In the referenced article, Langlois and Garzarelli make a very important point, after having distinguished modular systems (where parts can be developed independently of the whole), from integral systems. In some cases, a part is so tightly integrated into the whole, that it cannot be changed independently.
The thesis is therefore, that the open model of development, based on modularity, is not good in cases where innovation needs to happen ‘integrally’ or ‘wholistically’.
Here’s the argument.
Langlois and Garzarelli:
“A modular system is good at modular or autonomous innovation, that is, innovation affecting the hidden design parameters of a given modularization but not affecting the visible design rules. But a modular system is bad at systemic innovation, that is, innovation that requires simultaneous change in the hidden design parameters and the visible design rules – simultaneous change in the parts and in the way the parts are hooked together.
One might also add, however, that sometimes a modular system can improve in performance even faster than a fine-tuned system. To the extent that such a system benefits from “collective intelligence” and rapid-trial-and-error learning, the improvement in the parts can dominate any benefits from fine-tuning. Personal computers are again a case in point. PCs have come to outperform first mainframes, then minicomputers, then RISC workstations, all of which, in their day, made their money as fine-tuned non-modular systems (at least relative to PCs). Again, the extent to which modular innovation can outperform fine-tuning may depend on the degree of inherent integrality in the system.
The benefits of an integral system in systemic change are related to the benefits of fine-tuning to which Christensen points. Fine-tuning is after all systemic change to improve performance. Thus integral systems may have advantages not only when users demand high performance in a technical sense but also when they need performance in the form of change and adaptability. This latter may also be a function of how quickly the user needs the system to perform; the front-end costs of a modular system may take the form of time costs – the output forgone while waiting for the modularization to crystallize or the visible design rules to get worked out. If a modularization is already in place, of course, the system can adapt and respond quickly by simply plugging in new modules to suit user needs. But if there is not yet a modularization, or if the user needs a level of performance greater than can be achieved even with the best possible assortment of available modules, then an integral system may do better.
(The terms autonomous and systemic are from Teece (1986). There is a third possibility, what Henderson and Clark (1990) call architectural innovation. Here the modules remain intact, but innovation takes place in the way the modules are hooked together. (For a paradigmatic example of this kind of innovation, visit Legoland.) The possibility of architectural innovation underlies the benefits of economies of substitution discussed earlier.)
In terms of our earlier distinction between the corporate model and the spontaneous or voluntary model, the need for performance and rapid adaptability would tend to militate in the direction of the corporate (Langlois 1988). But this does not mean that unsatisfied needs for performance and rapid systemic adaptation therefore call for central planning on a Soviet scale. In Christiansen’s account, unmet performance needs do always call for an integrated corporate structure. But the network theorist Duncan Watts (2004) reminds us that a decentralized structure, with its ability to utilize “collective intelligence,” can sometimes be marshaled even in the service of an emergency response. His example is the way the Toyota Corporation responded in 1997 when the sole plant supplying a crucial component burned to the ground, threatening to bring production of an entire model to a halt. Rather than attempting to create centrally a new plant to make the component, Toyota instead tapped the knowledge and capabilities of a large number of its divisions and outside supplier with the intent of generating rapid trial-and-error learning.”