Improved supply chain management based on hybrid demand forecasts

Authors:
Luis Aburto;Richard Weber
Affiliations:
Penta Analytics, Santiago, Chile;Department of Industrial Engineering, Faculty of Physical and Mathematical Sciences, University of Chile, Santiago, Chile
Venue:
Applied Soft Computing
Year:
2007

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Abstract

Demand forecasts play a crucial role for supply chain management. The future demand for a certain product is the basis for the respective replenishment systems. Several forecasting techniques have been developed, each one with its particular advantages and disadvantages compared to other approaches. This motivates the development of hybrid systems combining different techniques and their respective strengths. In this paper, we present a hybrid intelligent system combining Autoregressive Integrated Moving Average (ARIMA) models and neural networks for demand forecasting. We show improvements in forecasting accuracy and propose a replenishment system for a Chilean supermarket, which leads simultaneously to fewer sales failures and lower inventory levels than the previous solution.