4 Discussion and conclusion
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Figure 4 clearly shows that the highest driving power lies with terrorist attacks (20), natural disasters (19) and employee strike (21), which all fall in the category of external risks. The occurrence of external risk cannot be influenced by risk management, which makes it even more important to asses the relationship of those risks to other supply chain risks. Short‐term production downtimes at own facilities (3), capacity variances/bottlenecks on the supply market (4) and delay in delivery (6) were classified as highly dependent risks followed by short‐term production downtimes of supplier (9). The operating levels of the focal company depend on these risks substantially. They have minor driving power and are at the top of the ISM hierarchy. Besides, assigning high priority to these risks, the management should understand the dependence of these risks on lower level risks, in achieving the SCRM goals and objectives. But that means, putting high priority to the linkage risks (group III), too. The MICMAC analysis indicates that these are IT‐related risks (2), (8) and (15), respectively, poor performance of subcontractors (16) and lack of transport capacities (17) which both originate from 3PL provider and finally theft (10), poor delivery quality (11), lack of sufficient equipment, staff or transport/warehouse capacity (12) and hauling claim (13) which are in the first‐tier supplier's sphere. These linkage risks have relatively strong driving power as well as strong dependence. Therefore, these form the middle level of the model. Though the lower level risks induce or affect these risks, these also have significant driver power to influence some other risks, which are at the top of the model.
Another insight from driver power and dependence figure is that dependency on suppliers (5) is the only autonomous risk. Autonomous risks are weak drivers and weak dependents and do not have much influence on the system.
Further insights can be gained from the ISM model shown. First of all, the graph depicts the risks and their dependencies. In this configuration, the ISM model, in contrast to the digraph built from the initial reachability matrix, is clearly arranged but still contains all dependencies. The initial reachability matrix indicates only the direct relationships between any two elements. By building the ISM model, plenty of these edges can be removed while the information is still represented by a set of indirect dependencies. Thereby, the complexity of the visualization is reduced. So this mapping of inter‐relationships is a useful method for supply chain risk managers to evaluate supply chain risks and learn about the impact chains of these risks. It can also be used to communicate and explain these dependencies within the company and within the supply chain, to enable an effective management which deals with the most important risks, not only from a company perspective, but primarily from an overall supply chain perspective.
Besides the potential as a structured and simple communication tool among supply chain risk managers, the ISM model also supports the chosen risk categorization shown in Figure 1. With an exception of the IT risks (2), (8) and (15), the resulting structure illustrates the same hierarchy of categories. On level I are only the focal company's process and control risks, followed by its supply risks (4)‐(6). On level IV are the first‐tier supplier's process and control risks (7) and (9), followed by its supply risks (10)‐(12). Finally, on level V and below are the 3PL's risks (13) and (14), followed by its resource risks (16)‐(18). The external risks, which have a direct impact on all players, are at the bottom of this digraph. Risks (2), (8) and (15), i.e. IT breakdown on all levels (focal company, first‐tier supplier, 3PL), are exceptions because these risks have an influence both on risks on higher levels (upwards the supply chain) as well as on lower levels within the hierarchy. Risk management should carefully assess the causes behind those risks which could be either technical or relationship related. The former involves interruptions in data communication. The latter deals with risks of not getting relevant information about the development within the partner's company (e.g. forecast of volume changes, availability of transport capacities).
Although these results provide a lot of information about the dependencies between supply chain risks, the ISM model cannot be used to identify a direct (critical) link between two risks, that, when eliminated, would have no longer any effect. The resulting ISM model shows overall impact chains but removes links, if the information is still contained, to keep track of the dependencies at the expense of detailed information about all direct links. Furthermore, the ISM model shows only that there is a connection between two risks without any information if the impact of this connection is significant or negligible. To get more detailed information about the strength of the relation between risks, fuzzy ISM has been applied and subsequently, driver power and dependencies were derived. The main advantage of the fuzzy digraph is its clustering of the edges. The scale used is shown in Table V. In Figure 5, the fuzzy digraph contains all edges, the direct and indirect dependencies, which have a very strong impact of 1. The less significant edges are hidden to reduce the complexity. In this diagram, a supply chain risk manager can point out directly, which risks have significant impact on other risks and yet he can identify which linkage risks should be managed first to reduce a potential series of impacts on other, mostly downstream, risks. After having dealt with the most significant dependencies in a next step, the edges with a relation strength of 1 will be hidden and those with a strength of 0.75 will be shown. This approach ensures a structured treatment of the risks depending on the strength of impact on other risks while keeping the complexity in the diagram on a manageable level. This research contributes to the field of managerial decision‐making literature as it emphasizes the usefulness of integrating ISM in the identification phase of SCRM.
สนทนา 4 และสรุปส่วน: ส่วนของ sectionNext ก่อนหน้านี้Figure 4 clearly shows that the highest driving power lies with terrorist attacks (20), natural disasters (19) and employee strike (21), which all fall in the category of external risks. The occurrence of external risk cannot be influenced by risk management, which makes it even more important to asses the relationship of those risks to other supply chain risks. Short‐term production downtimes at own facilities (3), capacity variances/bottlenecks on the supply market (4) and delay in delivery (6) were classified as highly dependent risks followed by short‐term production downtimes of supplier (9). The operating levels of the focal company depend on these risks substantially. They have minor driving power and are at the top of the ISM hierarchy. Besides, assigning high priority to these risks, the management should understand the dependence of these risks on lower level risks, in achieving the SCRM goals and objectives. But that means, putting high priority to the linkage risks (group III), too. The MICMAC analysis indicates that these are IT‐related risks (2), (8) and (15), respectively, poor performance of subcontractors (16) and lack of transport capacities (17) which both originate from 3PL provider and finally theft (10), poor delivery quality (11), lack of sufficient equipment, staff or transport/warehouse capacity (12) and hauling claim (13) which are in the first‐tier supplier's sphere. These linkage risks have relatively strong driving power as well as strong dependence. Therefore, these form the middle level of the model. Though the lower level risks induce or affect these risks, these also have significant driver power to influence some other risks, which are at the top of the model.Another insight from driver power and dependence figure is that dependency on suppliers (5) is the only autonomous risk. Autonomous risks are weak drivers and weak dependents and do not have much influence on the system.Further insights can be gained from the ISM model shown. First of all, the graph depicts the risks and their dependencies. In this configuration, the ISM model, in contrast to the digraph built from the initial reachability matrix, is clearly arranged but still contains all dependencies. The initial reachability matrix indicates only the direct relationships between any two elements. By building the ISM model, plenty of these edges can be removed while the information is still represented by a set of indirect dependencies. Thereby, the complexity of the visualization is reduced. So this mapping of inter‐relationships is a useful method for supply chain risk managers to evaluate supply chain risks and learn about the impact chains of these risks. It can also be used to communicate and explain these dependencies within the company and within the supply chain, to enable an effective management which deals with the most important risks, not only from a company perspective, but primarily from an overall supply chain perspective.Besides the potential as a structured and simple communication tool among supply chain risk managers, the ISM model also supports the chosen risk categorization shown in Figure 1. With an exception of the IT risks (2), (8) and (15), the resulting structure illustrates the same hierarchy of categories. On level I are only the focal company's process and control risks, followed by its supply risks (4)‐(6). On level IV are the first‐tier supplier's process and control risks (7) and (9), followed by its supply risks (10)‐(12). Finally, on level V and below are the 3PL's risks (13) and (14), followed by its resource risks (16)‐(18). The external risks, which have a direct impact on all players, are at the bottom of this digraph. Risks (2), (8) and (15), i.e. IT breakdown on all levels (focal company, first‐tier supplier, 3PL), are exceptions because these risks have an influence both on risks on higher levels (upwards the supply chain) as well as on lower levels within the hierarchy. Risk management should carefully assess the causes behind those risks which could be either technical or relationship related. The former involves interruptions in data communication. The latter deals with risks of not getting relevant information about the development within the partner's company (e.g. forecast of volume changes, availability of transport capacities).
Although these results provide a lot of information about the dependencies between supply chain risks, the ISM model cannot be used to identify a direct (critical) link between two risks, that, when eliminated, would have no longer any effect. The resulting ISM model shows overall impact chains but removes links, if the information is still contained, to keep track of the dependencies at the expense of detailed information about all direct links. Furthermore, the ISM model shows only that there is a connection between two risks without any information if the impact of this connection is significant or negligible. To get more detailed information about the strength of the relation between risks, fuzzy ISM has been applied and subsequently, driver power and dependencies were derived. The main advantage of the fuzzy digraph is its clustering of the edges. The scale used is shown in Table V. In Figure 5, the fuzzy digraph contains all edges, the direct and indirect dependencies, which have a very strong impact of 1. The less significant edges are hidden to reduce the complexity. In this diagram, a supply chain risk manager can point out directly, which risks have significant impact on other risks and yet he can identify which linkage risks should be managed first to reduce a potential series of impacts on other, mostly downstream, risks. After having dealt with the most significant dependencies in a next step, the edges with a relation strength of 1 will be hidden and those with a strength of 0.75 will be shown. This approach ensures a structured treatment of the risks depending on the strength of impact on other risks while keeping the complexity in the diagram on a manageable level. This research contributes to the field of managerial decision‐making literature as it emphasizes the usefulness of integrating ISM in the identification phase of SCRM.
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