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

In: Just-in-Time Systems

Author

Listed:
  • Mauricio Cabrera-Ríos

    (University of Puerto Rico at Mayagüez)

  • María Angélica Salazar-Aguilar

    (Université de Montréal)

  • María Guadalupe Villarreal-Marroquín

    (The Ohio State University)

  • Ángela Patricia Anaya Salazar

    (Universidad de San Buenaventura-Cali)

Abstract

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.

Suggested Citation

  • Mauricio Cabrera-Ríos & María Angélica Salazar-Aguilar & María Guadalupe Villarreal-Marroquín & Ángela Patricia Anaya Salazar, 2012. "Artificial Neural Networks-Based Forecasting: An Attractive Option for Just-in-Time Systems," Springer Optimization and Its Applications, in: Roger Z. Ríos-Mercado & Yasmín A. Ríos-Solís (ed.), Just-in-Time Systems, chapter 0, pages 245-259, Springer.
    • Handle: RePEc:spr:spochp:978-1-4614-1123-9_11
    DOI: 10.1007/978-1-4614-1123-9_11
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