Ebola could teach us how to sync capacity with need

I wrote about Ebola back in August, speculating that better consumer and healthcare supply chains in Africa would have enabled a much quicker response, and that the next time a disease like this appears we could expect to see a faster resolution. Well, it’s now spread to Dallas and Madrid, infecting several hospital workers treating patients who contracted Ebola in West Africa and who have since died.

We have the supply chains I was thinking of when I last wrote. Let’s see how they work.

Old tools

The basic supply chain capabilities that come to mind are distribution networks, inventory management systems and people who know how to manage them. This part seems fine. We have plenty of protective gear, beds and vehicles to move things around. I do not worry about basic supply.

Unfortunately, we also have systems integration issues that pose problems. In dissecting what went wrong in Dallas when Thomas Eric Duncan was initially discharged with both symptoms and a recent Liberian stamp in his passport, we find a classic case of siloed information systems and business processes. Healthcare in the US, despite all the lavish spending, remains a byzantine maze of conflicting accountabilities, and therefore supply chain breakdowns.

Nordstrom’s omnichannel excellence would never have allowed the customer (or patient in this case) to fall through the cracks the way the Dallas hospital did. The same can be said of Caterpillar, which would never leave an equipment customer hanging with crossed-signals on an essential spare part. Why can’t we have this kind of integrated, holistic supply-demand matching in healthcare?

We can, but it’s going to take a streamlining of demand data flow from point of sale, whether emergency room visit, website inquiry or CVS Pharmacy purchase, back through electronic medical records to the supply response. Difficult, yes, but well worth doing, and not only to improve public health but also to save money.

New tools

More enticing is the possibility that new technologies – in particular, big data analytics – could make a difference. As highlighted in our recent Chief Supply Chain Officer Report, big data analytics was the runaway favourite among eight supply chain technologies deemed to be “disruptive and important”.

A BBC news article this week posits that big data may also apply to solving problems like the Ebola crisis, pointing to early pilots based on mobile phone tracking and search analysis that could flag epidemics early.

I spoke at an e-business conference this week in Madison, Wisconsin that blended a supply chain track, an IT track and a marketing track, where the most obvious connective thread across all three was the power of big data.

In the opening session, Dr Raj Veeramani of the University of Wisconsin boiled it down to the transformative potential of massive connectivity in what is often called the “internet of things” (#3 on our list of top disruptive supply chain technologies). Every session, it seemed, paid at least some tribute to the topic of how we can best exploit this wave of data to improve business.

Clearly there is common ground in learning how to query everything from geospatial data to trending Twitter feeds if we want a better understanding of where supply chain needs to be. The big aha for me is that we’re quickly moving away from process-centric replenishment orientated supply chain designs and toward those that can handle event-specific supply spikes.

How different really from a supply response standpoint is a disease epidemic from a new toy’s first Christmas? It’s all about knowing what’s hot where and being prepared.

The questions

Big data, which incidentally is inextricably linked to the internet of things, is important because it promises faster, more precise targeting of the supply response. Johnson Controls does this with buildings’ energy consumption. Google does it with server capacity loading. PepsiCo is trying to do it with consumer-driven flavours for potato chips. In each of these cases the question is clear and therefore the analytical exercise focused.

With just 1 in 20 supply chain people calling big data analytics “irrelevant”, it’s clear that we know the information is there and valuable. What’s still not clear is what questions to ask.

Perhaps the Ebola scare will teach us something about how to sync capacity with need.