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
Link To Document :
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