Risk, Resiliency, and Supply Chain Modeling

The general approach to distinguishing risks is to cluster them by impact and probability. Supply chain risk factors include: probability, impact, source, and predictability. After the risks are identified they are then ranked using the SMAUG model and appropriate responses are formulated to terminate, transfer, treat or tolerate the risk.

A supply chain model can uncover hidden breaking points in unexpected places – in commodity suppliers, for example, at small nodes, in the network or in ostensibly minor components. Among other insights, such a model can point at assets and processes that are being utilized at capacity, spot single-sourced materials inbound and products outbound, show volume or value concentration at particular nodes, identify bottlenecks in lead times, or quantify the impact of foreign exchange fluctuations on revenue and cost. Such analyses may lead to the discovery of measures to increase the resiliency of the supply chain that are independent of risks and their identified implications. Hence, these may be implemented even if no disruptions to the supply chain are assumed.

Supply chain resiliency defines to what extent a supply chain can withstand disruptions. The most common definition of a resilient supply chain is its ability to quickly recover from disruptive events, ideally emerging stronger than before. With disruptions piling up and impacting supply chains more frequently, however, the definition of the resilient supply chain will have to change – it likely will be impossible to recover to an ante-disruption state. Rather, resilient supply chains of the future should have the capacity to continuously morph into new states that enable them to operate under the new (but also temporary) conditions prevailing post-disruption – and do so repeatedly.