Integration of Structural Equation Modeling and Machine Learning in Supply Chain Management

Author:

Sarkale Sandeep L.1ORCID,Bhinde Hetal N.2ORCID,Tatia Amrita2ORCID,Mahajan Yogesh3,Sharma Vinod4ORCID

Affiliation:

1. Dr. D.Y. Patil Institute of Management and Research, India

2. Indira School of Business Studies, Pune, India

3. Symbiosis Centre for Management and Human Resource Development, Symbiosis International University (Deemed), India

4. Symbiosis Centre for Management and Human Resource, Symbiosis International University (Deemed), India

Abstract

A promising strategy for improving supply chain management's efficacy and efficiency is the merging of structural equation modeling (SEM) with machine learning (ML). The use of SEM and ML approaches in the context of supply chain operations is presented in detail in this research study. The research identifies the distinct advantages of both techniques and investigates how they work in tandem to simulate intricate supply chain linkages. The chapter explores the theoretical foundations of SEM and ML and analyses the advantages of integrating them for better-managing supply chain risks, forecasting accuracy, inventory optimization, and decision-making processes. The report presents successful applications of the integrated method in several supply chain areas through a rigorous review of the literature and actual case studies. It clarifies the approaches utilized for model creation, data integration, and performance assessment, illuminating the real-world difficulties and advantages of using this strategy.

Publisher

IGI Global

Reference24 articles.

1. Carbon-efficient production, supply chains and logistics

2. Inventory ordering policies of delayed deteriorating items under permissible delay in payments

3. A Comprehensive Review of Integrating Structural Equation Modeling and Machine Learning in Supply Chain Management.;L.Chen;The Journal of Supply Chain Management,2020

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