Artificial Neural Networks-Based Forecasting: An Attractive Option for Just-in-Time Systems

  • Mauricio Cabrera-RíosEmail author
  • María Angélica Salazar-Aguilar
  • María Guadalupe Villarreal-Marroquín
  • Ángela Patricia Anaya Salazar
Part of the Springer Optimization and Its Applications book series (SOIA, volume 60)


Just-in-time (JIT) systems focus on lead-time reduction and equalization to make them respond rapidly to changes in demand. Lead-time variability in real life production, however, does affect the performance of JIT systems. This makes demand forecasting an important task to ponder. In this chapter, the use of artificial neural networks (ANNs) is advocated as an attractive approach to forecast demand for JIT systems. ANNs’ capabilities to accommodate nonlinear dependencies and to generate forecasts for multiple periods ahead are among the most important reasons to consider for their adoption. A general method to build ANNs for time series prediction is presented aiming to circumvent some of the perceived difficulties associated to these models. Two case studies are also provided to illustrate the intended use.


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Copyright information

© Springer Science+Business Media, LLC 2012

Authors and Affiliations

  • Mauricio Cabrera-Ríos
    • 1
    Email author
  • María Angélica Salazar-Aguilar
    • 2
  • María Guadalupe Villarreal-Marroquín
    • 3
  • Ángela Patricia Anaya Salazar
    • 4
  1. 1.Industrial Engineering DepartmentUniversity of Puerto Rico at MayagüezPuertoRico
  2. 2.CIRRELT–HECUniversité de MontréalQuebecCanada
  3. 3.Integrated Systems Engineering DepartmentThe Ohio State UniversityColumbusUSA
  4. 4.Industrial Engineering DepartmentUniversidad de San Buenaventura-CaliCaliColombia

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