Integrated Approaches for Quality and Productivity Enhancement in the Food Sector for Micro Small and Medium Enterprises (MSME) using Lean Six Sigma

Integrated Approaches for Quality and Productivity Enhancement in the Food Sector for Micro Small and Medium Enterprises (MSME) using Lean Six Sigma | 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 Case Report Integrated Approaches for Quality and Productivity Enhancement in the Food Sector for Micro Small and Medium Enterprises (MSME) using Lean Six Sigma Manisha Lande, Aniket Lande, vinod todkari, Mandar More This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8994547/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract Purpose- The Micro, Small, and Medium-Sized Enterprises (MSME) sector plays a significant role in the Indian economy and is a vital force behind inclusive growth. The industry contributes significantly to manufacturing output, employment creation, rural industrialisation, and exports because of its low capital intensity and strong labour absorption capability. In order to increase quality, lower process variation, and get rid of waste, Lean Six Sigma (LSS) is a structured improvement methodology that combines Lean manufacturing and Six Sigma concepts. The current study offers a Lean Six Sigma framework roadmap that was created from real-world experience from applying Lean and Six Sigma techniques in a variety of MSME food businesses. The objective of this framework is to offer a methodical way to enhance overall productivity and quality of organisational performance and operational efficiency. Design/methodology/approach: Structured questionnaires are used in this study to gather data from a variety of MSME sectors, such as the food, textile, and glass industries. Python machine learning techniques and SPSS software are used to create input-output performance models based on the gathered data. Additionally, a sector-specific model specifically for the food business is created, in addition to a generalised input-output performance model for all sectors. The efficacy of the created models is assessed by comparing them using Python integration and SPSS. Lastly, a case study carried out in the food processing industry validates the suggested models. Findings- In order to improve productivity and quality, this study provides MSMEs, researchers, and practitioners with a methodical way to find and choose pertinent Lean Six Sigma input and output performance parameters. The suggested approach helps MSMEs remain competitive and sustainable over the long run while also improving operational efficiency. Research Limitation: Data sources are used in this study. Structured questionnaires were used to gather data from MSMEs in the textile, glass, and food sectors (rice mills, dal mills, and oil refining facilities). MSME bulletins, District Industries Centers (DICs), MSME offices, financial institutions that assist MSMEs, scholarly publications, newspapers, magazines, and other pertinent internet sites were also used to gather additional data. The study is sectorally limited to MSMEs in the food, textile, and glass industries and geographically limited to Maharashtra region only. Originality/value: Lean Six Sigma (LSS) has a great deal of promise to improve manufacturing MSMEs' performance, but assessing its efficacy is still difficult. Specifically for the food industry in poor nations, this study offers a fresh approach to create an input–output performance model using various input and output variables. The suggested method makes it easier to benchmark productivity and quality across several sets of critical success factors (CSFs). Data analysis and model creation are done using Python machine learning techniques and SPSS. As far as the authors are aware, this is the first study to combine machine learning and statistical techniques to create a performance evaluation model of this kind that can be expanded to other industries and application domains also. Critical Success Factors (CSFs) Micro Small and Medium Enterprises (MSMEs) Lean Six Sigma (LSS) Machine Learning using Python Statistical Package for the Social Sciences (SPSS) Indian MSMEs Performance Evaluation Input–Output Model Full Text Additional Declarations No competing interests reported. Supplementary Files SupplemantrydataforFoodpaper.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 16 May, 2026 Reviewers agreed at journal 08 May, 2026 Reviews received at journal 05 May, 2026 Reviewers agreed at journal 21 Apr, 2026 Reviewers invited by journal 21 Apr, 2026 Editor invited by journal 06 Apr, 2026 Editor assigned by journal 01 Apr, 2026 Submission checks completed at journal 28 Mar, 2026 First submitted to journal 28 Mar, 2026 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. 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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-8994547","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Case Report","associatedPublications":[],"authors":[{"id":626845150,"identity":"0eb41b6d-4816-4f05-9e18-a0e24a39fe18","order_by":0,"name":"Manisha 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The industry contributes significantly to manufacturing output, employment creation, rural industrialisation, and exports because of its low capital intensity and strong labour absorption capability.\u003c/p\u003e \u003cp\u003eIn order to increase quality, lower process variation, and get rid of waste, Lean Six Sigma (LSS) is a structured improvement methodology that combines Lean manufacturing and Six Sigma concepts.\u003c/p\u003e \u003cp\u003eThe current study offers a Lean Six Sigma framework roadmap that was created from real-world experience from applying Lean and Six Sigma techniques in a variety of MSME food businesses. The objective of this framework is to offer a methodical way to enhance overall productivity and quality of organisational performance and operational efficiency.\u003c/p\u003e \u003cp\u003eDesign/methodology/approach: Structured questionnaires are used in this study to gather data from a variety of MSME sectors, such as the food, textile, and glass industries. Python machine learning techniques and SPSS software are used to create input-output performance models based on the gathered data.\u003c/p\u003e \u003cp\u003eAdditionally, a sector-specific model specifically for the food business is created, in addition to a generalised input-output performance model for all sectors. The efficacy of the created models is assessed by comparing them using Python integration and SPSS. Lastly, a case study carried out in the food processing industry validates the suggested models.\u003c/p\u003e \u003cp\u003eFindings- In order to improve productivity and quality, this study provides MSMEs, researchers, and practitioners with a methodical way to find and choose pertinent Lean Six Sigma input and output performance parameters. The suggested approach helps MSMEs remain competitive and sustainable over the long run while also improving operational efficiency.\u003c/p\u003e \u003cp\u003eResearch Limitation: Data sources are used in this study. 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