International Journal of Physical Distribution & Logistics Management

Prabir K. Bagchi

1996 International Journal of Physical Distribution & Logistics Management

doi: 10.1108/09600039610113173

As companies strive to create better value for their customers in today’s competitive marketplace, managers are begining to realize the important role logistics plays. As they seek new ways to compete, one technique that has made headlines in the management community is benchmarking. Discusses the use of benchmarking in logistics in a few companies. Highlights the processes used, the obstacles faced and the gains made in these companies. Analyses the ways these obstacles were overcome. Based on these case studies, presents a generalized benchmarking process model and identifies and discusses the steps involved. Finally, provides practical suggestions for successful implementation of a benchmarking project.

Denis R. Towill

1996 International Journal of Physical Distribution & Logistics Management

doi: 10.1108/09600039610113182

The use of industrial dynamics modelling of real‐life supply chains has only recently re‐emerged from the shadows after a lengthy gestation period. Shows that it is a powerful methodology for predicting and prioritizing methods of re‐engineering the chain in order to achieve enhanced performance when viewed from the perspective of all “players” in the chain. Building an adequate model of an existing, or proposed, real‐life supply chain requires the use of people‐based sources, observation‐based sources, and systems‐knowledge‐based sources. Illustrates each of these approaches. Concludes with predicting the benefits expected from substantial re‐engineering of an electronics products supply chain.

Carol C. Bienstock

1996 International Journal of Physical Distribution & Logistics Management

doi: 10.1108/09600039610113191

Although logistics research contains numerous applications of computer simulation modelling, logistics literature has outlined no standard procedure for determining the appropriate sample size when employing simulation methodology in logistics research. Addresses this deficiency by considering the issue of sample size determination in logistics simulation models. Describes a technique for determining the sample size required to achieve the desired level of relative precision in logistics simulation models. In addition, provides an example of the application of this sample size determination technique to a logistics simulation.

Jari Juga

1996 International Journal of Physical Distribution & Logistics Management

doi: 10.1108/09600039610113209

Examines the potential of logistics network organization to deliver synergy benefits by simultaneously improving flexibility and co‐ordination. The synergy effects of the new organization range from the economies of scale and scope in production and logistics to various “spill‐over” benfits attributable to shared skills and resources in networks.