Next is what Fine calls process clockspeed, which deals with the process for creating and delivering a good or service. One way to measure process clockspeed is to look at average asset lives. The HOLT database shows that the average asset life (which includes R&D capitalization) of the top 1,800 industrial companies in the United States has gone from approximately fourteen years in 1975 to under ten years currently. Today’s companies need to generate economic returns on investment over a shorter time horizon than they did a generation ago. Exhibit 21.1 shows Fine’s estimates of product and process clockspeeds for a host of industries.
EXHIBIT 21.1 Clockspeeds in Sample Industries
IndustryProduct ClockspeedProcess Clockspeed
Fast-Clockspeed Industries
Personal computers < 6 months 2-4 years
Toys and games < 1 year 5-15 years
Semiconductors 1-2 years 3-10 years
Cosmetics 2-3 years 10-20 years
Medium-Clockspeed Industries
Automobiles 4-6 years 10-15 years
Fast food 3-8 years 5-25 years
Machine tools 6-10 years 10-15 years
Pharmaceuticals 7-15 years 5-10 years
Slow-Clockspeed Industries
Commercial aircraft 10-20 years 20-30 years
Tobacco 1-2 years 20-30 years
Petrochemicals 10-20 years 20-40 years
Paper 10-20 years 20-40 years
Source: Fine, Clockspeed, 239. Reproduced with permission.
That average clockspeeds are shortening does not mean that all sectors are changing equally rapidly. One of the factors underlying the average change is a shift in the composition of public companies. Eugene Fama and Kenneth French show that the number of companies in the Compustat database rose 70 percent between the mid-1970s and the mid-1990s. Most of the new companies, launched via initial public offerings, were smaller and faster growing than the existing companies.4 Since more fast-clockspeed companies have been added to the market mix over the past twenty-five years or so, the average clockspeed has shrunk. But the evidence shows that some companies can and do sustain high economic returns for a long time.5
Investors care about clockspeed because of its close link to sustainable competitive advantage. Robert Wiggins and Timothy Ruefli did an empirical study of the sustainability of excess returns. They defined persistent superior economic performance as “statistically significant above average performance relative to a reference set that persists over a long period of calendar time.” 6 While they measured performance using accounting numbers (return on assets and Tobin’s q) rather than sound economic numbers, I suspect the size of their sample (nearly 6,800 firms in forty industries) and the time period (twenty-five years, 1972 to 1997) were sufficient to yield representative results.
Wiggins and Ruefli propose and test four hypotheses:1. Periods of persistent superior economic performance are decreasing in duration over time. Their analysis supports the hypothesis, showing that the probability of leaving the “superior performance stratum” has increased over time.
2. Hypercompetition is not limited to high-technology industries but will occur through most industries. Here, the evidence supports the hypothesis by showing that while nontechnology companies had a higher probability of staying in the superior-performance stratum than technology companies did, the probability of leaving the stratum did increase over time.
3. Over time, firms increasingly seek to sustain competitive advantage by concatenating a series of short-term competitive advantages. The idea here is that successful companies string together a series of short-lived competitive advantages. The data support this hypothesis, too. The researchers show that the pattern of one-period superior performance is more prevalent in the study’s later time periods.7
4. Industry concentration, large market share, or both are negatively associated with chance of loss of persistent superior economic performance in an industry. The research did not support the final hypothesis. Neither a concentrated industry nor large market shares is empirically consistent with sustainable competitive advantage.
The Wiggins and Ruefli work is consistent with other recent research, including Foster and Kaplan’s Creative Destruction and the finding of Campbell et al. that firm-specific volatility has been rising steadily since the mid-1970s.8 An accelerating rate of innovation is causing a greater rate of dislocation for individual companies.
Two factors lead me to believe that the trend of faster clockspeed will persist. The first is the increase in information technology, which will likely have an ongoing, significant microeconomic effect.9 Technology increases clockspeed by allowing companies to improve processes and provides consumers with greater transparency. Second, an ongoing shift from physical to knowledge assets provides greater flexibility in resource allocation. Companies can change employee tasks more readily than they can change a factory’s output.
Investor Evolution
Faster clockspeed affects investors in a number of ways. First, shortening periods of sustainable excess returns have important implications for valuation. Shorter product and process life cycles undermine the usefulness of historical multiples (especially price/earnings, which weren’t very useful to begin with), because the basis of comparison is different. I believe there has been a trade-off: higher economic returns for shorter periods are increasingly replacing lower economic returns for longer periods. Whether or not I am right, simplistic valuation assumptions invite danger.
Another possible valuation pitfall comes with terminal valuations in discounted cash-flow models. Many discounted cash-flow models assume perpetual growth beyond an explicit forecast period, hence embedding an assumption of long-term value creation. In a world of shortening sustainable advantages, such an assumption appears particularly inappropriate.10
Clockspeed also has implications for portfolio turnover. Just as companies must string together a series of competitive advantages, optimal portfolio turnover is higher today than in the past. That said, I still suspect that aggregate portfolio turnover, which has risen sharply over the past twenty-five years, is too high. But extremely low portfolio turnover (less than 20 percent) may not provide sufficient flexibility to capture the market’s dynamics.
In addition, faster clockspeed suggests the need for greater diversification. If competitive advantages are coming and going faster than ever, investors need to cast a wider net in order to assure that their portfolios reflect the phenomenon. (Ideally, of course, investors would only focus on the winners and avoid the losers. This is practically very difficult.) The data show evidence for this increased diversification.
Finally, the rate of change in the business world demands that investors spend more time understanding the dynamics of organizational change. Success and failure at fast-changing companies may provide investors with some useful mental models for appreciating change at the slower evolving companies. The business world is going the way of Drosophila.
22
All the Right Moves
How to Balance the Long Term with the Short Term
Strategy in complex systems must resemble strategy in board games. You develop a small and useful tree of options that is continuously revised based on the arrangement of pieces and the actions of your opponent. It is critical to keep the number of options open. It is important to develop a theory of what kinds of options you want to have open.
—John H. Holland, presentation at the 2000 CSFB Thought Leader Forum
Managing for the Long Term
At a business forum I attended, a senior executive of a Fortune 100 company proclaimed that his company manages “not for the next quarter, but for the next quarter century.” Ugh. Such platitudes do not instill confidence in investors. Most managers don’t have any idea what’s going to happen in the next five years, much less the next twenty-five years. How do you manage for an ambiguous future?
Yet managers must clearly strike some balance between the short term and the long term. It’s like speedin
g down the highway in a car. If you focus just beyond the hood, you’re going to have a hard time anticipating what’s coming. Look too far ahead, on the other hand, and you lose perspective on the actions that you need to take now to navigate safely. There’s a tradeoff between the short term and the long term, and the appropriate focal point shifts as conditions warrant.
The notion that managers should only focus on the long term is nonsensical. Have you ever heard of a company that blew twenty straight quarters but had a great five years? It doesn’t happen; the long term is, by definition, an aggregation of short terms. So what’s the best way to think about managing for the long term in a complex environment?
Deep Blue’s Lessons
The strategies of chess grandmasters provide us with some very important clues about how to approach business strategy. Even with a relatively small number of rules and an eight-by-eight board, chess games are very complex and have perpetually novel outcomes. Even though chess is not too mathematically complicated, assessing all (or most) potential positions requires staggering computational power.
Deep Blue, IBM’s chess-playing supercomputer, demonstrated this computational brute force when it beat world champion Garry Kasparov in a six-game match in 1999. The $3 million computer evaluated 200 million positions a second—over 35 billion in the three minutes allotted to a single move—compared with Kasparov’s approximately three positions a second. Deep Blue also had a database of grandmaster opening games over the last hundred years.1
The strategic lesson in Deep Blue’s victory is not machine over man but rather that pure computational power can succeed in a well-defined game. Add a small amount of complexity to the game, however, and the number of options rises dramatically, rendering even the most powerful computers useless. For example, no computer program comes close to the best humans in the game of Go, which also has simple rules but a larger nineteen-by-nineteen board.2
Since the business world is vastly more complex than any board game, it’s impossible to understand all possible future positions, much less assess them. So success for humans in either chess or business is not about crunching numbers; it’s about developing strategies to achieve a long-term goal.
Strategies for Winners
So how do great chess players approach the game? Chess master Bruce Pandolfini observes four behaviors that are consistent among chess champions and useful in thinking through the short-term/long-term debate.3
1. Don’t look too far ahead: Most people believe that great players strategize by thinking far into the future, by thinking 10 or 15 moves ahead. That’s just not true. Chess players look only as far into the future as they need to, and that usually means thinking just a few moves ahead. Thinking too far ahead is a waste of time: The information is uncertain.
2. Develop options and continuously revise them based on the changing conditions (see exhibit 22.1): Great players consider their next move without playing it. You should never play the first good move that comes into your head. Put that move on your list, and then ask yourself if there’s an even better move. If you see a good idea, look for a better one—that’s my motto. Good thinking is a matter of making comparisons.
3. Know your competition: Being good at chess also requires being good at reading people. Few people think of chess as an intimate, personal game. But that’s what it is. Players learn a lot about their opponents, and exceptional chess players learn to interpret every gesture that their opponents make.EXHIBIT 22.1 Avoid Game Plans
Source: Sente Corporation.
4. Seek small advantages: You play for seemingly insignificant advantages—advantages that your opponent doesn’t notice or that he dismisses, thinking, “Big deal, you can have that.” It could be slightly better development, or a slightly safer king’s position. Slightly, slightly, slightly. None of those “slightlys” mean anything on their own, but add up seven or eight of them, and you have control.
Pandolfini stresses to his students that his goal is not to make them great chess players but great thinkers:My goal is to help them develop what I consider to be two of the most important forms of intelligence: the ability to read other people, and the ability to understand oneself. Those are the two kinds of intelligence you need to succeed at chess—and in life.4
There are limits to the business-as-chess analogy. Besides the added complexity of business, the most significant limitation is that chess is a zero-sum game: for every winner, there’s a loser. The business world is not zero-sum, and the game between players has an unspecified tenure. So how can we apply these lessons from chess to the business world?
Strategy as Simple Rules
One of the characteristics of a complex system is that highly variable outcomes emerge from simple rules. Unless you deliberately replay a chess game, you’ll never see the same game twice. Herein lies the key to resolving the tension between the short term and the long term.
Companies should develop long-term decision rules that are flexible enough to allow managers to make the right decisions in the short term. In this way, the company is managing for the long run even when it has no information about what the future holds. No company knows how the business landscape will develop—just as chess players don’t know how the board will develop—but decision rules provide action guidelines no matter what happens.
Kathy Eisenhardt and Don Sull call this “strategy as simple rules.”5 They argue that companies, especially in fast-changing markets, should not embrace complex strategies but rather adopt and stick to “a few straightforward, hard-and-fast rules that define direction without containing it.”
Eisenhardt and Sull specifically suggest five types of rules:1. How-to rules spell out key features of how a company should execute a process. It answers the question, What makes our process unique?
2. Boundary rules focus managers on which opportunities they should pursue and which are outside the pale.
3. Priority rules help managers rank the opportunities they accept.
4. Timing rules synchronize managers with the pace of opportunities that emerge in other parts of the company.
5. Exit rules help managers decide when to pull out of yesterday’s opportunities.
Eisenhardt and Sull argue that a company should have somewhere between two and seven rules, that young companies typically have too few, and that more mature businesses have too many. A decision rule to maintain accounting integrity (i.e., to avoid managing earnings per share versus managing the business) might also help reduce undue short-termism.
This “strategy as simple rules” approach is not only strongly analogous to successful chess playing, but it also resonates with other complex adaptive systems. Most important, it puts to rest the nonproductive debate about whether companies should manage for the short or long term. Companies that embrace simple rules can manage both for the next quarter and the next quarter century.
23
Survival of the Fittest
Fitness Landscapes and Competitive Advantage
It is not the strongest of the species that survives, nor the most intelligent, but the one most responsive to change.
—Charles Darwin, The Origin of Species
A Peek at Another Peak
In the spring of 1997, Tiger Woods didn’t just win the prestigious Masters golf tournament; he dominated it. Competing with the best golfers in the world, he sprinted away from the pack, winning by a record twelve strokes. To put this achievement in perspective, Woods had joined the tour less than a year earlier, and was still the tender age of twenty-one. He had already won four of the fifteen PGA Tour tournaments he had entered. Golf aficionados started favorably comparing him to Jack Nicklaus, widely considered the best golfer ever.
How did Woods react to his extraordinary success? He didn’t assume he had reached his potential. He didn’t sit back and enjoy. Instead, he carefully studied the videotape of his Masters performance and came to a surprising conclusion: “My swing really sucks.”1
Woods called his coa
ch, Butch Harmon, to help revamp his swing. Harmon was sure Woods could take his game to an even higher level but knew that the results would not come instantly. Woods would have to risk getting worse in the short term in order to get better for the long term. He didn’t hesitate. Working with Harmon, Woods improved his strength and changed his grip, allowing him to maintain his power while gaining more control.
Even as Woods managed to win only one Tour event from July 1997 to February 1999, he insisted he was a better golfer than before. “Winning is not always the barometer of getting better,” he asserted. In the spring of 1999, the new swing gelled. Woods went on to win ten of the next fourteen events in 1999, including eight PGA Tour victories. He tacked on another nine PGA Tour wins in 2000, and after capturing the 2001 Masters, he was the first golfer to be the reigning champion in all four majors simultaneously.
Fitness Landscapes
This story is a useful introduction to the idea of fitness landscapes. Evolutionary biologists originally developed fitness landscapes to help them understand evolution—in particular, how a species increases its fitness.2 Along the way, the framework has spawned useful ideas for corporate strategists.3
What does a fitness landscape look like? Envision a large grid, with each point representing a different strategy that a species (or a company) can pursue. Further imagine that the height of each point depicts fitness. Peaks represent high fitness, and valleys represent low fitness. From a company’s perspective, fitness equals value-creation potential. Each company operates in a landscape full of high-return peaks and value-destructive valleys.4 The topology of the landscape depends on the industry characteristics.
More Than You Know Page 14
