Intermittent demand forecasting for inventory control : the impact of temporal and cross-sectional aggregation
Author(s)Chau, Ngan Ngoc.
Massachusetts Institute of Technology. Supply Chain Management Program.
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Managing intermittent demand is a challenging operation in many industries since this type of demand is difficult to forecast. This challenge makes it hard to estimate inventory levels and thus affects service levels. The purpose of this study is to examine the impact of multiple levels of data aggregation on forecasting intermittent demand, and subsequently, on inventory control performance. In particular, we propose a procedure that integrates lead-time and customer heterogeneity into the forecasting using temporal and cross-sectional aggregation. Using data from a real-world setting and simulation, our analysis revealed that when high service levels were important for the company operations, the forecasting approach using temporal aggregation that incorporates lead-time information yielded a higher level of inventory efficiency in terms of both the holding cost and the realized service level. It appeared that when forecasts using temporal aggregation were augmented with information about customer behavior, their purchase patterns might be a helpful consideration for enhancing inventory performance. These findings allow us to provide useful recommendations for improving the current forecasting procedure and inventory control to the sponsor company of this project.
Thesis: M. Eng. in Supply Chain Management, Massachusetts Institute of Technology, Supply Chain Management Program, May, 2020Cataloged from the official PDF of thesis.Includes bibliographical references (pages 57-64).
DepartmentMassachusetts Institute of Technology. Supply Chain Management Program
Massachusetts Institute of Technology
Supply Chain Management Program.