14th IFAC Symposium on System Identification, SYSID 2006

SYSID-2006 Paper Abstract

Close

Paper WeA4.5

Schwartz, Jay D. (Arizona State Univ.), Rivera, Daniel E. (Arizona State Univ.)

Control-Relevant Demand Modeling for Supply Chain Management

Scheduled for presentation during the Regular Session "Identification for Control" (WeA4), Wednesday, March 29, 2006, 11:50−12:10, Cummings Room

14th IFAC Symposium on System Identification, March 29 - 31, 2006, Newcastle, Australia

This information is tentative and subject to change. Compiled on July 17, 2018

Keywords Identification for Control, Process Control, Other

Abstract

The development of control-oriented decision policies for inventory management in supply chains has received considerable interest in recent years, and demand modeling to supply forecasts for these policies is an important component of an effective solution to this problem. Drawing from the problem of control-relevant identification, we present an approach for demand modeling based on data that relies on a control-relevant prefilter to tailor the emphasis of the fit to the intended purpose of the model, which is to provide forecast signals to a tactical inventory management policy based on Model Predictive Control. Integrating the demand modeling and inventory control problems offers the opportunity to obtain reduced-order models that exhibit superior performance, with potentially lower user effort relative to traditional "open-loop" methods. A systematic approach to generating these prefilters is presented and the benefits resulting from their use are demonstrated on a representative production/inventory system case study.