Artificial intelligence-based inventory management for retail supply chain optimization: a case study of customer retention and revenue growth
DOI:
https://doi.org/10.60087/jklst.v3.n4.p260Abstract
This study explores the evolution of AI-driven product management in the retail industry, focusing on product quality, customer retention, and revenue growth. From the extensive case study of ChemScene, a biopharma company, we used advanced AI models that integrate LSTM neural networks, Q-learning, and genetic algorithms. Analysis of 18 months of data revealed remarkable improvements across key performance metrics. The sales volume increased by 38.1%, while the sales volume decreased by 77.1%. Customer loyalty was significantly boosted, increasing retention from 82% to 91%. These improvements translated into profitable results, including a 20% increase in revenue and a 31.3% jump in operating profit. Our findings not only validate the effectiveness of machine learning in inventory management but also provide new insights into AI's broader impact on customer relationships. And the market as a whole. This research provides a useful model for retailers considering AI adoption, paving the way for future research in this rapidly changing industry.
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