Global Supply Chain: Enhance Production Cost Efficiency Through Machine Learning

Abstract

Machine learning has enabled the discovery of various disciplines and patterns in Supply Chain Management (SCM). The entire industrial sector is striving to harness the contextual intelligence provided by machine learning by exploring new areas within the supply chain network. This article delves into Supply Chain Management (SCM) and its broader implications beyond logistics. SCM involves the resources, methods, and tools required to manage activities efficiently. For large companies with multiple subcontractors, SCM is essential for identifying areas needing improvement. Evaluating key indicators is crucial to optimizing SCM stages. Machine learning assists in recognizing recurring patterns and relevant data to develop models for better understanding production processes and identifying enhancement opportunities. A global corporation specializing in flooring and kid's surfaces, with numerous sites and a global presence, faces complexity and high costs due to diverse production parameters and customer expectations. Centralizing data and automating processes are vital to reducing production costs and uncertainties. This article utilizes machine-learning algorithms such as classification, linear regression, and K-means Clustering on unstructured data to optimize production and delivery costs, with the goal of producing goods at the most cost-effective locations worldwide.