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 technology for enhancing supply chain resilience, particularly in improving efficiency and demand forecasting. The DEMATEL analysis further bifurcates the sub-factors into cause and effect categories, offering insights into the interrelationships between various supply chain elements.

The thesis also presents a Interpretive Structural Modeling (ISM) based on consultations with 16 supply chain professionals and a survey of 229 respondents, further validating the proposed framework and highlighting the critical role of supply chain analytics in fostering resilience. This model provides a hierarchical structure of factors influencing supply chain resilience, enabling a more nuanced understanding of the complex interactions within the system.

In response to the increasing frequency and severity of supply chain disruptions, including natural disasters, geopolitical tensions, and global pandemics like CoVID-19, this research offers timely and actionable insights. It demonstrates how AI, ML, and BDA can be effectively integrated into supply chain operations to enhance predictive capabilities, improve decision-making processes, and increase overall agility and responsiveness.

This research contributes significantly to both theory and practice by providing a robust, empirically validated framework for integrating AI, ML, and BDA into supply chain operations to enhance resilience. The findings offer valuable insights for supply chain professionals and policymakers, guiding strategic decisions in technology adoption and capability development to better respond to disruptions in the increasingly complex and volatile global supply chain landscape. Moreover, the study's focus on the FMCG sector in India provides a unique perspective on emerging markets, offering lessons that can be potentially applied to other rapidly developing economies. The comprehensive methodological approach, combining qualitative insights with rigorous quantitative analysis, ensures that the findings are both rich in depth and generalizable across the industry.

By elucidating the specific ways in which AI, ML, and BDA can enhance various supply chain capabilities, this thesis paves the way for future research and practical applications in supply chain resilience. It underscores the importance of a holistic, technology-driven approach to supply chain management in the era of Industry 4.0, positioning these advanced technologies as crucial tools for navigating the uncertainties and complexities of modern global supply networks.