AI-Driven Demand Forecasting for Optimized Inventory Management

EasyChair Preprint no. 13214

Abstract

AI-driven demand forecasting has emerged as a powerful tool for optimizing inventory management in today's complex business landscape. The utilization of artificial intelligence techniques in demand forecasting enables organizations to make accurate predictions, effectively align inventory levels with customer demand, and ultimately enhance operational efficiency.

This abstract provides an overview of AI-driven demand forecasting and its role in optimized inventory management. It explores the process of data collection and preprocessing, highlighting the importance of incorporating diverse data sources such as historical sales data, market trends, and external factors. Feature engineering techniques are discussed, emphasizing the need to transform and normalize data while considering domain knowledge and external influences.

The selection and training of appropriate machine learning models are crucial for accurate demand forecasting. The abstract delves into the significance of model selection, hyperparameter tuning, and validation using historical data. It also emphasizes the iterative nature of demand forecasting, necessitating continuous monitoring and adjustment to ensure optimal performance.

Once accurate demand forecasts are generated, they serve as a basis for inventory optimization. The abstract addresses the calculation of optimal inventory levels, determination of reorder points and safety stock, and the integration of demand forecasts with supply chain management systems. It highlights the dynamic nature of inventory management and the importance of implementing strategies such as just-in-time and economic order quantity.

Keyphrases: Continuous Improvement, Demand Forecasting, Inventory Optimization, Supply Chain Management

@Booklet{EasyChair:13214,
  author = {Edwin Frank and Elizabeth Henry},
  title = {AI-Driven Demand Forecasting for Optimized Inventory Management},
  howpublished = {EasyChair Preprint no. 13214},

  year = {EasyChair, 2024}}