TY - GEN
T1 - Automated lead time estimation for manufacturing networks with dynamic demand
AU - Askin, Ronald
AU - Hanumantha, Girish Jampani
N1 - Publisher Copyright:
© 2017 IEEE.
Copyright:
Copyright 2019 Elsevier B.V., All rights reserved.
PY - 2017/7/1
Y1 - 2017/7/1
N2 - 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.
AB - 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.
KW - Dynamic Demand
KW - Lead Time Forecasting
KW - Queueing Networks
UR - http://www.scopus.com/inward/record.url?scp=85044971015&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85044971015&partnerID=8YFLogxK
U2 - 10.1109/COASE.2017.8256232
DO - 10.1109/COASE.2017.8256232
M3 - Conference contribution
AN - SCOPUS:85044971015
T3 - IEEE International Conference on Automation Science and Engineering
SP - 994
EP - 999
BT - 2017 13th IEEE Conference on Automation Science and Engineering, CASE 2017
PB - IEEE Computer Society
T2 - 13th IEEE Conference on Automation Science and Engineering, CASE 2017
Y2 - 20 August 2017 through 23 August 2017
ER -