Enhancing Supply Chain Sustainability with Artificial Intelligence

Ashwani Kumar

Abstract


 Sustainability is a growing concern in supply chain management. This chapter explores the role of AI in promoting sustainable supply chain practices. It discusses how AI can be used to optimize resource utilization, reduce waste, and minimize carbon footprints. The chapter also presents case studies of companies that have leveraged AI to achieve sustainability goals in their supply chains.


References


Koushik, P. Data-Driven Simulation: Integrating Sensitivity Analysis into Supply Chain Optimization, International Journal of Science and Research (IJSR), Volume 13 Issue 5, May 2024

Koushik, P. OPTIMIZING FULFILLMENT: A MULTI-FACETED APPROACH INTEGRATING LINEAR PROGRAMMING, BRANCH AND BOUND TECHNIQUES, AND REINFORCEMENT LEARNING. International Journal of Computer Engineering and Technology (IJCET) Volume 15, Issue 3, May-June 2024, pp. 134-149

Kshetri, N. (2018). 1 Blockchain’s roles in meeting key supply chain management objectives. International Journal of Information Management, 39, 80-89. https://doi.org/10.1016/j.ijinfomgt.2017.12.005

Min, H. (2010). Artificial intelligence in supply chain management: Theory and applications. International Journal of Logistics Research and Applications, 13(1), 13-39. https://doi.org/10.1080/13675560902736537

Kouhizadeh, M., & Sarkis, J. (2018). Blockchain practices, potentials, and perspectives in greening supply chains. Sustainability, 10(10), 3652. https://doi.org/10.3390/su10103652

Wamba, S. F., Gunasekaran, A., Akter, S., Ren, S. J., Dubey, R., & Childe, S. J. (2017). Big data analytics and firm performance: Effects of dynamic capabilities. Journal of Business Research, 70, 356-365. https://doi.org/10.1016/j.jbusres.2016.08.009

Choi, T. M., Wallace, S. W., & Wang, Y. (2018). Big data analytics in operations management. Production and Operations Management, 27(10), 1868-1883. https://doi.org/10.1111/poms.12838

Dubey, R., Gunasekaran, A., Childe, S. J., Blome, C., & Papadopoulos, T. (2019). Big data and predictive analytics and manufacturing performance: Integrating institutional theory, resource-based view and big data culture. British Journal of Management, 30(2), 341-361. https://doi.org/10.1111/1467-8551.12355

Ivanov, D., & Dolgui, A. (2020). A digital supply chain twin for managing the disruption risks and resilience in the era of Industry 4.0. Production Planning & Control, 31(10), 775-788. https://doi.org/10.1080/09537287.2020.1768450

Janssen, M., van der Voort, H., & Wahyudi, A. (2017). Factors influencing big data decision-making quality. Journal of Business Research, 70, 338-345. https://doi.org/10.1016/j.jbusres.2016.08.007

Queiroz, M. M., & Wamba, S. F. (2019). Blockchain adoption challenges in supply chain: An empirical investigation of the main drivers in India and the USA. International Journal of Information Management, 46, 70-82. https://doi.org/10.1016/j.ijinfomgt.2018.11.021

Wang, G., Gunasekaran, A., Ngai, E. W., & Papadopoulos, T. (2016). Big data analytics in logistics and supply chain management: Certain investigations for research and applications. International Journal of Production Economics, 176, 98-110. https://doi.org/10.1016/j.ijpe.2016.03.014


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