I’d like to talk today about something I’ve been calling the connected company. This is a rough sketch. Helicopter view of the whole thing.
Let’s start with what.
The average life expectancy of a human being in the 21st century is about 67 years , and it’s increasing all the time. Do you know what the average life expectancy for a company is?
Surprisingly short, it turns out. In a recent talk , John Hagel pointed out that the average life expectancy of a company in the S&P 500 has dropped precipitously, from 75 years (in 1937) to 15 years in a more recent study.
As companies grow they invariably increase in complexity, and as things get more complex they become more difficult to control. I believe that many of these companies are collapsing under their own weight.
The statistics back up this assumption. A recent analysis in the CYBEA Journal looked at profit-per-employee at 475 of the S&P 500, and the results were astounding: As you triple the number of employees, their productivity drops by half ( Chart here ). This “3/2 law” of employee productivity, along with the death rate for large companies, is pretty scary stuff. Surely we can do better? I believe we can. The secret, I think, lies in understanding the nature of large, complex systems, and letting go of some of our traditional notions of how companies function. The more employees your company has, the less productive each of these employees are. It is a generalization, of course, but a useful one and one that is confirmed by most people who have worked for growing organizations. As the company grows, so does the internal processes and the layers of bureaucracy, and the time spent on communications grows rapidly. It is, however, useful to look at the actual numbers. How much does productivity decrease as the organization grows? The answers are frankly frighting. To look at the effect in large organizations, we considered the constituents of the Standard & Poor's S&P 500 index of leading companies in leading industries of the U.S. economy. For each company we collected information on revenues, gross profit, EBITDA, and the number of employees. After removing some companies with missing or hard-to-use data (e.g. negative profits), we ended up with 475 large, publicly quoted American companies. As a metric for employee productivity we chose profits per employee. You can re-run the analysis with EBITDA or some other metric and the basic results do not change. See below for how to get the files. We then plotted in a log-log plot the profits per employee against the number of employees for the 475 companies in nine different industry sectors. The profit per employee versus the number of employees for 475 of the companies in the S&P 500 index. (Click on the image for a larger version.) Distribution of profit per employeeMin.8,2011st Qu.88,961Median167,089Mean298,5783rd Qu.311,342Max.4,689,266 Naturally there is enormous variation in employee productivity in such a diverse set of companies and industry sectors. The largest employer is Wal-Mart with $75 billion profit and 1.8 million employees ($41,800/employee). The three top slots in terms of employee productivity are all in the financial sector, with Ambac Financial Group's 354 employees generating $1.66 billion profits ($4.7M per employee). At the bottom we find Darden Restaurants whose 157,300 employees each contribute $8,201 to the company's profits. However, the trend is clearly downwards. Fitting a power law give a slope of -0.68. This is scary. Three raised to the power of -0.68 is 0.47. This means that when you triple the number of employees, you halve their productivity . Or: When you add 10% employees the productivity of each drops by 6.3%. Of course, since 3 times half is greater than one, your total profits are typically growing. What causes it? Clearly, there is some element of self-selection: companies sometimes rationally choose to be in high-volume, low-margin markets. But I suspect that is also used as an excuse. There are more possibilities to encounter expensive relationship friction , but also more opportunities to resolve them. I think it is largely down to communications: the degree to which a vision is shared and the effective dissemination of new ideas ideas and working practices. Innovation velocity is dependent on collaboration ; and collaboration among larger groups and creation networks require different skills and tools than what most executives are used to from smaller situations and is therefore often underestimated. Productivity in large enterprise is clearly a subject that deserves attention. If the S&P companies all achieved their average productivity, then they would between them generate an additional $2.9 trillion profit between them (with both winners and losers, of course).
Cities aren't just complex and difficult to control. They are also more productive than their corporate counterparts. In fact, the rules governing city productivity stand in stark contrast to the ominous “3/2 rule” that applies to companies. As companies add people, productivity shrinks. But as cities add people, productivity actually grows. A study by the Federal Reserve Bank of Philadelphia found that as the working population in a given area doubles, productivity (measured in this case by the rate of invention) goes up by 20%. This finding is borne out by study after study. If you’re interested in going deeper, take a look at this recent New York Times article: A Physicist Solves the City . Conclusion Patent intensity—the per capita invention rate—is positively related to the density of employment in the highly urbanized portion of MAs. All else equal, the number of inventions per person is about 20 percent greater in an MA with a local economy that is twice as dense as another MA. Since local employment density doubles more than four times in the sample, the implied gains in patents per capita due to urban density are substantial. In short, we find empirical evidence consistent with a theoretical micro foundation of endogenous growth. In addition, we find evidence of increasing returns to scale in the invention process, but holding density constant, these returns are exhausted at a modest city size—certainly below 1 million in population. Similarly, we find evidence of diminishing returns to density, but only at levels attained by a quarter of our sample. 36 Our results also support theories that suggest that more competitive local market structures are more conducive to innovation. We find that industrial and technology mix are important in explaining the variation in patent intensity across cities, but we found no significant effects for our measures of industrial or technological specialization. We found that local R&D inputs, especially human capital, contribute to higher patent intensities and there is evidence of a very modest substitution effect between academic and private R&D intensity. Variations in the reliance of a city’s industries on trade secret protection did not have a significant effect in our regressions. In the empirical work we have been careful in our definition of the unit of analysis and the inclusion of control variables that reflect the available resources (e.g. R&D, human capital, etc.) that are relevant to the local output of innovations. Thus we believe our coefficients on city size and density reflect effects that are external to the firm, but not to the city itself. On the other hand, our regressions are not sufficient to identify a particular mechanism that explains why these externalities are important. We have suggested a few possibilities, such as better matches between firms and workers or easier transmission of tacit knowledge, but our technique cannot distinguish among them. In order to do so, we require more refined theories and yet more data. To investigate these questions more precisely, one might examine an additional direction of cross-sectional variation, that is, differences across industries. In particular, this would allow one to test the significance of urbanization economies (city size) and localization economies (the local size of the industry). 37 A stronger approach is to focus on firms, the source of most of the innovations in our data, and to investigate the contribution of city characteristics to the productivity of the research efforts located in them. These are topics of our ongoing research. Working Paper NO. 06-14 URBAN DENSITY AND THE RATE OF INVENTION Gerald Carlino Satyajit Chatterjee Robert Hunt Federal Reserve Bank of Philadelphia
The problem with this kind of thinking is that the nature of a machine is to remain static, while the nature of a company is to grow. This conflict causes all kinds of problems because you have to redesign and rebuild the company while you also need to operate it – an idea dramatized in an EDS commercial from a few years ago: Building an airplane in flight .
But companies are made out of people! Just because something works for ants doesn’t mean it will work for us.
So we want to focus on complex systems that are made up of people. What kinds of options does that leave us?
Or even better, what if we compared the company with other large, complex human systems, like, for example, the city? Cities are large, complex, systems, but we don’t really try to control them.
In Stephen B. Johnson 's book Emergence: The Connected Lives of Ants, Brains, Cities, and Software he quotes complexity pioneer John Holland : Cities have no central planning commissions that solve the problem of purchasing and distributing supplies… How do these cities avoid devastating swings between shortage and glut, year after year, decade after decade?No, we don’t try to control cities, but we can manage them well. And if we start to look at companies as complex systems instead of machines, we can start to design and manage them for productivity instead of continuously hovering on the edge of collapse.
And we tend to design companies the way we design machines: We need the company to perform a certain function, so we design and build it to perform that function. Over time, things change. The company grows beyond a certain point. New systems are needed. Customers want different products and services, so we need to redesign and rebuild the machine, or buy a new one, to serve the new functions. This kind of rebuilding goes by many names, including re-organization, reengineering, right-sizing, flattening and so on.
Okay, you say, but cities are fundamentally different than companies. Just because this works for cities doesn’t mean that it will work for companies. Right?
THE LONG-LIVED COMPANY Actually there’s some interesting data there too. Back in the early 1980’s, right after the revolution in Iran, Shell Oil was concerned about the future of the oil industry. What might Shell look like after oil, they wondered? So they commissioned a study with some very interesting parameters: 1. First, they looked only at large companies with relative dominance in their industries, companies similar to Shell in that regard. 2. Second, they looked only at companies with very long lifespans – 100 years or more. 3. Third, they looked at companies who had made a major shift from one industry or product category to another. In other words, they looked at the immortals: the companies that didn't die. The study was never published, but the findings were detailed in a book: The Living Company by Shell executive Arie de Geus . Shell studied 40 large, long-lived companies, some of which were still surviving after 400+ years.
Interestingly, these companies had a lot in common with large cities: Ecosystems: Long-lived companies were decentralized. They tolerated “eccentric activities at the margins.” They were very active in partnerships and joint ventures. The boundaries of the company were less clearly delineated, and local groups had more autonomy over their decisions, than you would expect in the typical global corporation. Strong identity: Although the organization was loosely controlled, long-lived companies were connected by a strong, shared culture. Everyone in the company understood the company’s values. These companies tended to promote from within in order to keep that culture strong. Cities also share this common identity: think of the difference between a New Yorker and a Los Angelino, or a Parisian, for example. At the Dachis Group we like to call this common culture hivemind . Active listening: Long-lived companies had their eyes and ears focused on the world around them and were constantly seeking opportunities. Because of their decentralized nature and strong shared culture, it was easier for them to spot opportunities in the changing world and act, proactively and decisively, to capitalize on them. At Dachis we sometimes call this dynamic signal (watching and listening) and metafilter (information leading to decisive action).
DESIGN BY CONNECTION And today, thanks to social technologies, we finally have the tools to manage companies like the complex organisms they are. Social Business Design is design for companies that are made out of people. It’s design for complexity, for productivity, and for longevity. It’s not design by division but design by connection. To design the connected company we must focus on the company as a complex ecosystem, a set of connections and potential connections, a decentralized organism that has eyes and ears everywhere that people touch the company, whether they are employees, partners, customers or suppliers.
Social Business Design is a new discipline, but some basic rules are already emerging.
These emerging rules have less in common with traditional business design, and more in common with urban design and city planning. It’s not about design for control so much as design for emergence. You can’t control a complex system, but you can manage its growth, and there are a lot of things you can do that will position it for success. Here are a few of those emerging practices that signal excellence in design by connection:
Start small. Urban designers might look at maps or aerial views as they make their plans, but the life of a city happens at street level. As you initiate social programs, think of them as if you are designing a city street. A successful street is filled with people. The last thing you want is a whole bunch of large, urban areas with no people in them. In a city, big, open, empty spaces feel unsafe and unloved. So start small. The smaller the space is initially, the faster it will fill up with people. A good way to start is with an organization-wide project or initiative that requires participation from a number of people across the company. This gives you a cross-section of ideas and perspectives to look at as you plan the next stage.
Spaces need owners. Again, think of the city street: every business or building has an owner. The sidewalks have owners – typically every business at street level “polices” their stretch of sidewalk. And even the street has owners – the street sweeper, the cop on the beat. In the same way, make sure that every online space you create has someone positioned to take care of it, to keep it safe and clean.
Every person needs a place. In the same way that public spaces need caretakers, every person needs a place to live; somewhere they can put their stuff. As you build your social business, make sure that every single person has a place where they can put, and see, their stuff: their projects, the links they want to get back to, the documents they have created, their role, qualifications, expertise and so on.
Jumping-off points. A good city street offers opportunities that are unanticipated but serendipitous. The promising side-street. The sound of music coming through an open door. As you design for connection, think about how you might create those unexpected, but delightful, surprises. Every time someone visits an online space, there’s a chance to offer them something new.
Watch, listen, adjust and adapt. Design by connection is not a top-down activity so much as bottom-up. Complex systems just don’t work that way. In a complex system, you need to pay attention to small things and make little adjustments along the way. Think about how city streets evolve: one small step at a time. One retailer moves to a larger space; another goes out of business. One old building is torn down and replaced; another is rehabbed and turned into lofts. Pay attention to the culture, and watch how people react to the tools you provide. Are they using something in a different way than you expected? Find out why and see if you can enhance that. And what are they ignoring? If they’re not using something you expected them to use, go talk to them and see if you can figure out the reason.
The typical company has a very short life, from 15 to 50 years. But cities – and some companies – live much longer lifespans: from hundreds to thousands of years. Wouldn’t you like that for your company? I know I would. There is a lot of things I can’t get into today because there just isn’t time. But I’m working on a book on the subject with my friend and co-author Thomas Vander Wal. Since there’s been a lot of interest in this subject I have started an email discussion group, and if you give me your business card I will be happy to send you an invitation to the group. here . If you have thoughts I would love to hear them. I think we have time for questions?
4. From McKinsey study, published in Creative Destruction by Richard Foster and Sarah Kaplan
6. CYBEA Journal
7. Federal Reserve Bank of Philadelphia
12. Quoted by Stephen B. Johnson in Emergence: The Connected Lives of Ants, Brains, Cities and Software