Improved supply chain management based on hybrid demand forecasts

Luis Aburto, Richard Weber

Research output: Contribution to journalArticlepeer-review

205 Scopus citations

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.

Original languageEnglish
Pages (from-to)136-144
Number of pages9
JournalApplied Soft Computing Journal
Volume7
Issue number1
DOIs
StatePublished - Jan 2007

Keywords

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

Fingerprint

Dive into the research topics of 'Improved supply chain management based on hybrid demand forecasts'. Together they form a unique fingerprint.

Cite this