AI-Driven Next-Gen U.S. Retail: An Empirical Study on Optimizing Supply Chains by leveraging Artificial Intelligence, Business Intelligence, and Machine Learning.

Business Intelligence (BI), Artificial Intelligence (AI), and Machine Learnings (ML) have been playing an important role for optimizing Supply Chain Management (SCM) in the U.S. retail industry. The integration of these innovative and cutting-edge technologies into SCM has transformed the efficiency, agility, and profitability of retail businesses across the nation. It’s important to know how these advanced technologies transformed the supply chain process for optimizing inventory level to avoid any bottleneck, overstocking or stockout situation. This research examines how the integration of these modern technologies transformed the supply chain process and enabled retailers in optimizing their supply chain management. In this research work, we have used extensive knowledgebase on Business Intelligence, Artificial Intelligence, Machine Learning, the U.S. retail industry, and the Supply Chain Management, and later we applied this knowledgebase in the U.S retail domain to see how retailers integrate these technologies into their supply chain management process. We also used secondary information available online from reliable sources to make it more realistic. The U.S retail sales revenue was reported at US$7.6 trillion in Y2024 with an expected growth of CAGR of 3.2% over the last five years (Y2019-Y2024). We see a steady growth in the retail sector after the COVID-19 pandemic. Therefore, there is a growing demand for integrating these technologies into the retailers’ SCM so that they can predict consumer demand more accurately and maximize their sales revenue. These technologies serve retailers with greater benefits like forecasting product demand, optimizing inventory level, data-driven decision making, cost reductions by avoiding overstocking, increasing efficiency etc. Though these modern technologies enable retailers with supply chain optimization, there are still some downsides, which include high initial payouts, data silos, resistance to adoption of new technology, consistent and quality dataflow, data integration from various sources etc.