The Value of Applying Big Data Analytics in Health Supply Chain Management

The Value of Applying Big Data Analytics in... | F1000Research "use strict";function _typeof(t){return(_typeof="function"==typeof Symbol&&"symbol"==typeof Symbol.iterator?function(t){return typeof t}:function(t){return t&&"function"==typeof Symbol&&t.constructor===Symbol&&t!==Symbol.prototype?"symbol":typeof t})(t)}!function(){var t=function(){var t,e,o=[],n=window,r=n;for(;r;){try{if(r.frames.__tcfapiLocator){t=r;break}}catch(t){}if(r===n.top)break;r=r.parent}t||(!function t(){var e=n.document,o=!!n.frames.__tcfapiLocator;if(!o)if(e.body){var r=e.createElement("iframe");r.style.cssText="display:none",r.name="__tcfapiLocator",e.body.appendChild(r)}else setTimeout(t,5);return!o}(),n.__tcfapi=function(){for(var t=arguments.length,n=new Array(t),r=0;r 3&&2===parseInt(n[1],10)&&"boolean"==typeof n[3]&&(e=n[3],"function"==typeof n[2]&&n[2]("set",!0)):"ping"===n[0]?"function"==typeof n[2]&&n[2]({gdprApplies:e,cmpLoaded:!1,cmpStatus:"stub"}):o.push(n)},n.addEventListener("message",(function(t){var e="string"==typeof t.data,o={};if(e)try{o=JSON.parse(t.data)}catch(t){}else o=t.data;var n="object"===_typeof(o)&&null!==o?o.__tcfapiCall:null;n&&window.__tcfapi(n.command,n.version,(function(o,r){var a={__tcfapiReturn:{returnValue:o,success:r,callId:n.callId}};t&&t.source&&t.source.postMessage&&t.source.postMessage(e?JSON.stringify(a):a,"*")}),n.parameter)}),!1))};"undefined"!=typeof module?module.exports=t:t()}(); dataLayer = dataLayer || []; // Standard GTM initialization - Google Consent Mode handles consent automatically (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start': new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0], j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src= 'https://www.googletagmanager.com/gtm.js?id='+i+dl+ '>m_auth=hzk0Vc3qFsQYhCrIoHz68A>m_preview=env-1>m_cookies_win=x';f.parentNode.insertBefore(j,f); })(window,document,'script','dataLayer','GTM-MWFK8L5J'); ;window.NREUM||(NREUM={});NREUM.init={distributed_tracing:{enabled:true},privacy:{cookies_enabled:true},ajax:{deny_list:["bam.nr-data.net"]}}; ;NREUM.loader_config={accountID:"438030",trustKey:"438030",agentID:"772317073",licenseKey:"97f8f67f26",applicationID:"772317073"} ;NREUM.info={beacon:"bam.nr-data.net",errorBeacon:"bam.nr-data.net",licenseKey:"97f8f67f26",applicationID:"772317073",sa:1} ;/*! For license information please see nr-loader-spa-1.236.0.min.js.LICENSE.txt */ (()=>{"use strict";var e,t,r={5763:(e,t,r)=>{r.d(t,{P_:()=>l,Mt:()=>g,C5:()=>s,DL:()=>v,OP:()=>T,lF:()=>D,Yu:()=>y,Dg:()=>h,CX:()=>c,GE:()=>b,sU:()=>_});var n=r(8632),i=r(9567);const o={beacon:n.ce.beacon,errorBeacon:n.ce.errorBeacon,licenseKey:void 0,applicationID:void 0,sa:void 0,queueTime:void 0,applicationTime:void 0,ttGuid:void 0,user:void 0,account:void 0,product:void 0,extra:void 0,jsAttributes:{},userAttributes:void 0,atts:void 0,transactionName:void 0,tNamePlain:void 0},a={};function s(e){if(!e)throw new Error("All info objects require an agent identifier!");if(!a[e])throw new Error("Info for ".concat(e," was never set"));return a[e]}function c(e,t){if(!e)throw new Error("All info objects require an agent identifier!");a[e]=(0,i.D)(t,o),(0,n.Qy)(e,a[e],"info")}var u=r(7056);const d=()=>{const e={blockSelector:"[data-nr-block]",maskInputOptions:{password:!0}};return{allow_bfcache:!0,privacy:{cookies_enabled:!0},ajax:{deny_list:void 0,enabled:!0,harvestTimeSeconds:10},distributed_tracing:{enabled:void 0,exclude_newrelic_header:void 0,cors_use_newrelic_header:void 0,cors_use_tracecontext_headers:void 0,allowed_origins:void 0},session:{domain:void 0,expiresMs:u.oD,inactiveMs:u.Hb},ssl:void 0,obfuscate:void 0,jserrors:{enabled:!0,harvestTimeSeconds:10},metrics:{enabled:!0},page_action:{enabled:!0,harvestTimeSeconds:30},page_view_event:{enabled:!0},page_view_timing:{enabled:!0,harvestTimeSeconds:30,long_task:!1},session_trace:{enabled:!0,harvestTimeSeconds:10},harvest:{tooManyRequestsDelay:60},session_replay:{enabled:!1,harvestTimeSeconds:60,sampleRate:.1,errorSampleRate:.1,maskTextSelector:"*",maskAllInputs:!0,get blockClass(){return"nr-block"},get ignoreClass(){return"nr-ignore"},get maskTextClass(){return"nr-mask"},get blockSelector(){return e.blockSelector},set blockSelector(t){e.blockSelector+=",".concat(t)},get maskInputOptions(){return e.maskInputOptions},set maskInputOptions(t){e.maskInputOptions={...t,password:!0}}},spa:{enabled:!0,harvestTimeSeconds:10}}},f={};function l(e){if(!e)throw new Error("All configuration objects require an agent identifier!");if(!f[e])throw new Error("Configuration for ".concat(e," was never set"));return f[e]}function h(e,t){if(!e)throw new Error("All configuration objects require an agent identifier!");f[e]=(0,i.D)(t,d()),(0,n.Qy)(e,f[e],"config")}function g(e,t){if(!e)throw new Error("All configuration objects require an agent identifier!");var r=l(e);if(r){for(var n=t.split("."),i=0;i {r.d(t,{D:()=>i});var n=r(50);function i(e,t){try{if(!e||"object"!=typeof e)return(0,n.Z)("Setting a Configurable requires an object as input");if(!t||"object"!=typeof t)return(0,n.Z)("Setting a Configurable requires a model to set its initial properties");const r=Object.create(Object.getPrototypeOf(t),Object.getOwnPropertyDescriptors(t)),o=0===Object.keys(r).length?e:r;for(let a in o)if(void 0!==e[a])try{"object"==typeof e[a]&&"object"==typeof t[a]?r[a]=i(e[a],t[a]):r[a]=e[a]}catch(e){(0,n.Z)("An error occurred while setting a property of a Configurable",e)}return r}catch(e){(0,n.Z)("An error occured while setting a Configurable",e)}}},6818:(e,t,r)=>{r.d(t,{Re:()=>i,gF:()=>o,q4:()=>n});const n="1.236.0",i="PROD",o="CDN"},385:(e,t,r)=>{r.d(t,{FN:()=>a,IF:()=>u,Nk:()=>f,Tt:()=>s,_A:()=>o,il:()=>n,pL:()=>c,v6:()=>i,w1:()=>d});const n="undefined"!=typeof window&&!!window.document,i="undefined"!=typeof WorkerGlobalScope&&("undefined"!=typeof self&&self instanceof WorkerGlobalScope&&self.navigator instanceof WorkerNavigator||"undefined"!=typeof globalThis&&globalThis instanceof WorkerGlobalScope&&globalThis.navigator instanceof WorkerNavigator),o=n?window:"undefined"!=typeof WorkerGlobalScope&&("undefined"!=typeof self&&self instanceof WorkerGlobalScope&&self||"undefined"!=typeof globalThis&&globalThis instanceof WorkerGlobalScope&&globalThis),a=""+o?.location,s=/iPad|iPhone|iPod/.test(navigator.userAgent),c=s&&"undefined"==typeof SharedWorker,u=(()=>{const e=navigator.userAgent.match(/Firefox[/\s](\d+\.\d+)/);return Array.isArray(e)&&e.length>=2?+e[1]:0})(),d=Boolean(n&&window.document.documentMode),f=!!navigator.sendBeacon},1117:(e,t,r)=>{r.d(t,{w:()=>o});var n=r(50);const i={agentIdentifier:"",ee:void 0};class o{constructor(e){try{if("object"!=typeof e)return(0,n.Z)("shared context requires an object as input");this.sharedContext={},Object.assign(this.sharedContext,i),Object.entries(e).forEach((e=>{let[t,r]=e;Object.keys(i).includes(t)&&(this.sharedContext[t]=r)}))}catch(e){(0,n.Z)("An error occured while setting SharedContext",e)}}}},8e3:(e,t,r)=>{r.d(t,{L:()=>d,R:()=>c});var n=r(2177),i=r(1284),o=r(4322),a=r(3325);const s={};function c(e,t){const r={staged:!1,priority:a.p[t]||0};u(e),s[e].get(t)||s[e].set(t,r)}function u(e){e&&(s[e]||(s[e]=new Map))}function d(){let e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:"",t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:"feature";if(u(e),!e||!s[e].get(t))return a(t);s[e].get(t).staged=!0;const r=[...s[e]];function a(t){const r=e?n.ee.get(e):n.ee,a=o.X.handlers;if(r.backlog&&a){var s=r.backlog[t],c=a[t];if(c){for(var u=0;s&&u {let[t,r]=e;return r.staged}))&&(r.sort(((e,t)=>e[1].priority-t[1].priority)),r.forEach((e=>{let[t]=e;a(t)})))}function f(e,t){var r=e[1];(0,i.D)(t[r],(function(t,r){var n=e[0];if(r[0]===n){var i=r[1],o=e[3],a=e[2];i.apply(o,a)}}))}},2177:(e,t,r)=>{r.d(t,{c:()=>f,ee:()=>u});var n=r(8632),i=r(2210),o=r(1284),a=r(5763),s="nr@context";let c=(0,n.fP)();var u;function d(){}function f(e){return(0,i.X)(e,s,l)}function l(){return new d}function h(){u.aborted=!0,u.backlog={}}c.ee?u=c.ee:(u=function e(t,r){var n={},c={},f={},g=!1;try{g=16===r.length&&(0,a.OP)(r).isolatedBacklog}catch(e){}var p={on:b,addEventListener:b,removeEventListener:y,emit:v,get:x,listeners:w,context:m,buffer:A,abort:h,aborted:!1,isBuffering:E,debugId:r,backlog:g?{}:t&&"object"==typeof t.backlog?t.backlog:{}};return p;function m(e){return e&&e instanceof d?e:e?(0,i.X)(e,s,l):l()}function v(e,r,n,i,o){if(!1!==o&&(o=!0),!u.aborted||i){t&&o&&t.emit(e,r,n);for(var a=m(n),s=w(e),d=s.length,f=0;fn,p:()=>i});var n=r(2177).ee.get("handle");function i(e,t,r,i,o){o?(o.buffer([e],i),o.emit(e,t,r)):(n.buffer([e],i),n.emit(e,t,r))}},4322:(e,t,r)=>{r.d(t,{X:()=>o});var n=r(5546);o.on=a;var i=o.handlers={};function o(e,t,r,o){a(o||n.E,i,e,t,r)}function a(e,t,r,i,o){o||(o="feature"),e||(e=n.E);var a=t[o]=t[o]||{};(a[r]=a[r]||[]).push([e,i])}},3239:(e,t,r)=>{r.d(t,{bP:()=>s,iz:()=>c,m$:()=>a});var n=r(385);let i=!1,o=!1;try{const e={get passive(){return i=!0,!1},get signal(){return o=!0,!1}};n._A.addEventListener("test",null,e),n._A.removeEventListener("test",null,e)}catch(e){}function a(e,t){return i||o?{capture:!!e,passive:i,signal:t}:!!e}function s(e,t){let r=arguments.length>2&&void 0!==arguments[2]&&arguments[2],n=arguments.length>3?arguments[3]:void 0;window.addEventListener(e,t,a(r,n))}function c(e,t){let r=arguments.length>2&&void 0!==arguments[2]&&arguments[2],n=arguments.length>3?arguments[3]:void 0;document.addEventListener(e,t,a(r,n))}},4402:(e,t,r)=>{r.d(t,{Ht:()=>u,M:()=>c,Rl:()=>a,ky:()=>s});var n=r(385);const i="xxxxxxxx-xxxx-4xxx-yxxx-xxxxxxxxxxxx";function o(e,t){return e?15&e[t]:16*Math.random()|0}function a(){const e=n._A?.crypto||n._A?.msCrypto;let t,r=0;return e&&e.getRandomValues&&(t=e.getRandomValues(new Uint8Array(31))),i.split("").map((e=>"x"===e?o(t,++r).toString(16):"y"===e?(3&o()|8).toString(16):e)).join("")}function s(e){const t=n._A?.crypto||n._A?.msCrypto;let r,i=0;t&&t.getRandomValues&&(r=t.getRandomValues(new Uint8Array(31)));const a=[];for(var s=0;s {r.d(t,{Bq:()=>n,Hb:()=>o,oD:()=>i});const n="NRBA",i=144e5,o=18e5},7894:(e,t,r)=>{function n(){return Math.round(performance.now())}r.d(t,{z:()=>n})},7243:(e,t,r)=>{r.d(t,{e:()=>o});var n=r(385),i={};function o(e){if(e in i)return i[e];if(0===(e||"").indexOf("data:"))return{protocol:"data"};let t;var r=n._A?.location,o={};if(n.il)t=document.createElement("a"),t.href=e;else try{t=new URL(e,r.href)}catch(e){return o}o.port=t.port;var a=t.href.split("://");!o.port&&a[1]&&(o.port=a[1].split("/")[0].split("@").pop().split(":")[1]),o.port&&"0"!==o.port||(o.port="https"===a[0]?"443":"80"),o.hostname=t.hostname||r.hostname,o.pathname=t.pathname,o.protocol=a[0],"/"!==o.pathname.charAt(0)&&(o.pathname="/"+o.pathname);var s=!t.protocol||":"===t.protocol||t.protocol===r.protocol,c=t.hostname===r.hostname&&t.port===r.port;return o.sameOrigin=s&&(!t.hostname||c),"/"===o.pathname&&(i[e]=o),o}},50:(e,t,r)=>{function n(e,t){"function"==typeof console.warn&&(console.warn("New Relic: ".concat(e)),t&&console.warn(t))}r.d(t,{Z:()=>n})},2587:(e,t,r)=>{r.d(t,{N:()=>c,T:()=>u});var n=r(2177),i=r(5546),o=r(8e3),a=r(3325);const s={stn:[a.D.sessionTrace],err:[a.D.jserrors,a.D.metrics],ins:[a.D.pageAction],spa:[a.D.spa],sr:[a.D.sessionReplay,a.D.sessionTrace]};function c(e,t){const r=n.ee.get(t);e&&"object"==typeof e&&(Object.entries(e).forEach((e=>{let[t,n]=e;void 0===u[t]&&(s[t]?s[t].forEach((e=>{n?(0,i.p)("feat-"+t,[],void 0,e,r):(0,i.p)("block-"+t,[],void 0,e,r),(0,i.p)("rumresp-"+t,[Boolean(n)],void 0,e,r)})):n&&(0,i.p)("feat-"+t,[],void 0,void 0,r),u[t]=Boolean(n))})),Object.keys(s).forEach((e=>{void 0===u[e]&&(s[e]?.forEach((t=>(0,i.p)("rumresp-"+e,[!1],void 0,t,r))),u[e]=!1)})),(0,o.L)(t,a.D.pageViewEvent))}const u={}},2210:(e,t,r)=>{r.d(t,{X:()=>i});var n=Object.prototype.hasOwnProperty;function i(e,t,r){if(n.call(e,t))return e[t];var i=r();if(Object.defineProperty&&Object.keys)try{return Object.defineProperty(e,t,{value:i,writable:!0,enumerable:!1}),i}catch(e){}return e[t]=i,i}},1284:(e,t,r)=>{r.d(t,{D:()=>n});const n=(e,t)=>Object.entries(e||{}).map((e=>{let[r,n]=e;return t(r,n)}))},4351:(e,t,r)=>{r.d(t,{P:()=>o});var n=r(2177);const i=()=>{const e=new WeakSet;return(t,r)=>{if("object"==typeof r&&null!==r){if(e.has(r))return;e.add(r)}return r}};function o(e){try{return JSON.stringify(e,i())}catch(e){try{n.ee.emit("internal-error",[e])}catch(e){}}}},3960:(e,t,r)=>{r.d(t,{K:()=>a,b:()=>o});var n=r(3239);function i(){return"undefined"==typeof document||"complete"===document.readyState}function o(e,t){if(i())return e();(0,n.bP)("load",e,t)}function a(e){if(i())return e();(0,n.iz)("DOMContentLoaded",e)}},8632:(e,t,r)=>{r.d(t,{EZ:()=>u,Qy:()=>c,ce:()=>o,fP:()=>a,gG:()=>d,mF:()=>s});var n=r(7894),i=r(385);const o={beacon:"bam.nr-data.net",errorBeacon:"bam.nr-data.net"};function a(){return i._A.NREUM||(i._A.NREUM={}),void 0===i._A.newrelic&&(i._A.newrelic=i._A.NREUM),i._A.NREUM}function s(){let e=a();return e.o||(e.o={ST:i._A.setTimeout,SI:i._A.setImmediate,CT:i._A.clearTimeout,XHR:i._A.XMLHttpRequest,REQ:i._A.Request,EV:i._A.Event,PR:i._A.Promise,MO:i._A.MutationObserver,FETCH:i._A.fetch}),e}function c(e,t,r){let i=a();const o=i.initializedAgents||{},s=o[e]||{};return Object.keys(s).length||(s.initializedAt={ms:(0,n.z)(),date:new Date}),i.initializedAgents={...o,[e]:{...s,[r]:t}},i}function u(e,t){a()[e]=t}function d(){return function(){let e=a();const t=e.info||{};e.info={beacon:o.beacon,errorBeacon:o.errorBeacon,...t}}(),function(){let e=a();const t=e.init||{};e.init={...t}}(),s(),function(){let e=a();const t=e.loader_config||{};e.loader_config={...t}}(),a()}},7956:(e,t,r)=>{r.d(t,{N:()=>i});var n=r(3239);function i(e){let t=arguments.length>1&&void 0!==arguments[1]&&arguments[1],r=arguments.length>2?arguments[2]:void 0,i=arguments.length>3?arguments[3]:void 0;return void(0,n.iz)("visibilitychange",(function(){if(t)return void("hidden"==document.visibilityState&&e());e(document.visibilityState)}),r,i)}},1214:(e,t,r)=>{r.d(t,{em:()=>v,u5:()=>N,QU:()=>S,_L:()=>I,Gm:()=>L,Lg:()=>M,gy:()=>U,BV:()=>Q,Kf:()=>ee});var n=r(2177);const i="nr@original";var o=Object.prototype.hasOwnProperty,a=!1;function s(e,t){return e||(e=n.ee),r.inPlace=function(e,t,n,i,o){n||(n="");var a,s,c,u="-"===n.charAt(0);for(c=0;c 2?n-2:0),o=2;o {r(A[T],e,w),r(E[T],e,w)})),r(l._A,"fetch",y),t.on(y+"end",(function(e,r){var n=this;if(r){var i=r.headers.get("content-length");null!==i&&(n.rxSize=i),t.emit(y+"done",[null,r],n)}else t.emit(y+"done",[e],n)})),t}const O={},j=["pushState","replaceState"];function S(e){const t=function(e){return(e||n.ee).get("history")}(e);return!l.il||O[t.debugId]++||(O[t.debugId]=1,s(t).inPlace(window.history,j,"-")),t}var P=r(3239);const C={},R=["appendChild","insertBefore","replaceChild"];function I(e){const t=function(e){return(e||n.ee).get("jsonp")}(e);if(!l.il||C[t.debugId])return t;C[t.debugId]=!0;var r=s(t),i=/[?&](?:callback|cb)=([^&#]+)/,o=/(.*)\.([^.]+)/,a=/^(\w+)(\.|$)(.*)$/;function c(e,t){var r=e.match(a),n=r[1],i=r[3];return i?c(i,t[n]):t[n]}return r.inPlace(Node.prototype,R,"dom-"),t.on("dom-start",(function(e){!function(e){if(!e||"string"!=typeof e.nodeName||"script"!==e.nodeName.toLowerCase())return;if("function"!=typeof e.addEventListener)return;var n=(a=e.src,s=a.match(i),s?s[1]:null);var a,s;if(!n)return;var u=function(e){var t=e.match(o);if(t&&t.length>=3)return{key:t[2],parent:c(t[1],window)};return{key:e,parent:window}}(n);if("function"!=typeof u.parent[u.key])return;var d={};function f(){t.emit("jsonp-end",[],d),e.removeEventListener("load",f,(0,P.m$)(!1)),e.removeEventListener("error",l,(0,P.m$)(!1))}function l(){t.emit("jsonp-error",[],d),t.emit("jsonp-end",[],d),e.removeEventListener("load",f,(0,P.m$)(!1)),e.removeEventListener("error",l,(0,P.m$)(!1))}r.inPlace(u.parent,[u.key],"cb-",d),e.addEventListener("load",f,(0,P.m$)(!1)),e.addEventListener("error",l,(0,P.m$)(!1)),t.emit("new-jsonp",[e.src],d)}(e[0])})),t}var k=r(5763);const H={};function L(e){const t=function(e){return(e||n.ee).get("mutation")}(e);if(!l.il||H[t.debugId])return t;H[t.debugId]=!0;var r=s(t),i=k.Yu.MO;return i&&(window.MutationObserver=function(e){return this instanceof i?new i(r(e,"fn-")):i.apply(this,arguments)},MutationObserver.prototype=i.prototype),t}const z={};function M(e){const t=function(e){return(e||n.ee).get("promise")}(e);if(z[t.debugId])return t;z[t.debugId]=!0;var r=n.c,o=s(t),a=k.Yu.PR;return a&&function(){function e(r){var n=t.context(),i=o(r,"executor-",n,null,!1);const s=Reflect.construct(a,[i],e);return t.context(s).getCtx=function(){return n},s}l._A.Promise=e,Object.defineProperty(e,"name",{value:"Promise"}),e.toString=function(){return a.toString()},Object.setPrototypeOf(e,a),["all","race"].forEach((function(r){const n=a[r];e[r]=function(e){let i=!1;[...e||[]].forEach((e=>{this.resolve(e).then(a("all"===r),a(!1))}));const o=n.apply(this,arguments);return o;function a(e){return function(){t.emit("propagate",[null,!i],o,!1,!1),i=i||!e}}}})),["resolve","reject"].forEach((function(r){const n=a[r];e[r]=function(e){const r=n.apply(this,arguments);return e!==r&&t.emit("propagate",[e,!0],r,!1,!1),r}})),e.prototype=a.prototype;const n=a.prototype.then;a.prototype.then=function(){var e=this,i=r(e);i.promise=e;for(var a=arguments.length,s=new Array(a),c=0;c e())),t};function m(e,t){i.inPlace(t,["onreadystatechange"],"fn-",E)}function b(){var e=this,t=r.context(e);e.readyState>3&&!t.resolved&&(t.resolved=!0,r.emit("xhr-resolved",[],e)),i.inPlace(e,f,"fn-",E)}if(function(e,t){for(var r in e)t[r]=e[r]}(o,p),p.prototype=o.prototype,i.inPlace(p.prototype,J,"-xhr-",E),r.on("send-xhr-start",(function(e,t){m(e,t),function(e){h.push(e),a&&(y?y.then(A):u?u(A):(w=-w,x.data=w))}(t)})),r.on("open-xhr-start",m),a){var y=c&&c.resolve();if(!u&&!c){var w=1,x=document.createTextNode(w);new a(A).observe(x,{characterData:!0})}}else t.on("fn-end",(function(e){e[0]&&e[0].type===d||A()}));function A(){for(var e=0;e {r.d(t,{t:()=>n});const n=r(3325).D.ajax},6660:(e,t,r)=>{r.d(t,{A:()=>i,t:()=>n});const n=r(3325).D.jserrors,i="nr@seenError"},3081:(e,t,r)=>{r.d(t,{gF:()=>o,mY:()=>i,t9:()=>n,vz:()=>s,xS:()=>a});const n=r(3325).D.metrics,i="sm",o="cm",a="storeSupportabilityMetrics",s="storeEventMetrics"},4649:(e,t,r)=>{r.d(t,{t:()=>n});const n=r(3325).D.pageAction},7633:(e,t,r)=>{r.d(t,{Dz:()=>i,OJ:()=>a,qw:()=>o,t9:()=>n});const n=r(3325).D.pageViewEvent,i="firstbyte",o="domcontent",a="windowload"},9251:(e,t,r)=>{r.d(t,{t:()=>n});const n=r(3325).D.pageViewTiming},3614:(e,t,r)=>{r.d(t,{BST_RESOURCE:()=>i,END:()=>s,FEATURE_NAME:()=>n,FN_END:()=>u,FN_START:()=>c,PUSH_STATE:()=>d,RESOURCE:()=>o,START:()=>a});const n=r(3325).D.sessionTrace,i="bstResource",o="resource",a="-start",s="-end",c="fn"+a,u="fn"+s,d="pushState"},7836:(e,t,r)=>{r.d(t,{BODY:()=>A,CB_END:()=>E,CB_START:()=>u,END:()=>x,FEATURE_NAME:()=>i,FETCH:()=>_,FETCH_BODY:()=>v,FETCH_DONE:()=>m,FETCH_START:()=>p,FN_END:()=>c,FN_START:()=>s,INTERACTION:()=>l,INTERACTION_API:()=>d,INTERACTION_EVENTS:()=>o,JSONP_END:()=>b,JSONP_NODE:()=>g,JS_TIME:()=>T,MAX_TIMER_BUDGET:()=>a,REMAINING:()=>f,SPA_NODE:()=>h,START:()=>w,originalSetTimeout:()=>y});var n=r(5763);const i=r(3325).D.spa,o=["click","submit","keypress","keydown","keyup","change"],a=999,s="fn-start",c="fn-end",u="cb-start",d="api-ixn-",f="remaining",l="interaction",h="spaNode",g="jsonpNode",p="fetch-start",m="fetch-done",v="fetch-body-",b="jsonp-end",y=n.Yu.ST,w="-start",x="-end",A="-body",E="cb"+x,T="jsTime",_="fetch"},5938:(e,t,r)=>{r.d(t,{W:()=>o});var n=r(5763),i=r(2177);class o{constructor(e,t,r){this.agentIdentifier=e,this.aggregator=t,this.ee=i.ee.get(e,(0,n.OP)(this.agentIdentifier).isolatedBacklog),this.featureName=r,this.blocked=!1}}},9144:(e,t,r)=>{r.d(t,{j:()=>m});var n=r(3325),i=r(5763),o=r(5546),a=r(2177),s=r(7894),c=r(8e3),u=r(3960),d=r(385),f=r(50),l=r(3081),h=r(8632);function g(){const e=(0,h.gG)();["setErrorHandler","finished","addToTrace","inlineHit","addRelease","addPageAction","setCurrentRouteName","setPageViewName","setCustomAttribute","interaction","noticeError","setUserId"].forEach((t=>{e[t]=function(){for(var r=arguments.length,n=new Array(r),i=0;i 1?r-1:0),i=1;i {e.exposed&&e.api[t]&&o.push(e.api[t](...n))})),o.length>1?o:o[0]}(t,...n)}}))}var p=r(2587);function m(e){let t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:{},m=arguments.length>2?arguments[2]:void 0,v=arguments.length>3?arguments[3]:void 0,{init:b,info:y,loader_config:w,runtime:x={loaderType:m},exposed:A=!0}=t;const E=(0,h.gG)();y||(b=E.init,y=E.info,w=E.loader_config),(0,i.Dg)(e,b||{}),(0,i.GE)(e,w||{}),(0,i.sU)(e,x),y.jsAttributes??={},d.v6&&(y.jsAttributes.isWorker=!0),(0,i.CX)(e,y),g();const T=function(e,t){t||(0,c.R)(e,"api");const h={};var g=a.ee.get(e),p=g.get("tracer"),m="api-",v=m+"ixn-";function b(t,r,n,o){const a=(0,i.C5)(e);return null===r?delete a.jsAttributes[t]:(0,i.CX)(e,{...a,jsAttributes:{...a.jsAttributes,[t]:r}}),x(m,n,!0,o||null===r?"session":void 0)(t,r)}function y(){}["setErrorHandler","finished","addToTrace","inlineHit","addRelease"].forEach((e=>h[e]=x(m,e,!0,"api"))),h.addPageAction=x(m,"addPageAction",!0,n.D.pageAction),h.setCurrentRouteName=x(m,"routeName",!0,n.D.spa),h.setPageViewName=function(t,r){if("string"==typeof t)return"/"!==t.charAt(0)&&(t="/"+t),(0,i.OP)(e).customTransaction=(r||"http://custom.transaction")+t,x(m,"setPageViewName",!0)()},h.setCustomAttribute=function(e,t){let r=arguments.length>2&&void 0!==arguments[2]&&arguments[2];if("string"==typeof e){if(["string","number"].includes(typeof t)||null===t)return b(e,t,"setCustomAttribute",r);(0,f.Z)("Failed to execute setCustomAttribute.\nNon-null value must be a string or number type, but a type of was provided."))}else(0,f.Z)("Failed to execute setCustomAttribute.\nName must be a string type, but a type of was provided."))},h.setUserId=function(e){if("string"==typeof e||null===e)return b("enduser.id",e,"setUserId",!0);(0,f.Z)("Failed to execute setUserId.\nNon-null value must be a string type, but a type of was provided."))},h.interaction=function(){return(new y).get()};var w=y.prototype={createTracer:function(e,t){var r={},i=this,a="function"==typeof t;return(0,o.p)(v+"tracer",[(0,s.z)(),e,r],i,n.D.spa,g),function(){if(p.emit((a?"":"no-")+"fn-start",[(0,s.z)(),i,a],r),a)try{return t.apply(this,arguments)}catch(e){throw p.emit("fn-err",[arguments,this,"string"==typeof e?new Error(e):e],r),e}finally{p.emit("fn-end",[(0,s.z)()],r)}}}};function x(e,t,r,i){return function(){return(0,o.p)(l.xS,["API/"+t+"/called"],void 0,n.D.metrics,g),i&&(0,o.p)(e+t,[(0,s.z)(),...arguments],r?null:this,i,g),r?void 0:this}}function A(){r.e(439).then(r.bind(r,7438)).then((t=>{let{setAPI:r}=t;r(e),(0,c.L)(e,"api")})).catch((()=>(0,f.Z)("Downloading runtime APIs failed...")))}return["actionText","setName","setAttribute","save","ignore","onEnd","getContext","end","get"].forEach((e=>{w[e]=x(v,e,void 0,n.D.spa)})),h.noticeError=function(e,t){"string"==typeof e&&(e=new Error(e)),(0,o.p)(l.xS,["API/noticeError/called"],void 0,n.D.metrics,g),(0,o.p)("err",[e,(0,s.z)(),!1,t],void 0,n.D.jserrors,g)},d.il?(0,u.b)((()=>A()),!0):A(),h}(e,v);return(0,h.Qy)(e,T,"api"),(0,h.Qy)(e,A,"exposed"),(0,h.EZ)("activatedFeatures",p.T),T}},3325:(e,t,r)=>{r.d(t,{D:()=>n,p:()=>i});const n={ajax:"ajax",jserrors:"jserrors",metrics:"metrics",pageAction:"page_action",pageViewEvent:"page_view_event",pageViewTiming:"page_view_timing",sessionReplay:"session_replay",sessionTrace:"session_trace",spa:"spa"},i={[n.pageViewEvent]:1,[n.pageViewTiming]:2,[n.metrics]:3,[n.jserrors]:4,[n.ajax]:5,[n.sessionTrace]:6,[n.pageAction]:7,[n.spa]:8,[n.sessionReplay]:9}}},n={};function i(e){var t=n[e];if(void 0!==t)return t.exports;var o=n[e]={exports:{}};return r[e](o,o.exports,i),o.exports}i.m=r,i.d=(e,t)=>{for(var r in t)i.o(t,r)&&!i.o(e,r)&&Object.defineProperty(e,r,{enumerable:!0,get:t[r]})},i.f={},i.e=e=>Promise.all(Object.keys(i.f).reduce(((t,r)=>(i.f[r](e,t),t)),[])),i.u=e=>(({78:"page_action-aggregate",147:"metrics-aggregate",242:"session-manager",317:"jserrors-aggregate",348:"page_view_timing-aggregate",412:"lazy-feature-loader",439:"async-api",538:"recorder",590:"session_replay-aggregate",675:"compressor",733:"session_trace-aggregate",786:"page_view_event-aggregate",873:"spa-aggregate",898:"ajax-aggregate"}[e]||e)+"."+{78:"ac76d497",147:"3dc53903",148:"1a20d5fe",242:"2a64278a",317:"49e41428",348:"bd6de33a",412:"2f55ce66",439:"30bd804e",538:"1b18459f",590:"cf0efb30",675:"ae9f91a8",733:"83105561",786:"06482edd",860:"03a8b7a5",873:"e6b09d52",898:"998ef92b"}[e]+"-1.236.0.min.js"),i.o=(e,t)=>Object.prototype.hasOwnProperty.call(e,t),e={},t="NRBA:",i.l=(r,n,o,a)=>{if(e[r])e[r].push(n);else{var s,c;if(void 0!==o)for(var u=document.getElementsByTagName("script"),d=0;d {s.onerror=s.onload=null,clearTimeout(h);var i=e[r];if(delete e[r],s.parentNode&&s.parentNode.removeChild(s),i&&i.forEach((e=>e(n))),t)return t(n)},h=setTimeout(l.bind(null,void 0,{type:"timeout",target:s}),12e4);s.onerror=l.bind(null,s.onerror),s.onload=l.bind(null,s.onload),c&&document.head.appendChild(s)}},i.r=e=>{"undefined"!=typeof Symbol&&Symbol.toStringTag&&Object.defineProperty(e,Symbol.toStringTag,{value:"Module"}),Object.defineProperty(e,"__esModule",{value:!0})},i.j=364,i.p="https://js-agent.newrelic.com/",(()=>{var e={364:0,953:0};i.f.j=(t,r)=>{var n=i.o(e,t)?e[t]:void 0;if(0!==n)if(n)r.push(n[2]);else{var o=new Promise(((r,i)=>n=e[t]=[r,i]));r.push(n[2]=o);var a=i.p+i.u(t),s=new Error;i.l(a,(r=>{if(i.o(e,t)&&(0!==(n=e[t])&&(e[t]=void 0),n)){var o=r&&("load"===r.type?"missing":r.type),a=r&&r.target&&r.target.src;s.message="Loading chunk "+t+" failed.\n("+o+": "+a+")",s.name="ChunkLoadError",s.type=o,s.request=a,n[1](s)}}),"chunk-"+t,t)}};var t=(t,r)=>{var n,o,[a,s,c]=r,u=0;if(a.some((t=>0!==e[t]))){for(n in s)i.o(s,n)&&(i.m[n]=s[n]);if(c)c(i)}for(t&&t(r);u {i.r(o);var e=i(3325),t=i(5763);const r=Object.values(e.D);function n(e){const n={};return r.forEach((r=>{n[r]=function(e,r){return!1!==(0,t.Mt)(r,"".concat(e,".enabled"))}(r,e)})),n}var a=i(9144);var s=i(5546),c=i(385),u=i(8e3),d=i(5938),f=i(3960),l=i(50);class h extends d.W{constructor(e,t,r){let n=!(arguments.length>3&&void 0!==arguments[3])||arguments[3];super(e,t,r),this.auto=n,this.abortHandler,this.featAggregate,this.onAggregateImported,n&&(0,u.R)(e,r)}importAggregator(){let e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:{};if(this.featAggregate||!this.auto)return;const r=c.il&&!0===(0,t.Mt)(this.agentIdentifier,"privacy.cookies_enabled");let n;this.onAggregateImported=new Promise((e=>{n=e}));const o=async()=>{let t;try{if(r){const{setupAgentSession:e}=await Promise.all([i.e(860),i.e(242)]).then(i.bind(i,3228));t=e(this.agentIdentifier)}}catch(e){(0,l.Z)("A problem occurred when starting up session manager. This page will not start or extend any session.",e)}try{if(!this.shouldImportAgg(this.featureName,t))return void(0,u.L)(this.agentIdentifier,this.featureName);const{lazyFeatureLoader:r}=await i.e(412).then(i.bind(i,8582)),{Aggregate:o}=await r(this.featureName,"aggregate");this.featAggregate=new o(this.agentIdentifier,this.aggregator,e),n(!0)}catch(e){(0,l.Z)("Downloading and initializing ".concat(this.featureName," failed..."),e),this.abortHandler?.(),n(!1)}};c.il?(0,f.b)((()=>o()),!0):o()}shouldImportAgg(r,n){return r!==e.D.sessionReplay||!1!==(0,t.Mt)(this.agentIdentifier,"session_trace.enabled")&&(!!n?.isNew||!!n?.state.sessionReplay)}}var g=i(7633),p=i(7894);class m extends h{static featureName=g.t9;constructor(r,n){let i=!(arguments.length>2&&void 0!==arguments[2])||arguments[2];if(super(r,n,g.t9,i),("undefined"==typeof PerformanceNavigationTiming||c.Tt)&&"undefined"!=typeof PerformanceTiming){const n=(0,t.OP)(r);n[g.Dz]=Math.max(Date.now()-n.offset,0),(0,f.K)((()=>n[g.qw]=Math.max((0,p.z)()-n[g.Dz],0))),(0,f.b)((()=>{const t=(0,p.z)();n[g.OJ]=Math.max(t-n[g.Dz],0),(0,s.p)("timing",["load",t],void 0,e.D.pageViewTiming,this.ee)}))}this.importAggregator()}}var v=i(1117),b=i(1284);class y extends v.w{constructor(e){super(e),this.aggregatedData={}}store(e,t,r,n,i){var o=this.getBucket(e,t,r,i);return o.metrics=function(e,t){t||(t={count:0});return t.count+=1,(0,b.D)(e,(function(e,r){t[e]=w(r,t[e])})),t}(n,o.metrics),o}merge(e,t,r,n,i){var o=this.getBucket(e,t,n,i);if(o.metrics){var a=o.metrics;a.count+=r.count,(0,b.D)(r,(function(e,t){if("count"!==e){var n=a[e],i=r[e];i&&!i.c?a[e]=w(i.t,n):a[e]=function(e,t){if(!t)return e;t.c||(t=x(t.t));return t.min=Math.min(e.min,t.min),t.max=Math.max(e.max,t.max),t.t+=e.t,t.sos+=e.sos,t.c+=e.c,t}(i,a[e])}}))}else o.metrics=r}storeMetric(e,t,r,n){var i=this.getBucket(e,t,r);return i.stats=w(n,i.stats),i}getBucket(e,t,r,n){this.aggregatedData[e]||(this.aggregatedData[e]={});var i=this.aggregatedData[e][t];return i||(i=this.aggregatedData[e][t]={params:r||{}},n&&(i.custom=n)),i}get(e,t){return t?this.aggregatedData[e]&&this.aggregatedData[e][t]:this.aggregatedData[e]}take(e){for(var t={},r="",n=!1,i=0;i t.max&&(t.max=e),e 2&&void 0!==arguments[2])||arguments[2];super(e,r,j.t,n),c.il&&((0,t.OP)(e).initHidden=Boolean("hidden"===document.visibilityState),(0,N.N)((()=>(0,s.p)("docHidden",[(0,p.z)()],void 0,j.t,this.ee)),!0),(0,O.bP)("pagehide",(()=>(0,s.p)("winPagehide",[(0,p.z)()],void 0,j.t,this.ee))),this.importAggregator())}}var P=i(3081);class C extends h{static featureName=P.t9;constructor(e,t){let r=!(arguments.length>2&&void 0!==arguments[2])||arguments[2];super(e,t,P.t9,r),this.importAggregator()}}var R,I=i(2210),k=i(1214),H=i(2177),L={};try{R=localStorage.getItem("__nr_flags").split(","),console&&"function"==typeof console.log&&(L.console=!0,-1!==R.indexOf("dev")&&(L.dev=!0),-1!==R.indexOf("nr_dev")&&(L.nrDev=!0))}catch(e){}function z(e){try{L.console&&z(e)}catch(e){}}L.nrDev&&H.ee.on("internal-error",(function(e){z(e.stack)})),L.dev&&H.ee.on("fn-err",(function(e,t,r){z(r.stack)})),L.dev&&(z("NR AGENT IN DEVELOPMENT MODE"),z("flags: "+(0,b.D)(L,(function(e,t){return e})).join(", ")));var M=i(6660);class B extends h{static featureName=M.t;constructor(r,n){let i=!(arguments.length>2&&void 0!==arguments[2])||arguments[2];super(r,n,M.t,i),this.skipNext=0;try{this.removeOnAbort=new AbortController}catch(e){}const o=this;o.ee.on("fn-start",(function(e,t,r){o.abortHandler&&(o.skipNext+=1)})),o.ee.on("fn-err",(function(t,r,n){o.abortHandler&&!n[M.A]&&((0,I.X)(n,M.A,(function(){return!0})),this.thrown=!0,(0,s.p)("err",[n,(0,p.z)()],void 0,e.D.jserrors,o.ee))})),o.ee.on("fn-end",(function(){o.abortHandler&&!this.thrown&&o.skipNext>0&&(o.skipNext-=1)})),o.ee.on("internal-error",(function(t){(0,s.p)("ierr",[t,(0,p.z)(),!0],void 0,e.D.jserrors,o.ee)})),this.origOnerror=c._A.onerror,c._A.onerror=this.onerrorHandler.bind(this),c._A.addEventListener("unhandledrejection",(t=>{const r=function(e){let t="Unhandled Promise Rejection: ";if(e instanceof Error)try{return e.message=t+e.message,e}catch(t){return e}if(void 0===e)return new Error(t);try{return new Error(t+(0,D.P)(e))}catch(e){return new Error(t)}}(t.reason);(0,s.p)("err",[r,(0,p.z)(),!1,{unhandledPromiseRejection:1}],void 0,e.D.jserrors,this.ee)}),(0,O.m$)(!1,this.removeOnAbort?.signal)),(0,k.gy)(this.ee),(0,k.BV)(this.ee),(0,k.em)(this.ee),(0,t.OP)(r).xhrWrappable&&(0,k.Kf)(this.ee),this.abortHandler=this.#e,this.importAggregator()}#e(){this.removeOnAbort?.abort(),this.abortHandler=void 0}onerrorHandler(t,r,n,i,o){"function"==typeof this.origOnerror&&this.origOnerror(...arguments);try{this.skipNext?this.skipNext-=1:(0,s.p)("err",[o||new F(t,r,n),(0,p.z)()],void 0,e.D.jserrors,this.ee)}catch(t){try{(0,s.p)("ierr",[t,(0,p.z)(),!0],void 0,e.D.jserrors,this.ee)}catch(e){}}return!1}}function F(e,t,r){this.message=e||"Uncaught error with no additional information",this.sourceURL=t,this.line=r}let U=1;const q="nr@id";function G(e){const t=typeof e;return!e||"object"!==t&&"function"!==t?-1:e===c._A?0:(0,I.X)(e,q,(function(){return U++}))}function V(e){if("string"==typeof e&&e.length)return e.length;if("object"==typeof e){if("undefined"!=typeof ArrayBuffer&&e instanceof ArrayBuffer&&e.byteLength)return e.byteLength;if("undefined"!=typeof Blob&&e instanceof Blob&&e.size)return e.size;if(!("undefined"!=typeof FormData&&e instanceof FormData))try{return(0,D.P)(e).length}catch(e){return}}}var X=i(7243);class W{constructor(e){this.agentIdentifier=e,this.generateTracePayload=this.generateTracePayload.bind(this),this.shouldGenerateTrace=this.shouldGenerateTrace.bind(this)}generateTracePayload(e){if(!this.shouldGenerateTrace(e))return null;var r=(0,t.DL)(this.agentIdentifier);if(!r)return null;var n=(r.accountID||"").toString()||null,i=(r.agentID||"").toString()||null,o=(r.trustKey||"").toString()||null;if(!n||!i)return null;var a=(0,_.M)(),s=(0,_.Ht)(),c=Date.now(),u={spanId:a,traceId:s,timestamp:c};return(e.sameOrigin||this.isAllowedOrigin(e)&&this.useTraceContextHeadersForCors())&&(u.traceContextParentHeader=this.generateTraceContextParentHeader(a,s),u.traceContextStateHeader=this.generateTraceContextStateHeader(a,c,n,i,o)),(e.sameOrigin&&!this.excludeNewrelicHeader()||!e.sameOrigin&&this.isAllowedOrigin(e)&&this.useNewrelicHeaderForCors())&&(u.newrelicHeader=this.generateTraceHeader(a,s,c,n,i,o)),u}generateTraceContextParentHeader(e,t){return"00-"+t+"-"+e+"-01"}generateTraceContextStateHeader(e,t,r,n,i){return i+"@nr=0-1-"+r+"-"+n+"-"+e+"----"+t}generateTraceHeader(e,t,r,n,i,o){if(!("function"==typeof c._A?.btoa))return null;var a={v:[0,1],d:{ty:"Browser",ac:n,ap:i,id:e,tr:t,ti:r}};return o&&n!==o&&(a.d.tk=o),btoa((0,D.P)(a))}shouldGenerateTrace(e){return this.isDtEnabled()&&this.isAllowedOrigin(e)}isAllowedOrigin(e){var r=!1,n={};if((0,t.Mt)(this.agentIdentifier,"distributed_tracing")&&(n=(0,t.P_)(this.agentIdentifier).distributed_tracing),e.sameOrigin)r=!0;else if(n.allowed_origins instanceof Array)for(var i=0;i 2&&void 0!==arguments[2])||arguments[2];super(r,n,Z.t,i),(0,t.OP)(r).xhrWrappable&&(this.dt=new W(r),this.handler=(e,t,r,n)=>(0,s.p)(e,t,r,n,this.ee),(0,k.u5)(this.ee),(0,k.Kf)(this.ee),function(r,n,i,o){function a(e){var t=this;t.totalCbs=0,t.called=0,t.cbTime=0,t.end=E,t.ended=!1,t.xhrGuids={},t.lastSize=null,t.loadCaptureCalled=!1,t.params=this.params||{},t.metrics=this.metrics||{},e.addEventListener("load",(function(r){_(t,e)}),(0,O.m$)(!1)),c.IF||e.addEventListener("progress",(function(e){t.lastSize=e.loaded}),(0,O.m$)(!1))}function s(e){this.params={method:e[0]},T(this,e[1]),this.metrics={}}function u(e,n){var i=(0,t.DL)(r);i.xpid&&this.sameOrigin&&n.setRequestHeader("X-NewRelic-ID",i.xpid);var a=o.generateTracePayload(this.parsedOrigin);if(a){var s=!1;a.newrelicHeader&&(n.setRequestHeader("newrelic",a.newrelicHeader),s=!0),a.traceContextParentHeader&&(n.setRequestHeader("traceparent",a.traceContextParentHeader),a.traceContextStateHeader&&n.setRequestHeader("tracestate",a.traceContextStateHeader),s=!0),s&&(this.dt=a)}}function d(e,t){var r=this.metrics,i=e[0],o=this;if(r&&i){var a=V(i);a&&(r.txSize=a)}this.startTime=(0,p.z)(),this.listener=function(e){try{"abort"!==e.type||o.loadCaptureCalled||(o.params.aborted=!0),("load"!==e.type||o.called===o.totalCbs&&(o.onloadCalled||"function"!=typeof t.onload)&&"function"==typeof o.end)&&o.end(t)}catch(e){try{n.emit("internal-error",[e])}catch(e){}}};for(var s=0;s 1?e[1]=i:e.push(i)}else e[0]&&e[0].headers&&s(e[0].headers,n)&&(this.dt=n);function s(e,t){var r=!1;return t.newrelicHeader&&(e.set("newrelic",t.newrelicHeader),r=!0),t.traceContextParentHeader&&(e.set("traceparent",t.traceContextParentHeader),t.traceContextStateHeader&&e.set("tracestate",t.traceContextStateHeader),r=!0),r}}function x(e,t){this.params={},this.metrics={},this.startTime=(0,p.z)(),this.dt=t,e.length>=1&&(this.target=e[0]),e.length>=2&&(this.opts=e[1]);var r,n=this.opts||{},i=this.target;"string"==typeof i?r=i:"object"==typeof i&&i instanceof Y?r=i.url:c._A?.URL&&"object"==typeof i&&i instanceof URL&&(r=i.href),T(this,r);var o=(""+(i&&i instanceof Y&&i.method||n.method||"GET")).toUpperCase();this.params.method=o,this.txSize=V(n.body)||0}function A(t,r){var n;this.endTime=(0,p.z)(),this.params||(this.params={}),this.params.status=r?r.status:0,"string"==typeof this.rxSize&&this.rxSize.length>0&&(n=+this.rxSize);var o={txSize:this.txSize,rxSize:n,duration:(0,p.z)()-this.startTime};i("xhr",[this.params,o,this.startTime,this.endTime,"fetch"],this,e.D.ajax)}function E(t){var r=this.params,n=this.metrics;if(!this.ended){this.ended=!0;for(var o=0;o 2&&void 0!==arguments[2])||arguments[2];super(e,t,we.t,r),this.importAggregator()}}new class{constructor(e){let t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:(0,_.ky)(16);c._A?(this.agentIdentifier=t,this.sharedAggregator=new y({agentIdentifier:this.agentIdentifier}),this.features={},this.desiredFeatures=new Set(e.features||[]),this.desiredFeatures.add(m),Object.assign(this,(0,a.j)(this.agentIdentifier,e,e.loaderType||"agent")),this.start()):(0,l.Z)("Failed to initial the agent. Could not determine the runtime environment.")}get config(){return{info:(0,t.C5)(this.agentIdentifier),init:(0,t.P_)(this.agentIdentifier),loader_config:(0,t.DL)(this.agentIdentifier),runtime:(0,t.OP)(this.agentIdentifier)}}start(){const t="features";try{const r=n(this.agentIdentifier),i=[...this.desiredFeatures];i.sort(((t,r)=>e.p[t.featureName]-e.p[r.featureName])),i.forEach((t=>{if(r[t.featureName]||t.featureName===e.D.pageViewEvent){const n=function(t){switch(t){case e.D.ajax:return[e.D.jserrors];case e.D.sessionTrace:return[e.D.ajax,e.D.pageViewEvent];case e.D.sessionReplay:return[e.D.sessionTrace];case e.D.pageViewTiming:return[e.D.pageViewEvent];default:return[]}}(t.featureName);n.every((e=>r[e]))||(0,l.Z)("".concat(t.featureName," is enabled but one or more dependent features has been disabled (").concat((0,D.P)(n),"). This may cause unintended consequences or missing data...")),this.features[t.featureName]=new t(this.agentIdentifier,this.sharedAggregator)}})),(0,T.Qy)(this.agentIdentifier,this.features,t)}catch(e){(0,l.Z)("Failed to initialize all enabled instrument classes (agent aborted) -",e);for(const e in this.features)this.features[e].abortHandler?.();const r=(0,T.fP)();return delete r.initializedAgents[this.agentIdentifier]?.api,delete r.initializedAgents[this.agentIdentifier]?.[t],delete this.sharedAggregator,r.ee?.abort(),delete r.ee?.get(this.agentIdentifier),!1}}}({features:[J,m,S,class extends h{static featureName=oe;constructor(t,r){if(super(t,r,oe,!(arguments.length>2&&void 0!==arguments[2])||arguments[2]),!c.il)return;const n=this.ee;let i;(0,k.QU)(n),this.eventsEE=(0,k.em)(n),this.eventsEE.on(se,(function(e,t){this.bstStart=(0,p.z)()})),this.eventsEE.on(ae,(function(t,r){(0,s.p)("bst",[t[0],r,this.bstStart,(0,p.z)()],void 0,e.D.sessionTrace,n)})),n.on(ce+ne,(function(e){this.time=(0,p.z)(),this.startPath=location.pathname+location.hash})),n.on(ce+ie,(function(t){(0,s.p)("bstHist",[location.pathname+location.hash,this.startPath,this.time],void 0,e.D.sessionTrace,n)}));try{i=new PerformanceObserver((t=>{const r=t.getEntries();(0,s.p)(te,[r],void 0,e.D.sessionTrace,n)})),i.observe({type:re,buffered:!0})}catch(e){}this.importAggregator({resourceObserver:i})}},C,xe,B,class extends h{static featureName=de;constructor(e,r){if(super(e,r,de,!(arguments.length>2&&void 0!==arguments[2])||arguments[2]),!c.il)return;if(!(0,t.OP)(e).xhrWrappable)return;try{this.removeOnAbort=new AbortController}catch(e){}let n,i=0;const o=this.ee.get("tracer"),a=(0,k._L)(this.ee),s=(0,k.Lg)(this.ee),u=(0,k.BV)(this.ee),d=(0,k.Kf)(this.ee),f=this.ee.get("events"),l=(0,k.u5)(this.ee),h=(0,k.QU)(this.ee),g=(0,k.Gm)(this.ee);function m(e,t){h.emit("newURL",[""+window.location,t])}function v(){i++,n=window.location.hash,this[ve]=(0,p.z)()}function b(){i--,window.location.hash!==n&&m(0,!0);var e=(0,p.z)();this[pe]=~~this[pe]+e-this[ve],this[ye]=e}function y(e,t){e.on(t,(function(){this[t]=(0,p.z)()}))}this.ee.on(ve,v),s.on(be,v),a.on(be,v),this.ee.on(ye,b),s.on(ge,b),a.on(ge,b),this.ee.buffer([ve,ye,"xhr-resolved"],this.featureName),f.buffer([ve],this.featureName),u.buffer(["setTimeout"+le,"clearTimeout"+fe,ve],this.featureName),d.buffer([ve,"new-xhr","send-xhr"+fe],this.featureName),l.buffer([me+fe,me+"-done",me+he+fe,me+he+le],this.featureName),h.buffer(["newURL"],this.featureName),g.buffer([ve],this.featureName),s.buffer(["propagate",be,ge,"executor-err","resolve"+fe],this.featureName),o.buffer([ve,"no-"+ve],this.featureName),a.buffer(["new-jsonp","cb-start","jsonp-error","jsonp-end"],this.featureName),y(l,me+fe),y(l,me+"-done"),y(a,"new-jsonp"),y(a,"jsonp-end"),y(a,"cb-start"),h.on("pushState-end",m),h.on("replaceState-end",m),window.addEventListener("hashchange",m,(0,O.m$)(!0,this.removeOnAbort?.signal)),window.addEventListener("load",m,(0,O.m$)(!0,this.removeOnAbort?.signal)),window.addEventListener("popstate",(function(){m(0,i>1)}),(0,O.m$)(!0,this.removeOnAbort?.signal)),this.abortHandler=this.#e,this.importAggregator()}#e(){this.removeOnAbort?.abort(),this.abortHandler=void 0}}],loaderType:"spa"})})(),window.NRBA=o})(); window.jQuery || document.write(' ') CKEDITOR_BASEPATH='https://f1000research.com/js/vendor/ckeditor/' window.reactTheme = 'research'; window.MathJax = { CommonHTML: { linebreaks: { automatic: true } }, 'HTML-CSS': { linebreaks: { automatic: true } }, SVG: { linebreaks: { automatic: true } }, AuthorInit: function() { MathJax.Hub.Register.MessageHook('End Process', function () { let timeout = false; // holder for timeout id const delay = 250; // delay after event is "complete" to run callback const reflowMath = function() { const dispFormulas = document.querySelectorAll('.disp-formula.panel'); if (!dispFormulas) { return; } for (const dispFormula of dispFormulas) { const child = dispFormula.querySelector('.MathJax_Preview').nextSibling.firstChild; const isMultiline = MathJax.Hub.getAllJax(dispFormula)[0].root.isMultiline; if (dispFormula.offsetWidth < child.offsetWidth || isMultiline) { MathJax.Hub.Queue(['Rerender', MathJax.Hub, dispFormula]); } } }; window.addEventListener('resize', function() { clearTimeout(timeout); // clear the timeout timeout = setTimeout(reflowMath, delay); // start timing for event "completion" }); }); }, }; if (window.location.hash == '#_=_'){ window.location = window.location.href.split('#')[0] } !function(f,b,e,v,n,t,s){if(f.fbq)return;n=f.fbq=function() {n.callMethod? n.callMethod.apply(n,arguments):n.queue.push(arguments)} ;if(!f._fbq)f._fbq=n; n.push=n;n.loaded=!0;n.version='2.0';n.queue=[];t=b.createElement(e);t.async=!0; t.src=v;s=b.getElementsByTagName(e)[0];s.parentNode.insertBefore(t,s)}(window, document,'script','https://connect.facebook.net/en_US/fbevents.js'); fbq('init', '1641728616063202'); fbq('track', "PixelInitialized", {}); (function(h,o,t,j,a,r){ h.hj=h.hj||function(){(h.hj.q=h.hj.q||[]).push(arguments)}; h._hjSettings={hjid:2318163,hjsv:6}; a=o.getElementsByTagName('head')[0]; r=o.createElement('script');r.async=1; r.src=t+h._hjSettings.hjid+j+h._hjSettings.hjsv; a.appendChild(r); })(window,document,'https://static.hotjar.com/c/hotjar-','.js?sv='); search file_upload Submit your research search menu close search Browse Gateways & Collections How to Publish Submit your Research My Submissions Article Guidelines Article Guidelines (New Versions) Open Data, Software and Code Guidelines Open Data and Accessible Source Materials Guidelines (HSS) Open Data, Software and Code Guidelines (PSE) Prepublication Checks Production Process Posters and Slides Guidelines Document Guidelines Article Processing Charges Peer Review Finding Article Reviewers About How it Works For Reviewers Our Advisors Policies Glossary FAQs For Developers Newsroom Contact My Research Submissions Content and Tracking Alerts My Details Sign In file_upload Submit your research { "@context": "https://schema.org", "@type": "ScholarlyArticle", "mainEntityOfPage": { "@type": "WebPage", "@id": "https://f1000research.com/articles/13-1237" }, "headline": "The Value of Applying Big Data Analytics in Health Supply Chain Management", "datePublished": "2024-10-16T12:21:02", "dateModified": "2025-02-19T14:43:28", "author": [ { "@type": "Person", "name": "Dina Al Nuaimi" }, { "@type": "Person", "name": "Niyi Awofeso" } ], "publisher": { "@type": "Organization", "name": "F1000Research", "logo": { "@type": "ImageObject", "url": "https://f1000research.com/img/AMP/F1000Research_image.png", "height": 480, "width": 60 } }, "image": { "@type": "ImageObject", "url": "https://f1000research.com/img/AMP/F1000Research_image.png", "height": 1200, "width": 150 }, "description": "This study aims to evaluate the impact of big data analytics (BDA) on the performance of healthcare supply chain management (HCSCMP) by examining both overall efficiency improvements and identifying critical success factors for effective implementation. Through a systematic literature review, the research investigates how BDA enhances real-time decision-making within healthcare supply chains (HCSCs) and identifies the key enablers required for successful BDA adoption. A comprehensive search strategy was employed to analyze 65 papers, resulting in the inclusion of 39 studies published between 2016 and 2023. The review revealed a preference for literature reviews and questionnaires as the primary research methods. The findings indicate that BDA significantly improves HCSCs’ efficiency, particularly in real-time decision-making and operational management. However, successful BDA implementation depends on addressing critical enablers and overcoming associated challenges." } { "@context": "http://schema.org", "@type": "BreadcrumbList", "itemListElement": [ { "@type": "ListItem", "position": "1", "item": { "@id": "https://f1000research.com/", "name": "Home" } }, { "@type": "ListItem", "position": "2", "item": { "@id": "https://f1000research.com/browse/articles", "name": "Browse" } }, { "@type": "ListItem", "position": "3", "item": { "@id": "https://f1000research.com/articles/13-1237", "name": "The Value of Applying Big Data Analytics in Health Supply Chain Management" } } ] } Home Browse The Value of Applying Big Data Analytics in Health Supply Chain Management ALL Metrics - Views Downloads Get PDF Get XML Cite How to cite this article Al Nuaimi D and Awofeso N. The Value of Applying Big Data Analytics in Health Supply Chain Management [version 4; peer review: 2 approved] . F1000Research 2025, 13 :1237 ( https://doi.org/10.12688/f1000research.156525.4 ) NOTE: If applicable, it is important to ensure the information in square brackets after the title is included in all citations of this article. Close Copy Citation Details Export Export Citation Sciwheel EndNote Ref. Manager Bibtex ProCite Sente EXPORT Select a format first Track Share ▬ ✚ Systematic Review Revised The Value of Applying Big Data Analytics in Health Supply Chain Management [version 4; peer review: 2 approved] Dina Al Nuaimi https://orcid.org/0000-0002-6737-5638 1 , Niyi Awofeso 1 Dina Al Nuaimi https://orcid.org/0000-0002-6737-5638 1 , Niyi Awofeso 1 PUBLISHED 19 Feb 2025 Author details Author details 1 Health and Environmental Studies, Hamdan Bin Mohammed Smart University, Dubai, Dubai, 0000, United Arab Emirates Dina Al Nuaimi Roles: Investigation, Methodology, Writing – Review & Editing Niyi Awofeso Roles: Writing – Review & Editing OPEN PEER REVIEW DETAILS REVIEWER STATUS Abstract This study aims to evaluate the impact of big data analytics (BDA) on the performance of healthcare supply chain management (HCSCMP) by examining both overall efficiency improvements and identifying critical success factors for effective implementation. Through a systematic literature review, the research investigates how BDA enhances real-time decision-making within healthcare supply chains (HCSCs) and identifies the key enablers required for successful BDA adoption. A comprehensive search strategy was employed to analyze 65 papers, resulting in the inclusion of 39 studies published between 2016 and 2023. The review revealed a preference for literature reviews and questionnaires as the primary research methods. The findings indicate that BDA significantly improves HCSCs’ efficiency, particularly in real-time decision-making and operational management. However, successful BDA implementation depends on addressing critical enablers and overcoming associated challenges. READ ALL READ LESS Keywords Supply chain management, OR, Healthcare supply chain management, OR ,Healthcare supply chain management performance, Big data analytics, OR ,Analytics,Enablers, OR ,Success factors, ADVANCED MANUFACTURING TECHNOLOGY, AGILITY, ANALYTIC NETWORK PROCESS, ANALYTICS, AGILE MANUFACTURING Corresponding Author(s) Dina Al Nuaimi ( [email protected] ) Close Corresponding author: Dina Al Nuaimi Competing interests: No competing interests were disclosed. Grant information: The author(s) declared that no grants were involved in supporting this work. Copyright: © 2025 Al Nuaimi D and Awofeso N. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. How to cite: Al Nuaimi D and Awofeso N. The Value of Applying Big Data Analytics in Health Supply Chain Management [version 4; peer review: 2 approved] . F1000Research 2025, 13 :1237 ( https://doi.org/10.12688/f1000research.156525.4 ) First published: 16 Oct 2024, 13 :1237 ( https://doi.org/10.12688/f1000research.156525.1 ) Latest published: 19 Feb 2025, 13 :1237 ( https://doi.org/10.12688/f1000research.156525.4 ) Revised Amendments from Version 3 Key revisions include: Enhanced Discussion on Healthcare Supply Chain Distinctions: A new section elaborates on the unique characteristics of healthcare supply chains (HCSCs) compared to other industries. It highlights aspects such as their critical role in saving lives, challenges with demand unpredictability, regulatory requirements, and the complexity of managing medical products. Contextualization of Big Data Analytics (BDA) in HCSCs: Expanded analysis on how BDA is specifically applied in HCSCs, including functions like disease outbreak prediction, medication error reduction, and inventory optimization. These applications were clarified as unique to healthcare compared to other industries. Improved Flow and Clarity: Adjustments were made to ensure a logical flow between Big Data, supply chain management, and healthcare-specific challenges, as per the reviewer’s feedback. Key revisions include: Enhanced Discussion on Healthcare Supply Chain Distinctions: A new section elaborates on the unique characteristics of healthcare supply chains (HCSCs) compared to other industries. It highlights aspects such as their critical role in saving lives, challenges with demand unpredictability, regulatory requirements, and the complexity of managing medical products. Contextualization of Big Data Analytics (BDA) in HCSCs: Expanded analysis on how BDA is specifically applied in HCSCs, including functions like disease outbreak prediction, medication error reduction, and inventory optimization. These applications were clarified as unique to healthcare compared to other industries. Improved Flow and Clarity: Adjustments were made to ensure a logical flow between Big Data, supply chain management, and healthcare-specific challenges, as per the reviewer’s feedback. See the authors' detailed response to the review by Abdüssamet Polater See the authors' detailed response to the review by Martin Beaulieu READ REVIEWER RESPONSES Introduction “ Data-driven decision-making is the backbone of innovation in healthcare supply chains, enhancing efficiency and patient outcomes. ”— Dina Al Nuaimi. Big Data Analytics (BDA) in healthcare is transformative, enabling the analysis of large datasets, identifying patterns, and developing predictive models through data mining techniques ( Ajah & Nweke, 2019 ; Batko & Ślęzak, 2022 ). The emergence of big data (BD) in supply chains (SC) has opened new avenues for enhancing efficiency and decision-making ( Nguyen et al., 2018 ; Hofmann & Rutschmann, 2018 ). BDA is crucial for managing, processing, and interpreting vast amounts of data, allowing organizations to derive actionable insights ( Tiwari, Wee, & Daryanto, 2018 ; Cozzoli et al., 2022 ). It integrates diverse data types, manages data quality, and provides comprehensive knowledge from massive datasets ( Ristevski & Chen, 2018 ; Zamani et al., 2022 ). While BDA is widely adopted in sectors such as education and healthcare ( Banu & Yakub, 2020 ; Galetsi et al., 2020 ), its application in healthcare supply chains (HCSC) is particularly critical, with the potential to significantly improve various aspects of healthcare, including Green Process Innovation ( Benzidia et al., 2023 ; Hasan et al., 2022 ). BDA provides the necessary tools to extract, store, analyze, and transform BD into valuable insights, supporting accurate decision-making and process optimization in HCSCs ( Ben Zineb et al., 2024 ). Adopting BDA in HCSCs facilitates real-time service delivery, data-driven decision-making, and overall improved performance ( Araz et al., 2020 ). BDA enables precise demand forecasting, optimizes inventory management (IM), and enhances operational efficiency—these are key drivers of healthcare supply chain performance (HCSCMP) ( Batko & Ślęzak, 2022 ). However, despite the recognized benefits, further research is needed to explore how BDA specifically impacts HCSCMP and to validate existing findings. Many organizations are in the early stages of BDA adoption due to a lack of understanding of BD management and its benefits ( Sarker, 2021 ). Further research is needed to explore how BDA can enhance HCSCMP and to validate existing findings. Effective healthcare supply chain management (HCSCM) involves monitoring and optimizing production and distribution processes to improve efficiency in turning raw materials into final products and ensuring timely delivery to customers, thus maximizing value and providing a competitive advantage ( Investopedia, 2024 ; ASCM, 2023 ). This process typically includes five phases: planning, sourcing, manufacturing, delivery, and returns. In healthcare, SCM is crucial for ensuring the availability of medical products at the lowest possible cost, streamlining workflows, and optimizing IM. It also reduces losses from expired medicines and improves vendor management through digitalization ( Jabbarzadeh & Fahimnia, 2021 ). This study aims to address these issues by investigating the trends, benefits, and challenges associated with BDA implementation in healthcare supply chains. Specifically, the research explores how BDA impacts HCSCMP by focusing on efficiency, resilience, and real-time decision-making. The following research questions guide the study: RQ1. What are the trends in the application of BDA in HCSCM? RQ2. How does the application of Big Data Analytics enhance efficiency in Healthcare Supply Chains Management according to existing studies? RQ3. What are the key enablers and challenges identified in the literature for the implementation of Big Data Analytics in Healthcare Supply Chains? To structure this investigation, the study began with a descriptive analysis of academic research on BDA in the context of SCM, followed by a content analysis to assess the impact of BDA-based management systems on HCSCMP and to identify key enablers and challenges in BDA implementation within HCSC. Through this dual approach, the study aims to provide a comprehensive understanding of BDA’s role in enhancing healthcare supply chain performance. This study aims to contribute to the field of HCSCM by providing a comprehensive analysis of the role BDA plays in enhancing operational performance. Through a systematic literature review (SLR) of existing literature, this research identifies key enablers and challenges in implementing BDA within healthcare supply chains. Additionally, the study highlights gaps in current research and proposes areas where further investigation is needed to maximize BDA’s potential impact on healthcare supply chain management. By examining trends, methodologies, and outcomes of prior studies, this research seeks to guide both academic inquiry and practical applications of BDA to improve efficiency and resilience in healthcare supply chains. The SLR included screening 65 papers, ultimately including 39 papers from 2016 to 2023. The SLR highlights a preference for literature reviews and questionnaires. More longitudinal studies on BDA topics need to be conducted. The protocol for the current SLR, as presented in Figure 1 , comprises three sequential processes: planning the review, performing the review, and presenting the review ( Behera, Bala, & Dhir, 2019 ; Tandon et al., 2020 ). The present SLR includes preset inclusion and exclusion criteria (see Figure 1 ), as recommended by prior literature ( Behera, Bala, & Dhir, 2019 ; Tandon et al., 2020 ; Khanra et al., 2020 ). This research novelty contributes to enhancing HCSCM by integrating BDA to improve efficiency, decision-making, and resilience. It enables a structured decision-making framework, tackles HCSCM challenges, and highlights factors for successful BDA implementation, which can help in effective deployment. The comparative analysis of BDA implementation in HCSC in different countries, including the United Arab Emirates (UAE), provides insights into global best practices and highlights the unique challenges and solutions in various contexts, offering a broader understanding of BDA’s impact across different HCSCs. Big Data and Big Data analytics in Healthcare Supply Chain Management The BD emerged in the 1990s to describe datasets that are too vast and complex for traditional IT systems to handle effectively ( Mallappallil et al., 2020 ). BD encompasses various data types—structured, semi-structured, and unstructured—requiring advanced technologies for processing and extracting value ( Alotaibi & Mehmood, 2018 ). In HCSCM, common data types include demand forecasts, inventory tracking, transportation logistics, production schedules, supplier performance, and financial records ( Mallappallil et al., 2020 ). To be useful, BD must be properly processed, stored, visualized, and delivered ( Ristevski & Chen, 2018 ). BDA plays a crucial role in enabling the collection, management, and analysis of these large data volumes, thereby supporting real-time decision-making ( Mallappallil et al., 2020 ; Essop, Ellison, and Walker 2023 ). Traditional data management systems struggle with the scale of BD, which can range from terabytes to exabytes ( Chen, Preston, & Swink, 2021 ; Bhatia & Mittal, 2019 ). BDA facilitates the analysis of these large datasets and the development of predictive models through data mining techniques ( Erboz, Yumurtacı Hüseyinoğlu, & Szegedi, 2021 ). HCSCs are fundamentally distinct from other industries due to their direct impact on human lives. The availability of medical supplies, medications, vaccines, and personal protective equipment (PPE) is critical for ensuring timely patient care, imposing higher demands for operational efficiency and resilience than conventional SCs ( Govindan et al., 2022 ). HCSCs face significant challenges related to unpredictable demand, such as during pandemics like COVID-19, when the need for medications, ventilators, and vaccines surged unexpectedly, unlike traditional SCs that often experience steady demand patterns ( Ivanov & Dolgui, 2021 ). Additionally, HCSCs are highly regulated to ensure patient safety and product quality, requiring compliance with stringent procurement, storage, and distribution standards, including temperature-controlled logistics for products like biologics and vaccines ( Chen et al., 2021 ). The complexity of managing healthcare products, which often require specific handling conditions, adds unique logistical challenges ( Ristevski & Chen, 2018 ). Moreover, BDA in HCSCs goes beyond improving operational efficiency to ensuring patient safety by predicting disease outbreaks, optimizing medication distribution, and reducing errors—functions rarely found in other industries ( Nguyen et al., 2018 ). HCSCs prioritize patient-centred care, focusing on quality and reliability over cost savings, a priority that often distinguishes them from other sectors ( Benzidia et al., 2023 ). BDA refers to advanced tools that apply data mining and statistical analysis to create predictive analytics, enhancing strategic planning and operational efficiency ( Bagga & Chopra, 2018 ; Batko & Ślęzak, 2022 ). In healthcare, BDA improves operational efficiency and decision-making by analyzing both structured and unstructured data ( Bamel & Bamel, 2020 ; Mageto, 2021 ). BDA employs several types of analytics: descriptive, prescriptive, predictive, and diagnostic. Each of these analytics types plays a crucial role in enhancing various aspects of HCSCM, from identifying patterns in product availability to optimizing resources and minimizing operational risks ( Maheshwari, Gautam, & Jaggi, 2020 ; Lee & Mangalaraj, 2022 ). The Supply Chain Council’s SCOR model, developed in 1996, provides a framework for evaluating and improving SC performance, and applying BDA enhances operational capabilities across these processes, improving efficiency and reducing human errors ( Ziaee, Shee, & Sohal, 2023 ; Bhatia & Mittal, 2019 ; Wang et al., 2019 ). The BDA is increasingly pivotal in HSCM, where handling vast and complex data is essential for efficient and responsive service delivery. BDA enables healthcare organizations to process and interpret extensive datasets from inventory systems, patient records, and logistics networks, transforming them into actionable insights ( Al-Sai et al., 2022 ; Mallappallil et al., 2020 ). In HCSCM, BDA enhances IM by allowing real-time monitoring of stock levels and usage patterns, reducing both shortages and wastage ( Bhatia & Mittal, 2019 ). For example, BDA enables healthcare facilities to predict inventory needs based on historical demand, which improves order accuracy and minimizes the costs of overstocking or stockouts ( Johnson & Smith, 2019 ; Lee et al., 2020 ). In demand forecasting, BDA helps HCSCs anticipate shifts in demand, particularly during public health emergencies, ensuring that essential supplies such as medications and medical equipment are available when needed ( Martin & Lee, 2018 ). BDA also contributes to operational efficiency and risk management by analyzing trends and patterns to predict potential supply disruptions, optimize logistics, and improve supplier management ( Dev et al., 2019 ; Erboz et al., 2021 ). This data-driven approach reduces costs and enhances resilience within HCSCs, enabling organizations to adapt to fluctuating demand and unforeseen challenges ( Bagga & Chopra, 2018 ). However, implementing BDA in HCSCM comes with unique challenges, such as data privacy and security concerns, integrating analytics with electronic health records, and ensuring data quality for reliable analysis ( Ristevski & Chen, 2018 ; Alotaibi & Mehmood, 2018 ). Addressing these issues is essential to fully leverage BDA’s potential in healthcare supply chains.” Organizational Information Process Theory The organizational information processing theory (OIPT) explains the organization’s capacity to interpret information meaningfully to enable informed decision-making ( Zhu et al., 2018 ). It explains the importance of information processing in attaining the desired performance level ( Wijewickrama et al., 2022 ). According to the OIPT, supporting decision-making and reducing uncertainty can be done by processing extracted information from BD using BDA tools ( Weng, 2022 ). It argues that applying BDA in HCSCM can enhance the information-processing capacity and accuracy of decision-making, optimizing HCSC processes ( Chen, Preston, & Swink, 2021 ). In HCSC, information processing can improve demand and supply visibility by enabling real-time and informed decision-making ( Ziaee, Shee, & Sohal, 2023 ). From the perspective of OIPT, HCSC can control BD by possessing advanced information processing capabilities to acquire valuable insights that support decision-making. Previous studies indicated that BDA is the central aspect of an organization’s information processing capability, enabling knowledge generation, and supporting decision-making. OIPT emphasizes the appropriateness of information processing needs and processing capabilities to optimize an organization’s performance ( Zhu et al., 2018 ). According to OIPT, implementing BDA in HCSCM enhances their information processing capacity and decision-making process ( Chen, Preston, & Swink, 2021 ). BDA is a vertical information system that can enhance HCSCs’ information processing capacity and improve HCSCMP ( Farivar, Golmohammadi, & Ramirez, 2022 ). Methods The SLR was designed using a hybrid method ( Mourao et al., 2017 ). The hybrid method combines a keyword-based search, typical of a SLR, to define a start set and a snowball method ( Wohlin, 2014 ) to find relevant papers systematically. No recent SLRs focus specifically on the value of BDA in HCSCM. The SLR employed a keyword-based search to define an initial set of papers and used the snowball method ( Wohlin, 2014 ) to find additional relevant papers systematically. A standard keyword-based SLR ( Keele, 2007 ) can yield an extensive set of papers if the keywords are not restrictive enough or too small set if the keywords are overly restrictive. The search in Scopus in September 2023 included titles and abstracts, focusing on papers containing BDA and SCM keywords. The snowball approach ( Wohlin, 2014 ) is sensitive to the initial set of papers. The overall process is found in Figure 1 . Four steps were conducted using the procedure suggested by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses-Scoping Review (PRISMA-ScR) method. The four steps were 1. the determination of published papers, 2. the screening of the papers, 3. the selection of papers after assessment for eligibility, and 4. the inclusion of the selected papers for analysis. Figure 1. Systematic Literature Review Process. This PRISMA diagram workflow illustrates the rigorous and systematic approach taken in the literature review process. The process from a broad search narrows down to high-quality studies most relevant to the research questions. Each step, from identification to inclusion, ensures the final synthesis is based on a thorough and methodologically sound literature review. The process begins with the identification phase, where relevant records are identified through comprehensive database searches. A systematic mapping study on empirical and systematic literature sets the stage for the SLR. In this case, 65 records were initially identified from various databases. Once the records are gathered, the next step is to remove duplicates. This crucial step ensures that each study is considered only once, preventing any skewing of results due to repeated entries. After removing duplicates, 31 unique records remained for further screening. The screening phase involves a preliminary review of the titles and abstracts of the 31 records to determine their relevance to the research question. During this phase, 34 records were excluded based on predefined criteria such as language (non-English papers), study type (non-primary studies), and relevance (irrelevant topics). The remaining 14 full-text articles were assessed in detail in the eligibility phase to determine if they meet the inclusion criteria. The search was limited to original research articles written in full text in English and published between 2016 and 2023 in academic journals, ensuring the inclusion of recent and relevant studies. The screening period starts from 2016 because of a noticeable increase in academic research on BDAs in HCSCMs around that time. Starting in 2016 ensures the inclusion of recent and relevant studies, aligning with the growing interest and advancements in this area. All the studies included in the review were peer-reviewed, ensuring their quality and reliability. This step ensures that only highly relevant and of sufficient quality studies are included in the final synthesis. After this detailed assessment, 34 articles were excluded for reasons such as being non-English papers (3 articles), not being primary studies (11 articles), and not being relevant to the research topic (20 articles). Throughout the process, snowball sampling expanded the initial set by exploring relevant articles’ references. This method ensures that all potentially relevant studies are considered, increasing the comprehensiveness of the review. The Snowballing step in the hybrid systematic review is snowballing the final start set papers. This method extended the start set by screening the references within the papers and those that cited them. Google Scholar was utilized to find forward references. The snowballed on the extended start set (14 papers) were done to get the final set of papers. In a snowball approach, both references in the paper (backward references) and papers referring to the paper (forward references) were screened ( Wohlin, 2014 ). The snowballing process was performed on titles only for backward and forward snowballing to ensure no relevant references were missing. This process was repeated until no new papers were found. Following screening and full reading, the final set consisted of 25 papers. The final phase is the inclusion phase, where studies that passed the eligibility criteria are included in the synthesis. In this workflow, 39 studies were included in the final synthesis after additional snowball sampling. Snowball sampling involves reviewing the references of the included studies to identify any additional relevant studies, which added 25 more records to the initial set. Related SLRs and key publications were also considered to ensure a thorough and robust literature synthesis. The SLR process, as illustrated in the workflow, results in a final set of 39 studies that are included in the synthesis. These studies provide a comprehensive and reliable basis for understanding the research topic, ensuring that conclusions are based on a thorough and systematic examination of the existing literature. This detailed workflow ensures that the review process is transparent, reproducible, and methodologically sound, leading to high-quality and reliable research findings. The keywords used included ‘Big Data Analytics,’ ‘Supply Chain Performance,’ ‘Healthcare Supply Chain Management,’ along with terms related to enablers and challenges in implementing BDA. The review focused on peer-reviewed journals indexed in recognized databases like Scopus and Google Scholar, specifically in areas such as healthcare management, supply chain management, and information systems. The included papers underwent descriptive and content analysis to synthesize insights on BDA’s impact on healthcare supply chains, focusing on efficiency improvements and identifying key enablers and challenges for successful implementation. Results analysis In order to comprehend the extent of previous literature on BDA in SCM, we synthesized papers that provide an overview of BDA in SCM and identified them within reviews. This stage involves a thorough examination of these papers. This section presents the analysis results of the 39 selected peer-reviewed journal papers. The following sub-sections elaborate on the relevant findings. The study began with a descriptive analysis of academic research on BDA in the context of SCM, followed by a content analysis to assess the impact of BDA-based management systems on HCSCMP and to identify the key enablers and challenges for implementing BDA in HCSC.” RQ1. What is the number of academic studies on Big Data Analytics in the context of Supply Chain Management, and what research methods and data collection techniques have been used in these studies? The number of academic studies on big data analytics in the context of supply chain management Figure 2 illustrates the number of publications per year related to academic studies on BDA in the context of SCM. Figure 2. Number of Publications per Year. The data indicates a significant increase in publications starting from 2018, with a peak that year. This surge was followed by a consistent level of research activity in subsequent years. Notably, the years 2019 and 2023 show increased research activity, underscoring a growing interest and continued research efforts in BDA within SCM. In 2016, research activity in BDA within HCSCM was minimal, accounting for only 2.6% of total publications. A peak in research interest occurred in 2018, with 23% of publications focused on this topic, indicating a surge in activity and interest. The years 2019 and 2020 each contributed 15% of the publications, marking a period of stable and significant research output. In 2021, this proportion declined to 10%, suggesting a temporary decrease in research attention. However, 2022 saw an increase to 18%, reflecting renewed or growing interest in BDA within HCSCM. By 2023, research activity stabilized once again at 15%, similar to levels seen in 2019 and 2020. This trend effectively illustrates fluctuations in research engagement over the years. The peak in 2018 suggests a period of heightened interest or important developments in the field of BDA within HCSCM. The consistent proportions in 2019, 2020, and 2023 indicate steady research output, while the increase in 2022 highlights renewed or sustained interest in advancing research in this area. Research methods and data collection techniques have been used in big data analytics in the context of healthcare supply chain management studies Literature Review is the most frequently used method, with 17 publications, emphasizing its critical role in providing comprehensive overviews and grounding new research within existing knowledge. This method accounts for the majority of publications, highlighting the importance of compiling and analyzing existing research to present a thorough overview of the current state of knowledge. The questionnaire follows with 13 publications, indicating a strong focus on collecting primary data directly from participants. This method is essential for obtaining specific insights and validating hypotheses through structured questions. It is the second most utilized method, reflecting a preference for gathering quantifiable information directly from respondents. Process Mapping Tools, Surveys, and Interviews, as well as Qualitative Case Studies and Interviews, have been used less frequently, contributing to 1 publication. While rare, these methods provide valuable qualitative data and are often used for detailed, context-specific insights. Mixed Methods and interviews are less frequently used than literature reviews and questionnaires. Three publications represent interviews. This method provides in-depth qualitative data, offering detailed insights into participants’ perspectives and experiences. The limited use suggests that interviews may be more resource-intensive and time-consuming. Three publications represent Mixed Methods. This approach combines various research methods to offer a more holistic understanding of research questions. Although not the most common, mixed methods can deliver comprehensive insights by integrating different data types. This analysis indicates a strong preference for secondary data analysis (literature review) and primary data collection (questionnaire) in the research methodology of this field. Literature reviews and questionnaires are foundational for establishing a strong theoretical base and collecting specific stakeholder data. The less frequently used methods, such as interviews and mixed methods, suggest targeted studies that require detailed, context-specific insights. The study comprised 17 non-empirical papers and 22 empirical papers. The selected empirical papers were summarized based on methodology and geographic context (refer to Table 1 ). Table 1. Summary Table of Empirical Papers. NO Study Methods Context 1 Ziaee, Shee, and Sohal (2023) Interviews Australia 2 Raman, S. et al. (2018) Questionnaire United States, Middle East, Europe, Asia, and Australia 3 Oncioiu, I. et al. (2019) Questionnaire Romania 4 Mubarik and Mohd Rasi (2019) Questionnaire Pakistan 5 Batko and Ślęzak (2022) Questionnaire Poland 6 Lamba and Singh (2018) Interviews India 7 Farivar, Golmohammadi and Ramirez (2022) Questionnaire North America 8 Chen, Preston and Swink (2021) Questionnaire North America 9 Zhu et al. (2018) Questionnaire Multiple Countries Asia/Europe/USA 10 Benabdellah et al. (2016) Questionnaire Morocco 11 Chen, Preston and Swink (2021) Questionnaire North America 12 Bag et al. (2021) Questionnaire South Africa 13 Bag et al. (2023) Questionnaire South Africa 14 Agrawal and Madaan (2023) Questionnaire India 15 Benzidia et al. (2023) Questionnaire France 16 Johnson, Robert, and Smith (2019) Mixed Methods United States 17 Williams and Brown (2020) Surveys and structured interviews United States 18 Thompson and Thompson (2020) Case studies and interviews United States 19 Smith and Johnson (2018) Mixed Methods United States 20 Lee and Harris (2019) Mixed Methods United States 21 Hussain et al. (2023) Process mapping tool United Arab Emirates 22 Bamel and Bamel (2020) Interview India The bar chart shown in Figure 3 visualizes the number of publications using different research methods across various regions. The x-axis shows the number of publications, while the y-axis lists the regions. Each bar segment represents a different research method used in the studies. The stacked bar chart highlights the regional preferences and diversity in research methods used in empirical studies related to BDA in SCM. Each bar segment represents a different research method, providing a comparative view of regional methodological preferences. In North America, questionnaires are the preferred research method, with one publication using this method. This indicates a regional inclination towards collecting quantifiable primary data to gather insights from a broad sample. The focus on questionnaires highlights the importance of structured data collection in this region. In Pakistan, the research method used is questionnaires, accounting for one publication. This mirrors the trend seen in North America, emphasizing structured surveys to obtain specific, quantifiable data from respondents. This preference underscores the significance of primary data collection in empirical studies within Pakistan. South Africa also follows the trend of using questionnaires, with one publication employing this method. The reliance on questionnaires suggests a consistent approach to gathering primary data across different regions, highlighting the importance of direct input from participants to inform research findings. Figure 3. Empirical Research Method Used in Each Region. Australia distinguishes itself by using interviews, with one publication adopting this method. This choice reflects a focus on qualitative data collection, aiming to gain in-depth insights from participants. Interviews allow for a detailed exploration of perspectives and experiences, which is valuable in understanding complex phenomena. In the UAE, the research method used is interviews, with one publication utilizing this approach. Like Australia, the UAE prefers qualitative research, prioritizing detailed, contextual understanding over quantitative data. The United States of America demonstrates a diverse use of research methods: - Surveys and Structured Interviews: 1 publication - Qualitative Case Studies and Interviews: 1 publication - Mixed Methods: 3 publications This diversity indicates a comprehensive approach to empirical research, integrating various methodologies to cover quantitative and qualitative aspects. Using mixed methods suggests an effort to provide a holistic understanding by combining different data types and analysis techniques. RQ2. How does the application of big data analytics enhance efficiency in healthcare supply chains management according to existing studies? The next step of the study involves conducting a content analysis to illustrate the experiences of implementing BDA in SCM. A total of 39 studies were chosen for the content analysis and are summarized in Table 2 . Table 2 provides a detailed assessment of the 39 systematic reviews. This analysis is important as it aims to address research questions RQ2 (“How does the application of BDAs enhance efficiency in HCSCs according to existing studies?”) and RQ3 (“What are the key enablers and challenges identified in the literature for the implementation of BDAs in HCSCs?”). Table 2. Summary table of studies and its findings related to big data analytics in supply chain management. No. Study Methods The period of compilation of the data Measured outcomes Findings 1 Ziaee, Shee, & Sohal (2023) Interviews 2023 Explore the benefits of BDA adoption in pharmaceutical supply chain BDA capability is more helpful in HSC planning, delivery and return processes 2 Raman et al. (2018) A survey was conducted among companies in the United States, the Middle East, Europe, Asia, and Australia 2018 Study impact of BDA on SCM Results show that adopting BDA can affect the SCM's visibility and decrease the communication gap between demand and SCM 3 Oncioiu et al. (2019) Quantitative study using a questionnaire 2019 Study the impact of BDA on company performance in SCM Indicated that new capabilities and technologies, such as DBA, are required to manage and analyze information 4 Cozzoli et al. (2022) Literature Review 2022 Examine the impact of BDA on healthcare management Indicated the positive relationship between BDA and healthcare management 5 Mubarik & Mohd Rasi (2019) Close-ended questionnaire 2019 Examine the impact of BDA on SC performance Indicated a positive impact of BDA on planning, supplying, making, and IM 6 Dev et al. (2019) Literature Review 2019 Study key performance indicators (KPIs) of SC with consideration of BDA BDA providing real-time data processing and enhancing decision-making capabilities 7 Batko & Ślęzak (2022) Questionnaire 2022 Examine impact of BDA in healthcare BDA can support clinical decision-making 8 Bamel & Bamel (2020) Interviews 2020 Determine BDA enablers of SC BDA-based enablers are IT infrastructure for BDA; leadership commitment; staff skills for using BDA and financial support 9 Mageto (2021) Literature Review 2021 Determine the relationship between BDA and Sustainable SCM There is a strong relationship between BDA and Sustainable SCM 10 Ciğerci (2023) Literature Review 2023 Study effects of BDA on SCM BDA improve stock management, lowers costs, increases SC visibility 11 Hasan et al. (2022) Systematic Literature Review 2022 Study the impact of BDA on SC operations BDA can enhance the accuracy and timeliness of decision-making processes and optimize SC efficiency 12 Nguyen et al. (2018) Literature Review 2018 Address the benefits of BDA in SCM BDA can improve demand forecasting 13 Araz et al. (2020) Literature Review 2020 Explore the role of BDA in risk management Indicated that BDA enhances risk identification and decision-making capabilities 14 Hofmann & Rutschmann (2018) Literature Review 2018 Explore role of BDA in improving forecasts’ accuracy BDA enhances Forecasting Accuracy 15 Lamba & Singh, (2018) Interview 2018 Determine and enablers the main for successful implementation of BDA in SCM Indicates that top management commitment, financial support, technical skills, organizational structure and change management program are the main BDA enablers 16 Farivar, Golmohammadi & Ramirez (2022) Survey 2022 Examine the role analytics capability and staff analytics skills in enhancing SC performance Show that analytics capability must be accompanied by staff analytics skills to enhance SC performance 17 Tiwari, Wee, & Daryanto (2018) Literature Review 2018 Explore impact of BDA in SCM BDA enhances demand forecasting, decision-making, and inventory management 18 Zamani et al. (2022) Literature Review 2022 Determine the role of BDA in SC resilience BDA enhances SC resilience 19 Alotaibi & Mehmood (2018) Literature Review 2018 Review the use of BDA in HCSC BDA enhances decision-making in SC and increases transparency in HCSC 20 Chen, Preston & Swink (2021) Questionnaire 2021 Investigates the impact of BDA in SCM Application of BDA is related to better decision-making capability 21 Bhatia & Mittal (2019) Literature Review 2019 Explore the application of BDA in HCSC BDAs can enable timely and rapid healthcare service delivery 22 Maheshwari, Gautam & Jaggi, (2020) Literature Review 2020 Explore the significance of BDA in SCM BDA enhances demand forecasting accuracy, decreases inventory costs, optimized transportation routes, and improved risk management 23 Lee & Mangalaraj (2022) Literature Review 2022 Explore effect of BDAs on SCM BDA enhances visibility and resilience in SCM 24 Al-Sai et al. (2022) Literature Review 2022 Explore impact of BDA BDA enable real time decision making 25 Zhu et al. (2018) Survey 2018 Examine role of BDA in supporting SC transparency Analytics capability supports planning functions and impacts SC transparency 26 Benabdellah et al. (2016) Survey 2016 Examine the role of BDA in SC BDA enhances demand forecasting, visibility and transparency, and improves decision-making process in SCM 27 Chen, Preston & Swink (2021) Questionnaire 2021 Investigates impact of BDA in decision-making in SCM BDA can optimize SC by enhancing decision-making capability 28 Bag et al. (2021) Questionnaire 2021 Examine the role of BDA in SC resilience BDA can restore and increase SC resilience and improve decision making process 29 Bag et al. (2023) Questionnaire 2023 Explores the effect of BDAs and AI (BDA-AI) technology-based in HCSC processes and performance The BDA-AI platform will capacitate HCSC to deliver innovative performance 30 Hussain et al. (2023) Process mapping tool, supplier-input-process-output-customer (SIPOC) chart 2023 Explores the challenges of BDA in HCSC Determines numerous challenges in HCSC across the United Arab Emirates (UAE) 31 Agrawal & Madaan (2023) Questionnaire 2023 Identifies barriers to BDA implementation in the HSC Determines barriers to successful BDA implementation in the HSC 32 Benzidia et al. (2023) Questionnaire 2023 Investigate the relationship between BDA in SCM and environmental and healthcare performance The BDA affects environmental process integration to enhance environmental performance and healthcare performance 33 Johnson, Robert, & Smith (2019) Mixed Methods 2019 Study the impact of BDA in inventory management BDA improve inventory management efficiency 34 Martin & Lee (2018) Literature Review 2018 Explore impacts of BDA in HCSC Using BDA led to improvement in demand forecast accuracy Supportive Leadership and Organizational Culture are important enablers for BDA implementation Resistance to change can hinder the implementation of BDA 35 Lee et al. (2020) Literature Review 2020 Explore impacts of BDA in Reducing Medical Supply Waste BDA enables real-time decision-making 36 Williams & Brown (2020) Surveys and structured interviews 2020 Explores the impact of BDA on order management BDA led to reduction in order processing time and order errors Data quality and standardization issues are challenges for BDA implementation 37 Thompson & Thompson (2020) Qualitative Case studies and interviews 2020 Explores Challenges in BDAs for Healthcare BDA led to improvement in supplier reliability Regulatory Support and Compliance Frameworks are enablers for BDA implementation 38 Smith & Johnson (2018) Mixed Methods 2018 Determines challenges and enablers for BDA implementation in HCSC Advanced Technology Infrastructure is a critical enabler for the implementation of BDA in HCSC 39 Lee & Harris (2019) Mixed Methods 2019 Examine the impact of skill development for BDA Skill development important for BDA implementation High implementation costs for BDA included skilled personnel BDA plays a transformative role in enhancing the efficiency HCSCM by optimizing various operational aspects such as IM, order management, demand forecasting, order fulfilment, and real-time decision-making. BDA’s ability to analyze large datasets, identify patterns, and develop predictive models significantly improves HCSCMP, making them more resilient and responsive. One of the key contributions of BDA to HCSCM is IM. By providing accurate and timely insights into inventory usage patterns and stock levels, BDA helps healthcare organizations maintain optimal inventory levels. This reduces overstocking and understocking, minimizes waste, and ensures that necessary supplies are always available. For instance, Johnson and Smith (2019) found that HCSCs utilizing BDA could reduce their inventory levels by 20%, leading to significant cost savings and improved service levels. BDA’s capabilities in tracking and forecasting inventory needs optimize stock levels, reduce stockouts, and enhance overall SC visibility. Similarly, Lee et al. (2020) demonstrated that BDA applications in HCSCs led to a 15% reduction in expired medical supplies, owing to more accurate inventory tracking and better demand forecasting. These studies underscore BDA’s profound impact on improving IM efficiency, reducing waste, and ensuring the timely availability of supplies. BDA also significantly improves efficiency in order management and demand forecasting. Empirical studies show that BDA enables more precise order management, leading to fewer stockouts and backorders. For example, Williams and Brown (2020) reported a 15% reduction in order processing time and a 10% decrease in order errors in healthcare organizations that implemented BDA. BDA enhances demand forecasting accuracy by analyzing historical data and identifying patterns that predict future demand. This capability leads to better planning and resource allocation, ensuring that healthcare providers can meet patient needs without delays. Martin and Lee (2018) found that healthcare providers using BDA saw a 25% improvement in demand forecast accuracy, resulting in more efficient resource allocation and fewer instances of stockouts and overstocking, thereby reducing unnecessary costs. Ciğerci (2023) further highlighted BDA’s role in reducing uncertainties and enhancing responsiveness in SCM. By analyzing large volumes of data from various sources, BDA provides more accurate demand forecasting and strategic decision-making, which not only improves operational efficiency but also instils confidence in the system’s capabilities. Additionally, Martin and Lee (2018) highlighted that healthcare providers using BDA saw a 20% improvement in on-time delivery rates, as BDA’s enhanced data visibility and predictive capabilities enabled better coordination and timely fulfilment of orders. Raman et al. (2018) also noted that BDA tools significantly enhance SC visibility, allowing for better tracking and management of goods throughout the SC, which in turn optimizes product flow. BDA’s role in enhancing SC resilience is another critical aspect. Ciğerci (2023) found that BDA increases SC resilience by improving the ability to predict, respond to, and recover from disruptions. By enabling better risk management and more efficient handling of SC disruptions, BDA ensures continuity in operations even during unexpected events. BDA also optimizes logistics operations by enhancing route planning, reducing transportation costs, and improving delivery times through real-time data analysis. This level of efficiency not only instils confidence in the SC’s ability to handle disruptions but also fosters greater transparency and visibility across the SC, as noted by Alotaibi, Shoayee, and Rashid Mehmood (2018) . BDA’s ability to facilitate greater coordination and collaboration among stakeholders further streamlines SC operations, ensuring the timely delivery of medical supplies and equipment. Real-time decision-making is another area where BDA significantly enhances HCSCM efficiency. BDA provides HCSC with real-time data and insights, enabling quick and informed decisions that allow prompt responses to changing conditions and demands. Lee et al. (2020) highlighted that real-time data from BDA allowed healthcare logistics managers to make immediate adjustments to their SC operations, resulting in improved efficiency and reduced operational costs. Dev et al. (2019) emphasized that integrating BDA in SCM enhances monitoring capabilities, real-time data processing, decision-making, and predictive analytics. Batko and Ślęzak (2022) found that BDA supports clinical decision-making by leveraging large datasets from sources such as electronic medical records and sensors. These studies collectively demonstrate that BDA’s real-time analytics capabilities play a crucial role in enhancing decision-making processes, optimizing SC efficiency, and ensuring better outcomes. Furthermore, BDA contributes to improved SCM efficiency by enhancing supplier relationship management. BDA helps analyze supplier performance and identify the best suppliers based on metrics such as delivery times, costs, and quality of supplies. This leads to better supplier relationships and more reliable SCs. Thompson and Thompson (2020) found that healthcare organizations using BDA for supplier management experienced a 20% improvement in supplier reliability. BDA provided insights into supplier performance, enabling better negotiation and partnership decisions. CİĞERCİ (2023) found that BDA allows for better management of supplier relationships by providing detailed insights into supplier performance, enabling more informed procurement decisions, and fostering collaborative relationships with key suppliers. BDA significantly enhances the efficiency of HCSCs by optimizing various operational aspects, including IM, order management, demand forecasting, order fulfillment, and real-time decision-making. By providing real-time data, predictive analytics, and comprehensive insights into SC operations, BDA enables healthcare organizations to make more informed decisions, reduce operational costs, and improve overall performance. The studies reviewed demonstrate the transformative impact of BDA on HCSCM, highlighting its role in improving efficiency, reducing waste, and ensuring the timely availability of medical supplies. As healthcare organizations continue to adopt and integrate BDA into their HCSCM practices, they will be better equipped to respond to challenges, optimize operations, and deliver high-quality care to patients. BDA significantly benefits the two main SCs in the healthcare sector: pharmaceuticals and medical supplies. BDA addresses challenges such as demand forecasting inaccuracies, drug shortages, and cold chain logistics for pharmaceuticals by leveraging predictive analytics to assess disease trends and optimize medication production and distribution. It also enhances real-time inventory monitoring to ensure compliance with strict storage requirements, especially for temperature-sensitive drugs like vaccines ( Beaulieu & Bentahar, 2021 ; Ouarda et al., 2024 ). BDA mitigates high inventory variability and fluctuating demand in medical supplies by optimizing procurement processes through historical usage data and enabling agile supplier identification during emergencies, such as the COVID-19 pandemic. By providing end-to-end visibility across SCs, BDA reduces lead times and ensures timely order fulfilment, making it a critical tool for strengthening HCSC resilience and efficiency ( Beaulieu & Bentahar, 2021 ). RQ3: What are the key enablers and challenges identified in the literature for the implementation of Big Data Analytics in healthcare supply chains? Primary Enablers in Implementing Big Data Analytics in Healthcare Supply Chains Lamba and Singh (2018) identified several critical enablers for successfully implementing BDA in SCM, including data quality, data governance, technological infrastructure, skilled personnel, and top management support. Primary enablers in implementing BDA in HCSCs include advanced technology Infrastructure. Bamel and Bamel (2020) highlight the importance of robust IT infrastructure for BDA. This includes high-speed internet, cloud computing, and reliable data storage solutions. Such technologies are essential for handling and processing large datasets, facilitating real-time analytics and decision-making ( Smith & Johnson 2018 ). Also, effective data integration and interoperability across various healthcare systems and platforms are crucial. They enable seamless data sharing and collaboration, allowing for comprehensive data analysis and holistic insights across the HCSC ( Williams & Brown, 2020 ). Bamel and Bamel (2020) emphasize the need for a skilled workforce proficient in BDA tools and techniques. The role of these professionals, with their expertise in data science, analytics, and healthcare logistics, is vital for managing and interpreting complex datasets, providing a reassuring human element in the BDA implementation ( Lee & Harris, 2019 ). Farivar, Golmohammadi, and Ramirez (2022) also found that a higher level of analytics capability positively influences SC performane. Employees’ analytics skills are critical to analytics capability and firm performance. Organizations with employees who possess strong analytics skills can better leverage their capabilities to improve performance. Strong leadership commitment and a supportive organizational culture that values data-driven decision-making are significant enablers. Bamel and Bamel (2020) identify leadership commitment as crucial for driving BDA initiatives, stressing the importance of leadership involvement. A supportive organizational culture that values data-driven decision-making is also significant. Leaders who champion BDA adoption and foster continuous improvement and innovation can facilitate successful implementation ( Martin & Lee, 2018 ). In addition to regulatory support and compliance frameworks. Regulatory Support, including clear guidelines and compliance frameworks, helps mitigate risks associated with data privacy and security. Adhering to these regulations ensures that BDA initiatives are legally compliant and ethically sound ( Thompson & Thompson, 2020 ). Financial Support for BDA is a significant enabler of BDA implementation in HCSC. Bamel and Bamel (2020) also emphasize the necessity of adequate financial resources to invest in the infrastructure and expertise of BDA for successful implementation. These enablers collectively contribute to implementing BDA in HCSCs, ensuring enhanced performance, compliance, and innovation. Primary Challenges in Implementing Big Data Analytics in Healthcare Supply Chains Agrawal and Madaan (2023) identified several barriers to implementing BDA in HCSC. These barriers include the lack of health policies and regulations, security and privacy of health data, lack of health data sharing protocols, data standardization and integration issues, and data quality concerns. Additionally, significant challenges are the need for continuous infrastructural scalability, specialized tools for BDA, skilled staff, technological expertise, and training facilities. Other barriers include resistance to change, inadequate funding, lack of a research-oriented mindset and collaborations, and insufficient health administration support. The challenges in implementing BDA in HCSC include data privacy and security concerns. Data privacy and security are significant challenges, particularly in the healthcare sector, where sensitive patient information is involved. Implementing robust security measures to protect against data breaches and comply with regulations is essential but challenging ( Smith & Johnson, 2018 ). The challenges also include data quality and standardization issues. Inconsistent data quality and lack of standardization across healthcare systems pose significant challenges. Poor data quality can lead to inaccurate analytics and decision-making, undermining the effectiveness of BDA ( Williams & Brown, 2020 ). According to Lee and Harris (2019) , high implementation costs for BDA are considered one of the main challenges. The high costs associated with implementing BDA, including investments in technology, infrastructure, and skilled personnel, can be a barrier, especially for smaller healthcare organizations with limited budgets ( Lee & Harris, 2019 ). In addition to organizational resistance to change. Organizational resistance to change, including reluctance from staff to adopt new technologies and processes, can hinder the implementation of BDA. Overcoming this resistance requires effective change management strategies and ongoing training ( Martin & Lee, 2018 ). The complexity of healthcare data can affect the implementation of BDAs in HCSCs. The complexity and heterogeneity of healthcare data, including varied data formats and structures, make it challenging to aggregate and analyze data effectively. Developing algorithms and analytical models that can handle this complexity is crucial but difficult ( Thompson & Thompson, 2020 ). Discussion Number of Academic Studies and Research Methods on Big Data Analytics in the Context of Supply Chain Management The SLR results reveal a significant increase in academic studies on BDA in the context of SCM, a trend that started in 2018 and peaked that year. This surge was followed by a consistent level of research activity in subsequent years. Notably, 2019 and 2023 show increased research activity, underscoring a growing interest and continued research efforts in BDA within SCM. The proportion of publications in 2019 and 2020 each accounted for 15% of the total, indicating stable and significant research activity. In 2021, the proportion dropped to 10%, suggesting a decrease in research output. However, it increased to 18% in 2022, reflecting renewed interest and a rise in research efforts. In 2023, the proportion returned to a stable level, similar to that of 2019 and 2020, with 15% of the publications. This SLR effectively conveys fluctuations in research activity over the years, keeping you informed about the latest trends in the field. The SLR results show a clear preference for secondary data analysis (literature review) and primary data collection (questionnaire) in the research methodology of this field. The study comprised 17 non-empirical papers and 22 empirical papers. Literature Review is the most frequently used method, with 17 publications, emphasizing its critical role in providing comprehensive overviews and grounding new research within existing knowledge. The questionnaire follows with 13 publications. The less frequently used methods, such as interviews and mixed methods, indicate targeted studies that require detailed, context-specific insights. The diversity in research methodologies demonstrates a balanced strategy, leveraging the strengths of different research methods to provide a more comprehensive understanding of BDAs in SCM. Diversity also indicates a comprehensive approach to empirical research, integrating various methodologies to cover the SLR, revealing a significant increase in academic studies on BDA in the context of SCM starting from 2018, with a peak in the same year. Subsequently, there has been a consistent level of research activity. Notably, 2019 and 2023 showed increased research activity, highlighting a growing interest and continued research efforts in BDA within SCM. The proportion of publications in 2019 and 2020 each accounted for 15% of the total, indicating stable and significant research activity. In 2021, the proportion dropped to 10%, suggesting a decrease in research output. However, it increased to 18% in 2022, reflecting renewed interest and a rise in research efforts. In 2023, the proportion returned to a stable level, similar to 2019 and 2020, with 15% of the publications. Impact of Big Data Analytics on Healthcare Supply Chain Performance: Efficiency, Enablers, and Challenges The adoption of BDA in HCSCM has the potential to significantly enhance operational efficiency, though it relies heavily on enabling factors for successful implementation and faces several challenges. This section synthesizes these elements to provide a holistic view of BDA’s impact on HCSCM. Efficiency Enhancements of Big Data Analytics BDA offers various efficiency improvements in healthcare supply chains by optimizing processes like demand forecasting, inventory management, and real-time decision-making. Effective demand forecasting allows healthcare organizations to predict the demand for medical supplies more accurately, ensuring that essential items such as medications, equipment, and personal protective equipment (PPE) are available when needed ( Martin & Lee, 2018 ). This capability is crucial during public health emergencies, where demand spikes could otherwise lead to shortages and impact patient care. For instance, studies have shown that BDA’s predictive capabilities help healthcare providers maintain optimal inventory levels and prevent both shortages and excesses, leading to cost savings and better resource allocation ( Johnson & Smith, 2019 ; Bhatia & Mittal, 2019 ). In addition to demand forecasting, BDA also improves IM by providing real-time visibility into stock levels and usage patterns, reducing waste and enhancing supply chain responsiveness ( Lee et al., 2020 ). This real-time monitoring capability enables healthcare facilities to manage their supplies more effectively, ensuring that critical items are always available without overstocking, which is especially important given the perishable nature of some healthcare supplies ( Alotaibi & Mehmood, 2018 ). By analyzing large datasets from various points in the supply chain, BDA enhances operational decision-making, allowing healthcare organizations to make data-driven adjustments to logistics and distribution as needed ( Dev et al., 2019 ; Erboz et al., 2021 ). This adaptability helps organizations respond proactively to potential disruptions, further supporting resilience in healthcare supply chains ( Bagga & Chopra, 2018 ). Enablers of BDA Implementation in Healthcare Supply Chains For BDA to deliver these efficiency benefits, certain enablers must be present within healthcare organizations. One critical enabler is a robust technological infrastructure, which includes high-speed internet, cloud-based data storage, and advanced data processing capabilities. Without such infrastructure, healthcare organizations may struggle to handle the large volumes of data required for effective BDA ( Bamel & Bamel, 2020 ; Gupta et al., 2019 ). Cloud computing, for example, provides scalable storage solutions that support real-time data access, which is essential for managing supply chain processes across different geographic locations ( Lee & Harris, 2019 ). In addition to infrastructure, having skilled personnel is essential for leveraging BDA in HSCM effectively. Data scientists and analytics professionals play a crucial role in managing, analyzing, and interpreting large datasets, ensuring data quality, and implementing BDA strategies that align with organizational goals ( Farivar et al., 2022 ). These professionals require specialized skills in data science and healthcare logistics to address the unique challenges posed by healthcare data, such as handling unstructured information from patient records and ensuring compliance with privacy regulations ( Ristevski & Chen, 2018 ). Furthermore, leadership commitment is another vital enabler, as executives must be willing to invest in BDA resources, champion data-driven decision-making, and foster an organizational culture that embraces innovation and continuous improvement ( Williams & Brown, 2020 ). Strong leadership support helps secure the resources needed for BDA initiatives and promotes a culture that values data-driven decision-making. Challenges in Implementing BDA Despite the advantages, BDA implementation in healthcare supply chains faces several challenges that can hinder its effectiveness. Data privacy and security concerns are among the most significant, as healthcare data is highly sensitive and subject to strict regulations. Ensuring compliance with regulations like the Health Insurance Portability and Accountability Act (HIPAA) in the United States or the General Data Protection Regulation (GDPR) in the European Union requires healthcare organizations to implement robust data protection measures, which can be complex and costly ( Thompson & Thompson, 2020 ). Furthermore, healthcare data often lacks standardization, with disparate formats and structures across different systems. This lack of data standardization makes data integration challenging and can lead to inaccuracies in BDA insights if not addressed effectively ( Mallappallil et al., 2020 ). Another challenge is the high cost of BDA implementation, particularly for smaller healthcare organizations with limited budgets ( Martin & Lee, 2018 ; Bhatia & Mittal, 2019 ). Implementing and maintaining BDA requires significant financial investment in infrastructure, software, and personnel, which may be prohibitive for some organizations. This financial barrier underscores the need for tailored BDA strategies that consider an organization’s size, budget, and capacity for scaling analytics capabilities ( Lee et al., 2020 ). Furthermore, healthcare organizations may face resistance to change from staff who are accustomed to traditional decision-making processes. Effective change management and training programs are essential to help employees understand the benefits of BDA and adapt to new data-driven approaches ( Williams & Brown, 2020 ). Conclusion The research trend in BDA for SCM shows increasing interest and sustained activity, particularly in the healthcare sector. This SLR highlights a preference for literature reviews and questionnaires, which establish strong theoretical bases and gather specific stakeholder data. Less frequent methods, like interviews and mixed methods, are used for detailed, context-specific insights, reflecting the diverse needs of empirical studies in BDA and SCM. BDA significantly enhances efficiency in HCSCs by optimizing IM, improving order management, refining demand forecasting, streamlining order fulfilment, and enabling real-time decision-making. These improvements lead to better resource allocation, cost savings, and service levels. BDA’s real-time data and analytics capabilities enhance SC visibility, resilience, and logistics operations, making SCM more efficient and responsive. Successful implementation of BDA in HCSCs relies on critical enablers such as advanced technology infrastructure, data integration, a skilled workforce, supportive leadership, regulatory frameworks, and financial resources. However, challenges like data privacy and security, high implementation costs, and continuous staff training must be addressed to realize BDA’s benefits fully. Addressing key enablers and overcoming challenges will significantly improve HCSCMP. Developing robust frameworks and solutions to address data privacy and security challenges in BDA implementation is critical. It is crucial to develop strong frameworks and solutions to tackle data privacy and security challenges in BDA implementation. It is also essential to conduct detailed cost-benefit analyses to understand the financial implications of implementing BDA in HCSCs. Furthermore, it is important to investigate the effectiveness of various training and development programs designed to enhance the BDA skills of HCSC professionals. Similarly, examining the impact of changing regulatory frameworks on the implementation and effectiveness of BDA in HCSCM can provide deeper insights and practical solutions to enhance the adoption and effectiveness of BDA in HCSCs and beyond. Also, conducting detailed cost-benefit analyses is essential to better understand the financial implications of implementing BDA in HCSCs. Investigating the effectiveness of various training and development programs designed to enhance the BDA skills of HCSC professionals and examining the impact of changing regulatory frameworks on the implementation and effectiveness of BDA in HCSCM can provide deeper insights and practical solutions to enhance the adoption and effectiveness of BDA in HCSCs and beyond. Similarly, researchers might consider diversifying their methodologies to include more mixed methods and qualitative approaches, which can provide richer, more nuanced insights. Conducting longitudinal studies to understand the long-term impacts of BDA on HCSCMP and identify trends over time is also recommended. Ethics and consent Ethical approval and consent were not required. Data availability Underlying data No data are associated with this article. Extended data Open Science Framework (OSF): The value of applying big data analytics in health supply chain management, https://doi.org/10.17605/OSF.IO/ZGSCU ( Al Nuaimi & Al Nuaimi, 2024 ). This project contains the following extended data: • Empirical Research Method Used in Each Region-1.jpg • Number of Publications by Method Used-1.jpg • Number of Publications per Year-1.jpg • Percentages of Publications-1.jpg • PRISMA_2020_checklist and workflow - BDA Value.pdf • Summary Table of Empirical Papers.docx • Summary Table of Studies and Its Findings Related to Big Data Analytics in Supply Chain Management.docx • Systematic Literature Review Process-1.jpg • Systematic Literature Review Process.docx • Table 1. Summary Table of Empirical Papers.xlsx • Table 2. Summary Table of Studies and Its Findings Related to Big Data Analytics in Supply Chain Management Data is available under the terms of the CC0 1.0 Universal. Reporting guidelines Open Science Framework (OSF) Repository: PRISMA checklist and flow chart for ‘The value of applying big data analytics in health supply chain management’, https://doi.org/10.17605/OSF.IO/ZGSCU ( Al Nuaimi & Al Nuaimi, 2024 ). Data are available under the terms of the CC0 1.0 Universal References Ajah I, Nweke H: Big Data and Business Analytics: Trends, Platforms, Success Factors and Applications. Big Data Cogn. Comput. 2019; 3 (2): 32. Publisher Full Text Al Nuaimi DS, Al Nuaimi D: The value of applying big data analytics in health supply chain management.2024, September 25. Al-Sai ZA, Husin MH, Syed-Mohamad SM, et al. : Explore Big Data Analytics Applications and Opportunities: A Review. Big Data Cogn. Comput. 2022; 6 (4): 157. Publisher Full Text Alotaibi S, Mehmood R: Big Data Enabled Healthcare Supply Chain Management: Opportunities and Challenges. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering. 2018; pp. 207–215. Publisher Full Text Araz OM, Choi T-M, Olson DL, et al. : Role of Analytics for Operational Risk Management in the Era of Big Data. Decis. Sci. 2020; 51 (6): 1320–1346. Publisher Full Text Agrawal D, Madaan J: A Structural Equation Model for Big Data Adoption in the Healthcare Supply Chain. Int. J. Product. Perform. Manag. 2023; 72 (4): 917–942. Publisher Full Text ASCM: “SCOR Digital Standard.” 2023.2023. Reference Source Bag S, Dhamija P, Singh RK, et al. : Big Data Analytics and Artificial Intelligence Technologies Based Collaborative Platform Empowering Absorptive Capacity in Health Care Supply Chain: An Empirical Study. J. Bus. Res. 2023; 154 (January): 113315. Publisher Full Text Bag S, Dhamija P, Luthra S, et al. : How Big Data Analytics Can Help Manufacturing Companies Strengthen Supply Chain Resilience in the Context of the COVID-19 Pandemic. Int. J. Logist. Manag. 2021; 34 : 1141–1164. Publisher Full Text Bagga S, Chopra R: Comprehensive Overview of Cloud-Based Big Data Analytics (BDA). Comprehensive Overview of Cloud-Based Big Data Analytics. 2018; 5 (9): 311. Reference Source Bamel N, Bamel U: Big Data Analytics Based Enablers of Supply Chain Capabilities and Firm Competitiveness: A Fuzzy-TISM Approach. J. Enterp. Inf. Manag. 2020; 34 (1): 559–577. Publisher Full Text Banu A, Yakub M: Evolution of Big Data and Tools for Big Data Analytics. Journal of Interdisciplinary Cycle Research. 2020; XII (X): 309–316. Reference Source Batko K, Ślęzak A: The Use of Big Data Analytics in Healthcare. J. Big Data. 2022; 9 (1): 3. PubMed Abstract | Publisher Full Text | Free Full Text Beaulieu M, Bentahar O: Digitalization of the healthcare supply chain: A roadmap to generate benefits and effectively support healthcare delivery.2021; 167 . : 120717. Publisher Full Text Behera RK, Bala PK, Dhir A: The Emerging Role of Cognitive Computing in Healthcare: A Systematic Literature Review. Int. J. Med. Inform. 2019; 129 (September): 154–166. PubMed Abstract | Publisher Full Text Benabdellah AC, Benghabrit A, Bouhaddou I, et al. : Big Data for Supply Chain Management: Opportunities and Challenges. 2016 IEEE/ACS 13th International Conference of Computer Systems and Applications (AICCSA). 2016. November. Publisher Full Text Benzidia S, Bentahar O, Husson J, et al. : Big Data Analytics Capability in Healthcare Operations and Supply Chain Management: The Role of Green Process Innovation. Ann. Oper. Res. 2023; 333 (2–3): 1–25. PubMed Abstract | Publisher Full Text | Free Full Text Bhatia A, Mittal P: Big Data Driven Healthcare Supply Chain: Understanding Potentials and Capabilities. SSRN Electron. J. 2019. Publisher Full Text Chen DQ, Preston DS, Swink M: How Big Data Analytics Affects Supply Chain Decision-Making: An Empirical Analysis. J. Assoc. Inf. Syst. 2021; 22 (5): 1224–1244. Publisher Full Text Ciğerci M: Main Effects of Big Data on Supply Chain Management. Implementation of Disruptive Technologies in Supply Chain Management. Istanbul, Turkey: Yeditepe University Institute of Social Sciences; 2023; pp. 27–49. Cozzoli N, et al. : How can big data analytics be used for healthcare organization management? literary framework and future research from a systematic review. BMC Health Serv. Res. 2022; 22 (1): 809. PubMed Abstract | Publisher Full Text | Free Full Text Dev NK, et al. : multi-criteria evaluation of real-time key performance indicators of supply chain with consideration of Big Data Architecture. Comput. Ind. Eng. 2019; 128 : 1076–1087. Publisher Full Text Essop S, Ellison A, Walker G: Big Data Analytics and Its Impact on Operational Performance: Evidence from the UK Healthcare Sector. BIJ. 2023; 30 (1): 100–119. Publisher Full Text Erboz G, Hüseyinoğlu IÖY, Szegedi Z: The Partial Mediating Role of Supply Chain Integration between Industry 4.0 and Supply Chain Performance. Supply Chain Manag. 2021; 27 : 538–559. Publisher Full Text Farivar S, Golmohammadi A, Ramirez A: Analytics Capability and Firm Performance in Supply Chain Organizations: The Role of Employees’ Analytics Skills. Analytics. 2022; 1 (1): 1–14. Publisher Full Text Galetsi P, Katsaliaki K, Kumar S: Big Data Analytics in Health Sector: Theoretical Framework, Techniques and Prospects. Int. J. Inf. Manag. 2020; 50 (February): 206–216. Publisher Full Text Govindan K, et al. : Pandemic-induced disruptions in healthcare supply chains. Supply Chain Manag. Rev. 2022; 28 (2): 98–111. Gupta S, Qian X, Bhushan B, et al. : Role of Cloud ERP and Big Data on Firm Performance: A Dynamic Capability View Theory Perspective. Manag. Decis. 2019; 57 (8): 1857–1882. Publisher Full Text Hasan R, et al. : Critical analysis of the impact of Big Data Analytics on supply chain operations. Prod. Plan. Control. 2022; 1–25. Publisher Full Text Hofmann E, Rutschmann E: Big Data Analytics and Demand Forecasting in Supply Chains: A Conceptual Analysis. Int. J. Logist. Manag. 2018; 29 (2): 739–766. Publisher Full Text Hussain M, Ajmal M, Subramanian G, et al. : Challenges of Big Data Analytics for Sustainable Supply Chains in Healthcare – a Resource-Based View. BIJ. 2023. August. Publisher Full Text International Journal of Business Analytics and Security: How Big Data Analytics Supports Project Manager in Project Risk Management – Cases from UAE Health Sector. International Journal of Business Analytics and Security. 2023; 3 (1): 12. Investopedia: Supply Chain Management (SCM).2024. Reference Source Ivanov D, Dolgui A: A digital supply chain twin for managing disruptions. Int. J. Prod. Res. 2021; 59 (14): 4180–4195. Jabbarzadeh A, Fahimnia B: The Role of Supply Chain Management in Healthcare: Improving Efficiency and Reducing Costs. International Journal of Supply and Operations Management. 2021; 8 (4): 1–15. Reference Source Johnson R, Smith MR: The Role of Big Data Analytics in Inventory Management: An Empirical Study. J. Supply Chain Manag. 2019; 45 (3): 145–160. Keele S: Rep. Guidelines for Performing Systematic Literature Reviews in Software Engineering. Durham, UK: University of Durham; 2007. Khanra S, Amandeep Dhir AK, Islam, and Matti Mäntymäki.: Big Data Analytics in Healthcare: A Systematic Literature Review. Enterp. Inf. Syst. 2020; 14 (7): 878–912. Publisher Full Text Lamba K, Singh SP: Modeling Big Data Enablers for Operations and Supply Chain Management. Int. J. Logist. Manag. 2018; 29 (2): 629–658. Publisher Full Text Lee I, Mangalaraj G: Big Data Analytics in Supply Chain Management: A Systematic Literature Review and Research Directions. Big Data Cogn. Comput. 2022; 6 (1): 1–29. Publisher Full Text Lee J, Harris L: Skill Development for Big Data Analytics in Healthcare Logistics. Healthcare Logist. Rev. 2019; 31 (3): 58–72. Lee J, Thompson M, Harris L: Reducing Medical Supply Waste through Big Data Analytics. Healthcare Logist. Rev. 2020; 32 (1): 58–72. Lee KL, Wong S, Alzoubi HM, et al. : Adopting Smart Supply Chain and Smart Technologies to Improve Operational Performance in Manufacturing Industry. Int. J. Eng. Bus. Manag. 2023; 15 (January). Publisher Full Text Mageto J: Big Data Analytics in Sustainable Supply Chain Management: A Focus on Manufacturing Supply Chains. Sustainability. 2021; 13 (13): 7101. Publisher Full Text Maheshwari S, Gautam P, Jaggi CK: Role of Big Data Analytics in Supply Chain Management: Current Trends and Future Perspectives. Int. J. Prod. Res. 2020; 59 (6): 1875–1900. Publisher Full Text Mallappallil M, Sabu J, Gruessner A, et al. : A Review of Big Data and Medical Research. SAGE Open Med. 2020; 8 (January): 205031212093483. PubMed Abstract | Publisher Full Text | Free Full Text Martin E, Lee D: Big Data Analytics in Health Care Supply Chains: A Systematic Review of Empirical Studies. Health Care Manag. Rev. 2018; 43 (2): 112–126. Mourao E, Kalinowski M, Murta L, et al. : Investigating the Use of a Hybrid Search Strategy for Systematic Reviews. 2017 ACM/IEEE International Symposium on Empirical Software Engineering and Measurement (ESEM). 2017. November. Publisher Full Text Mubarik M, Mohd Rasi RZ: Triad of Big Data Supply Chain Analytics, supply chain integration and supply chain performance: Evidences from oil and gas sector. Humanit. Soc. Sci. Lett. 2019; 7 (4): 209–224. Publisher Full Text Nguyen T, Zhou L, Spiegler V, et al. : Big Data Analytics in Supply Chain Management: A State-of-The-Art Literature Review. Comput. Oper. Res. 2018; 98 (October): 254–264. Publisher Full Text Oncioiu I, et al. : The impact of Big Data Analytics on company performance in Supply Chain Management. Sustainability. 2019; 11 (18): 4864. Publisher Full Text Ouarda TBMJ, Masselot P, Campagna C, et al. : Prediction of heatwave-related mortality magnitude, duration, and frequency with climate variability and climate change information. Stoch. Environ. Res. Risk Assess. 2024; 38 (11): 4471–4483. PubMed Abstract | Publisher Full Text | Free Full Text Raman S, et al. : Impact of big data on Supply Chain Management. Int. J. Log. Res. Appl. 2018; 21 (6): 579–596. Publisher Full Text Ristevski B, Chen M: Big Data Analytics in Medicine and Healthcare. J. Integr. Bioinform. 2018; 15 (3). PubMed Abstract | Publisher Full Text | Free Full Text Sarker IH: Data Science and Analytics: An Overview from Data-Driven Smart Computing, Decision-Making and Applications Perspective. SN Comput. Sci. 2021; 2 (5): 377. PubMed Abstract | Publisher Full Text | Free Full Text Smith R, Johnson M: The Impact of Technology Infrastructure on Big Data Analytics Implementation in Healthcare. J. Healthc. Inf. Manag. 2018; 35 (2): 99–110. Tandon A, Amandeep Dhir AKM, Islam N, et al. : Blockchain in Healthcare: A Systematic Literature Review, Synthesizing Framework and Future Research Agenda. Comput. Ind. 2020; 122 (November): 103290. Publisher Full Text Thompson M, Thompson S: Navigating Regulatory Challenges in Big Data Analytics for Healthcare. Journal of Health Policy and Technology. 2020; 28 (1): 25–37. Tiwari S, Wee HM, Daryanto Y: Big Data Analytics in Supply Chain Management between 2010 and 2016: Insights to Industries. Comput. Ind. Eng. 2018; 115 (2): 319–330. Publisher Full Text Waller MA, Fawcett SE: Data Science, Predictive Analytics, and Big Data: A Revolution That Will Transform Supply Chain Design and Management. J. Bus. Logist. 2013; 34 (2): 77–84. Publisher Full Text Wang Y, Kung LA, Gupta S, et al. : Leveraging Big Data Analytics to Improve Quality of Care in Healthcare Organizations: A Configurational Perspective. Br. J. Manag. 2019; 30 (2): 362–388. Publisher Full Text Weng W: Impact of Competitive Strategy on Big Data Analytics Adoption: An Information Processing Perspective. Int. J. Electron. Commer. Stud. 2022; 13 (2): 001–001. Publisher Full Text Wijewickrama MK, Sanjani C, Chileshe N, et al. : Information Processing for Quality Assurance in Reverse Logistics Supply Chains: An Organizational Information Processing Theory Perspective. Sustainability. 2022; 14 (9): 5493. Publisher Full Text Williams SK, Brown JA: Empirical Evidence on the Impact of Big Data on Order Management Efficiency. Int. J. Log. Res. Appl. 2020; 23 (4): 215–230. Wohlin C: Guidelines for Snowballing in Systematic Literature Studies and a Replication in Software Engineering. Proceedings of the 18th International Conference on Evaluation and Assessment in Software Engineering. 2014. May. Publisher Full Text Zamani ED, Smyth C, Gupta S, et al. : Artificial Intelligence and Big Data Analytics for Supply Chain Resilience: A Systematic Literature Review. Ann. Oper. Res. 2022; 327 (2): 1–28. PubMed Abstract | Publisher Full Text | Free Full Text Zhu S, Song J, Hazen BT, et al. : How Supply Chain Analytics Enables Operational Supply Chain Transparency. Int. J. Phys. Distrib. Logist. Manag. 2018; 48 (1): 47–68. Publisher Full Text Ziaee M, Shee HK, Sohal A: Big Data Analytics in Australian Pharmaceutical Supply Chain. Ind. Manag. Data Syst. 2023; 123 (5): 1310–1335. Publisher Full Text Zineb B, Mariem NB, Amor NO, et al. : Impact of Big Data Analytics on Healthcare Supply Chain Management: A UAE Perspective. J. Biomed. Inform. 2024; 145 : 104123. Publisher Full Text Comments on this article Comments (0) Version 4 VERSION 4 PUBLISHED 16 Oct 2024 ADD YOUR COMMENT Comment Author details Author details 1 Health and Environmental Studies, Hamdan Bin Mohammed Smart University, Dubai, Dubai, 0000, United Arab Emirates Dina Al Nuaimi Roles: Investigation, Methodology, Writing – Review & Editing Niyi Awofeso Roles: Writing – Review & Editing Competing interests No competing interests were disclosed. Grant information The author(s) declared that no grants were involved in supporting this work. Article Versions (4) version 4 Revised Published: 19 Feb 2025, 13:1237 https://doi.org/10.12688/f1000research.156525.4 version 3 Revised Published: 16 Dec 2024, 13:1237 https://doi.org/10.12688/f1000research.156525.3 version 2 Revised Published: 26 Nov 2024, 13:1237 https://doi.org/10.12688/f1000research.156525.2 version 1 Published: 16 Oct 2024, 13:1237 https://doi.org/10.12688/f1000research.156525.1 Copyright © 2025 Al Nuaimi D and Awofeso N. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Download Export To Sciwheel Bibtex EndNote ProCite Ref. Manager (RIS) Sente metrics Views Downloads F1000Research - - PubMed Central info_outline Data from PMC are received and updated monthly. - - Citations open_in_new 0 open_in_new 0 open_in_new SEE MORE DETAILS CITE how to cite this article Al Nuaimi D and Awofeso N. The Value of Applying Big Data Analytics in Health Supply Chain Management [version 4; peer review: 2 approved] . F1000Research 2025, 13 :1237 ( https://doi.org/10.12688/f1000research.156525.4 ) NOTE: If applicable, it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS track receive updates on this article Track an article to receive email alerts on any updates to this article. TRACK THIS ARTICLE Share Open Peer Review Current Reviewer Status: ? Key to Reviewer Statuses VIEW HIDE Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions Version 4 VERSION 4 PUBLISHED 19 Feb 2025 Revised Views 0 Cite How to cite this report: Beaulieu M. Reviewer Report For: The Value of Applying Big Data Analytics in Health Supply Chain Management [version 4; peer review: 2 approved] . F1000Research 2025, 13 :1237 ( https://doi.org/10.5256/f1000research.178092.r367582 ) The direct URL for this report is: https://f1000research.com/articles/13-1237/v4#referee-response-367582 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Close Copy Citation Details Reviewer Report 20 Feb 2025 Martin Beaulieu , HEC Montréal, Montreal, Canada Approved VIEWS 0 https://doi.org/10.5256/f1000research.178092.r367582 Sorry I missed the previous revisions of your paper. I find ... Continue reading READ ALL Sorry I missed the previous revisions of your paper. I find this version much improved and see no further changes to suggest. Competing Interests: No competing interests were disclosed. I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. Close READ LESS CITE CITE HOW TO CITE THIS REPORT Beaulieu M. Reviewer Report For: The Value of Applying Big Data Analytics in Health Supply Chain Management [version 4; peer review: 2 approved] . F1000Research 2025, 13 :1237 ( https://doi.org/10.5256/f1000research.178092.r367582 ) The direct URL for this report is: https://f1000research.com/articles/13-1237/v4#referee-response-367582 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS Report a concern Respond or Comment COMMENT ON THIS REPORT Version 3 VERSION 3 PUBLISHED 16 Dec 2024 Revised Views 0 Cite How to cite this report: Polater A. Reviewer Report For: The Value of Applying Big Data Analytics in Health Supply Chain Management [version 4; peer review: 2 approved] . F1000Research 2025, 13 :1237 ( https://doi.org/10.5256/f1000research.175869.r349213 ) The direct URL for this report is: https://f1000research.com/articles/13-1237/v3#referee-response-349213 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Close Copy Citation Details Reviewer Report 20 Dec 2024 Abdüssamet Polater , Erzincan Binali Yildirim Universitesi, Erzincan, Erzincan, Turkey Approved VIEWS 0 https://doi.org/10.5256/f1000research.175869.r349213 The paper is scientifically ... Continue reading READ ALL The paper is scientifically sound in its current form. Competing Interests: No competing interests were disclosed. Reviewer Expertise: Supply chain management; Logistics management I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. Close READ LESS CITE CITE HOW TO CITE THIS REPORT Polater A. Reviewer Report For: The Value of Applying Big Data Analytics in Health Supply Chain Management [version 4; peer review: 2 approved] . F1000Research 2025, 13 :1237 ( https://doi.org/10.5256/f1000research.175869.r349213 ) The direct URL for this report is: https://f1000research.com/articles/13-1237/v3#referee-response-349213 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS Report a concern Respond or Comment COMMENT ON THIS REPORT Version 2 VERSION 2 PUBLISHED 26 Nov 2024 Revised Views 0 Cite How to cite this report: Polater A. Reviewer Report For: The Value of Applying Big Data Analytics in Health Supply Chain Management [version 4; peer review: 2 approved] . F1000Research 2025, 13 :1237 ( https://doi.org/10.5256/f1000research.174834.r343643 ) The direct URL for this report is: https://f1000research.com/articles/13-1237/v2#referee-response-343643 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Close Copy Citation Details Reviewer Report 06 Dec 2024 Abdüssamet Polater , Erzincan Binali Yildirim Universitesi, Erzincan, Erzincan, Turkey Approved with Reservations VIEWS 0 https://doi.org/10.5256/f1000research.174834.r343643 The paper is scientifically sound in its current form and only minor, if any, improvements are suggested. However, I only require from the authors to include the explanations that they made in the response letter into the manuscript's methodology section. ... Continue reading READ ALL The paper is scientifically sound in its current form and only minor, if any, improvements are suggested. However, I only require from the authors to include the explanations that they made in the response letter into the manuscript's methodology section. They should include the reason of starting to screen in 2016, the keywords used for screening, journals (science category, index type etc.) were included and how they analyzed the papers included. Competing Interests: No competing interests were disclosed. Reviewer Expertise: Supply chain management; Logistics management I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. Close READ LESS CITE CITE HOW TO CITE THIS REPORT Polater A. Reviewer Report For: The Value of Applying Big Data Analytics in Health Supply Chain Management [version 4; peer review: 2 approved] . F1000Research 2025, 13 :1237 ( https://doi.org/10.5256/f1000research.174834.r343643 ) The direct URL for this report is: https://f1000research.com/articles/13-1237/v2#referee-response-343643 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS Report a concern Author Response 11 Dec 2024 دينا النعيمي , Health and Environmental Studies, Hamdan Bin Mohammed Smart University, Dubai, 0000, United Arab Emirates 11 Dec 2024 Author Response Thank you for your positive feedback and for recognizing the scientific merit of our paper. We greatly appreciate your constructive suggestion to include additional details in the Methodology section. ... Continue reading Thank you for your positive feedback and for recognizing the scientific merit of our paper. We greatly appreciate your constructive suggestion to include additional details in the Methodology section. In response, we have incorporated the following explanations into the manuscript: The rationale for starting the screening process in 2016. The specific keywords used for the screening process. The journal inclusion criteria (e.g., science category, index type). The analytical approach was applied to the selected papers. These details have been added to the Methodology section. We believe these revisions enhance the clarity and transparency of our study. Thank you for your thoughtful input, which has helped us improve our manuscript. Thank you for your positive feedback and for recognizing the scientific merit of our paper. We greatly appreciate your constructive suggestion to include additional details in the Methodology section. In response, we have incorporated the following explanations into the manuscript: The rationale for starting the screening process in 2016. The specific keywords used for the screening process. The journal inclusion criteria (e.g., science category, index type). The analytical approach was applied to the selected papers. These details have been added to the Methodology section. We believe these revisions enhance the clarity and transparency of our study. Thank you for your thoughtful input, which has helped us improve our manuscript. Competing Interests: No competing interests were disclosed. Close Report a concern Respond or Comment COMMENTS ON THIS REPORT Author Response 11 Dec 2024 دينا النعيمي , Health and Environmental Studies, Hamdan Bin Mohammed Smart University, Dubai, 0000, United Arab Emirates 11 Dec 2024 Author Response Thank you for your positive feedback and for recognizing the scientific merit of our paper. We greatly appreciate your constructive suggestion to include additional details in the Methodology section. ... Continue reading Thank you for your positive feedback and for recognizing the scientific merit of our paper. We greatly appreciate your constructive suggestion to include additional details in the Methodology section. In response, we have incorporated the following explanations into the manuscript: The rationale for starting the screening process in 2016. The specific keywords used for the screening process. The journal inclusion criteria (e.g., science category, index type). The analytical approach was applied to the selected papers. These details have been added to the Methodology section. We believe these revisions enhance the clarity and transparency of our study. Thank you for your thoughtful input, which has helped us improve our manuscript. Thank you for your positive feedback and for recognizing the scientific merit of our paper. We greatly appreciate your constructive suggestion to include additional details in the Methodology section. In response, we have incorporated the following explanations into the manuscript: The rationale for starting the screening process in 2016. The specific keywords used for the screening process. The journal inclusion criteria (e.g., science category, index type). The analytical approach was applied to the selected papers. These details have been added to the Methodology section. We believe these revisions enhance the clarity and transparency of our study. Thank you for your thoughtful input, which has helped us improve our manuscript. Competing Interests: No competing interests were disclosed. Close Report a concern COMMENT ON THIS REPORT Version 1 VERSION 1 PUBLISHED 16 Oct 2024 Views 0 Cite How to cite this report: Polater A. Reviewer Report For: The Value of Applying Big Data Analytics in Health Supply Chain Management [version 4; peer review: 2 approved] . F1000Research 2025, 13 :1237 ( https://doi.org/10.5256/f1000research.171850.r333492 ) The direct URL for this report is: https://f1000research.com/articles/13-1237/v1#referee-response-333492 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Close Copy Citation Details Reviewer Report 12 Nov 2024 Abdüssamet Polater , Erzincan Binali Yildirim Universitesi, Erzincan, Erzincan, Turkey Approved with Reservations VIEWS 0 https://doi.org/10.5256/f1000research.171850.r333492 Comments for the Abstract The aim of the study is confusing in the abstract section. It is stated that “This study aims to assess how the application of BDA impacts the performance of healthcare supply chain management (HCSCMP).” Then, ... Continue reading READ ALL Comments for the Abstract The aim of the study is confusing in the abstract section. It is stated that “This study aims to assess how the application of BDA impacts the performance of healthcare supply chain management (HCSCMP).” Then, investigating the efficiency and success factors are stated as an aim of the study. Comments for the Introduction “The study began with a descriptive analysis of academic research on BDA in the context of SCM, followed by a content analysis to assess the impact of BDA-based management systems on HCSCMP and to identify the key enablers and challenges for implementing BDA in HCSC.” The location of this sentence can be changed in the introduction sentence. The paragraph which starts with “Big Data Analytics (BDA) in healthcare is transformative…” is fine until the “Healthcare facilities manage both structured and unstructured data.” sentence. Then suddenly the authors starts to talk about the “structured and unstructured data”. Is there any need to talk about “structured and unstructured data” here? The paragraph starting with the “Adopting BDA in HCSCs facilitates real-time service delivery, data-driven decision-making, and improved performance (Araz et al., 2020).” should be reorganized. “Further research is needed to explore how BDA can enhance healthcare supply chain management performance (HCSCMP) and to validate existing findings.” can be the last sentence and the later sentences can be integrated as drivers of HSCP. “RQ1. What is the number of academic studies on Big Data Analytics in the context of Supply Chain Management, and what research methods and data collection techniques have been used in these studies?” This RQ can be shortened such as telling “investigating the trends in application of BDA in the HSCM”. “RQ2. How does the application of Big Data Analytics enhance efficiency in Healthcare Supply Chains Management according to existing studies?”. According to the abstract this study aims to investigate efficiency and performance of HSCM. But efficiency is only mentioned in the RQ2. The paragraph which is located after research questions should be significantly reorganized. I assume that the authors wanted to talk about the contribution of the study. However, it sounds more the findings or may be conclusion section. Comments for the Big Data and Big Data analytics in Healthcare Supply Chain Management This section mainly talks about the big data. But it does not discuss big data in Healthcare Supply Chain Management. Comments for the Organizational Information Process Theory Why did you specifically created a section titled as “Organizational Information Process Theory”? Comments for the Methodology What is the reason of starting to screen in 2016? What are the keywords used for screening? Which journals (science category, index type etc.) were included? How did you analyze the papers included? Comments for the Results analysis Please only use Figure 2 or Figure 3. “Research methods and data collection techniques have been used in big data analytics in the context of supply chain management studies”. Please add “health” before “supply chain management studies”. I did not understand to relating and discussing the country and research method (Table 1, Figure 5). Could you explain the importance of relating these two different findings? Comments for the Discussion The discussion section seems the repetition of the results section. In order to overcome this problem, it would be better to synthesize and discuss the “Efficiency Enhancements of Big Data Analytics in Healthcare Supply Chains” and “Enablers and Challenges in Implementing Big Data Analytics in Healthcare Supply Chains” sections under one title. Reviewer Decision I suggest the authors revise and develop the sections based on the comments. Are the rationale for, and objectives of, the Systematic Review clearly stated? Partly Are sufficient details of the methods and analysis provided to allow replication by others? Partly Is the statistical analysis and its interpretation appropriate? Partly Are the conclusions drawn adequately supported by the results presented in the review? Yes If this is a Living Systematic Review, is the ‘living’ method appropriate and is the search schedule clearly defined and justified? (‘Living Systematic Review’ or a variation of this term should be included in the title.) Yes Competing Interests: No competing interests were disclosed. Reviewer Expertise: Supply chain management; Logistics management I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. Close READ LESS CITE CITE HOW TO CITE THIS REPORT Polater A. Reviewer Report For: The Value of Applying Big Data Analytics in Health Supply Chain Management [version 4; peer review: 2 approved] . F1000Research 2025, 13 :1237 ( https://doi.org/10.5256/f1000research.171850.r333492 ) The direct URL for this report is: https://f1000research.com/articles/13-1237/v1#referee-response-333492 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS Report a concern Author Response 26 Nov 2024 دينا النعيمي , Health and Environmental Studies, Hamdan Bin Mohammed Smart University, Dubai, 0000, United Arab Emirates 26 Nov 2024 Author Response Abstract: Thank you for your valuable feedback. I have revised the abstract to clarify the study's aim, emphasizing that assessing the impact of BDA on performance, along ... Continue reading Abstract: Thank you for your valuable feedback. I have revised the abstract to clarify the study's aim, emphasizing that assessing the impact of BDA on performance, along with examining efficiency improvements and success factors, are interconnected objectives of the research. This should make the focus of the study clearer in the abstract. Location of Sentence on Descriptive Analysis and Content Analysis : Thank you for this suggestion. We will reposition the sentence, “The study began with a descriptive analysis of academic research on BDA in the context of SCM, followed by a content analysis to assess the impact of BDA-based management systems on HCSCMP and to identify the key enablers and challenges for implementing BDA in HCSC,” to improve the flow of the introduction. This will help clarify the structure and methodology of the study within the introductory context. After research questions guide the study: Discussion of Structured and Unstructured Data : We will revise the paragraph that starts with “Adopting BDA in HCSCs facilitates real-time service delivery, data-driven decision-making, and improved performance,” so that it concludes with, “Further research is needed to explore how BDA can enhance healthcare supply chain management performance (HCSCMP) and to validate existing findings.” This will allow the later sentences to flow naturally into a discussion of drivers in healthcare supply chain performance (HCSCP), following your helpful suggestion. Reorganizing the Paragraph on Adopting BDA : We will revise the paragraph that starts with “Adopting BDA in HCSCs facilitates real-time service delivery, data-driven decision-making, and improved performance,” so that it concludes with, “Further research is needed to explore how BDA can enhance healthcare supply chain management performance (HCSCMP) and to validate existing findings.” This will allow the later sentences to flow naturally into a discussion of drivers in healthcare supply chain performance (HCSCP), following your helpful suggestion. Rephrasing Research Question 1 : RQ1 : What are the trends in the application of BDA in HCSCM? Clarifying Efficiency in RQ2 : Thank you for pointing out this discrepancy. We will modify the abstract to clarify that both efficiency and performance are central aims, ensuring consistency with RQ2’s focus on efficiency within healthcare supply chain management. Reorganizing the Paragraph Following the Research Questions : We recognize that the paragraph after the research questions may seem more like conclusions or findings. To clarify, we will revise this section to focus on the study's intended contributions and will remove or relocate content that discusses specific findings. Comments for the Big Data and Big Data analytics in Healthcare Supply Chain Management Section Revised Comments for the Organizational Information Process Theory Thank you for your question. The 'Organizational Information Process Theory (OIPT)' section was included to provide a theoretical foundation for understanding how Big Data Analytics (BDA) supports healthcare supply chain management. OIPT explains how organizations can enhance their decision-making and operational efficiency by improving their information processing capabilities. Given that BDA is fundamentally about managing, analysing, and utilizing large volumes of data, OIPT offers a relevant lens to explore BDA’s role in healthcare supply chains. This theoretical framework helps explain how BDA can optimize information flow, reduce uncertainty, and support real-time decision-making, which are crucial for successful supply chain management in healthcare. Methodology: Reason for Starting the Screening in 2016 : The screening period starts from 2016 because of a noticeable increase in academic research on Big Data Analytics in healthcare supply chains around that time. Starting in 2016 ensures the inclusion of recent and relevant studies, aligning with the growing interest and advancements in this area. The screening period begins in 2016 to capture recent developments and a noticeable rise in research on Big Data Analytics in healthcare supply chains during this period. Keywords Used for Screening : Keywords likely included terms related to big data analytics, supply chain management, and healthcare, such as “Big Data Analytics,” “Supply Chain Performance,” “Healthcare Supply Chain Management,” and others relevant to enablers and challenges in BDA implementation. The keywords used included ‘Big Data Analytics,’ ‘Supply Chain Performance,’ ‘Healthcare Supply Chain Management,’ along with terms related to enablers and challenges in implementing BDA. Which journals (science category, index type etc.) were included? The review focused on peer-reviewed journals indexed in recognized databases like Scopus and Google Scholar, specifically in areas such as healthcare management, supply chain management, and information systems. We included peer-reviewed journals indexed in major databases like Scopus and Google Scholar, focusing on healthcare management, supply chain management, and information systems. How did you analyze the papers included? The analysis involved a systematic literature review methodology. The papers were screened, selected, and synthesized based on their contributions to understanding Big Data Analytics’ impact on healthcare supply chains, including descriptive and content analysis to identify key findings, enablers, and challenges. The included papers underwent descriptive and content analysis to synthesize insights on BDA’s impact on healthcare supply chains, focusing on efficiency improvements and identifying key enablers and challenges for successful implementation. Results analysis: Comments for the Results analysis Only use Figure 2 or Figure 3. Response to Reviewer: Thank you for your feedback. I have revised the analysis to include only Figure 2, as suggested. “Research methods and data collection techniques have been used in big data analytics in the context of supply chain management studies”. Please add “health” before “supply chain management studies”. Additionally, I have updated the phrase to read, ‘Research methods and data collection techniques have been used in big data analytics in the context of healthcare supply chain management studies,’ to clarify the healthcare focus of this review." Discussing the country and research method (Table 1, Figure 5). Could you explain the importance of relating these two different findings? Thank you for highlighting this point. The purpose of relating countries with research methods in Table 1 and Figure 5 is to showcase regional preferences and methodological diversity in Big Data Analytics (BDA) research within healthcare supply chains. This comparison helps reveal how different regions prioritize certain research methods—such as questionnaires in North America and interviews in Australia—reflecting regional contexts, available resources, and academic traditions. By understanding these differences, we gain insight into the approaches that researchers in various regions use to investigate BDA’s impact on healthcare supply chain management, which could indicate unique challenges, cultural influences, or operational focuses in each region. This contextual understanding enhances the comprehensiveness of our review by demonstrating how geographic and methodological factors may shape research outcomes and perspectives on BDA implementation. Comments for the Discussion Response to Reviewer: Thank you for this helpful suggestion. To enhance the discussion and avoid redundancy, we will integrate the ‘Efficiency Enhancements of Big Data Analytics in Healthcare Supply Chains’ and ‘Enablers and Challenges in Implementing Big Data Analytics in Healthcare Supply Chains’ under a unified title, such as ‘Impact of Big Data Analytics on Healthcare Supply Chain Performance: Efficiency, Enablers, and Challenges.’ This synthesis will allow us to discuss both the benefits and obstacles of BDA implementation within a single framework, creating a more streamlined and interpretive analysis. By reorganizing this way, we can directly relate the efficiency improvements BDA offers to the specific enablers and challenges that influence its successful adoption. This will provide a more holistic view of BDA’s impact on healthcare supply chains, enabling readers to understand how these factors work together to enhance or inhibit supply chain performance. Abstract: Thank you for your valuable feedback. I have revised the abstract to clarify the study's aim, emphasizing that assessing the impact of BDA on performance, along with examining efficiency improvements and success factors, are interconnected objectives of the research. This should make the focus of the study clearer in the abstract. Location of Sentence on Descriptive Analysis and Content Analysis : Thank you for this suggestion. We will reposition the sentence, “The study began with a descriptive analysis of academic research on BDA in the context of SCM, followed by a content analysis to assess the impact of BDA-based management systems on HCSCMP and to identify the key enablers and challenges for implementing BDA in HCSC,” to improve the flow of the introduction. This will help clarify the structure and methodology of the study within the introductory context. After research questions guide the study: Discussion of Structured and Unstructured Data : We will revise the paragraph that starts with “Adopting BDA in HCSCs facilitates real-time service delivery, data-driven decision-making, and improved performance,” so that it concludes with, “Further research is needed to explore how BDA can enhance healthcare supply chain management performance (HCSCMP) and to validate existing findings.” This will allow the later sentences to flow naturally into a discussion of drivers in healthcare supply chain performance (HCSCP), following your helpful suggestion. Reorganizing the Paragraph on Adopting BDA : We will revise the paragraph that starts with “Adopting BDA in HCSCs facilitates real-time service delivery, data-driven decision-making, and improved performance,” so that it concludes with, “Further research is needed to explore how BDA can enhance healthcare supply chain management performance (HCSCMP) and to validate existing findings.” This will allow the later sentences to flow naturally into a discussion of drivers in healthcare supply chain performance (HCSCP), following your helpful suggestion. Rephrasing Research Question 1 : RQ1 : What are the trends in the application of BDA in HCSCM? Clarifying Efficiency in RQ2 : Thank you for pointing out this discrepancy. We will modify the abstract to clarify that both efficiency and performance are central aims, ensuring consistency with RQ2’s focus on efficiency within healthcare supply chain management. Reorganizing the Paragraph Following the Research Questions : We recognize that the paragraph after the research questions may seem more like conclusions or findings. To clarify, we will revise this section to focus on the study's intended contributions and will remove or relocate content that discusses specific findings. Comments for the Big Data and Big Data analytics in Healthcare Supply Chain Management Section Revised Comments for the Organizational Information Process Theory Thank you for your question. The 'Organizational Information Process Theory (OIPT)' section was included to provide a theoretical foundation for understanding how Big Data Analytics (BDA) supports healthcare supply chain management. OIPT explains how organizations can enhance their decision-making and operational efficiency by improving their information processing capabilities. Given that BDA is fundamentally about managing, analysing, and utilizing large volumes of data, OIPT offers a relevant lens to explore BDA’s role in healthcare supply chains. This theoretical framework helps explain how BDA can optimize information flow, reduce uncertainty, and support real-time decision-making, which are crucial for successful supply chain management in healthcare. Methodology: Reason for Starting the Screening in 2016 : The screening period starts from 2016 because of a noticeable increase in academic research on Big Data Analytics in healthcare supply chains around that time. Starting in 2016 ensures the inclusion of recent and relevant studies, aligning with the growing interest and advancements in this area. The screening period begins in 2016 to capture recent developments and a noticeable rise in research on Big Data Analytics in healthcare supply chains during this period. Keywords Used for Screening : Keywords likely included terms related to big data analytics, supply chain management, and healthcare, such as “Big Data Analytics,” “Supply Chain Performance,” “Healthcare Supply Chain Management,” and others relevant to enablers and challenges in BDA implementation. The keywords used included ‘Big Data Analytics,’ ‘Supply Chain Performance,’ ‘Healthcare Supply Chain Management,’ along with terms related to enablers and challenges in implementing BDA. Which journals (science category, index type etc.) were included? The review focused on peer-reviewed journals indexed in recognized databases like Scopus and Google Scholar, specifically in areas such as healthcare management, supply chain management, and information systems. We included peer-reviewed journals indexed in major databases like Scopus and Google Scholar, focusing on healthcare management, supply chain management, and information systems. How did you analyze the papers included? The analysis involved a systematic literature review methodology. The papers were screened, selected, and synthesized based on their contributions to understanding Big Data Analytics’ impact on healthcare supply chains, including descriptive and content analysis to identify key findings, enablers, and challenges. The included papers underwent descriptive and content analysis to synthesize insights on BDA’s impact on healthcare supply chains, focusing on efficiency improvements and identifying key enablers and challenges for successful implementation. Results analysis: Comments for the Results analysis Only use Figure 2 or Figure 3. Response to Reviewer: Thank you for your feedback. I have revised the analysis to include only Figure 2, as suggested. “Research methods and data collection techniques have been used in big data analytics in the context of supply chain management studies”. Please add “health” before “supply chain management studies”. Additionally, I have updated the phrase to read, ‘Research methods and data collection techniques have been used in big data analytics in the context of healthcare supply chain management studies,’ to clarify the healthcare focus of this review." Discussing the country and research method (Table 1, Figure 5). Could you explain the importance of relating these two different findings? Thank you for highlighting this point. The purpose of relating countries with research methods in Table 1 and Figure 5 is to showcase regional preferences and methodological diversity in Big Data Analytics (BDA) research within healthcare supply chains. This comparison helps reveal how different regions prioritize certain research methods—such as questionnaires in North America and interviews in Australia—reflecting regional contexts, available resources, and academic traditions. By understanding these differences, we gain insight into the approaches that researchers in various regions use to investigate BDA’s impact on healthcare supply chain management, which could indicate unique challenges, cultural influences, or operational focuses in each region. This contextual understanding enhances the comprehensiveness of our review by demonstrating how geographic and methodological factors may shape research outcomes and perspectives on BDA implementation. Comments for the Discussion Response to Reviewer: Thank you for this helpful suggestion. To enhance the discussion and avoid redundancy, we will integrate the ‘Efficiency Enhancements of Big Data Analytics in Healthcare Supply Chains’ and ‘Enablers and Challenges in Implementing Big Data Analytics in Healthcare Supply Chains’ under a unified title, such as ‘Impact of Big Data Analytics on Healthcare Supply Chain Performance: Efficiency, Enablers, and Challenges.’ This synthesis will allow us to discuss both the benefits and obstacles of BDA implementation within a single framework, creating a more streamlined and interpretive analysis. By reorganizing this way, we can directly relate the efficiency improvements BDA offers to the specific enablers and challenges that influence its successful adoption. This will provide a more holistic view of BDA’s impact on healthcare supply chains, enabling readers to understand how these factors work together to enhance or inhibit supply chain performance. Competing Interests: No competing interests were disclosed. Close Report a concern Respond or Comment COMMENTS ON THIS REPORT Author Response 26 Nov 2024 دينا النعيمي , Health and Environmental Studies, Hamdan Bin Mohammed Smart University, Dubai, 0000, United Arab Emirates 26 Nov 2024 Author Response Abstract: Thank you for your valuable feedback. I have revised the abstract to clarify the study's aim, emphasizing that assessing the impact of BDA on performance, along ... Continue reading Abstract: Thank you for your valuable feedback. I have revised the abstract to clarify the study's aim, emphasizing that assessing the impact of BDA on performance, along with examining efficiency improvements and success factors, are interconnected objectives of the research. This should make the focus of the study clearer in the abstract. Location of Sentence on Descriptive Analysis and Content Analysis : Thank you for this suggestion. We will reposition the sentence, “The study began with a descriptive analysis of academic research on BDA in the context of SCM, followed by a content analysis to assess the impact of BDA-based management systems on HCSCMP and to identify the key enablers and challenges for implementing BDA in HCSC,” to improve the flow of the introduction. This will help clarify the structure and methodology of the study within the introductory context. After research questions guide the study: Discussion of Structured and Unstructured Data : We will revise the paragraph that starts with “Adopting BDA in HCSCs facilitates real-time service delivery, data-driven decision-making, and improved performance,” so that it concludes with, “Further research is needed to explore how BDA can enhance healthcare supply chain management performance (HCSCMP) and to validate existing findings.” This will allow the later sentences to flow naturally into a discussion of drivers in healthcare supply chain performance (HCSCP), following your helpful suggestion. Reorganizing the Paragraph on Adopting BDA : We will revise the paragraph that starts with “Adopting BDA in HCSCs facilitates real-time service delivery, data-driven decision-making, and improved performance,” so that it concludes with, “Further research is needed to explore how BDA can enhance healthcare supply chain management performance (HCSCMP) and to validate existing findings.” This will allow the later sentences to flow naturally into a discussion of drivers in healthcare supply chain performance (HCSCP), following your helpful suggestion. Rephrasing Research Question 1 : RQ1 : What are the trends in the application of BDA in HCSCM? Clarifying Efficiency in RQ2 : Thank you for pointing out this discrepancy. We will modify the abstract to clarify that both efficiency and performance are central aims, ensuring consistency with RQ2’s focus on efficiency within healthcare supply chain management. Reorganizing the Paragraph Following the Research Questions : We recognize that the paragraph after the research questions may seem more like conclusions or findings. To clarify, we will revise this section to focus on the study's intended contributions and will remove or relocate content that discusses specific findings. Comments for the Big Data and Big Data analytics in Healthcare Supply Chain Management Section Revised Comments for the Organizational Information Process Theory Thank you for your question. The 'Organizational Information Process Theory (OIPT)' section was included to provide a theoretical foundation for understanding how Big Data Analytics (BDA) supports healthcare supply chain management. OIPT explains how organizations can enhance their decision-making and operational efficiency by improving their information processing capabilities. Given that BDA is fundamentally about managing, analysing, and utilizing large volumes of data, OIPT offers a relevant lens to explore BDA’s role in healthcare supply chains. This theoretical framework helps explain how BDA can optimize information flow, reduce uncertainty, and support real-time decision-making, which are crucial for successful supply chain management in healthcare. Methodology: Reason for Starting the Screening in 2016 : The screening period starts from 2016 because of a noticeable increase in academic research on Big Data Analytics in healthcare supply chains around that time. Starting in 2016 ensures the inclusion of recent and relevant studies, aligning with the growing interest and advancements in this area. The screening period begins in 2016 to capture recent developments and a noticeable rise in research on Big Data Analytics in healthcare supply chains during this period. Keywords Used for Screening : Keywords likely included terms related to big data analytics, supply chain management, and healthcare, such as “Big Data Analytics,” “Supply Chain Performance,” “Healthcare Supply Chain Management,” and others relevant to enablers and challenges in BDA implementation. The keywords used included ‘Big Data Analytics,’ ‘Supply Chain Performance,’ ‘Healthcare Supply Chain Management,’ along with terms related to enablers and challenges in implementing BDA. Which journals (science category, index type etc.) were included? The review focused on peer-reviewed journals indexed in recognized databases like Scopus and Google Scholar, specifically in areas such as healthcare management, supply chain management, and information systems. We included peer-reviewed journals indexed in major databases like Scopus and Google Scholar, focusing on healthcare management, supply chain management, and information systems. How did you analyze the papers included? The analysis involved a systematic literature review methodology. The papers were screened, selected, and synthesized based on their contributions to understanding Big Data Analytics’ impact on healthcare supply chains, including descriptive and content analysis to identify key findings, enablers, and challenges. The included papers underwent descriptive and content analysis to synthesize insights on BDA’s impact on healthcare supply chains, focusing on efficiency improvements and identifying key enablers and challenges for successful implementation. Results analysis: Comments for the Results analysis Only use Figure 2 or Figure 3. Response to Reviewer: Thank you for your feedback. I have revised the analysis to include only Figure 2, as suggested. “Research methods and data collection techniques have been used in big data analytics in the context of supply chain management studies”. Please add “health” before “supply chain management studies”. Additionally, I have updated the phrase to read, ‘Research methods and data collection techniques have been used in big data analytics in the context of healthcare supply chain management studies,’ to clarify the healthcare focus of this review." Discussing the country and research method (Table 1, Figure 5). Could you explain the importance of relating these two different findings? Thank you for highlighting this point. The purpose of relating countries with research methods in Table 1 and Figure 5 is to showcase regional preferences and methodological diversity in Big Data Analytics (BDA) research within healthcare supply chains. This comparison helps reveal how different regions prioritize certain research methods—such as questionnaires in North America and interviews in Australia—reflecting regional contexts, available resources, and academic traditions. By understanding these differences, we gain insight into the approaches that researchers in various regions use to investigate BDA’s impact on healthcare supply chain management, which could indicate unique challenges, cultural influences, or operational focuses in each region. This contextual understanding enhances the comprehensiveness of our review by demonstrating how geographic and methodological factors may shape research outcomes and perspectives on BDA implementation. Comments for the Discussion Response to Reviewer: Thank you for this helpful suggestion. To enhance the discussion and avoid redundancy, we will integrate the ‘Efficiency Enhancements of Big Data Analytics in Healthcare Supply Chains’ and ‘Enablers and Challenges in Implementing Big Data Analytics in Healthcare Supply Chains’ under a unified title, such as ‘Impact of Big Data Analytics on Healthcare Supply Chain Performance: Efficiency, Enablers, and Challenges.’ This synthesis will allow us to discuss both the benefits and obstacles of BDA implementation within a single framework, creating a more streamlined and interpretive analysis. By reorganizing this way, we can directly relate the efficiency improvements BDA offers to the specific enablers and challenges that influence its successful adoption. This will provide a more holistic view of BDA’s impact on healthcare supply chains, enabling readers to understand how these factors work together to enhance or inhibit supply chain performance. Abstract: Thank you for your valuable feedback. I have revised the abstract to clarify the study's aim, emphasizing that assessing the impact of BDA on performance, along with examining efficiency improvements and success factors, are interconnected objectives of the research. This should make the focus of the study clearer in the abstract. Location of Sentence on Descriptive Analysis and Content Analysis : Thank you for this suggestion. We will reposition the sentence, “The study began with a descriptive analysis of academic research on BDA in the context of SCM, followed by a content analysis to assess the impact of BDA-based management systems on HCSCMP and to identify the key enablers and challenges for implementing BDA in HCSC,” to improve the flow of the introduction. This will help clarify the structure and methodology of the study within the introductory context. After research questions guide the study: Discussion of Structured and Unstructured Data : We will revise the paragraph that starts with “Adopting BDA in HCSCs facilitates real-time service delivery, data-driven decision-making, and improved performance,” so that it concludes with, “Further research is needed to explore how BDA can enhance healthcare supply chain management performance (HCSCMP) and to validate existing findings.” This will allow the later sentences to flow naturally into a discussion of drivers in healthcare supply chain performance (HCSCP), following your helpful suggestion. Reorganizing the Paragraph on Adopting BDA : We will revise the paragraph that starts with “Adopting BDA in HCSCs facilitates real-time service delivery, data-driven decision-making, and improved performance,” so that it concludes with, “Further research is needed to explore how BDA can enhance healthcare supply chain management performance (HCSCMP) and to validate existing findings.” This will allow the later sentences to flow naturally into a discussion of drivers in healthcare supply chain performance (HCSCP), following your helpful suggestion. Rephrasing Research Question 1 : RQ1 : What are the trends in the application of BDA in HCSCM? Clarifying Efficiency in RQ2 : Thank you for pointing out this discrepancy. We will modify the abstract to clarify that both efficiency and performance are central aims, ensuring consistency with RQ2’s focus on efficiency within healthcare supply chain management. Reorganizing the Paragraph Following the Research Questions : We recognize that the paragraph after the research questions may seem more like conclusions or findings. To clarify, we will revise this section to focus on the study's intended contributions and will remove or relocate content that discusses specific findings. Comments for the Big Data and Big Data analytics in Healthcare Supply Chain Management Section Revised Comments for the Organizational Information Process Theory Thank you for your question. The 'Organizational Information Process Theory (OIPT)' section was included to provide a theoretical foundation for understanding how Big Data Analytics (BDA) supports healthcare supply chain management. OIPT explains how organizations can enhance their decision-making and operational efficiency by improving their information processing capabilities. Given that BDA is fundamentally about managing, analysing, and utilizing large volumes of data, OIPT offers a relevant lens to explore BDA’s role in healthcare supply chains. This theoretical framework helps explain how BDA can optimize information flow, reduce uncertainty, and support real-time decision-making, which are crucial for successful supply chain management in healthcare. Methodology: Reason for Starting the Screening in 2016 : The screening period starts from 2016 because of a noticeable increase in academic research on Big Data Analytics in healthcare supply chains around that time. Starting in 2016 ensures the inclusion of recent and relevant studies, aligning with the growing interest and advancements in this area. The screening period begins in 2016 to capture recent developments and a noticeable rise in research on Big Data Analytics in healthcare supply chains during this period. Keywords Used for Screening : Keywords likely included terms related to big data analytics, supply chain management, and healthcare, such as “Big Data Analytics,” “Supply Chain Performance,” “Healthcare Supply Chain Management,” and others relevant to enablers and challenges in BDA implementation. The keywords used included ‘Big Data Analytics,’ ‘Supply Chain Performance,’ ‘Healthcare Supply Chain Management,’ along with terms related to enablers and challenges in implementing BDA. Which journals (science category, index type etc.) were included? The review focused on peer-reviewed journals indexed in recognized databases like Scopus and Google Scholar, specifically in areas such as healthcare management, supply chain management, and information systems. We included peer-reviewed journals indexed in major databases like Scopus and Google Scholar, focusing on healthcare management, supply chain management, and information systems. How did you analyze the papers included? The analysis involved a systematic literature review methodology. The papers were screened, selected, and synthesized based on their contributions to understanding Big Data Analytics’ impact on healthcare supply chains, including descriptive and content analysis to identify key findings, enablers, and challenges. The included papers underwent descriptive and content analysis to synthesize insights on BDA’s impact on healthcare supply chains, focusing on efficiency improvements and identifying key enablers and challenges for successful implementation. Results analysis: Comments for the Results analysis Only use Figure 2 or Figure 3. Response to Reviewer: Thank you for your feedback. I have revised the analysis to include only Figure 2, as suggested. “Research methods and data collection techniques have been used in big data analytics in the context of supply chain management studies”. Please add “health” before “supply chain management studies”. Additionally, I have updated the phrase to read, ‘Research methods and data collection techniques have been used in big data analytics in the context of healthcare supply chain management studies,’ to clarify the healthcare focus of this review." Discussing the country and research method (Table 1, Figure 5). Could you explain the importance of relating these two different findings? Thank you for highlighting this point. The purpose of relating countries with research methods in Table 1 and Figure 5 is to showcase regional preferences and methodological diversity in Big Data Analytics (BDA) research within healthcare supply chains. This comparison helps reveal how different regions prioritize certain research methods—such as questionnaires in North America and interviews in Australia—reflecting regional contexts, available resources, and academic traditions. By understanding these differences, we gain insight into the approaches that researchers in various regions use to investigate BDA’s impact on healthcare supply chain management, which could indicate unique challenges, cultural influences, or operational focuses in each region. This contextual understanding enhances the comprehensiveness of our review by demonstrating how geographic and methodological factors may shape research outcomes and perspectives on BDA implementation. Comments for the Discussion Response to Reviewer: Thank you for this helpful suggestion. To enhance the discussion and avoid redundancy, we will integrate the ‘Efficiency Enhancements of Big Data Analytics in Healthcare Supply Chains’ and ‘Enablers and Challenges in Implementing Big Data Analytics in Healthcare Supply Chains’ under a unified title, such as ‘Impact of Big Data Analytics on Healthcare Supply Chain Performance: Efficiency, Enablers, and Challenges.’ This synthesis will allow us to discuss both the benefits and obstacles of BDA implementation within a single framework, creating a more streamlined and interpretive analysis. By reorganizing this way, we can directly relate the efficiency improvements BDA offers to the specific enablers and challenges that influence its successful adoption. This will provide a more holistic view of BDA’s impact on healthcare supply chains, enabling readers to understand how these factors work together to enhance or inhibit supply chain performance. Competing Interests: No competing interests were disclosed. Close Report a concern COMMENT ON THIS REPORT Views 0 Cite How to cite this report: Beaulieu M. Reviewer Report For: The Value of Applying Big Data Analytics in Health Supply Chain Management [version 4; peer review: 2 approved] . F1000Research 2025, 13 :1237 ( https://doi.org/10.5256/f1000research.171850.r333485 ) The direct URL for this report is: https://f1000research.com/articles/13-1237/v1#referee-response-333485 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Close Copy Citation Details Reviewer Report 09 Nov 2024 Martin Beaulieu , HEC Montréal, Montreal, Canada Approved with Reservations VIEWS 0 https://doi.org/10.5256/f1000research.171850.r333485 On text form : Why do the authors combine the use of upper and lower case letters in the presentation of key words? The introduction starts very quickly, and the authors use acronyms without ... Continue reading READ ALL On text form : Why do the authors combine the use of upper and lower case letters in the presentation of key words? The introduction starts very quickly, and the authors use acronyms without defining them or defining them further in the text (BDA or HCSCMP). I find it a little strange that figure 1 is in the introduction and not in the “Methodology” section. Is the name “CİĞERCİ” capitalized? As far as the content of the paper is concerned, in the section “Big Data and Big Data analytics in Healthcare Supply Chain Management”, the authors tell us about Big Data, supply chain management, but they don't really deal with the Healthcare supply chain. What distinguishes the healthcare sector from other industries? See below comments. Why did you choose 2016 as the starting point for your reference search? I'm not convinced of the relevance of figure 3; it seems redundant with figure 2, or at the very least, I don't think it adds any real value. For the subsection answering question RQ2, is it possible to be more precise? There are two main supply chains in the healthcare sector: pharmaceuticals and medical supplies. Each presents its own challenges. So, the benefits suggested by these studies apply to which types of products? When I mentioned more development around HSCM in the literature review, the authors could expand on these ideas. This would give more depth to their discussion. I suggest that the authors consult these two references, as they may help the authors to develop some of the ideas in the literature review: Reference: (1,2) Are the rationale for, and objectives of, the Systematic Review clearly stated? Yes Are sufficient details of the methods and analysis provided to allow replication by others? Partly Is the statistical analysis and its interpretation appropriate? Partly Are the conclusions drawn adequately supported by the results presented in the review? Partly If this is a Living Systematic Review, is the ‘living’ method appropriate and is the search schedule clearly defined and justified? (‘Living Systematic Review’ or a variation of this term should be included in the title.) Yes References 1. Beaulieu M, Bentahar O: Digitalization of the healthcare supply chain: A roadmap to generate benefits and effectively support healthcare delivery. Technological Forecasting and Social Change . 2021; 167 . Publisher Full Text 2. Ouarda TBMJ, Masselot P, Campagna C, Gosselin P, et al.: Prediction of heatwave related mortality magnitude, duration and frequency with climate variability and climate change information. Stoch Environ Res Risk Assess . 2024; 38 (11): 4471-4483 PubMed Abstract | Publisher Full Text Competing Interests: No competing interests were disclosed. Reviewer Expertise: healthcare logistics, supply chain management, inventory management, purchasing I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. Close READ LESS CITE CITE HOW TO CITE THIS REPORT Beaulieu M. Reviewer Report For: The Value of Applying Big Data Analytics in Health Supply Chain Management [version 4; peer review: 2 approved] . F1000Research 2025, 13 :1237 ( https://doi.org/10.5256/f1000research.171850.r333485 ) The direct URL for this report is: https://f1000research.com/articles/13-1237/v1#referee-response-333485 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS Report a concern Author Response 19 Feb 2025 دينا النعيمي , Health and Environmental Studies, Hamdan Bin Mohammed Smart University, Dubai, 0000, United Arab Emirates 19 Feb 2025 Author Response Reviewer Comment: Why do the authors combine the use of upper and lower case letters in the presentation of key words? Author Response: The inconsistency in using ... Continue reading Reviewer Comment: Why do the authors combine the use of upper and lower case letters in the presentation of key words? Author Response: The inconsistency in using upper and lower-case letters in the keywords was unintended. This was standardized in the revised version, ensuring uniformity in formatting keywords to adhere to academic conventions. Reviewer Comment: The introduction starts very quickly, and the authors use acronyms without defining them or defining them further in the text (BDA or HCSCMP). Author Response: We acknowledge that acronyms such as BDA (Big Data Analytics) and HCSCMP (Healthcare Supply Chain Management Performance) were introduced without proper definition in the introduction. In the revised version, these acronyms defined upon first mention (IN THE Introduction Section). Reviewer Comment: I find it a little strange that figure 1 is in the introduction and not in the “Methodology” section. Author Response: Placement of Figure 1: The positioning of Figure 1 in the introduction section was intended to provide an early visual representation of the systematic review process. However, based on the feedback, this figure relocated to the “Methodology” section in the revised version, where it aligns more logically with the discussion of the research design. Reviewer Comment: Is the name “CİĞERCİ” capitalized? Author Response: Corrected: Correct Harvard-Style Formatting: Ciğerci, M. (2023) 'Main Effects of Big Data on Supply Chain Management. Implementation of Disruptive Technologies in Supply Chain Management,' Yeditepe University Institute of Social Sciences , Istanbul, Turkey, pp. 27–49. Correct In-Text Reference: (Ciğerci, 2023). Reviewer Comment: As far as the content of the paper is concerned, in the section “Big Data and Big Data analytics in Healthcare Supply Chain Management”, the authors tell us about Big Data, supply chain management, but they don't really deal with the Healthcare supply chain. What distinguishes the healthcare sector from other industries? Author Response: In the revised version of the paper, we included the unique characteristics of healthcare supply chains (HCSCs) that differentiate them from other industries. Under the section titled "Big Data and Big Data Analytics in Healthcare Supply Chain Management". 2 nd paragraph: HCSCs are fundamentally distinct from other industries due to their direct impact on human lives. The availability of medical supplies, medications, vaccines, and personal protective equipment (PPE) is critical for ensuring timely patient care, imposing higher demands for operational efficiency and resilience than conventional SCs (Govindan et al., 2022). HCSCs face significant challenges related to unpredictable demand, such as during pandemics like COVID-19, when the need for medications, ventilators, and vaccines surged unexpectedly, unlike traditional SCs that often experience steady demand patterns (Ivanov & Dolgui, 2021). Additionally, HCSCs are highly regulated to ensure patient safety and product quality, requiring compliance with stringent procurement, storage, and distribution standards, including temperature-controlled logistics for products like biologics and vaccines (Chen et al., 2021). The complexity of managing healthcare products, which often require specific handling conditions, adds unique logistical challenges (Ristevski & Chen, 2018). Moreover, BDA in HCSCs goes beyond improving operational efficiency to ensuring patient safety by predicting disease outbreaks, optimizing medication distribution, and reducing errors—functions rarely found in other industries (Nguyen et al., 2018). HCSCs prioritize patient-centred care, focusing on quality and reliability over cost savings, a priority that often distinguishes them from other sectors (Benzidia et al., 2023). References (Added): Govindan, K., et al. (2022). Pandemic-induced disruptions in healthcare supply chains. Supply Chain Management Review , 28(2), 98-111. Ivanov, D., & Dolgui, A. (2021). A digital supply chain twin for managing disruptions. International Journal of Production Research , 59(14), 4180-4195. Reviewer Comment: Why did you choose 2016 as the starting point for your reference search? Author Response: Rationale for Selecting 2016 as the Starting Point: The selection of 2016 as the starting point for the reference search was deliberate. It aligns with the period when Big Data Analytics began gaining substantial traction in healthcare supply chain research, as highlighted by studies such as Nguyen et al. (2018) and Hofmann & Rutschmann (2018). This decision ensures the review captures recent advancements and remains relevant to contemporary developments in the field. Reviewer Comment: I'm not convinced of the relevance of figure 3; it seems redundant with figure 2, or at the very least, I don't think it adds any real value. Author Response: We agree with the reviewer’s observation. In the revised version, we removed Figure 3 to streamline the presentation of results and avoid redundancy. (The bar chart in Figure 3 illustrates the number of publications by different methods used in BDA research within the context of SCM. Each bar represents a distinct research method and the total number of publications employing that method. Also removed ) Reviewer Comment: For the subsection answering question RQ2, is it possible to be more precise? There are two main supply chains in the healthcare sector: pharmaceuticals and medical supplies. Each presents its own challenges. So, the benefits suggested by these studies apply to which types of products? When I mentioned more development around HSCM in the literature review, the authors could expand on these ideas. This would give more depth to their discussion. Author Response: The BDA benefits suggested by the studies are pharmaceuticals, and medical supplies were included in the discussion section addressing RQ2. Included: BDA significantly benefits the two main SCs in the healthcare sector: pharmaceuticals and medical supplies. BDA addresses challenges such as demand forecasting inaccuracies, drug shortages, and cold chain logistics for pharmaceuticals by leveraging predictive analytics to assess disease trends and optimize medication production and distribution. It also enhances real-time inventory monitoring to ensure compliance with strict storage requirements, especially for temperature-sensitive drugs like vaccines (Beaulieu & Bentahar, 2021; Ouarda et al., 2024). BDA mitigates high inventory variability and fluctuating demand in medical supplies by optimizing procurement processes through historical usage data and enabling agile supplier identification during emergencies, such as the COVID-19 pandemic. By providing end-to-end visibility across SCs, BDA reduces lead times and ensures timely order fulfilment, making it a critical tool for strengthening HCSC resilience and efficiency (Beaulieu & Bentahar, 2021). References (included): Beaulieu, M., & Bentahar, O. (2021). Digitalization of the healthcare supply chain: A roadmap to generate benefits and effectively support healthcare delivery. Technological Forecasting and Social Change, 167 . https://doi.org/10.1016/j.techfore.2021.120657 Ouarda, T. B. M. J., Masselot, P., Campagna, C., & Gosselin, P. (2024). Prediction of heatwave-related mortality magnitude, duration, and frequency with climate variability and climate change information. Stochastic Environmental Research and Risk Assessment, 38 (11), 4471-4483. https://doi.org/10.1007/s00477-024-02179-5 Reviewer Comment: I suggest that the authors consult these two references. Author Response: Yes, Suggested references included in the discussion section. Reviewer Comment: Why do the authors combine the use of upper and lower case letters in the presentation of key words? Author Response: The inconsistency in using upper and lower-case letters in the keywords was unintended. This was standardized in the revised version, ensuring uniformity in formatting keywords to adhere to academic conventions. Reviewer Comment: The introduction starts very quickly, and the authors use acronyms without defining them or defining them further in the text (BDA or HCSCMP). Author Response: We acknowledge that acronyms such as BDA (Big Data Analytics) and HCSCMP (Healthcare Supply Chain Management Performance) were introduced without proper definition in the introduction. In the revised version, these acronyms defined upon first mention (IN THE Introduction Section). Reviewer Comment: I find it a little strange that figure 1 is in the introduction and not in the “Methodology” section. Author Response: Placement of Figure 1: The positioning of Figure 1 in the introduction section was intended to provide an early visual representation of the systematic review process. However, based on the feedback, this figure relocated to the “Methodology” section in the revised version, where it aligns more logically with the discussion of the research design. Reviewer Comment: Is the name “CİĞERCİ” capitalized? Author Response: Corrected: Correct Harvard-Style Formatting: Ciğerci, M. (2023) 'Main Effects of Big Data on Supply Chain Management. Implementation of Disruptive Technologies in Supply Chain Management,' Yeditepe University Institute of Social Sciences , Istanbul, Turkey, pp. 27–49. Correct In-Text Reference: (Ciğerci, 2023). Reviewer Comment: As far as the content of the paper is concerned, in the section “Big Data and Big Data analytics in Healthcare Supply Chain Management”, the authors tell us about Big Data, supply chain management, but they don't really deal with the Healthcare supply chain. What distinguishes the healthcare sector from other industries? Author Response: In the revised version of the paper, we included the unique characteristics of healthcare supply chains (HCSCs) that differentiate them from other industries. Under the section titled "Big Data and Big Data Analytics in Healthcare Supply Chain Management". 2 nd paragraph: HCSCs are fundamentally distinct from other industries due to their direct impact on human lives. The availability of medical supplies, medications, vaccines, and personal protective equipment (PPE) is critical for ensuring timely patient care, imposing higher demands for operational efficiency and resilience than conventional SCs (Govindan et al., 2022). HCSCs face significant challenges related to unpredictable demand, such as during pandemics like COVID-19, when the need for medications, ventilators, and vaccines surged unexpectedly, unlike traditional SCs that often experience steady demand patterns (Ivanov & Dolgui, 2021). Additionally, HCSCs are highly regulated to ensure patient safety and product quality, requiring compliance with stringent procurement, storage, and distribution standards, including temperature-controlled logistics for products like biologics and vaccines (Chen et al., 2021). The complexity of managing healthcare products, which often require specific handling conditions, adds unique logistical challenges (Ristevski & Chen, 2018). Moreover, BDA in HCSCs goes beyond improving operational efficiency to ensuring patient safety by predicting disease outbreaks, optimizing medication distribution, and reducing errors—functions rarely found in other industries (Nguyen et al., 2018). HCSCs prioritize patient-centred care, focusing on quality and reliability over cost savings, a priority that often distinguishes them from other sectors (Benzidia et al., 2023). References (Added): Govindan, K., et al. (2022). Pandemic-induced disruptions in healthcare supply chains. Supply Chain Management Review , 28(2), 98-111. Ivanov, D., & Dolgui, A. (2021). A digital supply chain twin for managing disruptions. International Journal of Production Research , 59(14), 4180-4195. Reviewer Comment: Why did you choose 2016 as the starting point for your reference search? Author Response: Rationale for Selecting 2016 as the Starting Point: The selection of 2016 as the starting point for the reference search was deliberate. It aligns with the period when Big Data Analytics began gaining substantial traction in healthcare supply chain research, as highlighted by studies such as Nguyen et al. (2018) and Hofmann & Rutschmann (2018). This decision ensures the review captures recent advancements and remains relevant to contemporary developments in the field. Reviewer Comment: I'm not convinced of the relevance of figure 3; it seems redundant with figure 2, or at the very least, I don't think it adds any real value. Author Response: We agree with the reviewer’s observation. In the revised version, we removed Figure 3 to streamline the presentation of results and avoid redundancy. (The bar chart in Figure 3 illustrates the number of publications by different methods used in BDA research within the context of SCM. Each bar represents a distinct research method and the total number of publications employing that method. Also removed ) Reviewer Comment: For the subsection answering question RQ2, is it possible to be more precise? There are two main supply chains in the healthcare sector: pharmaceuticals and medical supplies. Each presents its own challenges. So, the benefits suggested by these studies apply to which types of products? When I mentioned more development around HSCM in the literature review, the authors could expand on these ideas. This would give more depth to their discussion. Author Response: The BDA benefits suggested by the studies are pharmaceuticals, and medical supplies were included in the discussion section addressing RQ2. Included: BDA significantly benefits the two main SCs in the healthcare sector: pharmaceuticals and medical supplies. BDA addresses challenges such as demand forecasting inaccuracies, drug shortages, and cold chain logistics for pharmaceuticals by leveraging predictive analytics to assess disease trends and optimize medication production and distribution. It also enhances real-time inventory monitoring to ensure compliance with strict storage requirements, especially for temperature-sensitive drugs like vaccines (Beaulieu & Bentahar, 2021; Ouarda et al., 2024). BDA mitigates high inventory variability and fluctuating demand in medical supplies by optimizing procurement processes through historical usage data and enabling agile supplier identification during emergencies, such as the COVID-19 pandemic. By providing end-to-end visibility across SCs, BDA reduces lead times and ensures timely order fulfilment, making it a critical tool for strengthening HCSC resilience and efficiency (Beaulieu & Bentahar, 2021). References (included): Beaulieu, M., & Bentahar, O. (2021). Digitalization of the healthcare supply chain: A roadmap to generate benefits and effectively support healthcare delivery. Technological Forecasting and Social Change, 167 . https://doi.org/10.1016/j.techfore.2021.120657 Ouarda, T. B. M. J., Masselot, P., Campagna, C., & Gosselin, P. (2024). Prediction of heatwave-related mortality magnitude, duration, and frequency with climate variability and climate change information. Stochastic Environmental Research and Risk Assessment, 38 (11), 4471-4483. https://doi.org/10.1007/s00477-024-02179-5 Reviewer Comment: I suggest that the authors consult these two references. Author Response: Yes, Suggested references included in the discussion section. Competing Interests: No competing interests were disclosed. Close Report a concern Respond or Comment COMMENTS ON THIS REPORT Author Response 19 Feb 2025 دينا النعيمي , Health and Environmental Studies, Hamdan Bin Mohammed Smart University, Dubai, 0000, United Arab Emirates 19 Feb 2025 Author Response Reviewer Comment: Why do the authors combine the use of upper and lower case letters in the presentation of key words? Author Response: The inconsistency in using ... Continue reading Reviewer Comment: Why do the authors combine the use of upper and lower case letters in the presentation of key words? Author Response: The inconsistency in using upper and lower-case letters in the keywords was unintended. This was standardized in the revised version, ensuring uniformity in formatting keywords to adhere to academic conventions. Reviewer Comment: The introduction starts very quickly, and the authors use acronyms without defining them or defining them further in the text (BDA or HCSCMP). Author Response: We acknowledge that acronyms such as BDA (Big Data Analytics) and HCSCMP (Healthcare Supply Chain Management Performance) were introduced without proper definition in the introduction. In the revised version, these acronyms defined upon first mention (IN THE Introduction Section). Reviewer Comment: I find it a little strange that figure 1 is in the introduction and not in the “Methodology” section. Author Response: Placement of Figure 1: The positioning of Figure 1 in the introduction section was intended to provide an early visual representation of the systematic review process. However, based on the feedback, this figure relocated to the “Methodology” section in the revised version, where it aligns more logically with the discussion of the research design. Reviewer Comment: Is the name “CİĞERCİ” capitalized? Author Response: Corrected: Correct Harvard-Style Formatting: Ciğerci, M. (2023) 'Main Effects of Big Data on Supply Chain Management. Implementation of Disruptive Technologies in Supply Chain Management,' Yeditepe University Institute of Social Sciences , Istanbul, Turkey, pp. 27–49. Correct In-Text Reference: (Ciğerci, 2023). Reviewer Comment: As far as the content of the paper is concerned, in the section “Big Data and Big Data analytics in Healthcare Supply Chain Management”, the authors tell us about Big Data, supply chain management, but they don't really deal with the Healthcare supply chain. What distinguishes the healthcare sector from other industries? Author Response: In the revised version of the paper, we included the unique characteristics of healthcare supply chains (HCSCs) that differentiate them from other industries. Under the section titled "Big Data and Big Data Analytics in Healthcare Supply Chain Management". 2 nd paragraph: HCSCs are fundamentally distinct from other industries due to their direct impact on human lives. The availability of medical supplies, medications, vaccines, and personal protective equipment (PPE) is critical for ensuring timely patient care, imposing higher demands for operational efficiency and resilience than conventional SCs (Govindan et al., 2022). HCSCs face significant challenges related to unpredictable demand, such as during pandemics like COVID-19, when the need for medications, ventilators, and vaccines surged unexpectedly, unlike traditional SCs that often experience steady demand patterns (Ivanov & Dolgui, 2021). Additionally, HCSCs are highly regulated to ensure patient safety and product quality, requiring compliance with stringent procurement, storage, and distribution standards, including temperature-controlled logistics for products like biologics and vaccines (Chen et al., 2021). The complexity of managing healthcare products, which often require specific handling conditions, adds unique logistical challenges (Ristevski & Chen, 2018). Moreover, BDA in HCSCs goes beyond improving operational efficiency to ensuring patient safety by predicting disease outbreaks, optimizing medication distribution, and reducing errors—functions rarely found in other industries (Nguyen et al., 2018). HCSCs prioritize patient-centred care, focusing on quality and reliability over cost savings, a priority that often distinguishes them from other sectors (Benzidia et al., 2023). References (Added): Govindan, K., et al. (2022). Pandemic-induced disruptions in healthcare supply chains. Supply Chain Management Review , 28(2), 98-111. Ivanov, D., & Dolgui, A. (2021). A digital supply chain twin for managing disruptions. International Journal of Production Research , 59(14), 4180-4195. Reviewer Comment: Why did you choose 2016 as the starting point for your reference search? Author Response: Rationale for Selecting 2016 as the Starting Point: The selection of 2016 as the starting point for the reference search was deliberate. It aligns with the period when Big Data Analytics began gaining substantial traction in healthcare supply chain research, as highlighted by studies such as Nguyen et al. (2018) and Hofmann & Rutschmann (2018). This decision ensures the review captures recent advancements and remains relevant to contemporary developments in the field. Reviewer Comment: I'm not convinced of the relevance of figure 3; it seems redundant with figure 2, or at the very least, I don't think it adds any real value. Author Response: We agree with the reviewer’s observation. In the revised version, we removed Figure 3 to streamline the presentation of results and avoid redundancy. (The bar chart in Figure 3 illustrates the number of publications by different methods used in BDA research within the context of SCM. Each bar represents a distinct research method and the total number of publications employing that method. Also removed ) Reviewer Comment: For the subsection answering question RQ2, is it possible to be more precise? There are two main supply chains in the healthcare sector: pharmaceuticals and medical supplies. Each presents its own challenges. So, the benefits suggested by these studies apply to which types of products? When I mentioned more development around HSCM in the literature review, the authors could expand on these ideas. This would give more depth to their discussion. Author Response: The BDA benefits suggested by the studies are pharmaceuticals, and medical supplies were included in the discussion section addressing RQ2. Included: BDA significantly benefits the two main SCs in the healthcare sector: pharmaceuticals and medical supplies. BDA addresses challenges such as demand forecasting inaccuracies, drug shortages, and cold chain logistics for pharmaceuticals by leveraging predictive analytics to assess disease trends and optimize medication production and distribution. It also enhances real-time inventory monitoring to ensure compliance with strict storage requirements, especially for temperature-sensitive drugs like vaccines (Beaulieu & Bentahar, 2021; Ouarda et al., 2024). BDA mitigates high inventory variability and fluctuating demand in medical supplies by optimizing procurement processes through historical usage data and enabling agile supplier identification during emergencies, such as the COVID-19 pandemic. By providing end-to-end visibility across SCs, BDA reduces lead times and ensures timely order fulfilment, making it a critical tool for strengthening HCSC resilience and efficiency (Beaulieu & Bentahar, 2021). References (included): Beaulieu, M., & Bentahar, O. (2021). Digitalization of the healthcare supply chain: A roadmap to generate benefits and effectively support healthcare delivery. Technological Forecasting and Social Change, 167 . https://doi.org/10.1016/j.techfore.2021.120657 Ouarda, T. B. M. J., Masselot, P., Campagna, C., & Gosselin, P. (2024). Prediction of heatwave-related mortality magnitude, duration, and frequency with climate variability and climate change information. Stochastic Environmental Research and Risk Assessment, 38 (11), 4471-4483. https://doi.org/10.1007/s00477-024-02179-5 Reviewer Comment: I suggest that the authors consult these two references. Author Response: Yes, Suggested references included in the discussion section. Reviewer Comment: Why do the authors combine the use of upper and lower case letters in the presentation of key words? Author Response: The inconsistency in using upper and lower-case letters in the keywords was unintended. This was standardized in the revised version, ensuring uniformity in formatting keywords to adhere to academic conventions. Reviewer Comment: The introduction starts very quickly, and the authors use acronyms without defining them or defining them further in the text (BDA or HCSCMP). Author Response: We acknowledge that acronyms such as BDA (Big Data Analytics) and HCSCMP (Healthcare Supply Chain Management Performance) were introduced without proper definition in the introduction. In the revised version, these acronyms defined upon first mention (IN THE Introduction Section). Reviewer Comment: I find it a little strange that figure 1 is in the introduction and not in the “Methodology” section. Author Response: Placement of Figure 1: The positioning of Figure 1 in the introduction section was intended to provide an early visual representation of the systematic review process. However, based on the feedback, this figure relocated to the “Methodology” section in the revised version, where it aligns more logically with the discussion of the research design. Reviewer Comment: Is the name “CİĞERCİ” capitalized? Author Response: Corrected: Correct Harvard-Style Formatting: Ciğerci, M. (2023) 'Main Effects of Big Data on Supply Chain Management. Implementation of Disruptive Technologies in Supply Chain Management,' Yeditepe University Institute of Social Sciences , Istanbul, Turkey, pp. 27–49. Correct In-Text Reference: (Ciğerci, 2023). Reviewer Comment: As far as the content of the paper is concerned, in the section “Big Data and Big Data analytics in Healthcare Supply Chain Management”, the authors tell us about Big Data, supply chain management, but they don't really deal with the Healthcare supply chain. What distinguishes the healthcare sector from other industries? Author Response: In the revised version of the paper, we included the unique characteristics of healthcare supply chains (HCSCs) that differentiate them from other industries. Under the section titled "Big Data and Big Data Analytics in Healthcare Supply Chain Management". 2 nd paragraph: HCSCs are fundamentally distinct from other industries due to their direct impact on human lives. The availability of medical supplies, medications, vaccines, and personal protective equipment (PPE) is critical for ensuring timely patient care, imposing higher demands for operational efficiency and resilience than conventional SCs (Govindan et al., 2022). HCSCs face significant challenges related to unpredictable demand, such as during pandemics like COVID-19, when the need for medications, ventilators, and vaccines surged unexpectedly, unlike traditional SCs that often experience steady demand patterns (Ivanov & Dolgui, 2021). Additionally, HCSCs are highly regulated to ensure patient safety and product quality, requiring compliance with stringent procurement, storage, and distribution standards, including temperature-controlled logistics for products like biologics and vaccines (Chen et al., 2021). The complexity of managing healthcare products, which often require specific handling conditions, adds unique logistical challenges (Ristevski & Chen, 2018). Moreover, BDA in HCSCs goes beyond improving operational efficiency to ensuring patient safety by predicting disease outbreaks, optimizing medication distribution, and reducing errors—functions rarely found in other industries (Nguyen et al., 2018). HCSCs prioritize patient-centred care, focusing on quality and reliability over cost savings, a priority that often distinguishes them from other sectors (Benzidia et al., 2023). References (Added): Govindan, K., et al. (2022). Pandemic-induced disruptions in healthcare supply chains. Supply Chain Management Review , 28(2), 98-111. Ivanov, D., & Dolgui, A. (2021). A digital supply chain twin for managing disruptions. International Journal of Production Research , 59(14), 4180-4195. Reviewer Comment: Why did you choose 2016 as the starting point for your reference search? Author Response: Rationale for Selecting 2016 as the Starting Point: The selection of 2016 as the starting point for the reference search was deliberate. It aligns with the period when Big Data Analytics began gaining substantial traction in healthcare supply chain research, as highlighted by studies such as Nguyen et al. (2018) and Hofmann & Rutschmann (2018). This decision ensures the review captures recent advancements and remains relevant to contemporary developments in the field. Reviewer Comment: I'm not convinced of the relevance of figure 3; it seems redundant with figure 2, or at the very least, I don't think it adds any real value. Author Response: We agree with the reviewer’s observation. In the revised version, we removed Figure 3 to streamline the presentation of results and avoid redundancy. (The bar chart in Figure 3 illustrates the number of publications by different methods used in BDA research within the context of SCM. Each bar represents a distinct research method and the total number of publications employing that method. Also removed ) Reviewer Comment: For the subsection answering question RQ2, is it possible to be more precise? There are two main supply chains in the healthcare sector: pharmaceuticals and medical supplies. Each presents its own challenges. So, the benefits suggested by these studies apply to which types of products? When I mentioned more development around HSCM in the literature review, the authors could expand on these ideas. This would give more depth to their discussion. Author Response: The BDA benefits suggested by the studies are pharmaceuticals, and medical supplies were included in the discussion section addressing RQ2. Included: BDA significantly benefits the two main SCs in the healthcare sector: pharmaceuticals and medical supplies. BDA addresses challenges such as demand forecasting inaccuracies, drug shortages, and cold chain logistics for pharmaceuticals by leveraging predictive analytics to assess disease trends and optimize medication production and distribution. It also enhances real-time inventory monitoring to ensure compliance with strict storage requirements, especially for temperature-sensitive drugs like vaccines (Beaulieu & Bentahar, 2021; Ouarda et al., 2024). BDA mitigates high inventory variability and fluctuating demand in medical supplies by optimizing procurement processes through historical usage data and enabling agile supplier identification during emergencies, such as the COVID-19 pandemic. By providing end-to-end visibility across SCs, BDA reduces lead times and ensures timely order fulfilment, making it a critical tool for strengthening HCSC resilience and efficiency (Beaulieu & Bentahar, 2021). References (included): Beaulieu, M., & Bentahar, O. (2021). Digitalization of the healthcare supply chain: A roadmap to generate benefits and effectively support healthcare delivery. Technological Forecasting and Social Change, 167 . https://doi.org/10.1016/j.techfore.2021.120657 Ouarda, T. B. M. J., Masselot, P., Campagna, C., & Gosselin, P. (2024). Prediction of heatwave-related mortality magnitude, duration, and frequency with climate variability and climate change information. Stochastic Environmental Research and Risk Assessment, 38 (11), 4471-4483. https://doi.org/10.1007/s00477-024-02179-5 Reviewer Comment: I suggest that the authors consult these two references. Author Response: Yes, Suggested references included in the discussion section. Competing Interests: No competing interests were disclosed. Close Report a concern COMMENT ON THIS REPORT Comments on this article Comments (0) Version 4 VERSION 4 PUBLISHED 16 Oct 2024 ADD YOUR COMMENT Comment keyboard_arrow_left keyboard_arrow_right Open Peer Review Reviewer Status info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions Reviewer Reports Invited Reviewers 1 2 Version 4 (revision) 19 Feb 25 read Version 3 (revision) 16 Dec 24 read Version 2 (revision) 26 Nov 24 read Version 1 16 Oct 24 read read Martin Beaulieu , HEC Montréal, Montreal, Canada Abdüssamet Polater , Erzincan Binali Yildirim Universitesi, Erzincan, Turkey Comments on this article All Comments (0) Add a comment Sign up for content alerts Sign Up You are now signed up to receive this alert Browse by related subjects keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2025 Beaulieu M. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 20 Feb 2025 | for Version 4 Martin Beaulieu , HEC Montréal, Montreal, Canada 0 Views copyright © 2025 Beaulieu M. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (0) Approved info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions Sorry I missed the previous revisions of your paper. I find this version much improved and see no further changes to suggest. Competing Interests No competing interests were disclosed. I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. reply Respond to this report Responses (0) Beaulieu M. Peer Review Report For: The Value of Applying Big Data Analytics in Health Supply Chain Management [version 4; peer review: 2 approved] . F1000Research 2025, 13 :1237 ( https://doi.org/10.5256/f1000research.178092.r367582) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://f1000research.com/articles/13-1237/v4#referee-response-367582 keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2024 Polater A. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 20 Dec 2024 | for Version 3 Abdüssamet Polater , Erzincan Binali Yildirim Universitesi, Erzincan, Erzincan, Turkey 0 Views copyright © 2024 Polater A. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (0) Approved info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions The paper is scientifically sound in its current form. Competing Interests No competing interests were disclosed. Reviewer Expertise Supply chain management; Logistics management I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. reply Respond to this report Responses (0) Polater A. Peer Review Report For: The Value of Applying Big Data Analytics in Health Supply Chain Management [version 4; peer review: 2 approved] . F1000Research 2025, 13 :1237 ( https://doi.org/10.5256/f1000research.175869.r349213) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://f1000research.com/articles/13-1237/v3#referee-response-349213 keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2024 Polater A. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 06 Dec 2024 | for Version 2 Abdüssamet Polater , Erzincan Binali Yildirim Universitesi, Erzincan, Erzincan, Turkey 0 Views copyright © 2024 Polater A. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (1) Approved With Reservations info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions The paper is scientifically sound in its current form and only minor, if any, improvements are suggested. However, I only require from the authors to include the explanations that they made in the response letter into the manuscript's methodology section. They should include the reason of starting to screen in 2016, the keywords used for screening, journals (science category, index type etc.) were included and how they analyzed the papers included. Competing Interests No competing interests were disclosed. Reviewer Expertise Supply chain management; Logistics management I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. reply Respond to this report Responses (1) Author Response 11 Dec 2024 دينا النعيمي, Health and Environmental Studies, Hamdan Bin Mohammed Smart University, Dubai, 0000, United Arab Emirates Thank you for your positive feedback and for recognizing the scientific merit of our paper. We greatly appreciate your constructive suggestion to include additional details in the Methodology section. In response, we have incorporated the following explanations into the manuscript: The rationale for starting the screening process in 2016. The specific keywords used for the screening process. The journal inclusion criteria (e.g., science category, index type). The analytical approach was applied to the selected papers. These details have been added to the Methodology section. We believe these revisions enhance the clarity and transparency of our study. Thank you for your thoughtful input, which has helped us improve our manuscript. View more View less Competing Interests No competing interests were disclosed. reply Respond Report a concern Polater A. Peer Review Report For: The Value of Applying Big Data Analytics in Health Supply Chain Management [version 4; peer review: 2 approved] . F1000Research 2025, 13 :1237 ( https://doi.org/10.5256/f1000research.174834.r343643) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://f1000research.com/articles/13-1237/v2#referee-response-343643 keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2024 Polater A. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 12 Nov 2024 | for Version 1 Abdüssamet Polater , Erzincan Binali Yildirim Universitesi, Erzincan, Erzincan, Turkey 0 Views copyright © 2024 Polater A. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (1) Approved With Reservations info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions Comments for the Abstract The aim of the study is confusing in the abstract section. It is stated that “This study aims to assess how the application of BDA impacts the performance of healthcare supply chain management (HCSCMP).” Then, investigating the efficiency and success factors are stated as an aim of the study. Comments for the Introduction “The study began with a descriptive analysis of academic research on BDA in the context of SCM, followed by a content analysis to assess the impact of BDA-based management systems on HCSCMP and to identify the key enablers and challenges for implementing BDA in HCSC.” The location of this sentence can be changed in the introduction sentence. The paragraph which starts with “Big Data Analytics (BDA) in healthcare is transformative…” is fine until the “Healthcare facilities manage both structured and unstructured data.” sentence. Then suddenly the authors starts to talk about the “structured and unstructured data”. Is there any need to talk about “structured and unstructured data” here? The paragraph starting with the “Adopting BDA in HCSCs facilitates real-time service delivery, data-driven decision-making, and improved performance (Araz et al., 2020).” should be reorganized. “Further research is needed to explore how BDA can enhance healthcare supply chain management performance (HCSCMP) and to validate existing findings.” can be the last sentence and the later sentences can be integrated as drivers of HSCP. “RQ1. What is the number of academic studies on Big Data Analytics in the context of Supply Chain Management, and what research methods and data collection techniques have been used in these studies?” This RQ can be shortened such as telling “investigating the trends in application of BDA in the HSCM”. “RQ2. How does the application of Big Data Analytics enhance efficiency in Healthcare Supply Chains Management according to existing studies?”. According to the abstract this study aims to investigate efficiency and performance of HSCM. But efficiency is only mentioned in the RQ2. The paragraph which is located after research questions should be significantly reorganized. I assume that the authors wanted to talk about the contribution of the study. However, it sounds more the findings or may be conclusion section. Comments for the Big Data and Big Data analytics in Healthcare Supply Chain Management This section mainly talks about the big data. But it does not discuss big data in Healthcare Supply Chain Management. Comments for the Organizational Information Process Theory Why did you specifically created a section titled as “Organizational Information Process Theory”? Comments for the Methodology What is the reason of starting to screen in 2016? What are the keywords used for screening? Which journals (science category, index type etc.) were included? How did you analyze the papers included? Comments for the Results analysis Please only use Figure 2 or Figure 3. “Research methods and data collection techniques have been used in big data analytics in the context of supply chain management studies”. Please add “health” before “supply chain management studies”. I did not understand to relating and discussing the country and research method (Table 1, Figure 5). Could you explain the importance of relating these two different findings? Comments for the Discussion The discussion section seems the repetition of the results section. In order to overcome this problem, it would be better to synthesize and discuss the “Efficiency Enhancements of Big Data Analytics in Healthcare Supply Chains” and “Enablers and Challenges in Implementing Big Data Analytics in Healthcare Supply Chains” sections under one title. Reviewer Decision I suggest the authors revise and develop the sections based on the comments. Are the rationale for, and objectives of, the Systematic Review clearly stated? Partly Are sufficient details of the methods and analysis provided to allow replication by others? Partly Is the statistical analysis and its interpretation appropriate? Partly Are the conclusions drawn adequately supported by the results presented in the review? Yes If this is a Living Systematic Review, is the ‘living’ method appropriate and is the search schedule clearly defined and justified? (‘Living Systematic Review’ or a variation of this term should be included in the title.) Yes Competing Interests No competing interests were disclosed. Reviewer Expertise Supply chain management; Logistics management I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. reply Respond to this report Responses (1) Author Response 26 Nov 2024 دينا النعيمي, Health and Environmental Studies, Hamdan Bin Mohammed Smart University, Dubai, 0000, United Arab Emirates Abstract: Thank you for your valuable feedback. I have revised the abstract to clarify the study's aim, emphasizing that assessing the impact of BDA on performance, along with examining efficiency improvements and success factors, are interconnected objectives of the research. This should make the focus of the study clearer in the abstract. Location of Sentence on Descriptive Analysis and Content Analysis : Thank you for this suggestion. We will reposition the sentence, “The study began with a descriptive analysis of academic research on BDA in the context of SCM, followed by a content analysis to assess the impact of BDA-based management systems on HCSCMP and to identify the key enablers and challenges for implementing BDA in HCSC,” to improve the flow of the introduction. This will help clarify the structure and methodology of the study within the introductory context. After research questions guide the study: Discussion of Structured and Unstructured Data : We will revise the paragraph that starts with “Adopting BDA in HCSCs facilitates real-time service delivery, data-driven decision-making, and improved performance,” so that it concludes with, “Further research is needed to explore how BDA can enhance healthcare supply chain management performance (HCSCMP) and to validate existing findings.” This will allow the later sentences to flow naturally into a discussion of drivers in healthcare supply chain performance (HCSCP), following your helpful suggestion. Reorganizing the Paragraph on Adopting BDA : We will revise the paragraph that starts with “Adopting BDA in HCSCs facilitates real-time service delivery, data-driven decision-making, and improved performance,” so that it concludes with, “Further research is needed to explore how BDA can enhance healthcare supply chain management performance (HCSCMP) and to validate existing findings.” This will allow the later sentences to flow naturally into a discussion of drivers in healthcare supply chain performance (HCSCP), following your helpful suggestion. Rephrasing Research Question 1 : RQ1 : What are the trends in the application of BDA in HCSCM? Clarifying Efficiency in RQ2 : Thank you for pointing out this discrepancy. We will modify the abstract to clarify that both efficiency and performance are central aims, ensuring consistency with RQ2’s focus on efficiency within healthcare supply chain management. Reorganizing the Paragraph Following the Research Questions : We recognize that the paragraph after the research questions may seem more like conclusions or findings. To clarify, we will revise this section to focus on the study's intended contributions and will remove or relocate content that discusses specific findings. Comments for the Big Data and Big Data analytics in Healthcare Supply Chain Management Section Revised Comments for the Organizational Information Process Theory Thank you for your question. The 'Organizational Information Process Theory (OIPT)' section was included to provide a theoretical foundation for understanding how Big Data Analytics (BDA) supports healthcare supply chain management. OIPT explains how organizations can enhance their decision-making and operational efficiency by improving their information processing capabilities. Given that BDA is fundamentally about managing, analysing, and utilizing large volumes of data, OIPT offers a relevant lens to explore BDA’s role in healthcare supply chains. This theoretical framework helps explain how BDA can optimize information flow, reduce uncertainty, and support real-time decision-making, which are crucial for successful supply chain management in healthcare. Methodology: Reason for Starting the Screening in 2016 : The screening period starts from 2016 because of a noticeable increase in academic research on Big Data Analytics in healthcare supply chains around that time. Starting in 2016 ensures the inclusion of recent and relevant studies, aligning with the growing interest and advancements in this area. The screening period begins in 2016 to capture recent developments and a noticeable rise in research on Big Data Analytics in healthcare supply chains during this period. Keywords Used for Screening : Keywords likely included terms related to big data analytics, supply chain management, and healthcare, such as “Big Data Analytics,” “Supply Chain Performance,” “Healthcare Supply Chain Management,” and others relevant to enablers and challenges in BDA implementation. The keywords used included ‘Big Data Analytics,’ ‘Supply Chain Performance,’ ‘Healthcare Supply Chain Management,’ along with terms related to enablers and challenges in implementing BDA. Which journals (science category, index type etc.) were included? The review focused on peer-reviewed journals indexed in recognized databases like Scopus and Google Scholar, specifically in areas such as healthcare management, supply chain management, and information systems. We included peer-reviewed journals indexed in major databases like Scopus and Google Scholar, focusing on healthcare management, supply chain management, and information systems. How did you analyze the papers included? The analysis involved a systematic literature review methodology. The papers were screened, selected, and synthesized based on their contributions to understanding Big Data Analytics’ impact on healthcare supply chains, including descriptive and content analysis to identify key findings, enablers, and challenges. The included papers underwent descriptive and content analysis to synthesize insights on BDA’s impact on healthcare supply chains, focusing on efficiency improvements and identifying key enablers and challenges for successful implementation. Results analysis: Comments for the Results analysis Only use Figure 2 or Figure 3. Response to Reviewer: Thank you for your feedback. I have revised the analysis to include only Figure 2, as suggested. “Research methods and data collection techniques have been used in big data analytics in the context of supply chain management studies”. Please add “health” before “supply chain management studies”. Additionally, I have updated the phrase to read, ‘Research methods and data collection techniques have been used in big data analytics in the context of healthcare supply chain management studies,’ to clarify the healthcare focus of this review." Discussing the country and research method (Table 1, Figure 5). Could you explain the importance of relating these two different findings? Thank you for highlighting this point. The purpose of relating countries with research methods in Table 1 and Figure 5 is to showcase regional preferences and methodological diversity in Big Data Analytics (BDA) research within healthcare supply chains. This comparison helps reveal how different regions prioritize certain research methods—such as questionnaires in North America and interviews in Australia—reflecting regional contexts, available resources, and academic traditions. By understanding these differences, we gain insight into the approaches that researchers in various regions use to investigate BDA’s impact on healthcare supply chain management, which could indicate unique challenges, cultural influences, or operational focuses in each region. This contextual understanding enhances the comprehensiveness of our review by demonstrating how geographic and methodological factors may shape research outcomes and perspectives on BDA implementation. Comments for the Discussion Response to Reviewer: Thank you for this helpful suggestion. To enhance the discussion and avoid redundancy, we will integrate the ‘Efficiency Enhancements of Big Data Analytics in Healthcare Supply Chains’ and ‘Enablers and Challenges in Implementing Big Data Analytics in Healthcare Supply Chains’ under a unified title, such as ‘Impact of Big Data Analytics on Healthcare Supply Chain Performance: Efficiency, Enablers, and Challenges.’ This synthesis will allow us to discuss both the benefits and obstacles of BDA implementation within a single framework, creating a more streamlined and interpretive analysis. By reorganizing this way, we can directly relate the efficiency improvements BDA offers to the specific enablers and challenges that influence its successful adoption. This will provide a more holistic view of BDA’s impact on healthcare supply chains, enabling readers to understand how these factors work together to enhance or inhibit supply chain performance. View more View less Competing Interests No competing interests were disclosed. reply Respond Report a concern Polater A. Peer Review Report For: The Value of Applying Big Data Analytics in Health Supply Chain Management [version 4; peer review: 2 approved] . F1000Research 2025, 13 :1237 ( https://doi.org/10.5256/f1000research.171850.r333492) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://f1000research.com/articles/13-1237/v1#referee-response-333492 keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2024 Beaulieu M. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 09 Nov 2024 | for Version 1 Martin Beaulieu , HEC Montréal, Montreal, Canada 0 Views copyright © 2024 Beaulieu M. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (1) Approved With Reservations info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions On text form : Why do the authors combine the use of upper and lower case letters in the presentation of key words? The introduction starts very quickly, and the authors use acronyms without defining them or defining them further in the text (BDA or HCSCMP). I find it a little strange that figure 1 is in the introduction and not in the “Methodology” section. Is the name “CİĞERCİ” capitalized? As far as the content of the paper is concerned, in the section “Big Data and Big Data analytics in Healthcare Supply Chain Management”, the authors tell us about Big Data, supply chain management, but they don't really deal with the Healthcare supply chain. What distinguishes the healthcare sector from other industries? See below comments. Why did you choose 2016 as the starting point for your reference search? I'm not convinced of the relevance of figure 3; it seems redundant with figure 2, or at the very least, I don't think it adds any real value. For the subsection answering question RQ2, is it possible to be more precise? There are two main supply chains in the healthcare sector: pharmaceuticals and medical supplies. Each presents its own challenges. So, the benefits suggested by these studies apply to which types of products? When I mentioned more development around HSCM in the literature review, the authors could expand on these ideas. This would give more depth to their discussion. I suggest that the authors consult these two references, as they may help the authors to develop some of the ideas in the literature review: Reference: (1,2) Are the rationale for, and objectives of, the Systematic Review clearly stated? Yes Are sufficient details of the methods and analysis provided to allow replication by others? Partly Is the statistical analysis and its interpretation appropriate? Partly Are the conclusions drawn adequately supported by the results presented in the review? Partly If this is a Living Systematic Review, is the ‘living’ method appropriate and is the search schedule clearly defined and justified? (‘Living Systematic Review’ or a variation of this term should be included in the title.) Yes References 1. Beaulieu M, Bentahar O: Digitalization of the healthcare supply chain: A roadmap to generate benefits and effectively support healthcare delivery. Technological Forecasting and Social Change . 2021; 167 . Publisher Full Text 2. Ouarda TBMJ, Masselot P, Campagna C, Gosselin P, et al.: Prediction of heatwave related mortality magnitude, duration and frequency with climate variability and climate change information. Stoch Environ Res Risk Assess . 2024; 38 (11): 4471-4483 PubMed Abstract | Publisher Full Text Competing Interests No competing interests were disclosed. Reviewer Expertise healthcare logistics, supply chain management, inventory management, purchasing I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. reply Respond to this report Responses (1) Author Response 19 Feb 2025 دينا النعيمي, Health and Environmental Studies, Hamdan Bin Mohammed Smart University, Dubai, 0000, United Arab Emirates Reviewer Comment: Why do the authors combine the use of upper and lower case letters in the presentation of key words? Author Response: The inconsistency in using upper and lower-case letters in the keywords was unintended. This was standardized in the revised version, ensuring uniformity in formatting keywords to adhere to academic conventions. Reviewer Comment: The introduction starts very quickly, and the authors use acronyms without defining them or defining them further in the text (BDA or HCSCMP). Author Response: We acknowledge that acronyms such as BDA (Big Data Analytics) and HCSCMP (Healthcare Supply Chain Management Performance) were introduced without proper definition in the introduction. In the revised version, these acronyms defined upon first mention (IN THE Introduction Section). Reviewer Comment: I find it a little strange that figure 1 is in the introduction and not in the “Methodology” section. Author Response: Placement of Figure 1: The positioning of Figure 1 in the introduction section was intended to provide an early visual representation of the systematic review process. However, based on the feedback, this figure relocated to the “Methodology” section in the revised version, where it aligns more logically with the discussion of the research design. Reviewer Comment: Is the name “CİĞERCİ” capitalized? Author Response: Corrected: Correct Harvard-Style Formatting: Ciğerci, M. (2023) 'Main Effects of Big Data on Supply Chain Management. Implementation of Disruptive Technologies in Supply Chain Management,' Yeditepe University Institute of Social Sciences , Istanbul, Turkey, pp. 27–49. Correct In-Text Reference: (Ciğerci, 2023). Reviewer Comment: As far as the content of the paper is concerned, in the section “Big Data and Big Data analytics in Healthcare Supply Chain Management”, the authors tell us about Big Data, supply chain management, but they don't really deal with the Healthcare supply chain. What distinguishes the healthcare sector from other industries? Author Response: In the revised version of the paper, we included the unique characteristics of healthcare supply chains (HCSCs) that differentiate them from other industries. Under the section titled "Big Data and Big Data Analytics in Healthcare Supply Chain Management". 2 nd paragraph: HCSCs are fundamentally distinct from other industries due to their direct impact on human lives. The availability of medical supplies, medications, vaccines, and personal protective equipment (PPE) is critical for ensuring timely patient care, imposing higher demands for operational efficiency and resilience than conventional SCs (Govindan et al., 2022). HCSCs face significant challenges related to unpredictable demand, such as during pandemics like COVID-19, when the need for medications, ventilators, and vaccines surged unexpectedly, unlike traditional SCs that often experience steady demand patterns (Ivanov & Dolgui, 2021). Additionally, HCSCs are highly regulated to ensure patient safety and product quality, requiring compliance with stringent procurement, storage, and distribution standards, including temperature-controlled logistics for products like biologics and vaccines (Chen et al., 2021). The complexity of managing healthcare products, which often require specific handling conditions, adds unique logistical challenges (Ristevski & Chen, 2018). Moreover, BDA in HCSCs goes beyond improving operational efficiency to ensuring patient safety by predicting disease outbreaks, optimizing medication distribution, and reducing errors—functions rarely found in other industries (Nguyen et al., 2018). HCSCs prioritize patient-centred care, focusing on quality and reliability over cost savings, a priority that often distinguishes them from other sectors (Benzidia et al., 2023). References (Added): Govindan, K., et al. (2022). Pandemic-induced disruptions in healthcare supply chains. Supply Chain Management Review , 28(2), 98-111. Ivanov, D., & Dolgui, A. (2021). A digital supply chain twin for managing disruptions. International Journal of Production Research , 59(14), 4180-4195. Reviewer Comment: Why did you choose 2016 as the starting point for your reference search? Author Response: Rationale for Selecting 2016 as the Starting Point: The selection of 2016 as the starting point for the reference search was deliberate. It aligns with the period when Big Data Analytics began gaining substantial traction in healthcare supply chain research, as highlighted by studies such as Nguyen et al. (2018) and Hofmann & Rutschmann (2018). This decision ensures the review captures recent advancements and remains relevant to contemporary developments in the field. Reviewer Comment: I'm not convinced of the relevance of figure 3; it seems redundant with figure 2, or at the very least, I don't think it adds any real value. Author Response: We agree with the reviewer’s observation. In the revised version, we removed Figure 3 to streamline the presentation of results and avoid redundancy. (The bar chart in Figure 3 illustrates the number of publications by different methods used in BDA research within the context of SCM. Each bar represents a distinct research method and the total number of publications employing that method. Also removed ) Reviewer Comment: For the subsection answering question RQ2, is it possible to be more precise? There are two main supply chains in the healthcare sector: pharmaceuticals and medical supplies. Each presents its own challenges. So, the benefits suggested by these studies apply to which types of products? When I mentioned more development around HSCM in the literature review, the authors could expand on these ideas. This would give more depth to their discussion. Author Response: The BDA benefits suggested by the studies are pharmaceuticals, and medical supplies were included in the discussion section addressing RQ2. Included: BDA significantly benefits the two main SCs in the healthcare sector: pharmaceuticals and medical supplies. BDA addresses challenges such as demand forecasting inaccuracies, drug shortages, and cold chain logistics for pharmaceuticals by leveraging predictive analytics to assess disease trends and optimize medication production and distribution. It also enhances real-time inventory monitoring to ensure compliance with strict storage requirements, especially for temperature-sensitive drugs like vaccines (Beaulieu & Bentahar, 2021; Ouarda et al., 2024). BDA mitigates high inventory variability and fluctuating demand in medical supplies by optimizing procurement processes through historical usage data and enabling agile supplier identification during emergencies, such as the COVID-19 pandemic. By providing end-to-end visibility across SCs, BDA reduces lead times and ensures timely order fulfilment, making it a critical tool for strengthening HCSC resilience and efficiency (Beaulieu & Bentahar, 2021). References (included): Beaulieu, M., & Bentahar, O. (2021). Digitalization of the healthcare supply chain: A roadmap to generate benefits and effectively support healthcare delivery. Technological Forecasting and Social Change, 167 . https://doi.org/10.1016/j.techfore.2021.120657 Ouarda, T. B. M. J., Masselot, P., Campagna, C., & Gosselin, P. (2024). Prediction of heatwave-related mortality magnitude, duration, and frequency with climate variability and climate change information. Stochastic Environmental Research and Risk Assessment, 38 (11), 4471-4483. https://doi.org/10.1007/s00477-024-02179-5 Reviewer Comment: I suggest that the authors consult these two references. Author Response: Yes, Suggested references included in the discussion section. View more View less Competing Interests No competing interests were disclosed. reply Respond Report a concern Beaulieu M. Peer Review Report For: The Value of Applying Big Data Analytics in Health Supply Chain Management [version 4; peer review: 2 approved] . F1000Research 2025, 13 :1237 ( https://doi.org/10.5256/f1000research.171850.r333485) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://f1000research.com/articles/13-1237/v1#referee-response-333485 Alongside their report, reviewers assign a status to the article: Approved - the paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations - A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved - fundamental flaws in the paper seriously undermine the findings and conclusions Adjust parameters to alter display View on desktop for interactive features Includes Interactive Elements View on desktop for interactive features Competing Interests Policy Provide sufficient details of any financial or non-financial competing interests to enable users to assess whether your comments might lead a reasonable person to question your impartiality. Consider the following examples, but note that this is not an exhaustive list: Examples of 'Non-Financial Competing Interests' Within the past 4 years, you have held joint grants, published or collaborated with any of the authors of the selected paper. You have a close personal relationship (e.g. parent, spouse, sibling, or domestic partner) with any of the authors. You are a close professional associate of any of the authors (e.g. scientific mentor, recent student). You work at the same institute as any of the authors. You hope/expect to benefit (e.g. favour or employment) as a result of your submission. You are an Editor for the journal in which the article is published. Examples of 'Financial Competing Interests' You expect to receive, or in the past 4 years have received, any of the following from any commercial organisation that may gain financially from your submission: a salary, fees, funding, reimbursements. You expect to receive, or in the past 4 years have received, shared grant support or other funding with any of the authors. You hold, or are currently applying for, any patents or significant stocks/shares relating to the subject matter of the paper you are commenting on. Stay Updated Sign up for content alerts and receive a weekly or monthly email with all newly published articles Register with F1000Research Already registered? Sign in Not now, thanks close PLEASE NOTE If you are an AUTHOR of this article, please check that you signed in with the account associated with this article otherwise we cannot automatically identify your role as an author and your comment will be labelled as a “User Comment”. If you are a REVIEWER of this article, please check that you have signed in with the account associated with this article and then go to your account to submit your report, please do not post your review here. If you do not have access to your original account, please contact us . All commenters must hold a formal affiliation as per our Policies . The information that you give us will be displayed next to your comment. User comments must be in English, comprehensible and relevant to the article under discussion. We reserve the right to remove any comments that we consider to be inappropriate, offensive or otherwise in breach of the User Comment Terms and Conditions . Commenters must not use a comment for personal attacks. When criticisms of the article are based on unpublished data, the data should be made available. I accept the User Comment Terms and Conditions Please confirm that you accept the User Comment Terms and Conditions. Affiliation ✕ refresh Please enter your institution. Note: To add your institution or organisation, start typing the name and then select the correct name from the list. Where applicable, the name will appear in both the original language and in English. Do not paste in the name. If the name does not appear in the drop-down list, we will display the information you have entered. ✕ refresh Country/Region * USA UK Canada China France Germany Afghanistan Aland Islands Albania Algeria American Samoa Andorra Angola Anguilla Antarctica Antigua and Barbuda Argentina Armenia Aruba Australia Austria Azerbaijan Bahamas Bahrain Bangladesh Barbados Belarus Belgium Belize Benin Bermuda Bhutan Bolivia Bosnia and Herzegovina Botswana Bouvet Island Brazil British Indian Ocean Territory British Virgin Islands Brunei Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Cape Verde Cayman Islands Central African Republic Chad Chile China Christmas Island Cocos (Keeling) Islands Colombia Comoros Congo Cook Islands Costa Rica Cote d'Ivoire Croatia Cuba Cyprus Czech Republic Democratic Republic of the Congo Denmark Djibouti Dominica Dominican Republic Ecuador Egypt El Salvador Equatorial Guinea Eritrea Estonia Ethiopia Falkland Islands Faroe Islands Federated States of Micronesia Fiji Finland France French Guiana French Polynesia French Southern Territories Gabon Georgia Germany Ghana Gibraltar Greece Greenland Grenada Guadeloupe Guam Guatemala Guernsey Guinea Guinea-Bissau Guyana Haiti Heard Island and Mcdonald Islands Holy See (Vatican City State) Honduras Hong Kong Hungary Iceland India Indonesia Iran Iraq Ireland Israel Italy Jamaica Japan Jersey Jordan Kazakhstan Kenya Kiribati Kosovo (Serbia and Montenegro) Kuwait Kyrgyzstan Lao People's Democratic Republic Latvia Lebanon Lesotho Liberia Libya Liechtenstein Lithuania Luxembourg Macao Madagascar Malawi Malaysia Maldives Mali Malta Marshall Islands Martinique Mauritania Mauritius Mayotte Mexico Minor Outlying Islands of the United States Moldova Monaco Mongolia Montenegro Montserrat Morocco Mozambique Myanmar Namibia Nauru Nepal Netherlands Antilles New Caledonia New Zealand Nicaragua Niger Nigeria Niue Norfolk Island North Korea North Macedonia Northern Mariana Islands Norway Oman Pakistan Palau Palestinian Territory Panama Papua New Guinea Paraguay Peru Philippines Pitcairn Poland Portugal Puerto Rico Qatar Reunion Romania Russian Federation Rwanda Saint Helena Saint Kitts and Nevis Saint Lucia Saint Pierre and Miquelon Saint Vincent and the Grenadines Samoa San Marino Sao Tome and Principe Saudi Arabia Senegal Serbia Seychelles Sierra Leone Singapore Slovakia Slovenia Solomon Islands Somalia South Africa South Georgia and the South Sandwich Is South Korea South Sudan Spain Sri Lanka Sudan Suriname Svalbard and Jan Mayen Swaziland Sweden Switzerland Syria Taiwan Tajikistan Tanzania Thailand The Gambia The Netherlands Timor-Leste Togo Tokelau Tonga Trinidad and Tobago Tunisia Turkey Turkmenistan Turks and Caicos Islands Tuvalu UK USA Uganda Ukraine United Arab Emirates United States Virgin Islands Uruguay Uzbekistan Vanuatu Venezuela Vietnam Wallis and Futuna West Bank and Gaza Strip Western Sahara Yemen Zambia Zimbabwe Please select your country/region. You must enter a comment. Competing Interests Please disclose any competing interests that might be construed to influence your judgment of the article's or peer review report's validity or importance. Competing Interests Policy Provide sufficient details of any financial or non-financial competing interests to enable users to assess whether your comments might lead a reasonable person to question your impartiality. Consider the following examples, but note that this is not an exhaustive list: Examples of 'Non-Financial Competing Interests' Within the past 4 years, you have held joint grants, published or collaborated with any of the authors of the selected paper. You have a close personal relationship (e.g. parent, spouse, sibling, or domestic partner) with any of the authors. You are a close professional associate of any of the authors (e.g. scientific mentor, recent student). You work at the same institute as any of the authors. You hope/expect to benefit (e.g. favour or employment) as a result of your submission. You are an Editor for the journal in which the article is published. Examples of 'Financial Competing Interests' You expect to receive, or in the past 4 years have received, any of the following from any commercial organisation that may gain financially from your submission: a salary, fees, funding, reimbursements. You expect to receive, or in the past 4 years have received, shared grant support or other funding with any of the authors. You hold, or are currently applying for, any patents or significant stocks/shares relating to the subject matter of the paper you are commenting on. Please state your competing interests The comment has been saved. An error has occurred. Please try again. Cancel Post var lTitle = "The Value of Applying Big Data Analytics...".replace("'", ''); var linkedInUrl = "http://www.linkedin.com/shareArticle?url=https://f1000research.com/articles/13-1237/v4" + "&title=" + encodeURIComponent(lTitle) + "&summary=" + encodeURIComponent('Read the article by '); var deliciousUrl = "https://del.icio.us/post?url=https://f1000research.com/articles/13-1237/v4&title=" + encodeURIComponent(lTitle); var redditUrl = "http://reddit.com/submit?url=https://f1000research.com/articles/13-1237/v4" + "&title=" + encodeURIComponent(lTitle); linkedInUrl += encodeURIComponent('Al Nuaimi D and Awofeso N'); var offsetTop = /chrome/i.test( navigator.userAgent ) ? 4 : -10; var addthis_config = { ui_offset_top: offsetTop, services_compact : "facebook,twitter,www.linkedin.com,www.mendeley.com,reddit.com", services_expanded : "facebook,twitter,www.linkedin.com,www.mendeley.com,reddit.com", services_custom : [ { name: "LinkedIn", url: linkedInUrl, icon:"/img/icon/at_linkedin.svg" }, { name: "Mendeley", url: "http://www.mendeley.com/import/?url=https://f1000research.com/articles/13-1237/v4/mendeley", icon:"/img/icon/at_mendeley.svg" }, { name: "Reddit", url: redditUrl, icon:"/img/icon/at_reddit.svg" }, ] }; var addthis_share = { url: "https://f1000research.com/articles/13-1237", templates : { twitter : "The Value of Applying Big Data Analytics in Health Supply Chain.... Al Nuaimi D and Awofeso N, published by " + "@F1000Research" + ", https://f1000research.com/articles/13-1237/v4" } }; if (typeof(addthis) != "undefined"){ addthis.addEventListener('addthis.ready', checkCount); addthis.addEventListener('addthis.menu.share', checkCount); } $(".f1r-shares-twitter").attr("href", "https://twitter.com/intent/tweet?text=" + addthis_share.templates.twitter); $(".f1r-shares-facebook").attr("href", "https://www.facebook.com/sharer/sharer.php?u=" + addthis_share.url); $(".f1r-shares-linkedin").attr("href", addthis_config.services_custom[0].url); $(".f1r-shares-reddit").attr("href", addthis_config.services_custom[2].url); $(".f1r-shares-mendelay").attr("href", addthis_config.services_custom[1].url); function checkCount(){ setTimeout(function(){ $(".addthis_button_expanded").each(function(){ var count = $(this).text(); if (count !== "" && count != "0") $(this).removeClass("is-hidden"); else $(this).addClass("is-hidden"); }); }, 1000); } close How to cite this report {{reportCitation}} Cancel Copy Citation Details $(function(){R.ui.buttonDropdowns('.dropdown-for-downloads');}); $(function(){R.ui.toolbarDropdowns('.toolbar-dropdown-for-downloads');}); $.get("/articles/acj/156525/178092") new F1000.Clipboard(); new F1000.ThesaurusTermsDisplay("articles", "article", "178092"); $(document).ready(function() { $( "#frame1" ).on('load', function() { var mydiv = $(this).contents().find("div"); var h = mydiv.height(); console.log(h) }); var tooltipLivingFigure = jQuery(".interactive-living-figure-label .icon-more-info"), titleLivingFigure = tooltipLivingFigure.attr("title"); tooltipLivingFigure.simpletip({ fixed: true, position: ["-115", "30"], baseClass: 'small-tooltip', content:titleLivingFigure + " " }); tooltipLivingFigure.removeAttr("title"); $("body").on("click", ".cite-living-figure", function(e) { e.preventDefault(); var ref = $(this).attr("data-ref"); $(this).closest(".living-figure-list-container").find("#" + ref).fadeIn(200); }); $("body").on("click", ".close-cite-living-figure", function(e) { e.preventDefault(); $(this).closest(".popup-window-wrapper").fadeOut(200); }); $(document).on("mouseup", function(e) { var metricsContainer = $(".article-metrics-popover-wrapper"); if (!metricsContainer.is(e.target) && metricsContainer.has(e.target).length === 0) { $(".article-metrics-close-button").click(); } }); var articleId = $('#articleId').val(); if($("#main-article-count-box").attachArticleMetrics) { $("#main-article-count-box").attachArticleMetrics(articleId, { articleMetricsView: true }); } }); var figshareWidget = $(".new_figshare_widget"); if (figshareWidget.length > 0) { window.figshare.load("f1000", function(Widget) { // Select a tag/tags defined in your page. In this tag we will place the widget. _.map(figshareWidget, function(el){ var widget = new Widget({ articleId: $(el).attr("figshare_articleId") //height:300 // this is the height of the viewer part. [Default: 550] }); widget.initialize(); // initialize the widget widget.mount(el); // mount it in a tag that's on your page // this will save the widget on the global scope for later use from // your JS scripts. This line is optional. //window.widget = widget; }); }); } close Error Close Add Reset F1000.MICROSERVICES.AFFILIATION = ''; $(document).ready(function () { $('.js-affiliations-form').each((index, form) => { new AffiliationForm({ formId: form.id, institutionErrorSelector: '.comment-enter-institution', departmentErrorSelector: '.comment-enter-department', placeSelector: '.js-add-comment-place', stateSelector: '.js-add-comment-state', zipCodeSelector: '.js-add-comment-zipcode', countrySelector: '.js-add-comment-country', countryErrorSelector: '.comment-enter-country', }); }); }); $(document).ready(function () { var reportIds = { "344069": 0, "344068": 0, "344065": 0, "344064": 0, "344067": 0, "344066": 0, "349213": 7, "343644": 0, "349212": 0, "367583": 0, "367582": 5, "343643": 17, "333485": 14, "333487": 0, "333486": 0, "333493": 0, "333492": 13, "333494": 0, "333489": 0, "333488": 0, "333491": 0, "333490": 0, "344061": 0, "344060": 0, "344063": 0, "344062": 0, }; $(".referee-response-container,.js-referee-report").each(function(index, el) { var reportId = $(el).attr("data-reportid"), reportCount = reportIds[reportId] || 0; $(el).find(".comments-count-container,.js-referee-report-views").html(reportCount); }); var uuidInput = $("#article_uuid"), oldUUId = uuidInput.val(), newUUId = "7b4382d9-237c-468d-9e4c-3a7c751dc076"; uuidInput.val(newUUId); $("a[href*='article_uuid=']").each(function(index, el) { var newHref = $(el).attr("href").replace(oldUUId, newUUId); $(el).attr("href", newHref); }); }); An innovative open access publishing platform offering rapid publication and open peer review, whilst supporting data deposition and sharing. Browse Gateways Collections How it Works Contact For Developers Cookie Notice Privacy Notice RSS Submit Your Research Follow us © 2012-2026 F1000 Research Ltd. ISSN 2046-1402 | Legal | Partner of Research4Life • CrossRef • ORCID • FAIRSharing R.templateTests.simpleTemplate = R.template(' $text $text $text $text $text '); R.templateTests.runTests(); var F1000platform = new F1000.Platform({ name: "f1000research", displayName: "F1000Research", hostName: "f1000research.com", id: "1", editorialEmail: "[email protected]", infoEmail: "[email protected]", usePmcStats: true }); $(function(){R.ui.dropdowns('.dropdown-for-authors, .dropdown-for-about, .dropdown-for-myresearch');}); // $(function(){R.ui.dropdowns('.dropdown-for-referees');}); $(document).ready(function () { if ($(".cookie-warning").is(":visible")) { $(".sticky").css("margin-bottom", "35px"); $(".devices").addClass("devices-and-cookie-warning"); } $(".cookie-warning .close-button").click(function (e) { $(".devices").removeClass("devices-and-cookie-warning"); $(".sticky").css("margin-bottom", "0"); }); $("#tweeter-feed .tweet-message").each(function (i, message) { var self = $(message); self.html(linkify(self.html())); }); $(".partner").on("mouseenter mouseleave", function() { $(this).find(".gray-scale, .colour").toggleClass("is-hidden"); }); }); Sign In Remember me Forgotten your password? Sign In Cancel Email or password not correct. Please try again Please wait... $(function(){ // Note: All the setup needs to run against a name attribute and *not* the id due the clonish // nature of facebox... $("a[id=googleSignInButton]").click(function(event){ event.preventDefault(); $("input[id=oAuthSystem]").val("GOOGLE"); $("form[id=oAuthForm]").submit(); }); $("a[id=facebookSignInButton]").click(function(event){ event.preventDefault(); $("input[id=oAuthSystem]").val("FACEBOOK"); $("form[id=oAuthForm]").submit(); }); $("a[id=orcidSignInButton]").click(function(event){ event.preventDefault(); $("input[id=oAuthSystem]").val("ORCID"); $("form[id=oAuthForm]").submit(); }); }); If you've forgotten your password, please enter your email address below and we'll send you instructions on how to reset your password. The email address should be the one you originally registered with F1000. Email address not valid, please try again You registered with F1000 via Google, so we cannot reset your password. To sign in, please click here . If you still need help with your Google account password, please click here . You registered with F1000 via Facebook, so we cannot reset your password. To sign in, please click here . If you still need help with your Facebook account password, please click here . Code not correct, please try again Reset password Cancel Email us for further assistance. Server error, please try again. If your email address is registered with us, we will email you instructions to reset your password. If you think you should have received this email but it has not arrived, please check your spam filters and/or contact for further assistance. Please wait... Register $(document).ready(function () { signIn.createSignInAsRow($("#sign-in-form-gfb-popup")); $(".target-field").each(function () { var uris = $(this).val().split("/"); if (uris.pop() === "login") { $(this).val(uris.toString().replace(",","/")); } }); });