6 Practical use of ISM in SCRM
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In this paper, an attempt has been made to apply ISM to uncover interdependencies of supply chain risks. The ISM creates conditions for rational decision making in that complex issues are structured. The pairwise analysis of risks in a group of experts from different functional areas encourages contributions from those who understand the issues being discussed, but may not understand all issues related to the overall risk management process. Thus, the present model will help to increase the awareness of decision makers, whilst assisting them to better understand the mutual influence among different supply chain risks and the consequences this implies for decisions about risk mitigation strategies. Substantial discussion of the identified risks among the experts lead to significant learning about the inter‐relationship and total risk exposure of the company or supply chain under study.
The ISM steps could be implemented in software. Input to this software would be the list of identified risks. The decision about the pairwise relationship between the risks (step 2) can be done by using a “voting screen” (interface) that displays every relationship between risks upon the users have to vote. Use of such software will help to keep track of all the relationships and to ensure that all necessary comparisons are made, which form the basis for the SSIM. The following steps will be calculated and the final graph will be drawn automatically. Furthermore, it reduces the chance of human errors and shortens the overall process considerably. We propose to use such an ISM software as a complementary tool with other risk management (identification) tools. Moreover, the software could also be used in a collaborative environment with suppliers and customers to share risk information across company boundaries.
7 Future orientation
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The application of ISM showed its practicability as an analysis and decision‐support tool in order to facilitate thorough understanding of a complex problem. The complex problem studied was the inter‐relationship of supply chain risks. The process of building an ISM develops subject matter knowledge throughout the discussion and analysis. In the present work, only 21 risks have been used for modeling. More risks can be identified to develop ISM. In addition to the identification of critical risks with high driver and dependence power (linkage risks) and chains of dependent events in terms of the consequences, it is important to consider the probability. Using this approach, the first risk to manage would be the one with the highest probable impact onto the supply chain. Future research should integrate the probability in the model and combine it with the results derived from fuzzy MICMAC in order to provide decision makers with a more precise basis for the effective allocation of risk management resources. Moreover, the model has not been statistically validated. Testing of the validity of this model can be another area of future research. Structural equation modeling has the capability of testing the validity of an already developed theoretical model.