Modeling and simulation of order-driven planning policies in build-to-order automobile production

In adopting build-to-order order fulfillment systems, automotive companies strive to better synchronize their production output with market demand. This essentially gives rise to a new paradigm in production planning. Since all business is linked to customer orders, the operational performance is su...

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Bibliographic Details
Published inInternational journal of production economics Vol. 131; no. 1; pp. 183 - 193
Main Authors Volling, Thomas, Spengler, Thomas S.
Format Journal Article
LanguageEnglish
Published Amsterdam Elsevier B.V 01.05.2011
Elsevier
Elsevier Sequoia S.A
SeriesInternational Journal of Production Economics
Subjects
Automobile production
Automobile production Build-to-order Production planning Available-to-promise Simulation
Automobiles
Automotive components
Automotive engineering
Available-to-promise
Build-to-order
Computer simulation
Construction
Construction costs
Dynamics
Economics
Operations research
Order processing
Production planning
Production scheduling
Simulation
Studies
Automobile production
Simulation
Build-to-order
Available-to-promise
Production planning
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Summary:In adopting build-to-order order fulfillment systems, automotive companies strive to better synchronize their production output with market demand. This essentially gives rise to a new paradigm in production planning. Since all business is linked to customer orders, the operational performance is substantially determined by order-driven planning. Therefore, a clear understanding of the associated planning tasks, order promising and master production scheduling, as well as their dynamic interaction is essential. Based on the analysis of the decision situation of order-driven planning in build-to-order settings, we provide a framework comprising separate interlinked quantitative models for order promising and master production scheduling. The focus of the contribution is on the modeling and evaluation of both models in a dynamic setting. The approach is evaluated by means of a simulative analysis using empirical data from the automotive industry. Conclusions regarding the potentials of such systems with respect to customer service, the leveling of resource utilization, and holding are presented.
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ISSN:0925-5273
1873-7579
DOI:10.1016/j.ijpe.2011.01.008