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International Journal of Contemporary Business Studies Vol: 3, No: 1. January, 2012 ISSN 2156-7506 Available online at http://www.akpinsight.webs.com expressed as a numerical score given as a result of applying the model and measures. The higher the risk profile score, the higher the supplier’s disruption potential to the supply chain. In order to apply the risk results to potential events, the survey results were reorganized into operational, network and external riskrelated measures, and the results were recalculated for each supplier. The reorganized measures are presented in Appendix 1. The financial impact of the risk profiles on company revenues was calculated by identifying the components furnished by individual suppliers, associating those components with a finished product and product gross revenue, and computing the sum of associated monthly revenues for each supplier. 4.3Sample and Data Collection The study sample consists of five automotive casting suppliers to a major automotive company in the United States. The sample data was collected by first interviewing the supplier’s account representative to discuss the study and the internet-based survey. Subsequently, the survey instrument web link was sent in an email to the supplier’s account representative. The account representative completed the survey, supplier historical performance data was evaluated, and a company analyst conducted an environmental analysis of the organization. All risk ratings were assessed using a five-point Likert scale and a risk index was calculated for each supplier. In addition, each supplier provided a-priori probabilities for the twelve risk events identified in Appendix 1. The a-priori probabilities were determined via a review of relevant quantitative and qualitative information by a team of company personnel familiar with the identified risk events as they relate to the five suppliers. By logically examining the information, the team was able to estimate a-priori probability values pertaining to the twelve risk events for each supplier. These probabilities provided the basis for the construction of Bayesian networks used in the creation of the supplier risk profiles. 4.4Research Model Bayesian networks were constructed to examine the probability of a supplier’s impact on company revenues.Network, operational, and external risk levels were computed using the provided a-priori probabilities for the identified risk events. These risk levels were then used to determine a supplier’s probability of revenue impact on the company. A diagram of the Bayesian networks used in this study is illustrated in Figure 2. Network Key 1 = Misalignment of interest 2 = Supplier Financial Stress 3 = Supplier Leadership Change 4 = Tier 2 Stoppage 5 = Supplier Network Misalignment 6 = Quality Problems 7 = Delivery Problems 8 = Service Problems 9 = Supplier HR Problems 10 = Supplier Locked 11 = Merger/Divestiture 12 = Disasters Figure 2. Supplier Bayesian Network 12 Copyright © 2012. 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 Nodes (circles) represent variables in the Bayesian network. Each node contains states, or a set of probable values for each variable. The values ‘yes’ and ‘no’ represent the two states in which the variables can exist in the network illustrated in Figure 2. Nodes are connected to show causality with arrows known as ‘edges’ which indicate the direction of influence. When two nodes are joined by an edge, the causal node is referred to as the parent of the influenced (child) node. Child nodes are conditionally dependent upon their parent nodes. Thus, in Figure 2, the probability of suppliers experiencing network risks is dependent on the a-priori probabilities associated with the following variables: misalignment of interest; supplier financial stress; supplier leadership change; tier 2 stoppage; and supplier network misalignment.The a-priori probabilities associated with the variables quality problems, delivery problems, service problems, and supplier human resources (HR) problems directly influence operational risks. External risks are dependent upon the following variables: supplier locked (i.e., company cannot easily switch to another supplier), merger/divestitures, and disasters. The joint probabilities of the computed network, operational, and external risks are then used to determine the probability that a supplier will have an adverse impact on the company’s revenue stream. 5. RESULTS In this study, the product of the supplier’s revenue impact probability times its revenue impact provides ‘value-at-risk’ (VAR) dollars. VAR is defined as the minimum loss expected on a portfolio of assets over a certain holding period at a given probability (Venkataraman, 1997). VAR was developed by financial institutions in the early 1990s to provide senior management with a single number that could easily incorporate information on the risk of a portfolio of assets (Engle & Manganelli, 2004). Today, VAR has evolved into a risk measurement tool that can be applied outside of the financial management arena, such as in making procurement decisions (Sanders & Manfredo, 2002). VAR can also be used to evaluate and manage supply chain risks. The Supply Chain Council defines VAR as the sum of the probability of events times the monetary impact of the events for the specific process, supplier, product, or customer (Supply Chain Council, 2008). Thus, this metric allows for comparisons among suppliers to facilitate supply chain risk management. This study examines monthly VAR dollars for the company based upon the risk profiles of each supplier. The a-priori probabilities for the twelve supply chain risk events that affect network, operational, and external risks are presented in Table 1 for each supplier. These values were used to generate a risk profile using Bayesian networks composed of network, operational, and external risk probabilities along with the supplier’s probability of revenue impact on the company. As previously discussed, risk event probabilities linked to misalignment of interest, supplier financial stress, supplier leadership changes, tier 2 stoppage, and supplier network misalignment directly affect network risks. The table reveals that the suppliers have a 50 percent probability of experiencing a risk event due to either financial stress or changes in leadership. In addition, there is a 29 to 32 percent probability that the supplier base encounters a tier 2 stoppage risk event. Finally, the table shows that the probability of the suppliers being subjected to risk events due to misalignment of interests or network misalignments are in the range of 19 to 21 percent and 12 to 20 percent respectively. 5.1 Sensitivity Analysis A network risk profile sensitivity analysis was conducted for each supplier. The sensitivity analysis begins with a determination of a supplier’s probability of revenue impact on the company by applying the a-priori probabilities illustrated in Table 1 to the Bayesian network. Computations for the probability of revenue impact on the company based on the risk profile of Supplier 1 are presented in Appendix 2. An examination of Appendix 2 reveals that Supplier 1 has a 41 percent probability of affecting company revenues. Supplier revenue impact probabilities were also computed assuming a 100 percent probability of supplier network risks along with a zero percent probability of such risks to compare VAR revenue impacts at the extreme cases relative to the base case, represented by the a-priori probabilities. 13 Copyright © 2012. Academy of Knowledge Process
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