Data-driven risk measurement of firm-to-firm relationships in a supply chain

Business entities are always exposed to potential risks as they are interconnected in a supply chain. The performance of a business entity would be disturbed by the realization of risks, and substantial effort would be required to bring its performance back to the previous level. This study proposes an approach to measure the degree of risk caused by a supplier to the manufacturer by considering the interaction between them in a supply chain. A supply chain simulation is developed based on a real business case for an assemble-to-order industry, and the operational dataset is used to measure the degree of risk. A binary response model with a latent variable is employed to estimate the degree of risk under different conditions. Sensitivity analyses are conducted using a numerical experiment. The results show that decremental demand outperforms incremental demand when the lead time of supply is the performance measure. In terms of the degree of risk, the converse is found to be true when the fulfillment rate is the performance measure. The proposed approach could be used to quantify the risk level, identify the bottleneck supplier, and provide a guide for updating the operational settings.