By Michael Schrage
In industry after industry, in market after global market, the rise of virtual prototyping has led to a collapse in product development cycle times and radical reductions in costs. The numbers don't lie. The benefits are there. Unfortunately, the fundamental dynamics of the process have been wildly misunderstood. Contrary to the management mantras, speed and cost-reduction are not the ultimate destinies of digital design methodologies; they are just the beginning of the journey.
In fact, speed and cost-reduction are what CAD/CAE turn into mere commodities. Much as spreadsheet software commoditized the costs of financial modeling, CAD and CAE commoditize cycle-time compression. As these technologies slide down their cost curves, more organizations reap the benefits of faster development cycles. Con sequently, the critical management issue isn't about developing products ever faster at lower cost; it's about getting greater value from the time and money saved.
What happens to competitive advantage as cycle time differences between rival firms narrow and their offerings hit the market at comparable times? More important, what happens when product-development cycles go faster than their customers are ready, willing, or able to absorb? Speed for the sake of speed is as valueless as innovation for the sake of innovation. The challenge is to treat speed, cycle-time compression, and their concomitant savings less as ultimate ends and more as creative means.
The digital experience confirms that organizations have to rethink the role of speed in shaping their value propositions. They must constantly ask themselves hard questions about productivity trade-offs and then decide whether or not to reorganize around the answers.
Consider a simple "thought experiment" to illustrate these tensions and trade-offs. Imagine that a new CAD/CAE infrastructure lets a company double the number of development cycles its product teams can run. Under the old system, teams could perform 10 cycles within their 10-month development window. Now, for the same computational costs, they can run 20 full iterations. Think of those extra cycles as currency, such that each additional cycle can "purchase" either a product improvement, a cost-reduction, or a speed-up.
Now, just how should the team "invest" those 10 extra cycles? Should these innovation teams:
- Spend all 10 cycles on speed to come to market in half the time?
- Spend all cycles to develop a product that's 50% "better"?
- Spend all cycles to cut prices by 30%?
- Spend all cycles on some blend of speed, price, and quality?
- Spend a few cycles on an intriguing but risky enhancement?
- Spend a few cycles to test an alternate design approach?
- Spend all 10 to develop an entirely new product concept?
Obviously, there are no inherently right answers. Even worse, these questions are too simplistic. They disregard the menace of organizational conflict that managers confront when hard choices have to be made. Consider the politics as well as productivity: How will the team allocate its new cycles? Should Design get three; Manufacturing get three; Marketing get three; with the tenth held in reserve for emergencies? Or does the product manager "own" the cycles budget? At what point should key customers and suppliers be brought in to help spend those cycles?
The message for "product champions" who are betting on innovation is that productively spending new cycles may prove a greater management challenge than successfully investing new money. Granted, development cycles aren't quite as fungible as cash, but they are an enormously valuable resource. The ability to model, simulate, and prototype more options in less time ultimately becomes a different organizing principle for managing value creation.
Of course, one could unhappily arrive at a "Parkinson's Law of Prototyping" where, instead of "work expanding to fill the time available," endless iterations of prototypes and simulations soak up time like sponges while offering little but the dross of diminishing returns.
Is the 50th iteration of a prototype or simulation dramatically more valuable than the 35th? The 60th? The 110th? Or are the insights and information gleaned merely marginal? Do the design discussions fundamentally shift? Or do they simply become more refined? Do design assumptions harden? Or do they become more freewheeling and undisciplined as the cost of testing them contracts?
These questions are neither hypothetical nor simplistic. They are at the white-hot center of tomorrow's management decisions. Are we truly creating value? Or are we just futzing around?
These cycles are ultimately a form of "innovation capital." How the firm invests that capital reflects how it matches its own values with what the marketplace values. More cycles mean more capital, which in turn means more choices. The more choices you have, the more your values matter. The more your values matter, the surer you must be of why you hold them so dear-or why they must be discarded.
Author George Gilder, a keen observer of technology's productive charms, argues that an innovation can vaporize the economic assumptions a market has grown up with. "From time to time," he writes, "the structure of nations and economies goes through a technological wringer. A new invention radically reduces the price of a key factor of production and precipitates an industrial revolution. Before long, every competitive business in the economy must wring out the residue of the old costs and customs from all its products and practices."
Gilder's favorite example of the "technological wringer" is the integrated circuit; the microchip that reduced the price of electronic circuitry by a factor of over one million. That technology-driven economic transformation completely changed the silicon design paradigm. Electronics designers were forced to put all their traditional design methodologies through the technological wringer.
Gilder references computer industry guru Andrew Rap paport, who notes that "electronic designers now treat transistors as virtually free. In fact, on memory chips they cost some 400 millionths of a cent. To waste time or battery power or radio frequencies may be culpable acts, but to waste transistors is the essence of thrift. Today you use millions of them to slightly enhance your TV picture or to play a game of solitaire or to fax Doonesbury to Grandma. If you do not use transistors in your cars, your offices, your telephone systems, your design centers, your factories, your farm gear, or your missiles, you go out of business. If you don't waste transistors, your cost structure will cripple you. Your product will be either too expensive, too slow, too late, or too low in quality."
Rappaport's notion that "to waste transistors is the essence of thrift" begs the question: Where speed, design, innovation, and differentiation are essential to creating value and competitive ad vantage, could wasting CAD/CAE iterations be the essence of thrift? Consider another variation on Rappaport's theme: "If you don't waste development cycles your cost structure will cripple you. Your product will be either too expensive, too slow, too late, or too low in quality."
What "free" transistors are to electronic design, "free" digital models are to product, process, and service design. Ingeniously "wasting" prototypes and simulations becomes essential to risk management. Throwing CAD/CAE technology at design problems becomes vital to discovering errors and opportunities. Failure to wring new productivity from this embarrassment of virtual riches isn't just an embarrassment; it's a post-industrial tragedy of epic proportions.
To treat this population explosion of "free" models and innovation cycles as intriguing variants of the Information Rev olution is akin to describing humans as particularly nifty phenotypes of DNA: technically accurate but utterly misleading. As the unit of digital innovation evolves from the transistor to the computation to the model, organizational perceptions become paramount. Economics matter even more. The cultures of simulation become defined more by the qualities of their human interactions than by the quantities of their digital computations.
Listen to the technologies of digital media, virtual prototyping, and seamless simulations. They ask that you know what you want to model before you model it. They insist that you be able to articulate what you want to model in a way it can be modeled. They seductively whisper that if it's so cheap and easy to do yet another iteration, then just do it-again and again. Michael Schrage is co-director of the MIT Media Lab's eMarkets Initiative and a Merrill Lynch Forum Innovation Fellow. He is the author of Serious Play: How the World's Best Companies Simu late to Innovate. He can be reached at email@example.com.