Data-Driven Benchmarking of Raw Material Quality for Risk-Based QC Optimization in Pharmaceutical Manufacturing

Data-Driven Benchmarking of Raw Material Quality for Risk-Based QC Optimization in Pharmaceutical Manufacturing | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (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;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Data-Driven Benchmarking of Raw Material Quality for Risk-Based QC Optimization in Pharmaceutical Manufacturing Muhammad Bintang Ramadhan, Khadijah Zai This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8024060/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Ensuring consistent raw material quality is essential in pharmaceutical manufacturing to maintain product safety and regulatory compliance. However, routine full-scope quality control (QC) testing is resource-intensive, and risk-based reduction strategies remain underutilized. This study proposes a data-driven benchmarking framework that integrates Relative Standard Deviation (RSD) filtering, distribution normalization, control chart analysis, and Process Performance Index (PPI) evaluation to assess material consistency. Using historical QC data from a local pharmaceutical manufacturer, we analyzed 11 parameters across five raw materials, including aspirin, dextromethorphan hydrobromide, talc, phenylephrine HCl, and carboxymethyl cellulose sodium. Results show that six parameters exceeded the company-defined PPI threshold (≥ 0.70) and were justified for reduced testing without compromising compliance or product safety. The framework demonstrates how statistical benchmarking of QC data can support risk-based decision making, optimize analytical resources, and align with GMP principles. This work highlights the potential for integrating structured data analytics into pharmaceutical quality systems to enable efficient, compliant, and scalable QC practices. process performance index capability analysis reduced testing pharmaceutical quality data-driven benchmarking Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 13 1. Introduction Product quality is a critical pillar of pharmaceutical manufacturing, requiring strict adherence to regulatory standards to ensure safety, efficacy, and reliability. A central component of pharmaceutical quality assurance is the Product Quality Review (PQR), a systematic, data-driven assessment mandated under Good Manufacturing Practices (GMP). Among the key areas reviewed in a PQR is the evaluation of raw materials, whose quality and consistency directly impact the integrity of the finished product [1]. In recent years, reduced testing, the selective omission of certain test parameters for raw materials with demonstrated quality consistency, has gained interest as a risk-based strategy to optimize analytical resources. Regulatory frameworks support this practice under specific conditions, such as supplier qualification and a review of historical performance [2]. However, the implementation must be carefully controlled, with critical quality attributes (e.g., assay, impurities, identification, water content) and high-risk parameters consistently monitored to maintain compliance and product safety [3–5]. This study presents a data-driven approach for evaluating the feasibility of reduced testing based on historical quality control data. Using internal datasets from a local pharmaceutical manufacturer, we statistically assessed the consistency of selected parameters for materials such as aspirin, dextromethorphan hydrobromide, talc, phenylephrine HCl, and carboxymethyl cellulose sodium. Our analysis applies the Process Performance Index (PPI) as a benchmark to quantify material quality over time. Materials exceeding the predefined PPI threshold were identified as candidates for reduced analysis. The findings provide practical insights into risk-based quality control, illustrating how structured data curation and statistical benchmarking can facilitate the responsible optimization of testing practices within GMP-compliant environments. This work contributes to the intersection of data quality assessment, performance modeling, and responsible data management in regulated industrial settings. 2. Methodology This study is a field-based evaluation using historical quality control data from multiple pharmaceutical raw materials across several manufacturing lots. All materials included in the analysis had previously passed initial quality testing and met specification standards at the time of use. The research focused on statistically analyzing this data to assess material quality consistency and determine the feasibility of reduced quality control testing. The data were obtained from the internal database of a pharmaceutical manufacturing company (PT. "X") located in Jakarta Industrial Estate, Pulogadung, East Jakarta, Indonesia. The authors did not collect new data directly from the production site; instead, existing quality control data were extracted from the company's centralized system (from November 2023 to November 2024). Each material analyzed included data from at least ten manufacturing lots, and only data sets that complied with the most recent specification codes were included. The proposed framework benchmarks raw material quality using historical QC data through four sequential steps (Fig. 1 ). 2.1 Data curation and filtering QC datasets were collected from a local pharmaceutical manufacturer, covering five raw materials (aspirin, dextromethorphan hydrobromide, phenylephrine HCl, sodium carboxymethyl cellulose [Na-CMC], and talc) and 11 parameters. Only complete, specification-compliant results were included to ensure data integrity, in line with GMP guidelines [1]. 2.2 Distribution assessment and normalization The normality of each parameter distribution was assessed through the Anderson–Darling method. Non-normal data underwent Box–Cox transformation, when applicable, to ensure the data met the requirements for parametric statistical analysis. Where Box–Cox was insufficient, the Johnson transformation was applied [6]. 2.3 Process monitoring Individual control charts (X-bar and R) were generated to identify trends and potential instability, following standard statistical process control practices [7]. Out-of-control points were evaluated but not excluded, reflecting real production variability. 2.4 Capability benchmarking Process performance indices (PPI) were calculated for non-normal distributions, while process capability indices (CPI) were applied where normality was confirmed [7,8]. An internal threshold of PPI/CPI ≥ 0.70 was used to recommend reduced testing, consistent with risk-based quality management principles [2,4]. 3. Result and Discussion 3.1 Summary of Statistical Outcomes A total of 11 quality parameters across five raw materials were evaluated using the proposed benchmarking framework. Table 1 summarizes the results of PPI/CPI calculations, normality assessments, and eligibility for reduced testing based on the company’s internal threshold (PPI ≥ 0.70). Overall, 6 of 11 parameters (55%) exceeded the reduced-testing threshold and were recommended for leaner QC strategies. Table 1 Benchmarking outcome for raw material QC parameters Material Parameter PPI CPI Normality Threshold Met? Reduced Testing Recommendation Aspirin ROI 0.70 – Non-normal ✔ Recommended LOD 0.61 – Non-normal ✘ Not recommended Dextromethorphan HBr pH 0.81 1.00 Normal ✔ Recommended Water content 1.01 1.00 Normal ✔ Recommended ROI 1.23 – Non-normal ✔ Recommended Phenylephrine HCl LOD 0.28 – Non-normal ✘ Not recommended ROI 0.64 – Non-normal ✘ Not recommended Na-CMC pH 0.62 – Non-normal ✘ Not recommended Viscosity 0.85 – Non-normal ✔ Recommended LOD 0.86 – Non-normal ✔ Recommended Talc LOD 0.57 – Non-normal ✘ Not recommended 3.2 Case Studies The following three Sixpack capability reports, which represent a cross-section of possible scenarios, are presented to demonstrate the application of the proposed framework. The first report is an example of the most favorable outcome; the second, a borderline case; and the third, the least favorable outcome. 3.2.1 Success case: Dextromethorphan HBr (Residue on Ignition) Figure 2 shows the Sixpack report for residue on ignition (ROI) in dextromethorphan hydrobromide. This parameter achieved the highest PPI value (1.23) among all tests, indicating excellent process stability despite the non-normal distribution of the data. Control charts demonstrated consistent trends with no out-of-specification (OOS) lots, supporting eligibility for reduced QC testing. 3.2.2 Borderline case: Aspirin (Residue on Ignition) Figure 3 illustrates the borderline case of aspirin ROI, which reached a PPI of 0.70. This value exactly meets the internal reduced-testing threshold, making it a marginal candidate for reduced testing. While variability was higher than in the best-case scenario, inspection history confirmed that no batches were rejected. This case demonstrates how the framework aids decision-making under moderate variability. 3.2.3 Failure case: Phenylephrine HCl (Loss on Drying) As illustrated in Fig. 4 , the most unfavorable outcome observed for phenylephrine HCl involved a loss during the drying process. The parameter exhibited the lowest PPI (0.28), accompanied by unstable control charts and substantial variability. These findings suggest that there is a need for enhanced process control and support the continuation of comprehensive QC testing. This example underscores the framework's capacity to discern parameters that are deemed to be of high risk and are, consequently, deemed unsuitable for testing reduction. 3.3 Discussion The results underscore the value of applying structured benchmarking to QC data: 1) Complementary insights: Even when PPI values fell below the strict Six Sigma benchmark (1.33), six parameters were deemed suitable for reduced testing under the company’s internal threshold (≥ 0.70). 2) Regulatory alignment: No rejected batches were observed during the study period, suggesting that statistical benchmarking, when coupled with historical compliance data, can provide a defensible basis for risk-based testing. 3) Operational efficiency: By distinguishing stable from variable parameters, the framework enables QC teams to allocate resources where they are most needed, aligning with GMP and continuous improvement principles. 3.4 Interpretation The benchmarking results demonstrate that statistical indices, when applied to historical QC data, can effectively separate parameters suitable for reduced testing from those requiring continued full control [6,8]. Six parameters exceeded the internal PPI threshold (≥ 0.70), confirming process stability and supporting leaner QC strategies [2,4]. Conversely, parameters with low PPI values, such as phenylephrine HCl (LOD), highlighted areas of high variability where risk remains too significant for testing reduction [3,5]. Importantly, none of the evaluated parameters resulted in out-of-specification lots or rejected batches, indicating that even where variability exists, product quality has been maintained. This underscores the need to interpret capability indices not in isolation but alongside inspection trends, supplier quality history, and regulatory expectations [1,4,9–11]. Overall, the findings suggest that statistical benchmarking is a powerful enabler of risk-based QC, but its application must remain context-aware and supported by broader quality assurance practices [12]. 4. Conclusion This study introduced a structured, data-driven framework to evaluate the feasibility of reduced quality control (QC) testing for pharmaceutical raw materials. By combining RSD filtering, distribution normalization, control chart analysis, and Process Performance Index (PPI) benchmarking, the framework provides a transparent and statistically grounded basis for risk-based decision making in compliance with GMP principles. Declarations Conflicts of Interest The authors declared no conflict of interest in the manuscript. Author Contribution Concept – K.Z., M.B.R.; Design – K.Z., M.B.R; Supervision – K.Z.; Data Collection and/or Processing – M.B.R.; Analysis and/or Interpretation – M.B.R., K.Z.; LiteratureSearch – M.B.R, K.Z.; Writing – M.B.R, K.Z.; Critical Reviews – K.Z. Acknowledgement The authors thank a pharmaceutical manufacturing company (PT. “X”) located in Jakarta Industrial Estate, Pulogadung, East Jakarta, Indonesia, for the database that the authors can use for this study. References Indonesian Food and Drug Authority. Pedoman Cara Pembuatan Obat yang Baik (Guidelines on Good Manufacturing Practice). Jakarta: BPOM; 2018. Rack C. Approaches to reduced sampling and testing for starting materials. Pharm Technol. 2019;43(12):26–30. International Council for Harmonisation. ICH Q8(R2): Pharmaceutical Development, 2009. International Council for Harmonisation, ICH Q9. Quality Risk Management, 2005. U.S. Food and Drug Administration, Guidance for Industry: Process Validation: General Principles and Practices, 2011. Minitab. Understanding Johnson transformation for normality. Minitab Tech Articles, 2020. Montgomery DC. Introduction to Statistical Quality Control. 8th ed. Hoboken, NJ: Wiley; 2020. Shinde JH, Katikah RS. Importance of process capability and process performance index in machine tool. Int J Res Eng Appl Sci. 2012;2(2):1211–7. International Council for. Harmonisation, ICH Q10: Pharmaceutical Quality System, 2008. European Medicines Agency. Guideline on the Use of Near Infrared Spectroscopy by the Pharmaceutical Industry and the Data Requirements for New Submissions and Variations, 2014. Food US, Administration D. Guidance for Industry: Quality Systems Approach to Pharmaceutical CGMP Regulations, 2006. Womack JP, Jones DT. Lean Thinking: Banish Waste and Create Wealth in Your Corporation. New York: Simon & Schuster; 2003. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8024060","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":549285567,"identity":"213525f5-4176-4dcd-8055-00574e88c7c3","order_by":0,"name":"Muhammad Bintang Ramadhan","email":"","orcid":"","institution":"Universitas Gadjah Mada Yogyakarta","correspondingAuthor":false,"prefix":"","firstName":"Muhammad","middleName":"Bintang","lastName":"Ramadhan","suffix":""},{"id":549285569,"identity":"772e66f9-8a00-43b1-b8c4-eed29e8445a2","order_by":1,"name":"Khadijah Zai","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA8UlEQVRIiWNgGAWjYBACCQkgTuBhSGDjbz4AFWNskCBKC7/EsQQStADpBMmGHAOEKD4tkrObH954IGOXZ3DgzDfJrzkM8vwNzI038GmRljlmbJHAk1xscLh3m7TsNgbDGQcYmy3waZGTSDAD+oU5ccOBs9ukJbcxMG5gYGzD6zA5ifRvQC31QC05z0Ba7AlqkZbIAdlyOHFmQw6b5MdtDIkEtUjOyCkG+uV4Yr/EMWNrxm0SyTMOE/CLxI30jTd/9lQntvE3P7z5c5uNbX97+0O8IQYGjD0QmpkHFCPMBNWDwA+o1h9EqR4Fo2AUjIKRBgC13EoaF9tg/gAAAABJRU5ErkJggg==","orcid":"","institution":"Universitas Gadjah Mada Yogyakarta","correspondingAuthor":true,"prefix":"","firstName":"Khadijah","middleName":"","lastName":"Zai","suffix":""}],"badges":[],"createdAt":"2025-11-04 03:53:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8024060/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8024060/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":96914545,"identity":"6763006c-6a22-481e-bb15-006888fa992a","added_by":"auto","created_at":"2025-11-27 14:06:02","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":864116,"visible":true,"origin":"","legend":"","description":"","filename":"ManuscriptKhadijah.docx","url":"https://assets-eu.researchsquare.com/files/rs-8024060/v1/ed84abfc419f529970c78624.docx"},{"id":96914358,"identity":"0d13a048-883c-46a1-889c-1efd85d2d401","added_by":"auto","created_at":"2025-11-27 14:05:47","extension":"json","order_by":1,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":4229,"visible":true,"origin":"","legend":"","description":"","filename":"6c251017d6914208a6e1393ce736b758.json","url":"https://assets-eu.researchsquare.com/files/rs-8024060/v1/3e5ab21f27a1b7add583580c.json"},{"id":96914111,"identity":"a26ef7a2-ca3e-443f-91cb-fbb9a1d70e78","added_by":"auto","created_at":"2025-11-27 14:05:28","extension":"xml","order_by":2,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":35355,"visible":true,"origin":"","legend":"","description":"","filename":"6c251017d6914208a6e1393ce736b7581enriched.xml","url":"https://assets-eu.researchsquare.com/files/rs-8024060/v1/2cb28d9c52ba7e630e106f30.xml"},{"id":96914254,"identity":"e33e9f60-c784-4528-a148-4ca774aa7150","added_by":"auto","created_at":"2025-11-27 14:05:39","extension":"wmf","order_by":4,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":16740,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage1.wmf","url":"https://assets-eu.researchsquare.com/files/rs-8024060/v1/45dafbe6aad9b5e04e1e5227.wmf"},{"id":96913698,"identity":"e87ffd40-e54a-4b92-ad73-54e6a4fb518c","added_by":"auto","created_at":"2025-11-27 14:03:53","extension":"wmf","order_by":8,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":16740,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage1.wmf","url":"https://assets-eu.researchsquare.com/files/rs-8024060/v1/24c1908cd70da6342e1b87a3.wmf"},{"id":96747166,"identity":"9c67b195-42cd-4ebc-9123-43f119d1afaa","added_by":"auto","created_at":"2025-11-25 16:11:01","extension":"png","order_by":10,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":91617,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8024060/v1/820dd827ca10c8e9a907299a.png"},{"id":96747162,"identity":"d18f1ed6-8df4-4151-a20c-3d5ae3fb5438","added_by":"auto","created_at":"2025-11-25 16:11:01","extension":"png","order_by":11,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":103442,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8024060/v1/24d6e033ace51d1742075737.png"},{"id":96747172,"identity":"1de1a059-01d5-4c9a-a0c6-f0028e17badf","added_by":"auto","created_at":"2025-11-25 16:11:01","extension":"png","order_by":12,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":86279,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-8024060/v1/46c3f24d86c5d9daf93375ce.png"},{"id":96747174,"identity":"4f16969f-1198-4142-841d-2a716937e0fa","added_by":"auto","created_at":"2025-11-25 16:11:01","extension":"xml","order_by":14,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":33840,"visible":true,"origin":"","legend":"","description":"","filename":"6c251017d6914208a6e1393ce736b7581structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-8024060/v1/3c9031714f43182d6bcb6c69.xml"},{"id":96747173,"identity":"f80cec83-88ab-42f5-9218-30f9049d4d3c","added_by":"auto","created_at":"2025-11-25 16:11:01","extension":"html","order_by":15,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":39152,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8024060/v1/aa53ceb8cac93fbe3d85d8fb.html"},{"id":96914505,"identity":"b3e24f2f-35f0-4c13-81ba-01b8a9a48237","added_by":"auto","created_at":"2025-11-27 14:06:01","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":7649,"visible":true,"origin":"","legend":"\u003cp\u003eWorkflow for benchmarking raw material quality.\u003c/p\u003e","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8024060/v1/80a10bdf896b1616cabc9b7e.png"},{"id":96747171,"identity":"aacaf117-c02e-456c-b4b7-7dbf21413d49","added_by":"auto","created_at":"2025-11-25 16:11:01","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":489452,"visible":true,"origin":"","legend":"\u003cp\u003eProcess capability Sixpack report for Dextromethorphan HBr (Residue on Ignition). The parameter achieved PPI = 1.23, the highest among all tested variables. Stable control charts and the absence of OOS lots support reduced QC testing.\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8024060/v1/8113d4179a61d82bbf1fc21a.jpeg"},{"id":96915352,"identity":"2342f568-c061-4189-b5a4-1dd13b53b774","added_by":"auto","created_at":"2025-11-27 14:07:09","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":534080,"visible":true,"origin":"","legend":"\u003cp\u003eProcess capability Sixpack report for Aspirin (Residue on Ignition). With PPI = 0.70, the parameter meets the internal reduced-testing threshold. Although variability is higher than in best-case parameters, no compliance failures were observed, making it a borderline candidate for reduced QC testing.\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8024060/v1/6579af902842829d3428e810.jpeg"},{"id":96913563,"identity":"75185866-af62-4edc-ac6f-a23547ec86c4","added_by":"auto","created_at":"2025-11-27 14:02:46","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":446332,"visible":true,"origin":"","legend":"\u003cp\u003eProcess capability Sixpack report for Phenylephrine HCl (Loss on Drying). The parameter recorded PPI = 0.28, the lowest in the dataset. High variability and unstable control behavior necessitate maintaining full QC testing.\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8024060/v1/a56cbd16fda3736aa19b834f.jpeg"},{"id":96747175,"identity":"a89393d7-2862-434b-8072-f9f33f161472","added_by":"auto","created_at":"2025-11-25 16:11:01","extension":"png","order_by":13,"title":"Figure 13","display":"","copyAsset":false,"role":"figure","size":7649,"visible":true,"origin":"","legend":"\u003cp\u003eWorkflow for benchmarking raw material quality.\u003c/p\u003e","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8024060/v1/6138634004d30135223168e0.png"},{"id":96922346,"identity":"08b8d633-6bab-4a66-a645-d76d3886bd72","added_by":"auto","created_at":"2025-11-27 14:19:06","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2051251,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8024060/v1/20b84d8a-2b79-40e5-a694-99aef630adb1.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Data-Driven Benchmarking of Raw Material Quality for Risk-Based QC Optimization in Pharmaceutical Manufacturing","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eProduct quality is a critical pillar of pharmaceutical manufacturing, requiring strict adherence to regulatory standards to ensure safety, efficacy, and reliability. A central component of pharmaceutical quality assurance is the Product Quality Review (PQR), a systematic, data-driven assessment mandated under Good Manufacturing Practices (GMP). Among the key areas reviewed in a PQR is the evaluation of raw materials, whose quality and consistency directly impact the integrity of the finished product [1].\u003c/p\u003e\u003cp\u003eIn recent years, reduced testing, the selective omission of certain test parameters for raw materials with demonstrated quality consistency, has gained interest as a risk-based strategy to optimize analytical resources. Regulatory frameworks support this practice under specific conditions, such as supplier qualification and a review of historical performance [2]. However, the implementation must be carefully controlled, with critical quality attributes (e.g., assay, impurities, identification, water content) and high-risk parameters consistently monitored to maintain compliance and product safety [3\u0026ndash;5].\u003c/p\u003e\u003cp\u003eThis study presents a data-driven approach for evaluating the feasibility of reduced testing based on historical quality control data. Using internal datasets from a local pharmaceutical manufacturer, we statistically assessed the consistency of selected parameters for materials such as aspirin, dextromethorphan hydrobromide, talc, phenylephrine HCl, and carboxymethyl cellulose sodium. Our analysis applies the Process Performance Index (PPI) as a benchmark to quantify material quality over time. Materials exceeding the predefined PPI threshold were identified as candidates for reduced analysis.\u003c/p\u003e\u003cp\u003eThe findings provide practical insights into risk-based quality control, illustrating how structured data curation and statistical benchmarking can facilitate the responsible optimization of testing practices within GMP-compliant environments. This work contributes to the intersection of data quality assessment, performance modeling, and responsible data management in regulated industrial settings.\u003c/p\u003e"},{"header":"2. Methodology","content":"\u003cp\u003eThis study is a field-based evaluation using historical quality control data from multiple pharmaceutical raw materials across several manufacturing lots. All materials included in the analysis had previously passed initial quality testing and met specification standards at the time of use. The research focused on statistically analyzing this data to assess material quality consistency and determine the feasibility of reduced quality control testing.\u003c/p\u003e\u003cp\u003eThe data were obtained from the internal database of a pharmaceutical manufacturing company (PT. \"X\") located in Jakarta Industrial Estate, Pulogadung, East Jakarta, Indonesia. The authors did not collect new data directly from the production site; instead, existing quality control data were extracted from the company's centralized system (from November 2023 to November 2024). Each material analyzed included data from at least ten manufacturing lots, and only data sets that complied with the most recent specification codes were included.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe proposed framework benchmarks raw material quality using historical QC data through four sequential steps (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Data curation and filtering\u003c/h2\u003e\u003cp\u003eQC datasets were collected from a local pharmaceutical manufacturer, covering five raw materials (aspirin, dextromethorphan hydrobromide, phenylephrine HCl, sodium carboxymethyl cellulose [Na-CMC], and talc) and 11 parameters. Only complete, specification-compliant results were included to ensure data integrity, in line with GMP guidelines [1].\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Distribution assessment and normalization\u003c/h2\u003e\u003cp\u003eThe normality of each parameter distribution was assessed through the Anderson\u0026ndash;Darling method. Non-normal data underwent Box\u0026ndash;Cox transformation, when applicable, to ensure the data met the requirements for parametric statistical analysis. Where Box\u0026ndash;Cox was insufficient, the Johnson transformation was applied [6].\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Process monitoring\u003c/h2\u003e\u003cp\u003eIndividual control charts (X-bar and R) were generated to identify trends and potential instability, following standard statistical process control practices [7]. Out-of-control points were evaluated but not excluded, reflecting real production variability.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4 Capability benchmarking\u003c/h2\u003e\u003cp\u003eProcess performance indices (PPI) were calculated for non-normal distributions, while process capability indices (CPI) were applied where normality was confirmed [7,8]. An internal threshold of PPI/CPI\u0026thinsp;\u0026ge;\u0026thinsp;0.70 was used to recommend reduced testing, consistent with risk-based quality management principles [2,4].\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Result and Discussion","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Summary of Statistical Outcomes\u003c/h2\u003e\u003cp\u003eA total of 11 quality parameters across five raw materials were evaluated using the proposed benchmarking framework. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e summarizes the results of PPI/CPI calculations, normality assessments, and eligibility for reduced testing based on the company\u0026rsquo;s internal threshold (PPI\u0026thinsp;\u0026ge;\u0026thinsp;0.70). Overall, 6 of 11 parameters (55%) exceeded the reduced-testing threshold and were recommended for leaner QC strategies.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eBenchmarking outcome for raw material QC parameters\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMaterial\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eParameter\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePPI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCPI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNormality\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eThreshold Met?\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eReduced Testing Recommendation\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eAspirin\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eROI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNon-normal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e✔\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eRecommended\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLOD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNon-normal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e✘\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eNot recommended\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eDextromethorphan HBr\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003epH\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNormal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e✔\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eRecommended\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eWater content\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNormal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e✔\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eRecommended\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eROI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNon-normal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e✔\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eRecommended\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003ePhenylephrine HCl\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLOD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNon-normal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e✘\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eNot recommended\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eROI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNon-normal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e✘\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eNot recommended\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eNa-CMC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003epH\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNon-normal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e✘\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eNot recommended\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eViscosity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNon-normal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e✔\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eRecommended\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLOD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNon-normal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e✔\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eRecommended\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTalc\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLOD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNon-normal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e✘\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eNot recommended\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Case Studies\u003c/h2\u003e\u003cp\u003eThe following three Sixpack capability reports, which represent a cross-section of possible scenarios, are presented to demonstrate the application of the proposed framework. The first report is an example of the most favorable outcome; the second, a borderline case; and the third, the least favorable outcome.\u003c/p\u003e\u003cdiv id=\"Sec10\" class=\"Section3\"\u003e\u003ch2\u003e3.2.1 Success case: Dextromethorphan HBr (Residue on Ignition)\u003c/h2\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows the Sixpack report for residue on ignition (ROI) in dextromethorphan hydrobromide. This parameter achieved the highest PPI value (1.23) among all tests, indicating excellent process stability despite the non-normal distribution of the data. Control charts demonstrated consistent trends with no out-of-specification (OOS) lots, supporting eligibility for reduced QC testing.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section3\"\u003e\u003ch2\u003e3.2.2 Borderline case: Aspirin (Residue on Ignition)\u003c/h2\u003e\u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e illustrates the borderline case of aspirin ROI, which reached a PPI of 0.70. This value exactly meets the internal reduced-testing threshold, making it a marginal candidate for reduced testing. While variability was higher than in the best-case scenario, inspection history confirmed that no batches were rejected. This case demonstrates how the framework aids decision-making under moderate variability.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section3\"\u003e\u003ch2\u003e3.2.3 Failure case: Phenylephrine HCl (Loss on Drying)\u003c/h2\u003e\u003cp\u003eAs illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, the most unfavorable outcome observed for phenylephrine HCl involved a loss during the drying process. The parameter exhibited the lowest PPI (0.28), accompanied by unstable control charts and substantial variability. These findings suggest that there is a need for enhanced process control and support the continuation of comprehensive QC testing. This example underscores the framework's capacity to discern parameters that are deemed to be of high risk and are, consequently, deemed unsuitable for testing reduction.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e3.3 Discussion\u003c/h2\u003e\u003cp\u003eThe results underscore the value of applying structured benchmarking to QC data:\u003c/p\u003e\u003cp\u003e1) Complementary insights: Even when PPI values fell below the strict Six Sigma benchmark (1.33), six parameters were deemed suitable for reduced testing under the company\u0026rsquo;s internal threshold (\u0026ge;\u0026thinsp;0.70).\u003c/p\u003e\u003cp\u003e2) Regulatory alignment: No rejected batches were observed during the study period, suggesting that statistical benchmarking, when coupled with historical compliance data, can provide a defensible basis for risk-based testing.\u003c/p\u003e\u003cp\u003e3) Operational efficiency: By distinguishing stable from variable parameters, the framework enables QC teams to allocate resources where they are most needed, aligning with GMP and continuous improvement principles.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003e3.4 Interpretation\u003c/h2\u003e\u003cp\u003eThe benchmarking results demonstrate that statistical indices, when applied to historical QC data, can effectively separate parameters suitable for reduced testing from those requiring continued full control [6,8]. Six parameters exceeded the internal PPI threshold (\u0026ge;\u0026thinsp;0.70), confirming process stability and supporting leaner QC strategies [2,4]. Conversely, parameters with low PPI values, such as phenylephrine HCl (LOD), highlighted areas of high variability where risk remains too significant for testing reduction [3,5].\u003c/p\u003e\u003cp\u003eImportantly, none of the evaluated parameters resulted in out-of-specification lots or rejected batches, indicating that even where variability exists, product quality has been maintained. This underscores the need to interpret capability indices not in isolation but alongside inspection trends, supplier quality history, and regulatory expectations [1,4,9\u0026ndash;11].\u003c/p\u003e\u003cp\u003eOverall, the findings suggest that statistical benchmarking is a powerful enabler of risk-based QC, but its application must remain context-aware and supported by broader quality assurance practices [12].\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Conclusion","content":"\u003cp\u003eThis study introduced a structured, data-driven framework to evaluate the feasibility of reduced quality control (QC) testing for pharmaceutical raw materials. By combining RSD filtering, distribution normalization, control chart analysis, and Process Performance Index (PPI) benchmarking, the framework provides a transparent and statistically grounded basis for risk-based decision making in compliance with GMP principles.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003ch2\u003eConflicts of Interest\u003c/h2\u003e\u003cp\u003eThe authors declared no conflict of interest in the manuscript.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eConcept \u0026ndash; K.Z., M.B.R.; Design \u0026ndash; K.Z., M.B.R; Supervision \u0026ndash; K.Z.; Data Collection and/or Processing \u0026ndash; M.B.R.; Analysis and/or Interpretation \u0026ndash; M.B.R., K.Z.; LiteratureSearch \u0026ndash; M.B.R, K.Z.; Writing \u0026ndash; M.B.R, K.Z.; Critical Reviews \u0026ndash; K.Z.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThe authors thank a pharmaceutical manufacturing company (PT. \u0026ldquo;X\u0026rdquo;) located in Jakarta Industrial Estate, Pulogadung, East Jakarta, Indonesia, for the database that the authors can use for this study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eIndonesian Food and Drug Authority. Pedoman Cara Pembuatan Obat yang Baik (Guidelines on Good Manufacturing Practice). Jakarta: BPOM; 2018.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRack C. Approaches to reduced sampling and testing for starting materials. Pharm Technol. 2019;43(12):26\u0026ndash;30.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eInternational Council for Harmonisation. ICH Q8(R2): Pharmaceutical Development, 2009.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eInternational Council for Harmonisation, ICH Q9. Quality Risk Management, 2005.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eU.S. Food and Drug Administration, Guidance for Industry: Process Validation: General Principles and Practices, 2011.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMinitab. Understanding Johnson transformation for normality. Minitab Tech Articles, 2020.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMontgomery DC. Introduction to Statistical Quality Control. 8th ed. Hoboken, NJ: Wiley; 2020.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eShinde JH, Katikah RS. Importance of process capability and process performance index in machine tool. Int J Res Eng Appl Sci. 2012;2(2):1211\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eInternational Council for. Harmonisation, ICH Q10: Pharmaceutical Quality System, 2008.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eEuropean Medicines Agency. Guideline on the Use of Near Infrared Spectroscopy by the Pharmaceutical Industry and the Data Requirements for New Submissions and Variations, 2014.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFood US, Administration D. Guidance for Industry: Quality Systems Approach to Pharmaceutical CGMP Regulations, 2006.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWomack JP, Jones DT. Lean Thinking: Banish Waste and Create Wealth in Your Corporation. New York: Simon \u0026amp; Schuster; 2003.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"process performance index, capability analysis, reduced testing, pharmaceutical quality, data-driven benchmarking","lastPublishedDoi":"10.21203/rs.3.rs-8024060/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8024060/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eEnsuring consistent raw material quality is essential in pharmaceutical manufacturing to maintain product safety and regulatory compliance. However, routine full-scope quality control (QC) testing is resource-intensive, and risk-based reduction strategies remain underutilized. This study proposes a data-driven benchmarking framework that integrates Relative Standard Deviation (RSD) filtering, distribution normalization, control chart analysis, and Process Performance Index (PPI) evaluation to assess material consistency. Using historical QC data from a local pharmaceutical manufacturer, we analyzed 11 parameters across five raw materials, including aspirin, dextromethorphan hydrobromide, talc, phenylephrine HCl, and carboxymethyl cellulose sodium. Results show that six parameters exceeded the company-defined PPI threshold (\u0026ge;\u0026thinsp;0.70) and were justified for reduced testing without compromising compliance or product safety. The framework demonstrates how statistical benchmarking of QC data can support risk-based decision making, optimize analytical resources, and align with GMP principles. This work highlights the potential for integrating structured data analytics into pharmaceutical quality systems to enable efficient, compliant, and scalable QC practices.\u003c/p\u003e","manuscriptTitle":"Data-Driven Benchmarking of Raw Material Quality for Risk-Based QC Optimization in Pharmaceutical Manufacturing","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-25 16:10:56","doi":"10.21203/rs.3.rs-8024060/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"aa7a8514-f855-4526-b310-96baefdb1bba","owner":[],"postedDate":"November 25th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-04-28T01:08:04+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-25 16:10:56","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8024060","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8024060","identity":"rs-8024060","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}