Inventory Management Optimization for Intermittent Demand

This report discusses inventory management and demand forecasting issues faced by a well-known electrical equipment company. The company requires a precise inventory management system with a wide range of products to handle its high production volume. The company has trouble forecasting intermittent demand patterns due to a lack of appropriate analytical methodologies. To overcome these challenges, this study developed an inventory management system that integrates Newsvendor and Order Up Policy, whose analytical methods are optimized with the inventory management policy. A comprehensive review of the existing literature on inventory management is undertaken to gather valuable information and best practices. This study has been developed based on the research conducted by Syntetos (2009). A mathematical model has been included to maximize order levels, considering lead time and costs. In the model, SBA and Croston methods are used for intermittent demand forecasting. This model includes various parameters and assumptions that allow calculating expected total costs and determining the optimum order level that efficiently meets customer demand while minimizing expenses. The methods employed optimize inventory management, minimize inventory cost, and enhance customer satisfaction.