Psychological Foundations of Supply Chain Risk Management

Psychological research shows that risk is never a objective matter (1, 2). This is made even more obvious by the fact that demographics play a huge role in the decisions made by supply chain risk managers.

But how could one integrate these findings into a more general framework?

Problem

In business uncertainty is a problem of perception. As the name “uncertainty” implies there is no certainty about a business or in this case supply chain performance property. So the amount of uncertainty has to be estimated or guessed and since most of the estimation is done by mangers, guessing is a matter of how the problem is perceived by them.

In the process of analyzing a supply chain problem (well, any problem indeed) there are several psychological effects working mostly against a rational decision.
The process within the mind could be modeled as two competing thought processes:

The first branch of thought processes initially addresses the problem with a quick intuitive answer, and the second branch monitors and adjusts the proposed answers.

The authors admits that even though he supplies a long list of psychological biases, there are still many more so these points should be seen as what they are – an excerpt:

• One of the heuristics we use to simplify decision making is to assume that “one thing is like something else”.
• “Among the problems that have been observed based upon representativeness is the misrepresentation of the role of chance in events.”
• The frequency of the presentation of an issue and the manner in which it is presented influences the way we think about it, so “in the supply risk realm, major disasters are likely to color managerial perceptions of the frequency of major disruptions.”
• “Overconfidence […] is typical in assessment of uncertain quantities. In the context of supply risk management such overconfidence is likely to arise when a particular manager who has substantial experience infers (e.g., makes an intuitive judgment) about the probability of some event, such as the probability of supply disruptions associated with various suppliers, without collecting and utilizing performance data.”

Example case: The issue of having multiple suppliers

After the attacks on the World Trade Center in September 2001 there was an increase in the average number of suppliers deployed and similar effects can be noticed in other large disruptions, especially if the media coverage is large.

Thus, unless we actually collect the data and calculate the frequencies, the odds are good that we will confound the two terms in our algebra of the supply risk associated with major events, since as a short cut to understanding the risk, we substitute the extent of damage for how representative such an event is of daily experience.

First, there are rational reasons to increase the supply base to reduce / diversify the risks across multiple (hopefully, independent) suppliers.
So, after an event like this we think of the decision problem in a certain way, and decide to do a risk benefit analysis and
bq. we might decide that the seemingly small risk of more relationship issues is more than offset by the benefits as we add more suppliers. Note that this is a risk versus benefit comparison. By contrasting risk compared with benefits, we have framed the decision in one way.

But one could also reframe the problem to a risk-risk-analysis comparing on the one hand the risk of being subject to disruptions and on the other hand risks of inadequate coordination, due to too many suppliers.

This example should show two things. First, we generally add suppliers to reduce risk, but we trade reductions of risk that is relatively rarely experienced (but catastrophic when it is observed) for risk that is relatively frequently experienced. Second, there is tremendous value to actually knowing how your suppliers are performing in the form of how frequently you experience disruptions that require intervention, because this is the only way that you can really understand what you are trading off.

Management implications

Smith draws several management implications from his analysis:

1. “The first step in addressing any potential challenge is awareness”, not only an objectified view of the risks around us, but especially of the decision processes which lead to certain decisions for or against lean supply chains or backup suppliers.
2. Group decisions might be a way to improve this decision making, if common problems with it (e.g. group think are prohibited.
3. “We have already seen that statistical thinking and training can be valuable in overcoming the shortcuts that we can end up taking in the face of difficult decisions. In supply risk management it is important that the probability of potentially negative events be determined as accurately as reasonably possible, so that both the extent of the risks, and the potential for avoiding the negative consequences of the realized risk can be soundly assessed.”
4. “Finally, as was illustrated here, reframing represents a valuable approach to overcoming some of the biases associated with decision making under uncertainty.”

Conclusion

Smith highlights an often neglected aspect when talking about supply chain risk management. But this aspect is not only neglected by researchers, but also managers alike.
Psychological issues like those mentioned above have been discussed for decades, but only few businesses employ them or even think about employing them.
So on the one hand side one should ask: Do we need more risk mitigation measures to ensure sustainable profits?
But, also: How do our cost reduction measures affect our risk-exposure?