Automated lead time estimation for manufacturing networks with dynamic demand

Ronald Askin, Girish Jampani Hanumantha

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Efficient algorithms are essential for reliable lead time forecasting as they are often the basis for subsequent activities such as planned order releases and shop scheduling. Dynamic conditions are prevalent in modern manufacturing systems and reliable lead time forecasts can provide a competitive edge operationally. In this paper, we explore the use of efficient algorithms for steady state analysis to develop approximate estimates of lead times under dynamic conditions.

Original languageEnglish (US)
Title of host publication2017 13th IEEE Conference on Automation Science and Engineering, CASE 2017
PublisherIEEE Computer Society
Pages994-999
Number of pages6
Volume2017-August
ISBN (Electronic)9781509067800
DOIs
StatePublished - Jan 12 2018
Event13th IEEE Conference on Automation Science and Engineering, CASE 2017 - Xi'an, China
Duration: Aug 20 2017Aug 23 2017

Other

Other13th IEEE Conference on Automation Science and Engineering, CASE 2017
CountryChina
CityXi'an
Period8/20/178/23/17

Keywords

  • Dynamic Demand
  • Lead Time Forecasting
  • Queueing Networks

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Electrical and Electronic Engineering

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  • Cite this

    Askin, R., & Hanumantha, G. J. (2018). Automated lead time estimation for manufacturing networks with dynamic demand. In 2017 13th IEEE Conference on Automation Science and Engineering, CASE 2017 (Vol. 2017-August, pp. 994-999). IEEE Computer Society. https://doi.org/10.1109/COASE.2017.8256232