Interpretive structural modeling

Interpretive structural modelling (ISM) can be used for identifying and summarizing relationships among specific variables, also called “enablers”. ISM provides a means by which order and relationships can be imposed on such variables, thus categorizing the enablers according to their driving power and dependence.

Enablers of risk mitigation

Based on a solid literature review the authors come up with 11 enablers:

• Information sharing
  • More information means more visibility means less surprises
• Supply chain agility
  • Agility allows for faster adapting to changing circumstances without losing momentum
• Trust among supply chain partners
  • Lack of trust creates opportunism only
• Collaborative relationships among supply chain partners
  • Collaboration creates interdepence and the appreciation of flexibility and responsiveness
• Information security
  • Information is the most critical asset in the chain, so guard it
• Corporate social responsibility
  • Be prepared to manage the consequences of partners’ (wrong) policies
• Aligning of incentives and revenue sharing policies
  • Everybody’s interest must be served, not just one’s own
• Strategic risk planning
  • The supply chain is a strategic asset, not just an operational necessity
• Risk sharing
  • Both risks and rewards need to be distributed evenly throughout he chain
• Knowledge about risks in the supply chain
  • Risk understanding improves decision-making
• Continuous risk analysis and assessment
  • Business environments are dynamic, so are the risks

I cannot think of anything else, can you?

Dependant versus driver matrix

Similar to the later article on information risks management, the authors end up with a matrix showing the interrelations among the enablers, based on their dependency and driving power.

The enablers are further classified as

(1) Autonomous enablers (Southwest quadrant)
(2) Dependent enablers (Southeast quadrant)
(3) Linkage enablers (Northeast quadrant)
(4) Independent enablers (Northwest quadrant)

Autonomous enablers have a weak driver power and weak dependence. These enablers are relatively disconnected from the system. Linkage variables work both ways; they affect the enablers above and are at the same time dependent on the enablers below. Independent variables have a strong driver power and weak dependence. Dependent enablers are dependent on the other variables.

Conclusion

Interestingly, in this paper, the dependent enablers with the least driving force are seen as key enablers, while in the paper on information risks, the independent enablers are seen as the strongest contributors towards risk mitigation. Confusing at first, perhaps, but it depends on your point of view. Most important however, is that the interpreted structural modelling methodology, as the name implies, only provides the framework for interpretation of supply chain risk mitigation. It does not provide the answer.

Reference

Faisal, M., Banwet, D., & Shankar, R. (2006). Supply chain risk mitigation: modeling the enablers. Business Process Management Journal, 12 (4), 535-552 DOI: 10.1108/14637150610678113

Author links

• qu.edu: Mohd Nishat Faisal
• dmsiitd.org: D.K. Banwet
• dmsiitd.org: Ravi Shankar

Related

• husdal.com: Information risks management
• husdal.com: Supply Chain Confidence
• husdal.com: Corporate vulnerability

Two-in-one

Two approaches to risk assessment are used in this paper: First, interpretive structural modelling (ISM) is employed to understand the interrelationships among the enablers of information risks mitigation, resulting in a driving power and dependence matrix. Secondly, graph theory is used to quantify information risks. Unfortunately, unless I missed it somewhere, the paper provides little linkage between the two approaches.

Interdependence – Twelve enablers

In developing the driving power and dependence matrix, the authors come up with 12 “enablers” that contribute towards the mitigation of information risks:

(1) Information sharing among supply chain (SC) partners.
(2) Supply chain wide strategies to mitigate information risks.
(3) Level of supply chain integration.
(4) Collaborative relationships among supply chain (SC) partners.
(5) Support to partners.
(6) Reliable IT/IS infrastructure.
(7) Top management commitment.
(8) Trust among supply chain (SC) partners.
(9) Awareness about information risks.
(10) Availability of funds to implement information risk mitigation strategies.
(11) Incentives alignment.
(12) Metrics for continual information risks assessment and analysis.

Through a set of operations, linking enabler i (e.g. #2 above) with enabler j (e.g. #4 above) ,the relationship between each enabler is investigated, e.g.

(1) enabler i will help to achieve enabler j.
(2) enabler i will be achieved by enabler j.
(3) enabler i and j will help achieve each other.
(4) enablers i and j are unrelated.

Cutting a long story short, in the end they reach the following conclusion:

Driving power and dependence

The above is then presented as a matrix:

The enablers are further classified as

(1) Autonomous enablers (Southwest quadrant)
(2) Dependent enablers (Southeast quadrant)
(3) Linkage enablers (Northeast quadrant)
(4) Independent enablers (Northwest quadrant)

Autonomous enablers have a weak driver power and weak dependence. These enablers are relatively disconnected from the system. Dependent enablers are dependent on the other variables. Linkage variables work both ways; they affect the enablers above and are at the same time dependent on the enablers below. Independent variables have a strong driver power and weak dependence. These enablers are the most important variables and appear at the top of the ISM hierarchy, implying that management needs to address these enabler variables more carefully than perhaps the other enablers.

Quantification of risks – graph theory

I have chosen to deliberately skip this part of the paper in my review. Not because it is not interesting, but because it appears as a separate part unrelated to the aforementioned. Personally, if I were the author, I would have split this one paper into two separate papers; one paper using the interpretive structural modelling and another paper using graph theory. The way it is now I see a mixing, not a linking, of two separate approaches.

Conclusion

The article highlights that information risks are interrelated and that one factor (or “enabler”) cannot be mitigated without taking into account all the other factors that contribute to or are dependent on this factor. This is similar to the argument on corporate vulnerability in Svensson (2004), where

Corporate vulnerability may be influenced by the current and potential direct and indirect dependencies, the vertical and horizontal dependencies, as well as the unidirectional and bi-directional dependencies, between business activities in and between supply chains.

The article also reflect some of the points made in Christopher and Lee (2004) on supply chain confidence, where the argument is made that

Total end-to-end visibility will enable supply chains to be transparent, and the right information would be available to the right member of the supply chain at the right time.

This paper identifies twelve variables which may help mitigate information risks in supply chains. I may not agree with these twelve as such. Maybe there should be more, maybe less. Nonetheless, the approach used in the paper is worth looking into.

Reference

Faisal, M., Banwet, D., & Shankar, R. (2007). Information risks management in supply chains: an assessment and mitigation framework Journal of Enterprise Information Management, 20 (6), 677-699 DOI: 10.1108/17410390710830727

Author links

• qu.edu: Mohd Nishat Faisal
• dmsiitd.org: D.K. Banwet
• dmsiitd.org: Ravi Shankar

Related

• husdal.com: Supply Chain Confidence
• husdal.com: Corporate vulnerability