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Integrating Quality at Source into Supplier Management: A Pathway to Cost Efficiency and Regulatory Compliance

Binitkumar M Vaghani
Michigan Technological University Varian, A Siemens Healthineers Company MS In Mechanical Engineering

Submission to VIJ 2024-11-20

Abstract

In an increasingly competitive global market, organizations are seeking to enhance both cost efficiency and regulatory compliance in their supply chains. This paper explores the integration of Quality at Source (Q@S) into supplier management as a strategic pathway to achieve these goals. Quality at Source is a proactive approach that emphasizes defect prevention and quality assurance at the earliest stages of production, specifically at the supplier level. By embedding Q@S principles within supplier management, companies can not only reduce the financial burden associated with defect detection and correction but also mitigate risks associated with non-compliance with industry regulations. The study identifies key benefits of Q@S, including cost savings through reduced waste, improved production efficiency, and minimized need for extensive downstream quality control. Additionally, it demonstrates how Q@S facilitates regulatory compliance by ensuring that quality standards are met consistently, thus simplifying audits and reducing the likelihood of penalties. The paper details a framework for Q@S implementation, covering essential steps such as supplier selection, establishing quality metrics, conducting regular audits, and fostering a culture of continuous improvement among suppliers. Through a combination of case studies and data-driven insights, the paper illustrates the tangible benefits of Q@S integration. Real-world examples showcase successful applications of Q@S across different industries, highlighting significant cost reductions and enhanced compliance. Furthermore, this research discusses the challenges companies may face, such as initial investment costs and resistance to change from suppliers, and provides strategies for overcoming these barriers. The paper concludes by examining the role of advanced digital technologies—such as artificial intelligence (AI), Internet of Things (IoT), and blockchain—in furthering Q@S initiatives. These technologies enable real-time monitoring and data transparency, enhancing the capacity of organizations to track supplier performance and maintain quality standards. The findings underscore the importance of Quality at Source as an essential component of modern supplier management, providing a roadmap for organizations aiming to achieve cost-effective, compliant, and sustainable supply chains

References

  1. Denesiuk, P. (2024). International Supplier Quality Management in Global Supply Chains (based on “Dellini Restaurant” case) (Doctoral dissertation, Private Higher Educational Establishment-Institute “Ukrainian-American Concordia University").
  2. Dutta, G., Kumar, R., Sindhwani, R., & Singh, R. K. (2021). Digitalization priorities of quality control processes for SMEs: A conceptual study in perspective of Industry 4.0 adoption. Journal of Intelligent Manufacturing, 32(6), 1679-1698.
  3. Karanam, R. K., Sachani, D. K., Natakam, V. M., Yarlagadda, V. K., & Kothapalli, K. R. V. (2024). Resilient Supply Chains: Strategies for Managing Disruptions in a Globalized Economy. American Journal of Trade and Policy, 11(1), 7-16.
  4. Le, T. T. (2002). Pathways to leadership for business-to-business electronic marketplaces. Electronic Markets, 12(2), 112-119.
  5. Aung, M. M., & Chang, Y. S. (2014). Traceability in a food supply chain: Safety and quality perspectives. Food control, 39, 172-184.
  6. Bosona, T., & Gebresenbet, G. (2013). Food traceability as an integral part of logistics management in food and agricultural supply chain. Food control, 33(1), 32-48.
  7. Geffen, C. A., & Rothenberg, S. (2000). Suppliers and environmental innovation: the automotive paint process. International Journal of Operations & Production Management, 20(2), 166-186.
  8. Kaplan, R. S., & Cooper, R. (1998). Cost & effect: using integrated cost systems to drive profitability and performance. Harvard Business Press.
  9. MacNeill, A. J., Hopf, H., Khanuja, A., Alizamir, S., Bilec, M., Eckelman, M. J., ... & Sherman, J. D. (2020). Transforming the medical device industry: road map to a circular economy: study examines a medical device industry transformation. Health Affairs, 39(12), 2088-2097.
  10. Klassen, R. D., & Vereecke, A. (2012). Social issues in supply chains: Capabilities link responsibility, risk (opportunity), and performance. International Journal of production economics, 140(1), 103-115.
  11. Esan, O., Ajayi, F. A., & Olawale, O. (2024). Supply chain integrating sustainability and ethics: Strategies for modern supply chain management. World Journal of Advanced Research and Reviews, 22(1), 1930-1953.
  12. Roy, A. H., Wenger, S. J., Fletcher, T. D., Walsh, C. J., Ladson, A. R., Shuster, W. D., ... & Brown, R. R. (2008). Impediments and solutions to sustainable, watershed-scale urban stormwater management: lessons from Australia and the United States. Environmental management, 42, 344-359.
  13. Rao, P., & Holt, D. (2005). Do green supply chains lead to competitiveness and economic performance?. International journal of operations & production management, 25(9), 898-916.
  14. Power, D. (2005). Supply chain management integration and implementation: a literature review. Supply chain management: an International journal, 10(4), 252-263.
  15. Abatan, A., Jacks, B. S., Ugwuanyi, E. D., Nwokediegwu, Z. Q. S., Obaigbena, A., Daraojimba, A. I., & Lottu, O. A. (2024). The role of environmental health and safety practices in the automotive manufacturing industry. Engineering Science & Technology Journal, 5(2), 531-542.
  16. Mammadzada, A. Evolving Environmental Immigration Policies Through Technological Solutions: A Focused Analysis of Japan and Canada in the Context of COVID-19.
  17. Bhat, P., Shukla, T., Naik, N., Korir, D., Princy, R., Samrot, A. V., ... & Salmataj, S. A. (2023). Deep Neural Network as a Tool to Classify and Identify the 316L and AZ31BMg Metal Surface Morphology: An Empirical Study. Engineered Science, 26, 1064.
  18. Nie, P., Parovic, M., Zang, Z., Khurshid, S., Milicevic, A., & Gligoric, M. (2020). Unifying execution of imperative generators and declarative specifications. Proceedings of the ACM on Programming Languages, 4(OOPSLA), 1-26.
  19. SHUKLA, T. (2024). Beyond Diagnosis: AI’s Role in Preventive Healthcare and Early Detection.
  20. Zang, Z., Wiatrek, N., Gligoric, M., & Shi, A. (2022, October). Compiler testing using template java programs. In Proceedings of the 37th IEEE/ACM International Conference on Automated Software Engineering (pp. 1-13).
  21. Das, A., Shukla, T., Tomita, N., Richards, R., Vidis, L., Ren, B., & Hassanpour, S. (2024). Deep Learning for Classification of Inflammatory Bowel Disease Activity in Whole Slide Images of Colonic Histopathology. arXiv preprint arXiv:2410.19690.
  22. Ghelani, H. (2024). AI-Driven Quality Control in PCB Manufacturing: Enhancing Production Efficiency and Precision. Valley International Journal Digital Library, 1549-1564.
  23. Chanane, F. (2024). Exploring Optimization Synergies: Neural Networks and Differential Evolution for Rock Shear Velocity Prediction Enhancement. International Journal of Earth Sciences Knowledge and Applications, 6(1), 21-28.
  24. Buhse, B., Wei, T., Zang, Z., Milicevic, A., & Gligoric, M. (2019, May). Vedebug: regression debugging tool for java. In 2019 IEEE/ACM 41st International Conference on Software Engineering: Companion Proceedings (ICSE-Companion) (pp. 15-18). IEEE.
  25. Ghelani, H. (2024). Advanced AI Technologies for Defect Prevention and Yield Optimization in PCB Manufacturing. Valley International Journal Digital Library, 26534-26550.
  26. Miloud, M. O. B., & Liu, J. (2023, April). An Application Service for Supporting Security Management In Software-Defined Networks. In 2023 7th International Conference on Cryptography, Security and Privacy (CSP) (pp. 129-133). IEEE.
  27. Ghelani, H. (2021). Advances in lean manufacturing: improving quality and efficiency in modern production systems. Valley International Journal Digital Library, 611-625.
  28. Lakhani, R. (2023). Cybersecurity Threats in Internet of Things (IoT) Networks: Vulnerabilities and Defense Mechanisms. Valley International Journal Digital Library, 25965-25980.
  29. Lakhani, R., & Sachan, R. C. (2024). Securing Wireless Networks Against Emerging Threats: An Overview of Protocols and Solutions.
  30. Lakhani, R. Zero Trust Security Models: Redefining Network Security in Cloud Computing Environments.