DocumentCode
439055
Title
Towards control-relevant forecasting in supply chain management
Author
Schwartz, Jay D. ; Rivera, Daniel E. ; Kempf, Karl G.
Author_Institution
Dept. of Chem. & Mater. Eng., Arizona State Univ., Tempe, AZ, USA
fYear
2005
fDate
8-10 June 2005
Firstpage
202
Abstract
The focus of this paper is understanding the effects of demand forecast error on a tactical decision policy for a single node of a manufacturing supply chain. The demand forecast is treated as an external measured disturbance in a multi-degree-of-freedom feedback-feedforward internal model control (IMC) based inventory control system. Because forecast error will be multifrequency in nature, the effect of error in different frequency regimes is examined. A mathematical framework for evaluating the effect of forecast revisions in an IMC controller is developed. A simultaneous perturbation stochastic approximation (SPSA) optimization algorithm is implemented to develop an optimal tuning strategy under these conditions. For the IMC-based inventory controller presented it is concluded that the most desirable performance may be obtained by acting cautiously (e.g. implementing small changes to factory starts) to initial forecasts and gradually becoming more aggressive on starts until the actual demand change is realized.
Keywords
demand forecasting; feedback; feedforward; industrial control; multivariable systems; optimisation; perturbation techniques; production control; stochastic processes; stock control; supply chain management; IMC controller; SPSA optimization algorithm; control-relevant forecasting; demand change; demand forecast error; factory; inventory control system; manufacturing supply chain; mathematical framework; multidegree-of-freedom feedback-feedforward internal model control; optimal tuning strategy; simultaneous perturbation stochastic approximation; supply chain management; tactical decision policy; Approximation algorithms; Demand forecasting; Frequency; Inventory control; Predictive models; Pulp manufacturing; Stochastic processes; Supply chain management; Supply chains; Tuning;
fLanguage
English
Publisher
ieee
Conference_Titel
American Control Conference, 2005. Proceedings of the 2005
ISSN
0743-1619
Print_ISBN
0-7803-9098-9
Electronic_ISBN
0743-1619
Type
conf
DOI
10.1109/ACC.2005.1469932
Filename
1469932
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