Discovering existing patterns from supply chain data can be a turning point for any business. With Machine Learning (ML) algorithms, new patterns in supply chain data can be found daily without executive intervention or taxonomies to guide the analysis. The algorithms iteratively query the data using constraint-based modeling and recommend the solution with the highest predictive accuracy. In essence, Artificial Intelligence (AI) and machine learning algorithms provide businesses with insights into managing Supply Chain Management (SCM) factors affecting inventory, supplier quality, potential demand, procure-to-pay, order-to-cash, production planning, logistics management, and more. The new information and insights generated offer efficient decision-making processes in SCM and profitable investment proposals for businesses.

All these benefits can rapidly develop businesses, leading to accurate decisions, predictability, and a high return on investment and profitability. The nature of commerce depends on the return of investment with profitability. From this perspective, this chapter offers a solution for readers who want to be informed about current ML applications in SCM and follow the existing literature.