Improved supply chain management based on hybrid demand forecasts

Luis Aburto, Richard Weber

Research output: Contribution to journalArticlepeer-review

237 Scopus citations


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.

Original languageEnglish
Pages (from-to)136-144
Number of pages9
JournalApplied Soft Computing Journal
Issue number1
StatePublished - Jan 2007


  • Demand forecasting
  • Hybrid intelligent systems
  • Neural networks
  • Supply chain management


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