Designing Predictive Analytics Frameworks for Supply Chain Quality Management: A Machine Learning Approach to Defect Rate Optimization
Efficient supply chain management (SCM) is essential for enterprises seeking to enhance operational efficiency, reduce costs, and mitigate risks while ensuring product quality and customer satisfaction. Addressing quality concerns within the supply chain proactively helps minimize rework, recalls, a...
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MDPI AG
2025-04-01
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| author | Zainab Nadhim Jawad Balázs Villányi |
| author_facet | Zainab Nadhim Jawad Balázs Villányi |
| author_sort | Zainab Nadhim Jawad |
| collection | DOAJ |
| description | Efficient supply chain management (SCM) is essential for enterprises seeking to enhance operational efficiency, reduce costs, and mitigate risks while ensuring product quality and customer satisfaction. Addressing quality concerns within the supply chain proactively helps minimize rework, recalls, and returns, leading to significant cost savings and improved profitability. This study presents a machine learning (ML)-driven predictive analytics framework designed to forecast defect rates and optimize quality control processes. The research leverages a dataset sourced from a real-world fashion and beauty startup, hosted in a public repository. The framework employs advanced ML algorithms, including extreme gradient boosting (XGBoost), support vector machines (SVMs), and random forests (RFs), to accurately predict defect rates and derive actionable insights for supply chain optimization. Results demonstrate the effectiveness of predictive analytics in improving supply chain quality management, enabling enterprises to proactively reduce defect rates, minimize costs, and optimize return on investment (ROI). The proposed framework is designed to be scalable and transferable, ensuring adaptability across various industries, including fashion, e-commerce, and manufacturing. These findings underscore the economic and operational benefits of integrating machine learning into supply chain quality control, offering a data-driven, proactive approach to achieving high-efficiency, high-quality supply chain operations. |
| format | Article |
| id | doaj-art-b5fa1834f461422ea5ef1fa82ab0fa2b |
| institution | Kabale University |
| issn | 2813-4176 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
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| series | Platforms |
| spelling | doaj-art-b5fa1834f461422ea5ef1fa82ab0fa2b2025-08-20T04:01:58ZengMDPI AGPlatforms2813-41762025-04-0132610.3390/platforms3020006Designing Predictive Analytics Frameworks for Supply Chain Quality Management: A Machine Learning Approach to Defect Rate OptimizationZainab Nadhim Jawad0Balázs Villányi1Department of Electronics Technology, Faculty of Electrical Engineering and Informatics, Budapest University of Technology and Economics, Műegyetem rkp. 3., H-1111 Budapest, HungaryDepartment of Electronics Technology, Faculty of Electrical Engineering and Informatics, Budapest University of Technology and Economics, Műegyetem rkp. 3., H-1111 Budapest, HungaryEfficient supply chain management (SCM) is essential for enterprises seeking to enhance operational efficiency, reduce costs, and mitigate risks while ensuring product quality and customer satisfaction. Addressing quality concerns within the supply chain proactively helps minimize rework, recalls, and returns, leading to significant cost savings and improved profitability. This study presents a machine learning (ML)-driven predictive analytics framework designed to forecast defect rates and optimize quality control processes. The research leverages a dataset sourced from a real-world fashion and beauty startup, hosted in a public repository. The framework employs advanced ML algorithms, including extreme gradient boosting (XGBoost), support vector machines (SVMs), and random forests (RFs), to accurately predict defect rates and derive actionable insights for supply chain optimization. Results demonstrate the effectiveness of predictive analytics in improving supply chain quality management, enabling enterprises to proactively reduce defect rates, minimize costs, and optimize return on investment (ROI). The proposed framework is designed to be scalable and transferable, ensuring adaptability across various industries, including fashion, e-commerce, and manufacturing. These findings underscore the economic and operational benefits of integrating machine learning into supply chain quality control, offering a data-driven, proactive approach to achieving high-efficiency, high-quality supply chain operations.https://www.mdpi.com/2813-4176/3/2/6predictive modelingMLXGBoostSVMrandom forestROI |
| spellingShingle | Zainab Nadhim Jawad Balázs Villányi Designing Predictive Analytics Frameworks for Supply Chain Quality Management: A Machine Learning Approach to Defect Rate Optimization Platforms predictive modeling ML XGBoost SVM random forest ROI |
| title | Designing Predictive Analytics Frameworks for Supply Chain Quality Management: A Machine Learning Approach to Defect Rate Optimization |
| title_full | Designing Predictive Analytics Frameworks for Supply Chain Quality Management: A Machine Learning Approach to Defect Rate Optimization |
| title_fullStr | Designing Predictive Analytics Frameworks for Supply Chain Quality Management: A Machine Learning Approach to Defect Rate Optimization |
| title_full_unstemmed | Designing Predictive Analytics Frameworks for Supply Chain Quality Management: A Machine Learning Approach to Defect Rate Optimization |
| title_short | Designing Predictive Analytics Frameworks for Supply Chain Quality Management: A Machine Learning Approach to Defect Rate Optimization |
| title_sort | designing predictive analytics frameworks for supply chain quality management a machine learning approach to defect rate optimization |
| topic | predictive modeling ML XGBoost SVM random forest ROI |
| url | https://www.mdpi.com/2813-4176/3/2/6 |
| work_keys_str_mv | AT zainabnadhimjawad designingpredictiveanalyticsframeworksforsupplychainqualitymanagementamachinelearningapproachtodefectrateoptimization AT balazsvillanyi designingpredictiveanalyticsframeworksforsupplychainqualitymanagementamachinelearningapproachtodefectrateoptimization |