Skip to main content

Modelling Supply Chain Information and Material Flow Perturbations

  • Chapter

Abstract

It is well established that supply chains can be multitiered dynamic systems where each tier may have multiple entities (such as suppliers or manufacturers) and the linear flow of goods is uncommon (Riddalls, Bennett and Tipi 2000). This complexity is exacerbated when entities in the supply chain may be involved in a multitude of other supply chains, each with differing requirements or objectives (Sahin and Robinson 2002). In a supply chain system, there are forward flows of materials as product is moved from the supply base, to the manufacturers and eventually to the end customer. These material flows are triggered by information flows, which move in the reverse direction through the supply chain as shown in Figure 6.1.

This is a preview of subscription content, log in via an institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  • Chatfield, K., Kim, J., Harrison, T. and Hayya, J. 2004 ‘The bullwhip effect — impact of stochastic lead time, information quality, and information sharing: A simulation study’, Production and Operations Management, 13: 340–53.

    Article  Google Scholar 

  • Cooke, J.A. 2002 ‘Brave new world’, Logistics Management and Distribution Report, 41: 31–4.

    Google Scholar 

  • Fleisch, E. and Tellkamp, C. 2005 ‘Inventory inaccuracy and supply chain performance: A simulation study of a retail supply chain’, International Journal of Production Economics, 95: 373–85.

    Article  Google Scholar 

  • Forrester, J. 1961 Industrial Dynamics, New York: MIT Press.

    Google Scholar 

  • Lee, H., Padmanabhan, V. and Whang, S. 1997a ‘Information distortion in a supply chain: The bullwhip effect’, Management Science, 43(4): 546–58.

    Article  Google Scholar 

  • Lee, H., Padmanabhan, V. and Whang, S. 1997b ‘The bullwhip effect in supply chains’, Sloan Management Review, 38: 93–102.

    Google Scholar 

  • Lee, H. and Whang, S. 2000 ‘Information sharing in a supply chain’, International Journal of Manufacturing Technology and Management, 1: 79–83.

    Article  Google Scholar 

  • Lin, F., Huang, S. and Lin, S. 2002 ‘Effects of information sharing on supply chain performance in electronic commerce’, IEEE Transactions on Engineering Management, 49: 258–68.

    Article  Google Scholar 

  • Machalaba, D. and Kim, Q. 2002 ‘West Coast docks are shut down after series of work disruptions’, The Wall Street Journal (Eastern Edition), 30 September: A2.

    Google Scholar 

  • Mitroff, I. and Alpasan, M. 2003 ‘Preparing for evil’, Harvard Business Review, April: 109–15.

    Google Scholar 

  • Murata, T. 1989 ‘Petri-nets: Properties, analysis and applications’, Proceedings of the IEEE, 77: 541–80.

    Article  Google Scholar 

  • Petri, C. 1962 Kommunikation mit Autimaten, PhD dissertation, University of Bonn.

    Google Scholar 

  • Rice, J. and Caniato, F. 2003 ‘Building a secure and resilient supply network’, Supply Chain Management Review, 7: 22–30.

    Google Scholar 

  • Riddalls, C., Bennett, S. and Tipi, N. 2000 ‘Modeling the dynamics of supply chains’, International Journal of Systems Science, 31: 969–76.

    Article  Google Scholar 

  • Sahin, F. and Robinson, E. 2002 ‘Flow coordination and information sharing in supply chains: Review, implications and directions for future research’, Decision Sciences, 33: 505–36.

    Article  Google Scholar 

  • Salimifard, K. and Wright, M. 2001 ‘Petri-net based modeling of workflow systems: An overview’, European Journal of Operation Research, 134: 664–76.

    Article  Google Scholar 

  • Schneeweiss, W.G. 1999 Petri Nets for Reliability Modeling: In the Fields of Engineering Safety and Dependability, Hagen, Germany: Verlag.

    Google Scholar 

  • Wu, T. and Blackhurst, J. 2005 ‘A modeling methodology for supply chain synthesis and disruption analysis’, International Journal of Knowledge-Based and Intelligent Engineering Systems, 9: 93–106.

    Google Scholar 

  • Wu, T. and O’Grady, P. 2005 ‘A network-based approach to integrated supply chain design’, Production Planning and Control, 16: 444–53.

    Article  Google Scholar 

  • Zhou, M. and Zurawski, R. 1995 ‘Introduction to Petri nets in flexible and agile automation’, in M. Zhou (ed.), Petri Nets in Flexible and Agile Automation, Dordrecht, NL: Kluwer Academic Publishers.

    Chapter  Google Scholar 

  • Zurawski, R. and Zhou, M. 1994 ‘Petri nets and industrial applications: A tutorial’, IEEE Transactions on Industrial Electronics, 41: 567–83.

    Article  Google Scholar 

Download references

Authors

Editor information

Editors and Affiliations

Copyright information

© 2009 Teresa Wu and Jennifer Blackhurst

About this chapter

Cite this chapter

Wu, T., Blackhurst, J. (2009). Modelling Supply Chain Information and Material Flow Perturbations. In: Dwivedi, A., Butcher, T. (eds) Supply Chain Management and Knowledge Management. Palgrave Macmillan, London. https://doi.org/10.1057/9780230234956_6

Download citation

Publish with us

Policies and ethics