TY - GEN
T1 - Multi-objective control-relevant demand modeling for supply chain management
AU - Schwartz, Jay
AU - Rivera, Daniel
PY - 2006/12/1
Y1 - 2006/12/1
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.
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M3 - Conference contribution
AN - SCOPUS:80053753964
SN - 081691012X
SN - 9780816910120
T3 - AIChE Annual Meeting, Conference Proceedings
BT - 2006 AIChE Annual Meeting
T2 - 2006 AIChE Annual Meeting
Y2 - 12 November 2006 through 17 November 2006
ER -