Journal of Supply Chain Management Systems

Open Access Open Access  Restricted Access Subscription Access
Open Access Open Access Open Access  Restricted Access Restricted Access Subscription Access

Supply Chain Management with Application of Lean Six Sigma and Artificial Intelligence: An Integrated Empirical Investigation


Affiliations
1 Professor & Head, Department of Supply Chain & Logistics Management, Indian Institute of Social Welfare & Business Management, Management House, College Square West, Kolkata, West Bengal,, India
2 C.E.O (Realware), C.S.O. (S. C. Trading & Co.), Department of Supply Chain & Logistics Management, Indian Institute of Social Welfare and Business Management, Kolkata, West Bengal, India
3 Vice President (Materials), CESC Limited, Materials Division, Kolkata, West Bengal, India
     

   Subscribe/Renew Journal


The objective of this study is to establish a new business model for a business unit implementing the technology and methodology mentioned in 2.0 (visualised). It further encompassed the comparison with the benchmark snapshot between the quarter 01.10.2020 – 31.12.2020 and 01.1.2021 – 03.03.2021). The improvements due to the integration of lean six sigma along with artificial intelligence in conventional supply chain management system were quantified in terms of increase in percentage of profit, reduction in distributor attrition, failure of supply, excess or blocked inventory, and furthermore, reduction in operating losses and project completion tenure. Finally, the study identified areas of improvement with respect to the services rendered with supply chain, and suggested corrective actions for the same. The results derived in this study are explicit, conceptually stimulating and may provide guidance to managers engaged in supply chain management activities. The results showed that conventional supply chain management system is difficult to build, due to the complexities and uncertainties that exist across organizational boundaries. Hence, it is not surprising, therefore, that most alliances among partners fail to generate expected outcomes, especially in context to leveraging the potential of lean six sigma and Artificial Intelligence based SCM. It is hoped that our results provide a framework that may act as a blueprint for the manufacturing sector to assess and improve conventional supply chain management system with lean six sigma and AI-powered supply chain management, as well as increased organizational and financial performance.

Keywords

Supply Chain Management, Lean Six Sigma, Artificial Intelligence, Industrial Internet of Things, DMIAC, Client Relationship Management System, Operational Process
Subscription Login to verify subscription
User
Notifications
Font Size


  • Akter, S., Michael, K., Uddin, M. R., McCarthy, G., & Rahman, M. (2020). Transforming business using digital innovations: The application of AI, blockchain, cloud and data analytics. Annals of Operations Research, 1-33. doi:https://doi.org/10.1007/s10479-0 20- 03620-w

  • Antony, J., Escamilla, J. L., & Caine, P. (2003). Lean sigma [production and supply chain management]. Manufacturing Engineer, 82(2), 40-42. doi:https://doi.org/10.1049/me: 20030203

  • Barreto, L., Amaral, A., & Pereira, T. (2017, June 13). Industry 4.0 implications in logistics: An overview. In Proc. Int. Conf. Manuf. Eng. Soc. (MESIC 2017) (pp.1245-1252).

  • Ben-Daya, M., Hassini, E., & Bahroun, Z. (2019). Internet of things and supply chain management: A literature review. International Journal of Production Research, 57(15-16), 4719-4742. doi:https://doi.org/10.1080/002 07543.2017.1402140

  • Bendell, T. (2006). A review and comparison of six sigma and the lean organisations. The TQM Magazine, 18(3), 255-262.

  • Clegg, B., Pepper, M. P. J., & Spedding, T. A. (2010). The evolution of lean six sigma. International Journal of Quality & Reliability Management, 27(2), 138-155.

  • Dubey, R., Bryde, D. J., Blome, C., Roubaud, D., & Giannakis, M. (2021a). Facilitating artificial intelligence powered supply chain analytics through alliance management during the pandemic crises in the B2B context. Industrial Marketing Management, 96, 135-146.

  • Dubey, R., Bryde, D. J., Foropon, C., Tiwari, M., Dwivedi, Y., & Schiffling, S. (2021b). An investigation of information alignment and collaboration as complements to supply chain agility in humanitarian supply chain. International Journal of Production Research, 59(5), 1586-1605.

  • Frohlich, M. T., & Westbrook, R. (2001). Arcs of integration: An international study of supply chain strategies. Journal of Operations Management, 19, 185-200.

  • Guofu, J., & Shumeng, H. (2015). Artificial intelligence in computer networks [J]. Digital Technology and Applications, (7), 100-103.

  • Jayaram, A. (2016). Lean 6 sigma approach for global supply chain management using industry 4.0 and IIoT. 2nd International Conference on Contemporary Computing and Informatics (IC3I). Retrieved from https://www.researchgate.net/publication/314114889

  • Kuei, C. H., Madu, C. N., & Lin, C. (2011). Developing global supply chain quality management systems. International Journal of Production Research, 49, 4457-4481.

  • Liu, S., & Wu, B. (2010). Study on the supply chain management of global companies. International Conference in E-Business and E-Government (ICEE). doi:10.1109/ ICEE.2010.829

  • Martin, J. (2006). Lean six sigma for supply chain management. McGraw Hill Professional.

  • Rejeb, A., Simske, S., Rejeb, K., Treiblmaier, H., & Zailani, S. (2020). Internet of things research in supply chain management and logistics: A bibliometric analysis. Internet of Things, 12. Elsevier.

  • Riahi, Y., Saikouk, T., Gunasekaran, A., & Badraoui, I. (2021). Artificial intelligence applications in supply chain: A descriptive bibliometric analysis and future research directions. Expert Systems with Applications, 173. Elsevier.

  • Srinivasan, R., & Swink, M. (2018). An investigation of visibility and flexibility as complements to supply chain analytics: An organizational information processing theory perspective. Production and Operations Management, 27(10), 1849-1867.

  • Stadtler, H. (2015). Supply chain management: An overview. Supply Chain Management and Advanced Planning, 3-28. Springer.

  • Sun, Z. W., Li, W. F., Song, W., & Jiang, P. (2011). Research on manufacturing supply chain information platform architecture based on internet of things. Advanced Materials Research. doi:https://doi.org/10.4028/www.scientific.net/AMR.314-316.2344

  • Witkowski, K. (2017). Internet of things, big data, industry 4.0 – Innovative solutions in logistics and supply chains management. In Proc. 7th Int. Conf. Eng. Project Prod. Manag., 182, 763-769.

  • Yang, L. (2018). Research on application of artificial intelligence based on big data background in computer network technology, IOP Conf. Series: Materials Science and Engineering, 392. doi:10.1088/1757-899X/392/6/062185

  • Yi-Tao, D. E. N. G. (2010). The internet of things technology application in the supply chain management. Logistics Sci-Tech, 9, 92-94.

  • Yumei, H. (2017), Analysis of application of artificial intelligence in computer network technology. Electronic World, (11), 38-39.

  • Zhang, L., & Zhang, Y. (2016). Big data analysis by infinite deep neural networks. Journal of Computer Research and Development, 53(1), 68-79.

  • Zixing, C. (2016). Artificial intelligence and its applications (5th ed.). Beijing: Tsinghua University Press. Website References Retrieved June 29, 2021, from https://qualitysupportgroup.com/supply-chain-management-scm/


Abstract Views: 130

PDF Views: 0




  • Supply Chain Management with Application of Lean Six Sigma and Artificial Intelligence: An Integrated Empirical Investigation

Abstract Views: 130  |  PDF Views: 0

Authors

Sarbani Mitra
Professor & Head, Department of Supply Chain & Logistics Management, Indian Institute of Social Welfare & Business Management, Management House, College Square West, Kolkata, West Bengal,, India
Bishakh Chanda
C.E.O (Realware), C.S.O. (S. C. Trading & Co.), Department of Supply Chain & Logistics Management, Indian Institute of Social Welfare and Business Management, Kolkata, West Bengal, India
Partha Bhattacharya
Vice President (Materials), CESC Limited, Materials Division, Kolkata, West Bengal, India

Abstract


The objective of this study is to establish a new business model for a business unit implementing the technology and methodology mentioned in 2.0 (visualised). It further encompassed the comparison with the benchmark snapshot between the quarter 01.10.2020 – 31.12.2020 and 01.1.2021 – 03.03.2021). The improvements due to the integration of lean six sigma along with artificial intelligence in conventional supply chain management system were quantified in terms of increase in percentage of profit, reduction in distributor attrition, failure of supply, excess or blocked inventory, and furthermore, reduction in operating losses and project completion tenure. Finally, the study identified areas of improvement with respect to the services rendered with supply chain, and suggested corrective actions for the same. The results derived in this study are explicit, conceptually stimulating and may provide guidance to managers engaged in supply chain management activities. The results showed that conventional supply chain management system is difficult to build, due to the complexities and uncertainties that exist across organizational boundaries. Hence, it is not surprising, therefore, that most alliances among partners fail to generate expected outcomes, especially in context to leveraging the potential of lean six sigma and Artificial Intelligence based SCM. It is hoped that our results provide a framework that may act as a blueprint for the manufacturing sector to assess and improve conventional supply chain management system with lean six sigma and AI-powered supply chain management, as well as increased organizational and financial performance.

Keywords


Supply Chain Management, Lean Six Sigma, Artificial Intelligence, Industrial Internet of Things, DMIAC, Client Relationship Management System, Operational Process

References