Contemporary Business Studies - Academy of Knowledge Process ...

International Journal of Contemporary Business Studies
Vol: 3, No: 1. January, 2012 ISSN 2156-7506
Available online at http://www.akpinsight.webs.com
As mentioned previously, Suppliers 1 and 5 exhibited the smallest potential percentage increase in VAR
during the sensitivity analysis. However, Supplier 4 displays the largest potential decrease and increase in
VAR. Thus, after considering the supplier’s operational and external risk factors, the company may
consider the development of an aggressive supply chain risk management program that helps move
Supplier 4 towards the accomplishment of these reductions. The program would also reduce the potential
for Supplier 4 to incur a network risk event that has a high probability of VAR impact. Possible
incentives that the company could offer the supplier are incremental increases in business based upon
documented improvements in its network risk profile. As in the cases of Suppliers 1 and 2, this supplier
has a high probability (82 percent) of experiencing a delivery problem risk event. Immediate actions
should be taken to minimize the effects of such an event on company revenues. Moreover, given the high
probability of delivery problems associated with all of the study participants, the company should institute
a comprehensive supply chain risk management program designed to discover and eliminate the causes of
this issue among its suppliers.
7. CONCLUSIONS
The methodology presented in this study can be used to monitor network risks in supply chain networks.
As part of a supply chain governance agreement, suppliers could be required to periodically update their
risk probability profiles for the risk events outlined in Appendix 1. These updates could be applied to
Bayesian networks to create new risk profiles for each supplier. Adjustments to existing risk management
strategies, policies, and tactics could then be made to reflect the current risk realities associated with the
supply network. Thus, the methodology can provide a proactive means of managing network risks as well
as other categories of supply chain risks.
The methodology can also be used by organizations to develop supplier network risk profiles to determine
revenue risk exposure levels. Organizations can then decide if it is in their best interest to either assist a
supplier in improving its network risk profile or to terminate the relationship. Supplier network risk
profiles can be used to determine those network risk events that have the highest probability of occurrence
and the largest potential revenue impact. Thus, this methodology can assist organizations along with their
suppliers in developing comprehensive supplier risk management programs designed to minimize the
occurrence of network and other risk events.
Finally, this methodology can be used as a tool to assist managers in evaluating current and potential
suppliers. Suppliers who have been shown to improve their network risk profiles over time may be
rewarded by an organization with more business. Conversely, suppliers who have experienced increases
in network risk events over an extended period of time may be viewed as ‘at risk’ suppliers whose
relationship may require reassessment by the organization. The reassessment could result in removal from
the supply network. Potential suppliers willing to provide information for the generation of their risk
profiles may then become viable candidates for network inclusion.
8. LIMITATIONS
This study provides an examination of network risk profiles associated with casting suppliers in the
automotive industry. Therefore, the results are specific to the study participants. A potential limitation to
the use of the methodology presented in this study is the ability to acquire the necessary data from
suppliers needed for the construction of the Bayesian networks. There may be circumstances in which
some participants within a supply chain network are reluctant to share risk profile data with their
customers. The reluctance to share such information may be due to mistrust among network members.
Inaccurate or misleading information acquired from suppliers may also limit the effectiveness of Bayesian
networks as a tool for monitoring network risks in supply chains. Such information can result in the
creation of flawed profiles that fail to reflect the true risk associated with a particular supplier. Moreover,
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