A control-relevant approach to demand modeling for supply chain management

Jay D. Schwartz, Daniel Rivera

Research output: Contribution to journalArticle

1 Scopus citations

Abstract

The development of control-oriented decision policies for inventory management in supply chains has drawn considerable interest in recent years. Modeling demand to supply forecasts is an important component of an effective solution to this problem. Drawing from the problem of control-relevant parameter estimation, this paper presents an approach for demand modeling in a production-inventory system that relies on a specialized weight to tailor the emphasis of the fit to the intended purpose of the model, which is to provide forecasts to inventory management policies based on internal model control or model predictive control. A systematic approach to generate this weight function (implemented using data prefilters in the time domain) is presented and the benefits demonstrated on a series of representative case studies. The multi-objective formulation developed in this work allows the user to emphasize minimizing inventory variance, minimizing starts variance, or their combination, as dictated by operational and enterprise goals.

Original languageEnglish (US)
Pages (from-to)78-90
Number of pages13
JournalComputers and Chemical Engineering
Volume70
DOIs
StatePublished - Nov 5 2014

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Keywords

  • Control-relevant model reduction
  • Demand modeling
  • Internal model control
  • Inventory control
  • Model predictive control
  • Supply chain management

ASJC Scopus subject areas

  • Chemical Engineering(all)
  • Computer Science Applications

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