Assessing Supply Chain Risk Adopting Reliability Tools

• The gradual expansion of supply chains has developed its own risk. A single disruption at any element or flow will eventually affect the whole system. It can be observed now that there has been an increase in awareness of the risks involved with supply chain disruption. Many have shown concern over the significance of reliable systems which can identify risk prior to the event, assess the consequences of risk events, and, at the same time, be capable of controlling and managing the events that lead to disruption risk throughout supply chain. However, risk issue has not been thoroughly evaluated from the perspective of supply chain performance measures. This is possibly due to the lack of detailed and specific measurement tools for systematically identifying and evaluating supply chain risks. In this study, reviews conducted on relevant journals have guided our search to identify significant components in developing supply chain risk analysis models i.e., risk identification, estimation and evaluation. We explore each component and highlight the respective requirements. Acknowledging the well-developed risk analysis methods in the Reliability Engineering field, we consider the possibility of adopting the same approaches in the field of Supply Chain Risk Management. Thus we evaluate the advantages and disadvantages of several risk analysis methods applied in Reliability Engineering field, for example, Failure Modes and Effect Analysis (FMEA), Fault Tree Analysis (FTA), Event Tree Analysis (ETA), Hazards and Operability Analysis (HAZOP), Cause and Effect Diagram (CAED) and Analytical Hierarchy Process (AHP). We analyse the similarities and gaps between the two fields with the aim to propose a tool that is suitable for supply chain risk analysis. We find that the use of a stand-alone tool of FMEA or a hybrid application of FTA and AHP is potentially most appropriate in Supply Chain Risk Management.

Nyckelord

Supply Chain

Risk Management

Risk Analysis

Risk Assessment

Reliability

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