Jan Husdal's Blog

October 2009 Previous month Next month

Anyone who has flown more than just a couple ot times has probably experienced the Seatbelt sign coming on in mid-flight and heard the captain announce that there could be some turbulence ahead. And if you're a seasoned traveller you will know that unless the captain asks the cabin crew to sit down and suspend inflight services, there's not really much to worry about. Supply chain turbulence is a new term that I had not heard about before I read Supply chain risk in turbulent environments – A conceptual model for managing supply chain network risk by Peter Trkman and Kevin Mc Cormack. At first I thought it to be nothing more than old wine in new bottles, having exchanged "uncertainty" with "turbulence", but Trkman and Mc Cormack come up with a profoundly new model of four factors working together to create supply chain disruptions:

 

trkman.jpg

 

 

The model divides risks into two categories, internal (endogenous) and external (exogenous):

 

Endogenous uncertainty: The source of uncertainty/risk is inside the supply chain and can lead to changing relationships between focal firm and suppliers. Endogenous uncertainty: The source of uncertainty/risk is from outside the supply chain. Internal to the supply supply chain are market and technology turbulence. Market turbulence arises from the heterogeneity of the market, and technology turbulence refers to the degree to which technology changes over time within an industry. External to the supply chain are continuous risks and discrete events. Continuous risk: Events where the costs of potential changes are continuous in nature and relatively easy to predict. Discrete events: This category consists of low-likelihood, high-impact events.

 

Matched with supplier attributes on one side and supply chain design and structure on the other side, this provides the optimal breeding ground for supply chain disruptions.

 

Trkman, P., & McCormack, K. (2009). Supply chain risk in turbulent environments—A conceptual model for managing supply chain network risk International Journal of Production Economics, 119 (2), 247-258

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