The Effects of Production Planning on the Dynamic Behavior of a Simple Supply Chain: An Experimental Study

Part of the International Series in Operations Research & Management Science book series (ISOR, volume 152)


Sophisticated supply chain planning systems, also known as Advanced Planning and Scheduling (APS) systems, have become commonplace in industry, and constitute a multibillion dollar software industry (Musselman and Uzsoy 2001; de Kok and Fransoo 2003; Stadtler and Kilger 2004). Many of these models rely to some degree on mathematical programming formulations of multistage productioninventory systems, which have been discussed extensively by (Saad 1982; Voss and Woodruff 2003; Johnson andMontgomery 1974; Hax and Candea 1984) and in this volume by Missbauer and Uzsoy. However, there has been little study in the literature of the effects of these production planning models on the dynamic behavior of supply chains. The dynamic behavior of supply chains over time has been studied in the system dynamics literature for several decades (Sterman 2000; Forrester 1962), leading to a growing understanding of the effects of information and material delays on the behavior of these systems, such as the bullwhip effect (Chen et al. 1998; Chen et al. 2000; Dejonckheere et al. 2003; Dejonckheere et al. 2004). However, the production planning procedures used in these models are generally feedback control procedures, with little ability to predict future states of the system and behave in a reactive manner. It is also quite difficult to interface optimization-based production planning models to standard system dynamics software. Hence, there is very little work of which we are aware that examines the effect of optimization-based planning procedures on the dynamic behavior of the supply chain in a systematic manner.


Supply Chain System Dynamic Model Enterprise Resource Planning Enterprise Resource Planning System Bullwhip Effect 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



We thank Dr. Karl Kempf of Intel Corporation for providing the data for the testbed, and for his thoughtful suggestions throughout this work. The development of the SCOPE environment has been supported by The Laboratory for Extended Enterprises at Purdue (LEEAP), the UPS Foundation, and NSF Grants DMI-0075606 and DMI-0122207.


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© Springer New York 2011

Authors and Affiliations

  1. 1.Laboratory for Extended Enterprises at Purdue, e-Enterprise Center at Discovery ParkPurdue UniversityWest LafayetteUSA

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