Impact of Industry 4.0 Practices on Supply Chain Resilience in FMCG Sector

Abstract

Natural calamities like earthquakes, floods, and epidemics/pandemics like CoVID-19 significantly disrupt almost all the supply networks, ranging from medicines to numerous daily/emergency use items. Supply Chain Resilience is one such option to overcome the impact of the disruption. This study investigates the transformative role of Artificial Intelligence (AI), Machine Learning (ML), and Big Data Analytics (BDA) in enhancing supply chain resilience, with a specific focus on the Fast-Moving Consumer Goods (FMCG) sector in India. The research employs a comprehensive mixed-methods approach, combining qualitative and quantitative methodologies to provide a holistic understanding of how these technologies can be leveraged to mitigate supply chain disruptions and improve overall performance. The study begins with a critical examination of existing literature on supply chain capabilities, AI, ML, and BDA, establishing a theoretical foundation based on the Dynamic Capability View (DCV). Through semi-structured interviews with 25 FMCG supply chain professionals, the research identifies 11 key capabilities that are crucial for building resilient supply chains: Routing Optimization, Efficiency, Periodic Monitoring, Demand Forecasting, Visibility, Supply Chain Analytics, Inventory Management, Consumer Behaviour Analysis, Operations Planning, Point-of-Sale Integration, and Transportation Management. Utilizing open, axial, and selective coding approaches, the study develops a comprehensive framework for AI, ML, and BDA-enabled Supply Chain Resilience Performance (SCRP). This framework is further validated through quantitative analysis using Partial Least Squares Structural Equation Modeling (PLS-SEM), providing empirical evidence for the positive impact of these technologies on supply chain resilience. Additionally, the research employs integrated Analytic Hierarchy Process (AHP) and Decision Making Trial and Evaluation Laboratory (DEMATEL) techniques to prioritize factors and sub-factors, identifying AI as the most prominent