Relatively Right vs. Wholly Wrong
Are you making mistakes in pursuit of perfection?
By Michael E. Raynor
Michael E. Raynor is a director with Deloitte Consulting LLP and author of The Strategy Paradox and, most recently, The Innovator’s Manifesto. He can be reached at mraynor@deloitte.com.
Recently, over two consecutive days, I had the opportunity to work with the management team of a global multibillion-dollar company that occupies the commanding heights of its industry, and then a six-person startup that has yet to see its first dollar of revenue. These two sets of conversations so close together were for me a powerful reminder of a very valuable lesson: that very often the most important things are the simplest to understand, the hardest to do, and, as a result, the easiest to lose sight of.
The big multinational was wrestling with its energy consumption and greenhouse-gas footprint. It had a myriad of initiatives under way around the world, each of which made perfect sense on its own. Collectively, however, they weren’t having the desired impact, despite what seemed to be entirely reasonable divisions of labor among the various layers of the company’s relatively flat hierarchy. Headquarters set long-term objectives (greenhouse-gas reductions, growth, etc.) and reinforced or specified the unavoidable constraints (legislative and regulatory compliance, profitability, shareholder returns, etc.). Regional management helped translate those into more specific requirements for each country or location. And local management made the necessary operating decisions in light of the facts on the ground.
Reasonably enough, this led to a quest for the best tradeoffs among different ways to reduce energy use and carbon emissions. For example, there is no point in making major investments in energy efficiency in locations that were due to close in less than five years; that money could be used for state-of-the-art equipment at new locations that would result in a larger net reduction.
However, the tactical complexity of reducing this company’s energy use and carbon footprint is difficult to overstate. With so many moving parts, the optimal solution is almost certainly impossible to find. When we attempt to make “all the right tradeoffs” instead of keeping the achievement of our goal front and center, the resulting complexity can cause us to lose sight entirely of what we are attempting to achieve.
For example, it matters a lot whether diesel is burned as fuel in a truck or fuel in an electricity generator. In the case of the former, there are, as yet, no viable substitutes. But for electricity, there are alternatives; some are based on renewables, such as solar, and others have a smaller carbon footprint, such as drawing power from the grid. However, since most of the company’s diesel was burned as fuel, the incremental cost and carbon associated with diesel-generated electricity was easy to overlook.
From a capital-expenditure perspective, though, the cost of diesel generators was significant, and so there was strong pressure to keep costs low by buying less expensive and, hence, less efficient devices. This meant that not only was the company burning far more diesel for electricity than it had to—there was also a systematic lack of learning when it came to reducing the organization’s carbon footprint through the more efficient use of less carbon-intensive electricity.
Solving that problem isn’t about creating a “big picture” view from the top to inform the cross-functional communication and interaction across the hierarchical levels that is so often seen as the antidote to the divide-and-conquer paradigm of complex organizations. All the information required was available—that’s the only reason it was possible to diagnose the problem. What was needed was not more data at the top but a big-picture perspective throughout. Absent a set of shared priorities that keep in the foreground the most important objectives and constraints, the inevitable blizzard of complicating factors that seem relevant will obscure the ultimate objective.
With this in mind, consider now the small startup. The business model for this Web-based venture was premised on connecting customers directly with small manufacturers. Historically, the manufacturers in this industry had been forced to deal with distributors, who, understandably, were most interested in keeping happy their biggest customers—who were, predictably, the biggest manufacturers. Consequently, small manufacturers faced a catch-22: They couldn’t get large-scale distribution because they had few customers, and because they had few customers they couldn’t get large-scale distribution. Our startup would solve this problem.
When it came to signing up manufacturers, however, the startup found itself facing its own catch-22: Small manufacturers were hesitant to sign on because the startup had few customers; yet customers would likely be scarce until there were plenty of manufacturers on board.
The way out, they felt, was to sign on distributors, which would be easier than signing on manufacturers, since the distributors would see the new service as low-risk, as well as experience incremental sales—which, in turn, would allow the startup to get off the ground and make it simpler to sign up new manufacturers. In addition, working with distributors would make it much easier for the startup to develop its IT infrastructure, since, unlike small manufacturers, distributors would have well-developed systems themselves that the startup folks could plug into. The small manufacturers, on the other hand, often needed assistance just formatting their data—when it was possible to get reliable data at all. Finally, the broader product selection would give the startup a powerful marketing message and draw in customers quickly.
But as we explored the implications of this tactical workaround, it became less and less attractive. Would this early dependence on distributors compromise the company’s key differentiator and perhaps risk alienating early customers? The team conceded that it would, but it was a compromise worth making in the interests of more revenue, sooner.
Yet how easy would it be for the new company to wean itself off distributors’ revenue stream as it moved to an exclusively manufacturer-direct model? That was hard to say: Ironically, the more successful the distributor-based strategy in the early days, the more difficult it would be to drop the distributors in the future, since they’d be a large percentage of the startup’s revenue and a key driver of the selection that the by-then-established customer base would come to expect. But, the company felt, this was a risk worth taking. Would profitability be undermined? Yes, but again, only for as long as it would take to sign on enough manufacturers that the distributors could be jettisoned.
In short, signing on distributors, which seemed to solve so many individual problems, just might make it impossible for the venture to realize its initial vision.
Reflecting on this discussion over the following several days, a new consensus emerged, replacing what had been the firm’s avowed strategy for the previous several months: They could maintain their strategic integrity and get started without signing on distributors, after all.
Only time will tell whether this will turn out to be the right decision. The point of the story for present purposes is that, just as with the big multinational, deciding what to do was not a question of access to information or an integrated view of all the moving parts. This was a team that had been spending more time with each other than with their families for months. They talked to each other constantly about every last detail of every last detail. Everyone knew a goodly amount about everything going on. Yet even so, the tradeoffs remain painful, it’s tough to keep priorities straight, and it is always difficult to know what the right answer is.
The world has a certain fractal, scale-insensitive complexity to it. Analysis can lead to paralysis for just that reason: There is always another level of detail. As a result, the pursuit of an optimal solution can very often lead you much further astray than imprecise guesses informed by a steadfast conviction of the righteousness of your objective. That very often can be difficult, for cost-benefit analyses—well-intentioned ones informed by the best data available and a long-term time horizon—can suggest that more attractive alternatives exist, if only we look hard enough. But in the end, it is much better to be approximately right than precisely wrong.