Modeling Inventory vs. Modeling Disaster Prep –Where to begin?
Tags: abstracting network logistics problems, disaster preparation in healthcare, modeling inventory, scenario optimization, scenario planning, Sourcing optimization, supply chain contingency planning, supply chain interruption
The application of combinatorial optimization (a.k.a. sourcing optimization, collaborative sourcing, high definition sourcing) to network logistics problems is a best practice. When properly abstracted, however, the very same technology can be used to proactively create and optimize contingency plans in the event of a supply disruption.
What if there’s a labor strike at our largest suppliers’ receiving port? How much excess capacity do my second and third largest suppliers have, in case my number one supplier has a supply disruption. What is the cost benefit of geographically distributing my principal suppliers? And the list of what frankly are basic questions goes on –and can be answered!
We keep reading about the need to proactively make such contingency plans (i.e. optimize scenarios that solve potential problems and save them for rainy days that we hope never come), but how many of us have participated in meetings with our top suppliers, GPOs and distributors to synch our collective response in the event of a disaster, or at the very least, learn what they’ve done to protect themselves?
According to a 2011 global study sponsored by Zurich Financial Services and conducted by the Business Continuity Institute, 85% of companies reported at least one supply chain disruption in 2011. Remarkably, only 8% of the companies surveyed, which included several major medical device suppliers and a representative number of distributors and major IDNs, have ever sat down with their respective life lines and discussed business continuity planning.
The 2011 disasters highlight the need to stop paying lip service to supply chain disaster planning and, at the very least, gain an understanding of what plans your top suppliers have in place to take care of customers –like you—in the event of a disruption. Chances are, you’re fully aware of how efficient most major suppliers have become at managing their inventories to cut costs (e.g. JIT programs), but these same “lean programs” create additional exposure and vulnerability –right?
By the way, factories don’t have to take direct hits by an earthquake or tsunami to experience supply chain shattering problems. Only the power needs to go out. Besides, 40% of the disruptions identified in the survey were a result of problems with a supplier’s supplier. And despite the fact that the recent tsunami and earthquakes hit north central and northern Japan, brown outs in southern Japan were rampant. The ability to get most anything out of the country was indefinitely put on hold, as all empty ships, planes, trains and automobiles were mobilized as part of the crisis response effort. Yes, the Japanese government had a plan and it impacted businesses that were not directly affected by the disaster.
In fact, the same Zurich-sponsored survey showed that supply networks are more frequently disrupted by non-physical events like IT outages, transportation issues and labor strikes. Providers and GPOs who are only protecting themselves against physical plant damage are missing the point. They are missing the competitive advantage offered by building, synching and saving contingency scenarios with their up stream counterparts and other, hopefully geographically dispersed, suppliers.
When providers look across their supply chains, the idea of modeling interruption contingencies may seem overwhelming. But take heart, the process is usually less painful –and far more interesting– than the steps most providers have already undertaken to simply organize their spend data. Talk about a disaster!
In summary, the idea that sophisticated supply chain organizations would take the time to model their inventory at the expense of modeling their potential response(s) to a supply disruption doesn’t seem to make a whole lot of sense.
—Tom Finn














