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The order progress diagram: A supportive tool for diagnosing delivery reliability performance in make-to-order companies

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  • Soepenberg, G.D.
  • Land, Martin
  • Gaalman, Gerard

Abstract

This paper describes the development of a new tool for facilitating the diagnosis of logistic improvement opportunities in make-to-order (MTO) companies. Competitiveness of these companies increasingly imposes needs upon delivery reliability. In order to achieve high delivery reliability, both the average lateness and the variance of lateness of orders need to be controlled. A good diagnosis tool should make a link between performance on these two aspects and production planning and control (PPC) decisions. PPC decisions can relate to input control (order acceptance/delivery date promising, order release and priority dispatching) or output control (adjusting capacities). For a diagnosis related to the average lateness, a well-developed supportive tool is available: the throughput diagram. By using industrial data we show that the throughput diagram can help to gain insight into the consequences of input and output control decisions across time. Tools for facilitating the diagnosis of the variance of lateness in a similar way have not been published in literature. In order to fill this gap, the order progress diagram has been developed in this research. It helps to relate the variance of lateness to disturbances in order to progress and to uneven flow patterns. The diagram indicates the difference between the progress of an individual order and the average progress pattern of orders for different stages of the process. Thus it shows where due date deviations emerge. Similar to the throughput diagram, it links performance to the input and output control decisions mentioned. Its added value is illustrated by industrial data.

Suggested Citation

  • Soepenberg, G.D. & Land, Martin & Gaalman, Gerard, 2008. "The order progress diagram: A supportive tool for diagnosing delivery reliability performance in make-to-order companies," International Journal of Production Economics, Elsevier, vol. 112(1), pages 495-503, March.
  • Handle: RePEc:eee:proeco:v:112:y:2008:i:1:p:495-503
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    References listed on IDEAS

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    1. Kingsman, Brian & Hendry, Linda & Mercer, Alan & de Souza, Antonio, 1996. "Responding to customer enquiries in make-to-order companies Problems and solutions," International Journal of Production Economics, Elsevier, vol. 46(1), pages 219-231, December.
    2. Land, Martin & Gaalman, Gerard, 1996. "Workload control concepts in job shops A critical assessment," International Journal of Production Economics, Elsevier, vol. 46(1), pages 535-548, December.
    3. Kingsman, Brian G., 2000. "Modelling input-output workload control for dynamic capacity planning in production planning systems," International Journal of Production Economics, Elsevier, vol. 68(1), pages 73-93, October.
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    Cited by:

    1. Thürer, Matthias & Stevenson, Mark & Protzman, Charles W., 2015. "COBACABANA (Control of Balance by Card Based Navigation): An alternative to kanban in the pure flow shop?," International Journal of Production Economics, Elsevier, vol. 166(C), pages 143-151.
    2. Thürer, Matthias & Land, Martin J. & Stevenson, Mark & Fredendall, Lawrence D., 2016. "Card-based delivery date promising in high-variety manufacturing with order release control," International Journal of Production Economics, Elsevier, vol. 172(C), pages 19-30.
    3. Soepenberg, G.D. & Land, M.J. & Gaalman, G.J.C., 2012. "Adapting workload control for job shops with high routing complexity," International Journal of Production Economics, Elsevier, vol. 140(2), pages 681-690.
    4. Stevenson, Mark & Huang, Yuan & Hendry, Linda C. & Soepenberg, Erik, 2011. "The theory and practice of workload control: A research agenda and implementation strategy," International Journal of Production Economics, Elsevier, vol. 131(2), pages 689-700, June.
    5. Lin, Yi-Kuei, 2010. "System reliability of a stochastic-flow network through two minimal paths under time threshold," International Journal of Production Economics, Elsevier, vol. 124(2), pages 382-387, April.

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