While indeed there are economies of scale, there are countervailing costs of complexity – the more product families produced in a plant, the higher the overhead burden rates.
That sentence comes from page 92 of “The Innovator’s Prescription: A disruptive solution for health care“, co-authored by Clayton Christensen, Jerome Grossman, and Jason Hwang. Like all the books authored (or co-authored) by Christensen, the book is full of implications for fields outside the particularly industry being discussed.
In the case of this book, the subject matter is critically important in its own right: how can we find ways to allow technological breakthroughs to reduce the spiralling costs of healthcare?
In the book, the authors brilliantly extend and apply Christensen’s well-known ideas on disruptive change to the field of healthcare. But the book should be recommended reading for anyone interested in either strategy or operational effectiveness in any hi-tech industry. (It’s also recommended reading for anyone interested in the future of medicine – which probably includes all of us, since most of us can anticipate spending increasing amounts of time in hospitals or doctor’s surgeries as we become older.)
I’m still less than half way through reading this book, but the section I’ve just read seems to speak loudly to issues in the mobile industry, as well as to the healthcare industry.
It describes a manufacturing plant which was struggling with overhead costs. At this plant, 6.2 dollars were spent in overhead expenses for every dollar spend on direct labour:
These overhead costs included not just utilities and depreciation, but the costs of scheduling, expediting, quality control, repair and rework, scrap maintenance, materials handling, accounting, computer systems, and so on. Overhead comprised all costs that were not directly spent in making products.
The quality of products made at that plant was also causing concern:
About 15 percent of all overhead costs were created by the need to repair and rework products that failed in the field, or had been discovered by inspectors as faulty before shipment.
However, it didn’t appear to the manager that any money was being wasted:
The plant hadn’t been painted inside or out in 20 years. The landscaping was now overrun by weeds. The receptionist in the bare-bones lobby had been replaced long ago with a paper directory and a phone. The manager had no secretarial assistance, and her gray World War II vintage steel desk was dented by a kick from some frustrated predecessor.
Nevertheless, this particular plant had considerably higher overhead burden rates than the other plants from the same company. What was the difference?
The difference was in the complexity. This particular plant was set up to cope with large numbers of different product designs, whereas the other plants (which had been created later) had been able to optimise for particular design families.
The original plant essentially had the value proposition,
We’ll make any product that anyone designs
In contrast, the newer plants had the following kind of value proposition:
If you need a product that can be made through one of these two sequences of operations and activities, we’ll do it for you at the lowest possible cost and the highest possible quality.
Further analysis, across a number of different plants, reached the following results:
Each time the scale of a plant doubled, holding the degree of pathway complexity constant, the overhead rate could be expected to fall by 15 percent. So, for example, a plant that made two families and generated $40 million in sales would be expected to have an overhead burden ratio of about 2.85, while the burden rate for a plant making two families with $80 million in sales would be 15% lower (2.85 x 0.85 = 2.42). But every time the number of families produced in a plant of a given scale doubled, the overhead burden rate soared 27 percent. So if a two-pathway, $40 million plant accepted products that required two additional pathways, but that did not increase its sales volume, its overhead burden rate would increase by 2.85 x 1.27, to 3.62…
This is just one aspect of a long and fascinating analysis. Modern day general purpose hospitals support huge numbers of different patient care pathways, so high overhead rates are inevitable. The solution is to allow the formation of separate specialist units, where practitioners can then focus on iteratively optimising particular lines of healthcare. We can already see this in firms that specialise in laser eye surgery, in hernia treatment, and so on. Without these new units separating and removing some of the complexity of the original unit, it becomes harder and harder for innovation to take place. The innovation becomes stifled under conflicting business models. (I’m simplifying the argument here: please take a look at the book for the full picture.)
In short: reducing overhead costs isn’t just a matter of “eliminating obvious inefficiencies, spending less time on paperwork, etc”. It often requires initially painful structural changes, in which overly complex multi-function units are simplified by the removal and separation of business lines and product pathways. Only with the new, simplified set up – often involving new companies, and sometimes involving “creative destruction” – can disruptive innovations flourish.
Rising organisational complexity impacts the mobile industry too. I’ve written about this before. For example, in May last year I wrote an article “Platform strategy failure modes“:
The first failure mode is when a device manufacturer fails to have a strategy towards mobile software platforms. In this case, the adage holds true that a failure to strategise is a strategy to fail. A device manufacturer that simply “follows the wind” – picking platform P1 for device D1 because customer C1 expressed a preference for P1, picking platform P2 for device D2 because customer C2 expressed a preference for P2, etc – is going to find that the effort of interacting successfully with all these different platforms far exceeds their expectations. Mobile software platforms require substantial investment from manufacturers, before the manufacturer can reap commercial rewards from these platforms. (Getting a device ready to demo is one thing. That can be relatively easy. Getting a device approved to ship onto real networks – a device that is sufficiently differentiated to stand out from a crowd of lookalike devices – can take a lot longer.)
The second failure mode is similar to the first one. It’s when a device manufacturer spreads itself too thinly across multiple platforms. In the previous case, the manufacturer ended up working with multiple platforms, without consciously planning that outcome. In this case, the manufacturer knows what they are doing. They reason to themselves as follows:
- We are a highly competent company;
- We can manage to work with (say) three significant mobile software platforms;
- Other companies couldn’t cope with this diversification, but we are different.
But the outcome is the same as the previous case, even though different thinking gets the manufacturer into that predicament. The root failure is, again, a failure to appreciate the scale and complexity of mobile software platforms. These platforms can deliver tremendous value, but require significant ongoing skill and investment to yield that kind of result.
The third failure mode is when a manufacturer seeks re-use across several different mobile software platforms. The idea is that components (whether at the application or system level) are developed in a platform-agnostic way, so they can fit into each platform equally well.
To be clear, this is a fine goal. Done right, there are big dividends. But my observation is that this strategy is hard to get right. The strategy typically involves some kind of additional “platform independent layer”, that isolates the software in the component from the particular programming interfaces of the underlying platform. However, this additional layer often introduces its own complications…
Seeking clever economies of scale is commendable. But there often comes time when growing scale is bedevilled by growing complexity. It’s as mentioned at the beginning of this article:
While indeed there are economies of scale, there are countervailing costs of complexity – the more product families produced in a plant, the higher the overhead burden rates.
Even more than a drive to scale, companies in the mobile space need a drive towards simplicity. That means organisational simplicity as well as product simplicity.
As I stated in my article “Simplicity, simplicity, simplicity“:
The inherent complexity of present-day smartphones risks all kinds of bad outcomes:
- Smartphone device creation projects may become time-consuming and delay-prone, and the smartphones themselves may compromise on quality in order to try to hit a fast-receding market window;
- Smartphone application development may become difficult, as developers need to juggle different programming interfaces and optimisation methods;
- Smartphone users may fail to find the functionality they believe is contained (somewhere!) within their handset, and having found that functionality, they may struggle to learn how to use it.
In short, smartphone system complexity risks impacting manufacturability, developability, and usability. The number one issue for the mobile industry, arguably, is to constantly find better ways to tame this complexity.
The companies that are successfully addressing the complexity issue seem, on the whole, to be the ones on the rise in the mobile space.
Footnote: It’s a big claim, but it may well be true that of all the books on the subject of innovation in the last 20 years, Clayton’s Christensen’s writings are the most consistently important. The subtitle of his first book, “The innovator’s dilemma”, is a reminder why: “When new technologies cause great firms to fail“.
dw2 