Application AI in Traditional Supply Chain Management Decision-Making

Abstract

In the generation of demand uncertainty and complex market, the ability to fully integrate and orchestrate the entire supply chain spectrum of end-to-end processes from acquiring materials to converting, to delivery to final customers is highly desired by many organizations. While data sourcing, managing and manipulating are becoming one of the core advantages in the businesses, a number of leading-edge organizations have been studying and exploring the limits of machine learning and artificial intelligence (AI) to enrich excellence. The common usage of AI is being referr toed in extensive computational modelling for reasoning, recognizing patterns, calculating endless possibilities, learning, and understanding from the experience to facilitate one's needs. Especially in demand planning and forecasting, AI and/or machine learning is being used to guide effective planning of future demands with industrial precision of 85%, but lacks in full implementation among other sub-applications in supply chain (SC) such as MRP, MPS, predictive maintenance, and learning from experience instantly. One area of AI’s potential application that has not fully explored is in emerging management philosophy of SCM that requires the comprehension of complex interactions, real-time joint problem solving, and interrelated decision-making processes. This absence of competency in AI is due to lack of replicating information input on practical implications, technical merits, problem scopes, complex heuristics and long-term analysis that human brain can perform. With this obstacle in mind, this paper will concentrate one following hypotheses: - Identification of sub-application problems in SCM that can be solved through AI and machine learning algorithms - Exploring other literature and exploratory works on AI development and designs in SCM - Summarizing modern SCM models that can be addressed and replicated in AI application areas, problem scopes and methodology - Discuss and develop wproblem-solvingtraditional manager’s decision-makithe ng process in SCM using AI/ML techniques - Examine and synthesize SC data inputs required to enhance technical integrity and joint problem-solving in AI - Review future outlook on multitude of application of AI and machine learning in SCM