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Optimization of Inventory Management Using Artificial Neural Networks and K-Means Clustering for Cost Reduction and Improved Customer Service
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Author(s): Govind Shay Sharma (Vivekananda Global University, Jaipur, India)and Randhir Baghel (Poornima University, India)
Copyright: 2026
Pages: 18
Source title:
Impact of Generative AI on Food Supply Chain Management
Source Author(s)/Editor(s): Bhupinder Pal Singh Chahal (Yorkville University, Canada), Arokiaraj David (SBS Swiss Business School, RAK, UAE), Amrinder Singh (Jain University, India), Geetika Madaan (Marwadi University, India)and Gurmeet Singh (The University of the South Pacific, Fiji)
DOI: 10.4018/979-8-3693-9856-2.ch011
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Abstract
This paper addresses the critical issue of optimizing inventory strategies using advanced machine learning techniques, particularly Artificial Neural Networks (ANN), to enhance customer service and reduce costs. Here's a brief summary and analysis of the concepts you mentioned: 1. Optimization of Stock Management: Inventory management plays a vital role in fulfilling customer demand while minimizing lead times and costs. Optimizing stock levels can balance supply with fluctuating market demand. 2. Artificial Neural Networks (ANN): ANN is employed to forecast the optimal stock levels. By analyzing past stock data, ANN models can predict the required inventory and reduce errors in forecasting, thus minimizing overstocking or under stocking. 3. K-Means Algorithm for Grouping Items: The k-means algorithm is used to classify raw materials and finished goods into homogeneous groups. These groups share similar characteristics, allowing for tailored inventory policies. For example, fast-moving items may have different inventory strategies than slow-moving ones.
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