• 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