Automated lead time estimation for manufacturing networks with dynamic demand

Ronald Askin; Girish Jampani Hanumantha

doi:10.1109/COASE.2017.8256232

Automated lead time estimation for manufacturing networks with dynamic demand

Ronald Askin, Girish Jampani Hanumantha

Research output: Chapter in Book/Report/Conference proceeding › Conference 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 language	English (US)
Title of host publication	2017 13th IEEE Conference on Automation Science and Engineering, CASE 2017
Publisher	IEEE Computer Society
Pages	994-999
Number of pages	6
Volume	2017-August
ISBN (Electronic)	9781509067800
DOIs	https://doi.org/10.1109/COASE.2017.8256232
State	Published - Jan 12 2018
Event	13th IEEE Conference on Automation Science and Engineering, CASE 2017 - Xi'an, China Duration: Aug 20 2017 → Aug 23 2017

Other

Other	13th IEEE Conference on Automation Science and Engineering, CASE 2017
Country	China
City	Xi'an
Period	8/20/17 → 8/23/17

Keywords

Dynamic Demand
Lead Time Forecasting
Queueing Networks

ASJC Scopus subject areas

Control and Systems Engineering
Electrical and Electronic Engineering

Access to Document

10.1109/COASE.2017.8256232

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Cite this

Askin, R & Hanumantha, GJ 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, IEEE Computer Society, pp. 994-999, 13th IEEE Conference on Automation Science and Engineering, CASE 2017, Xi'an, China, 8/20/17. https://doi.org/10.1109/COASE.2017.8256232

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