Quantitative analysis uncovers hidden risk in supply chains
A manufacturer’s supply chain may include thousands of sources of materials and components, and a problem at any one of those supply nodes could pose risk to the firm. But how can manufacturers identify which suppliers and types of risk would be most disruptive? Traditional methods depend on knowing the probability that a specific type of risk will occur at any supplier and knowing the magnitude of disruption an event would cause. However risks exist on a continuum of frequency and predictability, and the sources of low-probability, high-impact risk are difficult to quantify. So manufacturers often equate a corporation’s greatest supply chain risk with its highest expenditure suppliers. But does this hold up to a rigorous quantitative analysis?
Because the mitigation actions (maintaining more inventory and/or an alternative supply source or facility, for example) are the same regardless of the type of disruptive event, Professor David Simchi-Levi advocates determining the impact to a firm’s operations if any disruptive event occurs, rather than estimating the probability of specific types of risks. He created a model that incorporates bill-of-material information (the list of ingredients required to build a company’s products), maps each part or material to one or more of the firm’s facilities and product lines, captures multiple tiers of supplier relationships (tier 1 are direct suppliers, tier 2 are suppliers to tier 1 suppliers, etc.), includes operational and financial impact measures, and incorporates node recovery time following disruption. As nodes are removed one at a time from the chain, the model determines how best to reallocate inventory and obtain alternatives, and predicts financial impact. The resulting analysis divides suppliers into three groups depending on the cost of individual components and financial impact of shortage: low-cost components/high financial impact; expensive components/high financial impact; and low-cost components/little financial impact.
Simchi-Levi and former graduate students William Schmidt and Yehua Wei used this model to analyze Ford Motor Company's exposure to supply chain disruption risk. Ford