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

Mauricio Cabrera-Ríos (), María Angélica Salazar-Aguilar (), María Guadalupe Villarreal-Marroquín () and Ángela Patricia Anaya Salazar ()
Additional contact information
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

Chapter Chapter 11 in Just-in-Time Systems, 2012, pp 245-259 from Springer

Abstract: 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.

Keywords: Artificial Neural Networks (ANN); Lead Time Variability; Real-life Production; Double Exponential Smoothing; Multiple Criteria Optimization (search for similar items in EconPapers)
Date: 2012
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:spochp:978-1-4614-1123-9_11

Ordering information: This item can be ordered from
http://www.springer.com/9781461411239

DOI: 10.1007/978-1-4614-1123-9_11

Access Statistics for this chapter

More chapters in Springer Optimization and Its Applications from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().