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Analyzing the Role of AI/ML in Optimizing Retail

Arulmozhi Kasthurirengan

arulmozhi.j@gmail.com

Abstract

The aim of the study was to analyze the role of artificial intelligence and machine learning in optimizing supply chain processes Methodology: This study adopted a desk methodology. A desk study research design is commonly known as secondary data collection. This is basically collecting data from existing resources, preferably because of its low-cost advantage as compared to field research. Our current study looked into already published studies and reports as the data was easily accessed through online journals and libraries. Findings: Artificial intelligence (AI) and machine learning (ML) play a pivotal role in optimizing supply chain processes by enhancing demand forecasting accuracy through sophisticated algorithms. They streamline inventory management by predicting demand patterns and automating replenishment tasks, leading to reduced stockouts and excess inventory. AI-powered analytics enable real-time insights into supply chain performance, identifying bottlenecks and inefficiencies for proactive decision-making. Unique Contribution to Theory, Practice and Policy: Theory of technology acceptance model (TAM), resource-based view (RBV) theory & dynamic capabilities theory may be used to anchor future studies on analyzing the role of artificial intelligence and machine learning in optimizing supply chain processes. Encourage supply chain stakeholders to adopt blockchain solutions for enhanced transparency, traceability, and efficiency. Advocate for regulatory frameworks that promote the adoption of blockchain technology in supply chains while addressing concerns related to data privacy, interoperability, and standardization.

Keywords

Demand Forecasting, Resource utilization, Reduce costs, Minimize errors, and Increase Productivity, Artificial Intelligence, Machine Learning, Inventory, Transportation, Warehousing