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Which utilization and service level lead to the maximum EVA?

Listed author(s):
  • Altendorfer, Klaus
  • Jodlbauer, Herbert
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    In this paper a model for evaluating the influence of utilization, WIP, FGI, service level and production lead time on EVA (economic value added) is developed in order to link the available logistical key figures with a market perspective and discuss their influence on company value. The question answered in this paper is: What is the optimal utilization and service level of a production system in order to achieve the maximum possible EVA for a company? A single-product, single-machine, make-to-order production system with investment dependent machine capacity is modeled. The model combines a market share concept based on delivery/production lead time and service level with concepts describing the logistical relationships between WIP, utilization, production lead time and service level. In addition to the explanatory use of the model, it can provide support for strategic decisions concerning investing or divesting machinery. The main application of this model is in describing the link between capacity investment and company value comprehensively based on data available from cost accounting in real companies. Furthermore, this paper shows that high flexibility of personnel and machine capacity increases the maximum possible EVA of production systems even if this flexibility is linked to additional costs.

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    Article provided by Elsevier in its journal International Journal of Production Economics.

    Volume (Year): 130 (2011)
    Issue (Month): 1 (March)
    Pages: 16-26

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    Handle: RePEc:eee:proeco:v:130:y:2011:i:1:p:16-26
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    1. Lutz, Stefan & Loedding, Hermann & Wiendahl, Hans-Peter, 2003. "Logistics-oriented inventory analysis," International Journal of Production Economics, Elsevier, vol. 85(2), pages 217-231, August.
    2. Coelli, Tim & Grifell-Tatje, Emili & Perelman, Sergio, 2002. "Capacity utilisation and profitability: A decomposition of short-run profit efficiency," International Journal of Production Economics, Elsevier, vol. 79(3), pages 261-278, October.
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    8. Van Nieuwenhuyse, Inneke & Vandaele, Nico & Rajaram, Kumar & Karmarkar, Uday S., 2007. "Buffer sizing in multi-product multi-reactor batch processes: Impact of allocation and campaign sizing policies," European Journal of Operational Research, Elsevier, vol. 179(2), pages 424-443, June.
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    10. Jodlbauer, Herbert & Altendorfer, Klaus, 2010. "Trade-off between capacity invested and inventory needed," European Journal of Operational Research, Elsevier, vol. 203(1), pages 118-133, May.
    11. Schnetzler, Matthias J. & Sennheiser, Andreas & Schonsleben, Paul, 2007. "A decomposition-based approach for the development of a supply chain strategy," International Journal of Production Economics, Elsevier, vol. 105(1), pages 21-42, January.
    12. Miles, James A. & Ezzell, John R., 1980. "The Weighted Average Cost of Capital, Perfect Capital Markets, and Project Life: A Clarification," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 15(03), pages 719-730, September.
    13. Teck H. Ho & Yu-Sheng Zheng, 2004. "Setting Customer Expectation in Service Delivery: An Integrated Marketing-Operations Perspective," Management Science, INFORMS, vol. 50(4), pages 479-488, April.
    14. Zapfel, Gunther, 1998. "Customer-order-driven production: An economical concept for responding to demand uncertainty?," International Journal of Production Economics, Elsevier, vol. 56(1), pages 699-709, September.
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