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I’ve been trying hard over the last several weeks to wrestle a very tough idea to the ground: economies of variety. Yes, there is such a thing, and I don’t mean either the Starbucks menu of mass-customized combinatorial choices or some charming favela economy that has variety, but not economies of variety. Economies of variety are related to, but not the same thing as, the idea of superlinearity.

I’ll leave that subject for another post, when I beat the thing into some sort of submission, but the process of wrangling the idea has led me to a much deeper appreciation of the two existing economies — of scale and scope respectively — that characterized the industrial age. So this is a sort of prequel post. If a well-posed notion of “economies of variety” can be constructed, it will need to be really solidly built in order to punch in the same weight class as these two mature ideas. A business that achieves all three will be close to unbeatable by competing businesses that only manage one or two out of three.

Amazon is the first company that is getting dangerously close to 3/3. That should give you a hint about where I am going with the economies of variety idea. But let’s figure out scale and scope first.

If you’re like me, you probably encountered the idea of economies of scale in a freshman economics course. You probably never encountered the economies of scope idea at all (I’ll explain why you didn’t in a bit). You were probably introduced to the former in book-keeping terms: the idea that large production runs allow you to amortize fixed costs over more instances, driving down unit costs.

This is going to sound really obvious and dumb once I explain why, but this is not the right definition of economies of scale. This is the effect of successfully achieving economies of scale.

Economies of scale and scope (and variety, though we won’t go there today) are both types of learning.

Economies of scale are the advantages that can result when repeatable processes are used to deliver large volumes of identical products or service instances. Scaling relies on interchangeable parts either in the product itself, or in the delivery mechanisms, in the case of intangible services.

Economies of scope are the advantages that can result when similar processes are used to deliver a set of distinct products or services.

As a first approximation, you could say that economies of scale result from learning the engineering, while economies of scope result from learning the marketing. The first is primarily a one-front war between a business and nature. The second is primarily a two-front war where a business fights nature on one front, and market incumbents on another. As an aside, both kinds of learning are war-time learning: they proceed in an environment where failure equals death for the firm.

More on this after we look at the details of the two learning processes.

Learning in Scaling

The key to economies of scale is process learning of the sort that the consulting firm BCG codified with its experience curves in the 1970s. Amortization of fixed costs across many instances is merely what makes the learning worthwhile, but the work of scaling lies in the learning. Getting to repeatability in an engineered process takes conscious and deliberate effort.

You can also think of scaling as the process of proving a steady-state financial hypothesis in a specific case. In other words, the amortization argument, which does not include the learning costs in getting to the design scale, is a hypothesis that you must set out to prove by construction. The equation is only true once the learning is over (and as we’ll see, it is therefore a “peacetime” model of business that applies during periods of detente between periods of business-war). The unknown learning costs are what might kill you. And usually they do, which is why pioneers rarely own markets that they create.

The ingenuity involved, I am now convinced, actually exceeds the ingenuity involved in coming up with the unscaled idea in the first place. Why do I say this? Because people who come up with great product ideas are a dime a dozen. People who figure out how to successfully scale an idea are far rarer. We tend to lionize “inventors” but the real heroes are probably the “scalers.”

Why exactly is there learning involved in scaling at all?

The law of large numbers: the more you scale, the more you expose your operations to rare phenomena that are expensive to deal with. Scaling is about dealing efficiently with events that occur with a predictable frequency. Hard disk failures are rare catastrophes for individuals. They are an operating condition for data centers. Staircase effects: Capacity increases follow a staircase curve, but demand changes smoothly. You can only buy one airplane at a time. You cannot buy half an airplane for an airline. So you’re constantly undershooting or overshooting your capacity requirements while scaling. A particularly severe (but non-commercial) example is scaling an ordinary navy into a blue water navy with aircraft carriers, a challenge China is currently taking on. You generally need 3 carrier groups to have one in deployment at all times, and it takes a couple of decades (or a very active war) to climb the three-step staircase. Loss windups: When you are running a small bakery, if your oven is malfunctioning, you might lose one batch of cookies before shutting down to fix the problem. In a scaled operation, due to the larger distances between loci of problem creation and discovery, and the sheer speed of operations, huge losses can pile up before you intervene. Soft failure cases are predictable inventory problems. Hard failures? Think about events like the Firestone tire recall and various instances of contaminated food products being recalled. Accounting illegibility: Chances are, while scaling, you are slashing prices as fast as you can to grab the largest possible share of a new market. Such phases are called “land grabs” for a reason. Margins may seem strong but that’s only because the accounting simply cannot model and track a growing and learning operation accurately. Effective margins, after factoring in risks and crisis response costs, may be much lower than you think. Contributing to this is poor financial governance during scaling phases leading to a lot of waste, both justified (getting a major new order by any means necessary) and unjustified (people taking advantage of the chaos to indulge in profiteering) Process Design Evolution: There is an enormous amount of iterative process redesign involved in successful scaling. As quickly as you discover rare conditions, unexpected operational risks and other blindside phenomena, you need to bake the knowledge into the process. This process must not only proceed very fast, but it has to be very elegant. A bad process adaptation to handle a contingency (think TSA security procedures following 9/11) can end up being both costly and ineffective, and add entropy to the process without increasing its capability. Human Factor Variances: If people are involved, such as in scaling a sales operation, you have to very suddenly turn tacit, creative knowledge in the heads of the pioneers into explicit knowledge that can very cheaply be imparted to the cheapest available brains capable of handling it. In the process you may discover that your tacit knowledge is simply too expensive to codify and scale. This training failure can kill your business. Gravitational Effects: When you scale, you start to influence and shape your environment rather than merely reacting to it. When you launch a small satellite into space, you can ignore its effect on the earth in orbit calculations. When you are talking about the Moon, you get a proper 2-body problem. One manifestation of gravitational effects is litigation. Get to a sufficient size, especially in America, and you are suddenly worth suing. Another interesting gravitational effect is late-stage growth investment flooding in: dumb money with growth expectations that might be unreasonable/greedy enough to kill the company. Lucy Effects: Think about the classic scene in I Love Lucy where Lucy is working on a chocolate assembly line that moves faster and faster. When she fails to keep up, she has to start stuffing her mouth with chocolate. As with fluid flows going from laminar to turbulent, process flows too, experience phase transitions. To keep them efficient (“laminar”) with increasing velocity, you may need to reinvent (or refactor) the process entirely. These hidden reinventions can sometimes be harder than the original inventions.

When you step back and think about all this, you realize that scaling is basically the equivalent of deliberate practice (the 10,000 hours idea) for companies. The COO is typically the unsung hero leading this scaling process (and often is promoted to CEO during the transition to a scaling phase).

By leaving the unpredictable learning costs out of the equation, Economics 101 professors tend to make scaling sound like a matter of so it shall be written, so it shall be done pronouncement. In practice, the outcome of scaling efforts is anything but certain, even for a wildly successful product. If you can find the right sort of talented people to drive the process the first time you attempt it, you will find that you can improve your process capabilities just slightly faster than you are increasing production volumes. Enough to deliver something approximating the cost lowering promised by the micro-economic calculations. The equation is only true if your learning costs come in under the hidden, assumed threshold. Otherwise you win a Pyrrhic victory, or get killed along the way.

If you succeed with one product, you’ve achieved something far more precious than that one product: an organization that has learned-how-to-learn the scaling challenge for a class of processes. The next time around, you can use your past (i.e., “experience curves” — now you know why they are called that) to learn faster, better.

Learning in Scoping

The key to economies of scope is transaction-cost learning. Walmart for example, can take advantage of the fact that whether you are buying a can of beans or a new toaster, the process of finding it in a large store, putting it into a shopping cart, and checking out, is the same. On the other end, buying a large catalog of things in bulk from China involves similar procurement process skills.

Economies of scope therefore revolve around questions like:

What is the right level of diversification for our product line? What new market needs can we address with products/services we already make? Is the industry naturally horizontally or vertically integrated? Can we force it to flip orientation? What is the right way to bundle and price related products? Should we cover the entire range of price/performance possibilities for this product category? Are there customers with full-scope needs who would actually want one-stop shopping? Which customers are we under-serving? Which ones are we over-serving? What should we build? What should we buy? What knowledge should we learn and store? What knowledge should we rent?

Each of these questions is driven by a market structure concern that can be phrased as “should this be traded via the marketplace or managed through internal cost accounting?” The answers have some engineering implications, but they are marketing questions first. They help determine the operational shape, size and boundaries of the firm relative to its financial size.

The last question in particular reveals a relationship with scaling. Scoping decisions lead to scaling commitments.

Scope learning proceeds much more slowly than scale learning. While every instance of a production or service process is a learning data point, scope changes are much rarer, so the data accumulates more slowly. Events like mergers and acquisitions, changing suppliers, trying out a new outsourcing pattern, building out a new channel, all drive scope learning. The most important kind of learning event for scope learning is fighting off a new competitor and surviving.

Unlike scale learning, which can be reasonably be expected to plateau into an efficient state that will then deliver high-margin revenues for a period, scope learning may never plateau at all.

You may never get to a point where you can claim you have right-sized and right-shaped the business, but you have to keep trying. In fact, managing the ongoing scope-learning process is the essential activity in business strategy. If you ever think you’ve right-sized/right-shaped for the steady state, that’s when you are most vulnerable to attacks.

The Historical Scale/Scope Asymmetry

Economies of scope were only recognized and studied in the 1970s. By contrast, economies of scale have been recognized since at least Adam Smith in the 1770s. Why the two-century gap? And why the continued obscurity of scoping dynamics relative to scaling dynamics?

There are three basic reasons.

Pristine vs. Competitive Market Formation

First, the idea of economies of scale applies to pristine markets (the first successful company or companies in a new market, that is/are able to form on virgin territory). Economies of scope, by contrast, primarily exist in competitive markets: markets where incumbents exist and evolution is driven by the entrance and exit of a changing cast of players.

I am borrowing the terms pristine and competitive from political science, where they refer to two types of state formation. The result of this difference is that economies of scope emerged as a factor in the business landscape only after a few industrial sectors had matured enough to enter the competitive phase in their evolution.

Priorities in Mainstream Economics

Second economies of scope are fundamentally about transaction costs and the theory of firms — Coase stuff — and as such, is not part of mainstream economics education. If you’re like 80% of people who endured an Economics 101 course, chances are you only encountered Keynes. If you were lucky, you also encountered Friedman. If you were really lucky, you also encountered the Austrian school. The study of larger actors than individuals or small firms in economics isn’t exactly heterodox, but it is not exactly wildly popular either. Studying large state actors leads to developmental economics, comparative advantage and so forth (the original subject matter when the field was still called “Political Economy”). Political science education tends to pay some attention to this.

But larger firms with significant gravitational fields? Forget it. The subject falls right through the crack between management science and economics. You may have formed some loose ideas about how dynamics at the firm level work, through exposure to Joseph Schumpeter’s “creative destruction” phrase, but chances are, you have no real idea about how the process actually works, how firm size distributions shift and morph, and how an economic landscape gradually settles into a relatively mature market structure.

So those are the two abstract reasons economies of scope aren’t as well appreciated: the pristine/competitive distinction and the relative obscurity of firm-based economic thinking. The third reason is a person rather than an abstraction.

Michael Porter

It is rare for a single individual to have as much historical influence as Michael Porter has, but I believe — and this is going to be a controversial claim — that by providing a rudimentary zeroth-order model (a largely ahistorical and structuralist model) for analyzing economies of scope, Porter helped prematurely close a line of investigation that required far more attention than it has received. So the study of economies of scope stalled by the mid-eighties.

It is sort of unfair to blame him though. Forces beyond him conspired to basically shut down a fertile direction of inquiry, and turned his promising early models into a set of static truths, around which a variety of business cargo cults formed. More on Porter later.

Scale First or Scope First?

Scale and scope have a chicken-and-egg relationship. So it is worth investigating their interplay to some extent.

In a pristine market environment, you typically scale first, in whatever scaling direction you discover, and start discretionary scoping only when serious competition emerges. Initial scope in a pristine market is determined mostly by what you are forced to create in order to exist.

Sears had to develop and scale the catalog model to establish modern retailing.

Amazon, Google and other Web majors had to invent large-scale data-center technologies. They didn’t have a choice about whether or not to put those activities in scope because there wasn’t anyone else to do it.

In a competitive market environment by contrast, you always scope first, and the task is very non-trivial. Your initial scoping is in fact your entry strategy, and it is structurally similar to an exploit in hacking computer systems.

Some of the elements of the initial scoping are widely recognized. You have to think about whether to develop your own data-center technology or use Amazon services, a choice Amazon itself did not have to make. A century ago, product makers had to decide whether to sell through the new supermarkets and department stores, or run their own little stores.

Other elements of initial scoping are much more obscure, and only recognized in hindsight. The successes of Dropbox and the iPad reflect the workings of scoping decisions that were very obscure before the fact.

If you manage to create an initial scoping exploit and break through barriers of entry created by incumbents, you can then scale in a very selective way. Indeed, you must scale to survive. If you are not scaling along any dimension (alone, or along with an aggregate of small players that adopt the same strategy, such as bloggers versus old media), you aren’t actually playing in the market. The time pressure exists for the same reason as it does in hacking: you have a limited period of time before the intrusion is detected and lockdown processes kick in. You have to steal what you can and hope to get away with enough to earn a bargaining position.

Finding an initial scope that provides access to a vector along which to scale, that incumbents have failed to recognize, or have cooperatively ignored, is the definition of breaking into an existing market during the competitive market formation phase.

Once you do, you’ve caught a tiger by the tail. You have to scale as fast and as effectively as you can, usually rescoping as smoothly and quickly as possible along the way, shaping the clay as fast as dump it onto the table. Speed is of the essence here, because it is your only hope of growing to a sufficient size, and accumulating enough experience-curve learning before the incumbents react and get ready to contest your position.

This idea is often misunderstood. Iterating fast before you find a scoping exploit to break into a market is useful, but not essential. If you have a day job, or other means to stay in bootstrapping mode for a long time, you can go as slowly as you like. But once you find a scoping exploit, the clock starts ticking immediately. That’s when operating at a much faster tempo than the opponent helps. The more you can get done while the defenses are down, the better positioned you will be when hostilities begin in earnest.

How big do you need to grow in your limited window of opportunity before the incumbents launch a credible counter-attack? Our list from earlier provides the answer: you need to grow to a gravitationally significant size. Big enough to shape and warp the environment around you, because ultimately, it is the environment (smaller economic actors who, in aggregate, form the crowd that determines destinies) that decides whether you get to live.

To get the environment to keep you alive, you need to be big enough to influence thinking in a specific way: you must appear to be a part of their landscape rather than a part of the mobile population of small actors navigating the landscape.

In the eyes of smaller actors, you must transform from being a peer to being part of the terrain, too big to be wiped out in terrain shifts.

Right-Sizing, Right-Shaping

Since most industrial era sectors (defined as those that do not possess any economies of variety) are now mature, there are very few pristine markets. Almost all markets are competitive. This means, the lifecycle of a business is as follows:

Becoming a Contender: An initial scoping exploit that we typically call the entry strategy. Success in initial scoping leads to the discovery of an unknown or dormant scaling vector. This is almost entirely a marketing challenge.

An initial scoping exploit that we typically call the entry strategy. Success in initial scoping leads to the discovery of an unknown or dormant scaling vector. This is almost entirely a marketing challenge. Earning a Title Shot : A rapid scaling phase to get to gravitational effects scale before the incumbents can react, accompanied by rapid adjustments in scoping, to get to a “fighting shape and size.” This is not the final right-size/right-scale personality of the business, but one that is capable of winning the fight that has been set in motion. Here tempo is everything. You have to move as fast as you can.

: A rapid scaling phase to get to gravitational effects scale before the incumbents can react, accompanied by rapid adjustments in scoping, to get to a “fighting shape and size.” This is not the final right-size/right-scale personality of the business, but one that is capable of winning the fight that has been set in motion. Here tempo is everything. You have to move as fast as you can. Title Fight: A wartime phase, when the incumbents finally recover from the effects of surprise and launch a counterattack to preserve the existing market structure (usually marked by a technological detente, which is why the newcomer can suddenly scale before the incumbents realize what is going on). This sets up the title fight: the newcomer through the undetected exploit and scaling phase, has set up a positional advantage, despite being smaller (generally), and is set for the tougher melee phase, after the surprise has been milked (see my post positioning moves vs. melee moves for more on this). This is a two-front war, since the newcomer must continue to learn along the scaling vector to preserve the position won.

A wartime phase, when the incumbents finally recover from the effects of surprise and launch a counterattack to preserve the existing market structure (usually marked by a technological detente, which is why the newcomer can suddenly scale before the incumbents realize what is going on). This sets up the title fight: the newcomer through the undetected exploit and scaling phase, has set up a positional advantage, despite being smaller (generally), and is set for the tougher melee phase, after the surprise has been milked (see my post positioning moves vs. melee moves for more on this). This is a two-front war, since the newcomer must continue to learn along the scaling vector to preserve the position won. A New Champion (Possibly): The challenger either wins the title fight or loses. In either case, there is a new market structure, and a new order. And as I argued in my previous realtechnik post (linked above), some incumbents may exit. Here, as in guerrilla warfare, the newcomer wins if he survives the fight with enough left over to stay in the market. Beating the incumbent is not necessary.

The challenger either wins the title fight or loses. In either case, there is a new market structure, and a new order. And as I argued in my previous realtechnik post (linked above), some incumbents may exit. Here, as in guerrilla warfare, the newcomer wins if he survives the fight with enough left over to stay in the market. Beating the incumbent is not necessary. Right-Sizing, Right-Shaping: The title fight leads to a new detente, and a period during which the newly enthroned (or admitted) challenger has a chance to grow and mature in relative peace to the right size and shape. Incumbents have accepted the presence of the newcomer, resign themselves to making enough room for it, and retreat to lick their wounds in peace as well.

The title fight leads to a new detente, and a period during which the newly enthroned (or admitted) challenger has a chance to grow and mature in relative peace to the right size and shape. Incumbents have accepted the presence of the newcomer, resign themselves to making enough room for it, and retreat to lick their wounds in peace as well. A New Contender Emerges: The old contender is now one of the incumbents. Like everyone else, it is caught napping when another incumbent discovers a new scaling vector, or reopens hostilities along a detente frontier that was declared closed in the last war.

A key characteristic of this lifecycle is this: businesses almost never die of old age; they die of fatal war wounds in periodic wars.

Drucker, Porter and Grabowski Explained

The distinction between scale and scope maps very clearly to the distinction between engineering and marketing. In the days before computer-aided engineering tools, you could make a distinction between engineering and innovation aspects of technology. Today, they coincide.

Economies of scale and scope help explain two familiar, and one not-so-familiar idea in business.

The first is Drucker’s idea that there are only two basic functions in a business: innovation and marketing. This can be restated by substituting the word engineering for innovation today. The bulk of engineering in this sense actually revolves around scaling, not invention. We can therefore restate Drucker as follows: there are two, and only two economies in business: economies of scale and economies of scope.

The second is Porter’s famous five forces model of competition (and various associated constructs such as value-chain analysis). Scale and scope explain both its strengths (it gets forces and masses and “pieces” in play right, as well as the basic rules of engagement in competition), and its weaknesses (it either gets the evolutionary arguments and explanations for the dynamics of the warfare mostly wrong, or ignores them). Porter’s models are somewhat helpful in explaining relatively pristine markets where the cycle of war and peace has not yet heated up and positioning is an occasional, rather than continuous exercise. Once the cycle accelerates beyond a point, the models become too brittle. You need a more dynamic model that accounts for the fact that economies of scope are a learning process, and therefore one driven by human factors rather than structural ones.

The third idea — this is the obscure one — is the Grabowski Ratio, which has strongly influenced my thinking. If you’ve been reading this blog for a while, you’ve encountered the idea, but if not, it is probably new to you. The ratio captures the idea that the success or failure of a new product or service depends on the ratio of marketing spend to engineering spend. The ideal ratio (empirically discovered) seems to be around 1. The scale/scope model helps explain why: in a pristine market environment, scope is a forced variable that is relatively simple to manage (with little scope learning involved), while scaling must be actively managed through learning. Engineering spend can outpace marketing spend in pristine markets without hurting the business. But once a competitive market forms, initial scoping is non-trivial (since it is basically “exploit design”), and once the game begins, both scaling and scoping must be fluid and agile. So both activities will require comparable amounts of funding.

If all this is too complex, just keep a simple idea in mind: scaling is engineering, scoping is marketing, both are types of learning, you have to do both to survive in competitive markets, which are the only kind around today.

The Inevitable Biological Metaphor

Scale and scope are two dimensions of growth. In the process of biological ontogeny, a single fertilized egg grows to a full organism along both dimensions.

Scale is a matter of replicating the same kind of cell. The ecological niche of an organism determines its overall mode of survival, which in turn determines how big it needs to be, and how many cells it needs.

Scope is the fixed variety that emerges in the collection of cells as it expands to cover the structural needs corresponding to the functional and behavioral range of the grown organisms. On Galapagos, cormorants lost their wings, for example, a case of scope evolution.

Both scale and scope in ontogeny can be regarded as the effects of learning that actually happens at the genetic level, but are encoded into the phenotype and tested through survival of individuals. So skin cells represent learning about how to optimally design the organism’s physical boundary. The size of a whale represents learning about how big you should be if you feed on plankton and live in a certain gravitational environment.

And so organisms grow in size and scope until they become complete: capable of autonomous survival in their environment (the “autonomy” is of course defined relative to the sociability of the species). Older species that are the first to colonize an environment primarily represent engineering learning about that environment. Species that enter environments already colonized by competitors, must learn both the physical and biological environments around them.

Fixed scale-and-scope designs are only enough for survival in a single ecological niche during a given, relatively stable time epoch with a stable detente among species in that environment. Throw domestic cats onto isolated Pacific islands and all hell can break loose. Put Japanese culinary preferences together with industrial whaling technologies and size is no defense for a whale. Put traditional Chinese medicine together with guns and poor people living near national parks, and tigers, rhinos and elephants turn into pills and potions.

More robust sorts of survivability require learning processes that have greater capacity and speed and crucially, are more open-ended, and don’t stop during detente periods. The two extremes in biology are bacteria and primates. Bacteria learn faster by keeping their scale and scope very limited and speeding up the genetic process. The represent variety in the favela sense that I referred to in the beginning. Primates also learn faster by keeping physical scale and scope relatively fixed, and moving learning to more programmable (and more sociable) brains. More agile firmware versus more agile software. They represent the true economies of variety I am trying to understand right now.

In between, you have animals with the bulk of their learning in hardware, through high scale and scope — a lot of fixed and embodied intelligence — but sharply circumscribed survivability. Small brains, slow genetic mutation rates, complicated, specialized and big bodies. The problem here isn’t that hardware intelligence isn’t adaptable, but it is only adaptable within the narrow range of possibilities that are explored before the ecosystem niche stabilized into a long detente. As a first approximation, we can say that hardware learning is bounded in both total capacity and closed in the sense that it can only learn certain fixed kinds of things.

Economies of both scale and scope learning represent hardware learning varieties that must stop once the organization reaches a certain condition we can call “adulthood.” Species that cannot do either firmware or software learning don’t survive very well when there is a lot of environmental variability, either through movement into new environments, or changes in their existing environments, or incursion of new species. Firmware and software learning in biology are the (mixed) metaphors for economies of variety that I am exploring.

If you want to get a head start, try the paper Environmental Hypotheses of Hominin Evolution by Richard Potts, Yearbook of Physical Anthropology 41:93–136 (1998). Google should help you find you a bootleg PDF.

The Boyd Connection

This post has been brewing for a while, but what finally made the ideas come together was discussions at the Boyd and Beyond conference at Quantico (the headquarters of the US Marine Corps), which I just attended (and spoke at — one of the most fun talks I’ve done in recent memory).

For readers familiar with Boydian/OODA thinking, many of the elements of the broad argument in this post can be restated in terms of the basic ideas in Boydian strategic thinking, in particular Fingerspitzengefühl and Schwerpunkt. The entire model of how to operate in competitive (as opposed to pristine) markets can be summarized as “get inside the tempo of the market and then ratchet up the tempo to compound your gains in the window of time before counter-reactions form”

Pristine market creation can be equivalently thought of as “getting inside the tempo of nature.” I talked about these ideas at the conference, but since it was a no-slides conference, I’ll try to reconstruct the talk in the form of slides and post shortly (probably on the Tempo blog, not here).

Within the Boydian world, I want to acknowledge Chet Richards and Ho-Sheng Hsiao. Both provided extremely helpful pieces of the arguments I needed for this post. I also plan to blame them if these ideas don’t work out.

Economies of Variety: Preview

I am still working out the economies of variety idea, but here is a quick preview.

If I am right that there is such a thing, Drucker’s idea that there are “two, and only two” essential functions in business will have to be revised to “three, and only three,” the third one being a business function (like engineering and marketing) that maps directly to the type of learning involved in economies of variety.

Amazon’s recommendation learning models are an early example.

Whatever this third function, it will be heavily dependent on technology: machine learning and data technology in particular. But those will be necessary rather than sufficient elements (just as interchangeable-parts manufacturing is necessary, but not sufficient, for economies of scale to exist). Just as the prize, for winning economies of scale is a highly favorable amortization-of-fixed-costs equation, and the prize for winning economies of scope is a highly favorable pattern of transaction costs, the prize for winning economies of variety will be a highly efficient learning capability that will result in a sort of “dynamic Coasean” firm that will have the equivalent of a primate brain.

Grabowski’s model will need to be turned into a three-way ratio. Instead of M/E, we’ll have M:E:?

The Boydian-dynamic version of Porter’s models will need to be evolved to reflect a potential three-front battlefield in business (and war, for that matter). The as-yet-unlabeled ? third function of business will provide a capacity for what Boyd called “fast transients” (today, some businesses exhibit this capacity occasionally, in specific moves, but no business has a capacity for sustained fast transients).

One of the things holding me back from finishing the model properly is the lack of sufficient examples to think about. So if you know of a company that is navigating a three-way M:E:? challenge, and wants to help me work this stuff out, send them this post. I’ll offer some sort of discount on my consulting services in exchange for the opportunity to learn about their business and analyze it.

Parts of this post were also strongly influenced by my ongoing research for the Leading Edge Forum (a division of CSC) on the “Future of Data.” Though I didn’t talk about the role of data in this model, the catchphrase of the Big Data movement, “Volume, Velocity, Variety”, should give you an idea about why there is a connection here.