New Insights Into Strategic Consumer Behavior From the Field of Operations Management

Introduction

This study highlights the value of adopting a consumer-centric approach to supply chain management and highlights the role of data-driven analytics (Babu, S. Z. D. et al., 2022), particularly Artificial Intelligence (Pandey, B. K. et al., 2022.) and Machine Learning (AI/ML), in extracting actionable insights from consumer data. This study also emphasises the need of adopting a consumer-centric approach to supply chain management. These insights have the potential to improve demand forecasting, personalization tactics, the efficiency of supply chain operations, customer satisfaction (Saxena, A. et al., 2021), and risk reduction. In this article, we take a look at the changing environment of supply chain management and emphasise how important it is to adopt a consumer-centric approach. This paper investigates the role that data-driven analytics (Kumar, M. S. et al., 2021), such as artificial intelligence (Gupta, A. et al., 2021) and machine learning (Tripathi, R. P. et al., 2023), play in the process of deriving actionable insights from consumer data. In addition to this, the study delves into the ways in which such insights might improve demand forecasting, personalization strategies, the efficiency of supply chain operations, customer satisfaction, and risk mitigation. This research paper provides a valuable resource for stakeholders seeking to navigate the evolving landscape of consumer behaviour within operations management. It does this by bridging the gap between theory and practical applications, and it ultimately paves the way for more adaptive and consumer-responsive supply chain strategies.

Traditional methods of managing supply chains have been completely overhauled as a result of the incorporation of various cutting-edge technology into today's fast changing corporate environment. This is the beginning of the age of the intelligent supply chain, which will be characterised by the seamless orchestration of operations, data-driven decision-making, and adaptive strategies. At the heart of this shift is the requirement to comprehend and respond to the behaviour of consumers with a level of precision that has never been seen before.

The compass that directs the operations of the supply chain is the consumer behaviour, which is influenced by a variety of factors including variations in the socioeconomic landscape, advances in technical innovation, and shifting preferences. This chapter dives into “New Insights into Strategic Consumer Behaviour from the Field of Operations Management” and examines the crucial role that it plays in building the modern intelligent supply chain. We investigate how the dynamics of supply chain management are being redefined as a result of strategic assessments of consumer behaviour, combined with the capabilities of artificial intelligence (AI) (Boopathi, S. et al., 2023) and machine learning (ML).

The ability of robots to execute tasks that traditionally require human intelligence, such as reasoning, learning, and problem-solving, is what is referred to as artificial intelligence (Pandey, B. K., & Pandey, D., 2023), or AI for short. Expert systems, natural language processing, speech recognition, and machine vision are just few of the many applications that make use of artificial intelligence (AI) (Copeland, 2023). Learning by Machines (ML)

The purpose of this chapter is to shed light on the mutually beneficial relationship that exists between the study of consumer behaviour and the application of AI and ML technologies in the management of supply chain operations. We hope that by doing so, we will be able to demonstrate the tremendous influence that an acute awareness of consumer behaviour can have on boosting efficiency, lowering costs, increasing customer pleasure, and ultimately generating competitive advantage in an era characterised by an intelligent supply chain.