OPTIMIZING DECISION-MAKING IN SUPPLY CHAINS: A FRAMEWORK FOR AI AND HUMAN COLLABORATION USING SAP TECHNOLOGIES
Keywords:
Artificial Intelligence (AI), Supply Chain Management, Human-AI Collaboration, SAP Systems, Predictive AnalyticsAbstract
This article examines the transformative impact of integrating Artificial Intelligence (AI) with human expertise in SAP-based supply chain management systems. Through an analysis of current implementations and case studies, we explore how AI-driven tools enhance data analytics, predictive modeling, and automated decision-making while human managers provide crucial contextual knowledge and strategic oversight. The article highlights key benefits, including improved demand forecasting accuracy, increased operational efficiency, and enhanced supply chain agility. We also address critical challenges such as data quality management, system integration, and organizational change. Our findings suggest that the synergy between AI capabilities and human judgment creates a robust framework for optimizing supply chain performance in dynamic business environments. This article proposes a collaborative model that leverages SAP's AI-powered solutions alongside human expertise to achieve a more resilient and responsive supply chain. The article contributes to the growing body of literature on AI applications in supply chain management. It offers practical insights for organizations seeking to implement or improve AI-human collaborative systems within their SAP-based supply chain operations.
References
G. Baryannis, S. Validi, S. Dani, and G. Antoniou, "Supply chain risk management and artificial intelligence: state of the art and future research directions," International Journal of Production Research, vol. 57, no. 7, pp. 2179-2202, 2019. [Online]. Available: https://doi.org/10.1080/00207543.2018.1530476
R. Y. Zhong, X. Xu, E. Klotz, and S. T. Newman, "Intelligent Manufacturing in the Context of Industry 4.0: A Review," Engineering, vol. 3, no. 5, pp. 616-630, 2017. [Online]. Available: https://doi.org/10.1016/J.ENG.2017.05.015
A. Gunasekaran, N. Subramanian, and S. Rahman, "Supply chain resilience: Role of complexities and strategies," International Journal of Production Research, vol. 53, no. 22, pp. 6809-6819, 2015. [Online]. Available: https://doi.org/10.1080/00207543.2015.1093667
M. Tarafdar, C. M. Beath, and J. W. Ross, "Using AI to Enhance Business Operations," MIT Sloan Management Review, vol. 60, no. 4, pp. 37-44, 2019. [Online]. Available: https://sloanreview.mit.edu/article/using-ai-to-enhance-business-operations/
R. Dubey, A. Gunasekaran, S. J. Childe, S. F. Wamba, and T. Papadopoulos, "The impact of big data on world-class sustainable manufacturing," The International Journal of Advanced Manufacturing Technology, vol. 84, pp. 631-645, 2016. [Online]. Available: https://doi.org/10.1007/s00170-015-7674-1
O. Rodríguez-Espíndola, S. Chowdhury, A. Beltagui, and P. Albores, "The potential of emergent disruptive technologies for humanitarian supply chains: The integration of blockchain, Artificial Intelligence and 3D printing," International Journal of Production Research, vol. 58, no. 15, pp. 4610-4630, 2020. [Online]. Available: https://doi.org/10.1080/00207543.2020.1761565
M. H. Jarrahi, "Artificial intelligence and the future of work: Human-AI symbiosis in organizational decision making," Business Horizons, vol. 61, no. 4, pp. 577-586, 2018. [Online]. Available: https://doi.org/10.1016/j.bushor.2018.03.007
A. Fagerstrøm, S. Pawar, V. Sigurdsson, G. R. Foxall, and M. Yani-de-Soriano, "That personal profile image might jeopardize your rental opportunity! On the relative impact of the seller's facial expressions upon buying behavior on Airbnb™," Computers in Human Behavior, vol. 72, pp. 123-131, 2017. [Online]. Available: https://doi.org/10.1016/j.chb.2017.02.029
S. Makridakis, "The forthcoming Artificial Intelligence (AI) revolution: Its impact on society and firms," Futures, vol. 90, pp. 46-60, 2017. [Online]. Available: https://doi.org/10.1016/j.futures.2017.03.006
K. Govindan, T. C. E. Cheng, N. Mishra, and N. Shukla, "Big data analytics and application for logistics and supply chain management," Transportation Research Part E: Logistics and Transportation Review, vol. 114, pp. 343-349, 2018. [Online]. Available: https://doi.org/10.1016/j.tre.2018.03.011
E. Brynjolfsson and T. Mitchell, "What can machine learning do? Workforce implications," Science, vol. 358, no. 6370, pp. 1530-1534, 2017. [Online]. Available: https://doi.org/10.1126/science.aap8062
M. Agrawal, S. Dutta, R. Kelly, and I. Millán, "COVID-19: An inflection point for Industry 4.0," McKinsey Digital, 2021. [Online]. Available: https://www.mckinsey.com/capabilities/mckinsey-digital/our-insights/covid-19-an-inflection-point-for-industry-40
E. Brynjolfsson, D. Rock, and C. Syverson, "Artificial Intelligence and the Modern Productivity Paradox: A Clash of Expectations and Statistics," National Bureau of Economic Research, Working Paper 24001, 2017. [Online]. Available: https://www.nber.org/papers/w24001
