Optimizing Supply Chain Management: A Decision-Making Approach for Performance Enhancement

Abstract

Introduction: Supply chains have become indispensable elements in the process of conducting modern businesses, continuously adapting to changes in markets and demands from clients. Effective Supply Chain Management (SCM) is essential for achieving success and holding a competitive advantage, with many challenges ahead: encouraging collaboration, new technologies, and best practices.

Objectives: This work develops the application of artificial neural networks (ANNs) within SCM based on the Supply Chain Operations Reference (SCOR) model. The SCOR model has been extensively applied because of its great capacity to detect issues within the supply chain; however, it does not indicate the best way to address these issues.

Methods: A diagnostic approach based on the SCOR model is proposed in a hybrid manner where ANNs are exploited to provide recommendations regarding the issues detected by the SCOR model. The integration of ANNs allows for data-driven insights to enhance the decision-making process and optimize supply chain operations.

Results: The new strategy aims to improve decision-making processes, modernize operations, reduce redundant tasks, and be flexible enough to adapt to future market changes by determining activities that could create greater impact within its supply chain.

Conclusions: Furthermore, the proposed methodology should be adaptable to various industries and is expected to optimize the efficiency of SCM, providing a scalable solution to enhance supply chain performance across different business sectors.