Multi-objective control-relevant demand modeling for supply chain management

Jay Schwartz, Daniel Rivera

Research output: Chapter in Book/Report/Conference proceedingConference contribution


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 producti n/inventory system case study. A multi-objective formulation is developed that allows the user to emphasize minimizing inventory variance, minimizing starts variance, or their combination.

Original languageEnglish (US)
Title of host publication2006 AIChE Annual Meeting
StatePublished - Dec 1 2006
Event2006 AIChE Annual Meeting - San Francisco, CA, United States
Duration: Nov 12 2006Nov 17 2006

Publication series

NameAIChE Annual Meeting, Conference Proceedings


Other2006 AIChE Annual Meeting
Country/TerritoryUnited States
CitySan Francisco, CA

ASJC Scopus subject areas

  • General Chemical Engineering
  • General Chemistry


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