The Ability to Detect Risk

This is a summary of the basic details from an article titled “A Comprehensive Risk Assessment and Evaluation Model: Proposing a Risk Priority Continuum”.  It was written by Stanley E. Griffis (University of Michigan) and Judith M. Whipple (Penn State University) for the _Transportation Journal_ of the American Society of Transportation and Logistics (AST&L), printed by the Pennsylvania State University Press. Vol. 51, No. 4. (c) 2012.

Instead of determining the probability or the impact of risk, this article focuses on identifying the chances of “the ability of risk detection”.  The last identification factor is the one that most businesses overlook while they are concentrating on the first two.

The authors define risk in accordance to that outlined by Zsidisin, Melnyk, & Ragatz (2005), who define it as “variability in outcomes or results”.  Griffis and Whipple want to add to the sparse amount of literature that treats the probability of detection of these large swings of variability (risk).

Inheritant in the strategy to identity risk is to choose how to best avoid it.  Griffis and Whipple offer the ultimate undesirable events (UUE) strategy developed by Ebeling (1997) in order to describe how detailed risk avoidance can be.  Briefly, an UUE is a distinct charted strategy to depict how several failure modes can lead to different (or even the same) undesirable events strategy.

The Failure Mode Effects and Criticality Analysis (FMECA) tool is the principle instrument used to identity UUEs.  A fault tree is the number one tool used to identify risk:

Example 1:


Slater, Robert. “18-849b Dependable Embedded Systems”. Carnegie Mellon University. Draft Paper. Spring 1998.

Example 2:


Hurley, Brion. “Risk Reduction in Healthcare: Slide Presentation from the Agency for Healthcare Research and Quality (AHRQ) Annual Conference”. 14 September 2009.

The defect with fault trees is the following: they “only identify the potential challenges/risk factors– risk factors are not assessed in terms of the likelihood of occurrence, potential impact, or detectability” (Griffis and Whipple 438).

These authors go on to develop a “Criticality Matrix” and define the criticality index for a given risk model.

[Once I update my WordPress site, I hope I will be able to put in formulas.]

Once likelihood of risk is determined, “quality fade” must be accounted for (Midler 2007).  Producers or suppliers keep on taking little pieces out of the cost equation and putting them into their profit margin.  Eventually, these changes accure and an unforeseen risk is realized.

The authors continue by developing a model that accounts for all three of the factors: probability of risk, impact of risk, and ability of risk detection, which they term the “Comprehensive Risk Assessment and Evaluation Model”.  This uses “a prioritization from a safe zone to a danger zone” which allows managers to “better understand the full spectrum of risks faced by their firms and supply chains”.

Example 3:

comprehensive risk assessment and evaluation model1

COSCO Sustainable Development Report 2009.

After establishing the above 3D graph, risk mitigation strategies should be implemented in accordance to the degree of risk priority even to each type of risk.  These strategies can be grouped as the following:

  1. Low Priority: Monitor or Take
  2. Mixed Priority: Imitate, Flexibility, Postpone, or Speculate
  3. High Priority: Avoid or Control (Griffis and Whipple 444).

It should also be remembered that firms might not always value “risk prevention” if the risk has a unlikely potential to disrupt the supply chain.

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More complete citations:

Ebeling, C. E. 1997. An Introduction to Reliability and Maintainability Engineering. Long Grove IL: Waveland Press.

Midler, P. 2007. “Quality Fade: China’s Great Business Challenge”. Knowledge@Wharton, July 25.

Zsidisin, G. A., S. A. Melnyk, and G. L. Ragatz. 2005 “An institutional Theory Perspective of Business Continuity Planning for Purchasing and Supply Management.” International Journal of Production Research 43 (16): 3401-20.


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