Daily Archives: January 10, 2014

Where Is Your Greatest Risk? Not Where You Think It Is.

As per a recent piece by Simchi-Levi, Schmidt, and Wei in the current issue of the Harvard Business Review on managing unpredictable supply chain disruptions, there is little correlation
between how much a firm spends annually on procurement at a particular site and the impact that the site’s disruption would
have on company performance
. In reality, the greatest exposures
often lie in unlikely places

Moreover, in many supply chains, these exposures are typically not realized until a low-probability, high-impact event — such as a Hurricane, Earthquake, SARS outbreak, or other mega-disaster — occurs. In these situations, companies find out that they significantly underestimated the impact and are not adequately prepared because their traditional models for evaluating and preparing supply chain risk break down as there is typically a lack of historical data for low probability, infrequently occurring, high-impact events. (Big companies have to deal with poor supplier performance, forecast errors, and transportation breakdowns everyday and traditional risk models can thus adequately predict, and allow the organization to prepare for, these impacts.)

But, as the authors point out, it doesn’t have to be this way. Companies can not only determine the potential magnitude of a disruption without historical data, but can even do so without even knowing what the disruption is. This is because, at the end of the day, the specifics of a disruption don’t really matter — only its impacts do. Be it flood, famine, or fire — you don’t care why your factory isn’t producing — you only care that it isn’t and you have to find an alternate source of supply. And it is possible to model the impact of a disruption at any point of your supply chain without knowing the event that caused it, as an impact is either going to eliminate or cut off supply or production.

To this end if, as the authors indicate, you develop a mathematical model (that can be computerized) that focuses on the impact of potential failures at points along the supply chain (such as the shuttering of a supplier’s factory or the inaccessibility of a distribution center), rather than the cause of the disruption, you can quantify what the financial and
operational impact would be if a critical supplier’s facility were out of commission for, say, two weeks — whatever the reason
. And that’s what you really care about.

In their paper, the authors describe a sophisticated linear optimization model that integrates predicted Time-To-Recovery (TTR) factors for each node (based upon historical recovery times for the supplier or distributor after a disruption) with Bill-of-Material (BoM), operational measures, financial measures, in-transit inventory levels, on-site inventory levels and demand forecasts for each product. When one node is removed at a time from this model, it can be used to find the supply chain response that would minimize the performance impact of the disruption (such as reducing inventory, shifting production, expediting transportation, or reallocating resources) and then calculate the resulting operational performance impact (PI). The node with the largest PI presents the greatest risk and is assigned the largest risk exposure index (REI) of 1.0 (and all other nodes are indexed relative to this value).

While you may need such a model to determine the full impact of a disruption, you don’t need such a complex model to determine the big hidden risks in your supply chain (which are often the result of sole-source supply arrangements somewhere in the supply chain, possibly at tier two or three). All you really need to do is map the full supply chain for every product you produce down to the raw material supply. Then you can quickly identify sole-source supply, single-factory or single location production, bottle-necks in the distribution network, etc. which lead to hidden risks.

And once you have identified the major risks, and collected the data to appropriately access the potential impacts of a disruption, you can build local models to analyze the extent of the risk exposure. And as you build more and more models, you work your way up to the point where you can begin working on the model described by Simchi-Levi, Schmidt, and Wei, incrementally. No big bang modelling approach needed. All you need to do is get underway with a good supply chain visibility solution, such as Resilinc‘s.