Artificial Intelligence And Machine Learning In Supply Chain Decision-Making In An Organisation
Abstract
The apparel industry is characterised by a complex and culturally diverse global supply chain that requires a high degree of collaboration and is complex with multiple perspectives. We will use Soft System Methodology (SSM) to tackle this complex and ill-structured problem that needs a clear-cut solution.The project will involve conducting extensive market research, analysing business intelligence reports, surveying employees, and conducting interviews with top management, clients and suppliers of Asmara's founding office in Indonesia. There is a potential to improve efficiency, reduce costs, and enhance the customer experience. This thesis aims to analyze the impact of Artificial Intelligence and Machine Learning on the apparel industry. The suggested course of action using SSM involves involving stakeholders actively in deciding on transformational measures and instilling a sense of ownership in the change process.
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Alicke, K., Rexhausen, D., & Seyfert, A. (2017). Supply Chain 4.0 in consumer goods. Mckinsey & Company, 1(11)
Baryannis, G., Dani, S., & Antoniou, G. (2019). Predicting supply chain risks using machine learning: The trade-off between performance and interpretability. Future Generation Computer Systems, 101, 993-1004.
Checkland, P., & Scholes, J. (1999). Soft systems methodology in action. John Wiley & Sons.
Christopher, M., Peck, H., & Towill, D. (2006). A taxonomy for selecting global supply chain strategies. The International Journal of Logistics Management.
D. Ni, Z. Xiao, & M. K. Lim. (n.d.). A systematic review of the research trends of machine learning in supply chain management. International Journal of Machine Learning and Cybernetics, pp. 1–20, 2019.
De Cremer, D. (2020). What does building a fair AI really entail. Harvard Business Review.
Jackson, M. C. (2001). Critical systems thinking and practice. European Journal of operational research, 128(2), 233-244.
Petkov, D., Petkova, O., Andrew, T., & Nepal, T. (2007). Mixing multiple criteria decision making with soft systems thinking techniques for decision support in complex situations. Decision Support Systems, 43(4), 1615-1629.
S. Thomassey. (n.d.). Sales forecasts in the clothing industry: the critical success factor of the supply chain management. International Journal of Production Economics, 128, nos. 2, 2010, 470–483.
DOI: https://doi.org/10.17509/strategic.v23i1.57616
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