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

Jay Schwartz, Daniel Rivera

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

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 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 publicationAIChE Annual Meeting, Conference Proceedings
StatePublished - 2006
Event2006 AIChE Annual Meeting - San Francisco, CA, United States
Duration: Nov 12 2006Nov 17 2006

Other

Other2006 AIChE Annual Meeting
CountryUnited States
CitySan Francisco, CA
Period11/12/0611/17/06

Fingerprint

Supply chain management
Inventory control
Model predictive control
Supply chains

ASJC Scopus subject areas

  • Chemical Engineering(all)
  • Chemistry(all)

Cite this

Schwartz, J., & Rivera, D. (2006). Multi-objective control-relevant demand modeling for supply chain management. In AIChE Annual Meeting, Conference Proceedings

Multi-objective control-relevant demand modeling for supply chain management. / Schwartz, Jay; Rivera, Daniel.

AIChE Annual Meeting, Conference Proceedings. 2006.

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

Schwartz, J & Rivera, D 2006, Multi-objective control-relevant demand modeling for supply chain management. in AIChE Annual Meeting, Conference Proceedings. 2006 AIChE Annual Meeting, San Francisco, CA, United States, 11/12/06.
Schwartz J, Rivera D. Multi-objective control-relevant demand modeling for supply chain management. In AIChE Annual Meeting, Conference Proceedings. 2006
Schwartz, Jay ; Rivera, Daniel. / Multi-objective control-relevant demand modeling for supply chain management. AIChE Annual Meeting, Conference Proceedings. 2006.
@inproceedings{0dc4193c99614ffeaa28e09a93263cfd,
title = "Multi-objective control-relevant demand modeling for supply chain management",
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 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.",
author = "Jay Schwartz and Daniel Rivera",
year = "2006",
language = "English (US)",
isbn = "081691012X",
booktitle = "AIChE Annual Meeting, Conference Proceedings",

}

TY - GEN

T1 - Multi-objective control-relevant demand modeling for supply chain management

AU - Schwartz, Jay

AU - Rivera, Daniel

PY - 2006

Y1 - 2006

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=80053753964&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=80053753964&partnerID=8YFLogxK

M3 - Conference contribution

AN - SCOPUS:80053753964

SN - 081691012X

SN - 9780816910120

BT - AIChE Annual Meeting, Conference Proceedings

ER -