Inventory management using data mining : forecasting in retail trade

Abstract

Inventory management, as an important business issue, plays a significant role in promoting business development. This study aims to apply data mining techniques, such as time series clustering and time series prediction techniques, in inventory management. Based on historical business data sets, time series clustering techniques, such as K-Means and Expectation Maximization are used to categorize inventories into reasonable groups. This study then identifies the most effective prediction technique to accurately predict inventory demands for each group. The traditional statistical evaluation metrics, such as Mean Absolute Percentage Error may not always be good indicators in an inventory management system, where the goal is to have as little inventory as possible without ever running out. The thesis proposes a more appropriated evaluation metric based on cost/benefit analysis of inventory forecasts. Results from a simulation program based on the proposed cost/benefit analysis are compared with statistical metrics.