A process control approach to tactical inventory management in production-inventory systems

Jay D. Schwartz, Daniel Rivera

Research output: Contribution to journalArticle

50 Scopus citations

Abstract

Supply chain management (SCM) is concerned with the efficient movement of goods through a network of suppliers and retailers. As delayed and uncertain dynamical systems, supply chains provide an excellent opportunity for demonstrating the benefits of control engineering principles to what is traditionally perceived as a "business" problem. This paper presents a fundamental yet practical approach for applying control-theoretic principles to tactical inventory management problem in a production-inventory system, the basic unit in a supply chain. Beginning with the use of a fluid analogy, we present internal model control (IMC) and model predictive control (MPC) as means for generating a series of increasingly sophisticated decision policies for inventory management. A combined feedback-feedforward multi-degree-of-freedom IMC policy is shown to properly adjust factory starts in the presence of inventory target changes, forecasted shifts in customer demand, and stochastic changes in demand. The MPC policy displays equivalent performance, but incorporates the added functionality of managing inventory in the presence of constraints, an important practical consideration. The MPC policy shows improved performance, greater flexibility, and higher functionality relative to an advanced order-up-to policy based on control engineering principles found in the literature.

Original languageEnglish (US)
Pages (from-to)111-124
Number of pages14
JournalInternational Journal of Production Economics
Volume125
Issue number1
DOIs
StatePublished - May 1 2010

Keywords

  • Internal model control
  • Model predictive control
  • Process control
  • Production control
  • Production-inventory systems
  • Supply chain management

ASJC Scopus subject areas

  • Business, Management and Accounting(all)
  • Economics and Econometrics
  • Management Science and Operations Research
  • Industrial and Manufacturing Engineering

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