Economies of scale and scope (and variety, though we won’t go there today) are both types of learning.
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?
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.
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.
by Venkat, Ribbonfarm | Read more:
