Confused? Overwhelmed?? Can’t see what’s coming next???
Welcome to the human race! None of us can — try as we might. Here are some tips about how to approach this crucial challenge.
Having made my living for over a decade as a quantitative forecaster, I can tell you that forecasting in times of continual, disruptive change is no longer the tool in my kit that I first reach for. What I use and recommend instead is scenario-based planning.
The key differences between those techniques are summarized in the pictured table from a presentation I gave recently. In a nutshell, scenario planning starts with sets of competing assumptions about potential future states of the world — each set called a scenario. Typically, at least three scenarios are considered — best case, worse case, and most likely (which is usually between those two.)
Evaluations are then made of our response to each of the feasible scenarios — without reference to how likely each one is. A playbook is developed for each scenario.
Finally, ongoing monitoring is conducted to determine, in real time going forward, which scenario is closest to developing. If some other possibility is developing outside the original scenarios, that is then built into our thinking.
As factories in China start coming back online, now is the time to start planning for our post-Covid world. I have reviewed scenarios developed by The Conference Board and Goldman Sachs, and have distilled them down to three simple essentials:
Each of these three scenarios is shown in the figure below. For each of these — what does our business look like? What do our customers and supply chains look like? Does anything else change, for example, the regulatory environment? Is this an opportunity to do a full value pivot, as others have done? If so, is that pivot temporary, or permanent?
Note the importance of three key metrics, in the simplified picture here labelled X, Y, and Z. X is the length of time it takes for a recovery to begin. Y is the “slope” of the recovery, i.e., how fast it occurs. And Z is the gap between the recovery and the pre-Covid world. In the best case, Z is zero — we get back to the way we were. In the worst case, Z remains very large, and we are stuck for a long time in a slow economy. In the middle case, Z indicates that things come back in some fashion, but not the way they were.
What makes the Covid situation especially difficult to model are the interactions between the health crisis that is slowly improving and the wealth crisis that resulted and is rapidly worsening. The availability of tests, treatments or potential cures, and potential vaccines each directly impacts the willingness of people to return to work and to their former lives as consumers. Most of the “data” available there to model scenarios — for example, the time it could take to develop and deploy a safe and effective vaccine — consist of hopeful speculation at best.
As if that weren’t enough complexity, there are varying vertical sensitivities. Government guidelines are generally following a first out, last back in model — bars, restaurants, live entertainment, and retail were among the first to close down, and will likely be among the last to open fully — if they are able to do so at all in any kind of economically feasible way.
Merriam-Webster defines intelligence as “the ability…to deal with new or trying situations.” I love this definition because it describes organizations as well as it does people. An intelligent enterprise is one that adapts quickly to changing conditions — and that exhibits resilience in the face of extreme change.
To be intelligent and resilient under this definition, it’s essential that the “enterprise radar” be functioning — i.e., that you be collecting the best metrics possible — X, Y, Z, and several others. Before you can manage change, you must measure it. A crisis presents an opportunity — or reveals a need — to rethink your metrics portfolio from the ground up.