Scoring Your Value Network for Risk

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A previous post in Supply Chain Action introduced some important questions for establishing and sustaining a resilient supply chain.  They included:

  1. How do you evaluate the resiliency of your value network?
  2. Do you have the capability to electronically represent your value network from one end to the other?
  3. Have you determined how to represent the value of inventory, currency and data that pulses along the paths in the value network?
  4. Have you quantified the consequences of a disruption (from whatever cause) that would impact that flow?
  5. If so, do you have a plan for dealing with such an interruption?
  6. What have you done to make sure key people know the plan and can execute it?

In addition to my previous two posts, “Building Resiliency into Your Value Network” in Supply Chain Action and my guest post on Bob Ferrari’s Supply Chain Matters, I promised more on the topic. This is a partial fulfillment of that promise.

In the network of suppliers, manufacturing plants, and distribution centers through which you create value for your customers, there reside potential points of failure.  These points of failure can be identified by an item and/or a location.  If the location, say a country or facility in a flood plain is the potential point of failure, then all of the items that are sourced, manufactured or stored there inherit that risk.  But the magnitude of the risk is not the same for every item at that location.  A scheme for scoring value network resiliency is required in order to answer the questions I have noted above.  Here is basic formula that I dreamed up to get your thinking started if you don’t already have one:

Simplistically, resiliency might be said to be (1 – risk) where risk is the outcome of the following expression:

LT/Rd * Rv * M * C * G

where the terms of this expression are defined in the following fashion:

  • Lead Time (LT) – can be analyzed, quantified and expressed in number of weeks or months
  • Redundancy (Rd) – how many substitute products/components or alternative sources exist?
  • Revenue (Rv) – total estimated annual or quarterly revenue (e.g. in $millions) from the sale of an item or from the sale of products in which the item is a component
  • Margin (M) – proportion of the revenue (from the sale of an a single unit of an item and/or from the sale of a single unit of each product in which the item is a component or ingredient) that is gross profit
  • Competition (C) – qualitative strength of the competition (How easily would the competition gain share if your supply were disrupted?), perhaps on a scale from 1 to 5
  • Geopolitical Stability (G) – a qualitative score, perhaps on a scale from 1 to 5

Such a score could then be viewed from either an item perspective or a location perspective.  Naturally, one could look at it by item and location as well.

This is an admittedly imperfect approach (is there a perfect one?), so I hope that you will leave a comment with a suggestion.  Obviously, scalers could be applied to each factor.  The point is that you need some way to evaluate and prioritize risk.

Could this be the basis of something useful?  How does your company measure risk or resiliency?

Philosophical thought for the weekend:  Winston Churchill once said, “Without courage, all other virtues lose their meaning.”

Thanks for dropping by Supply Chain Action.


About Arnold Mark Wells
Industry, software, and consulting background. I help companies do the things about which I write. If you think it might make sense to explore one of these topics for your organization, I would be delighted to hear from you. I am employed by Opalytics.

One Response to Scoring Your Value Network for Risk

  1. I had a very good comment through LinkedIn. I’ll duplicate it here.


    A valuable thought leadership piece, and I thank you for sharing. Interestingly, these concepts are similar to some of the concepts raised during normal disaster recovery planning and/or business continuity planning. A very few thoughts: (1) I think the variable of time might be underestimated, because there is lead time, obviously but, in the case of a customer decision to go to a competitor, there is also an issue of the time before the customer comes back; (2) I would be concerned about the relationships that might not be monotonic or linear, including competitor variable.


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