As glimmers of dawn begin to appear (shock! awe! disbelief!) at the end of our long night of Covid, it seems timely to address the question of how leaders can plan for a new period that is completely unknowable. (I know, I know; “Don’t jinx it, Bruce, by even thinking this tunnel has an end.” All I can say is that if I’m wrong this time, at some point I will be right, so humor me.)
The question of how to plan for the unknowable is really just a special case of strategic planning under uncertainty, so this is not just a timely but really an evergreen topic for Adam Smith, Esq. What can we intelligently say about how to approach this? Let me suggest a multipurpose tool; think of it as the Swiss Army Knife of strategic planning: Static vs. dynamic analysis.
Undergraduate economics introduced me to the concept, and, applied judiciously, it’s a powerful analytic technique that is very much with me still. It’s time to revisit this sometimes obscure, but extremely worthy, tool, for the benefit of the readers of Adam Smith, Esq. Hence Part 1 of this short series. Let’s ease our way in with a simple example.
Transportation authorities propose imposing tolls on previously free bridges across (say) the East River of New York City. They project annual revenue will be the product of (a) the amount of the toll; times (b) the average daily traffic across the river over the past, say, three to five years.
Wrong.
In response to the toll, people will adapt. They will, for example: Find alternate routes; carpool; use mass transit more; concentrate their activities on the “other side” into fewer but longer/more intense trips; up their reliance on WFH; etc. Static analysis takes the changed condition at face value and assumes that’s that. Dynamic analysis takes account of how the world will change in response to the new tolls.
For purposes of my economics professors, there were a few critical distinctions between static and dynamic analysis, which could be condensed under the analogy to a photograph vs. a video:
- In static analysis, there’s no role for time; in dynamic analysis, time occupies perhaps the most important role of all.
- Statics ignores/omits the path the system took to get where it is and possible paths forward; dynamics is all about the past and plausible future paths.
- Statics gives you a snapshot of an equilibrium; dynamics gives you a video of disequilibrium–>equilibrium–>disequilibrium….
- Statics essentially presents a simplified (unreal) model; dynamics aspires to reality itself.
A few more attributes could be noted. For brevity, I’ll describe statics and if you imagine the obverse you will have described dynamics. Statics:
- Has no room for variables, only constants.
- Rejects any role for uncertainty.
- Posits or assumes without exploration that supply=demand and savings=investment.
As pithy as the “photo/video” comparison is, a more famous way of getting at the meaning is to recall Eisenhower’s observation “plans are useless, but planning is indispensable.” If that’s not terse enough for you, many generals have distilled this into, “The enemy gets a vote.” You must plan, but blithely assuming you’ll be able to execute your plan to a T is wrongheaded.
How might this apply in the more pacific realm of organizational strategy?
For our purposes (business results and strategic analysis), let’s consider what’s happening now on a macro scale in LawLand (all of the legal services sector): NewLaw is trying to step on BigLaw’s toes. This is our own home-grown, backyard, case of new market entrants challenging the established incumbents. (An even more common pattern is that of an established market laggard matching/beating the market leader’s offering, unprofitably, in the hope of dislodging the frozen-in-place rankings.
Why would a peripheral hamburger chain, for example—although this is a real-life case—match the price of the market leader’s “Quarter Pounder,” but for a burger weighing one-third not one-quarter of a pound? The only way in which this could be “rational” would be if the peripheral chain was betting on affecting customers’ behavior over time. Lose money now, change the “dynamics” (to coin a phrase) of purchasing patterns over time? Statics would dictate that you’re raising your costs without raising your revenues; dynamics pins its hopes on a different result.
Let’s focus on the new entrants vs. incumbents battleground, however; like it or not, this is what we all are or will soon live through.
Getting more pointed, here’s how a Harvard Business Review article (“Adaptability: The New Competitive Advantage”) opens:
We live in an era of risk and instability. Globalization, new technologies, and greater transparency have combined to upend the business environment and give many CEOs a deep sense of unease. Just look at the numbers. Since 1980 the volatility of business operating margins, largely static since the 1950s, has more than doubled, as has the size of the gap between winners (companies with high operating margins) and losers (those with low ones).
Market leadership is even more precarious. The percentage of companies falling out of the top three rankings in their industry increased from 2% in 1960 to 14% in 2008. What’s more, market leadership is proving to be an increasingly dubious prize: The once strong correlation between profitability and industry share is now almost nonexistent in some sectors. According to our calculation, the probability that the market share leader is also the profitability leader declined from 34% in 1950 to just 7% in 2007. And it has become virtually impossible for some executives even to clearly identify in what industry and with which companies they’re competing.
This may sound like a description of today or tomorrow to you, but it was published over a decade ago (August 2011).
Don’t throw up your hands (I know you were tempted for a moment there). We actually have mental and market-based models for helping us, and our firms, act wisely, effectively, and ultimately successfully.
No doubt some of you have already thought this is approaching an essay on “disruptive innovation,” famously introduced to the world in 1995 by HBS Prof. Clayton Christensen. OK, fine. Let’s go there directly, shall we?
