One of the most potent things we ever did was to adopt a mantra of QSI- Quality, Speed and Innovativeness in designing. For instance concentrating on improving the speed of digital and physical prototyping to ensure that we "Put the right prototype in the right hands at the right time." had interesting side-effects.. including using prototyping in the search for solutions to design problems ("What do you think?"), not as prototyping to produce possible solutions ("Do you like it?").
Kevin Kelly's blog
Recent Innovations in the Method reports on KK survey of scientific community; he asked
"What would you say are the innovations in the scientific method of the last 50 years? What has changed the nature of science in practice in your lifetime? I am primarily interested in innovations in the process of science itself, rather than the discoveries made by that process."
Recent Innovations in the Method aggregated the replies including one from Chris Langton
Search Engines -- One (maybe two) obvious things are the recent emergence of online pre-print archives, resources like Wikis, and sites like MathWorld, all of which show up from searches with Google or other search engines. This capacity has so reduced the turnaround time for finding the answer to a question that may emerge in one's research or thinking, that you can get an answer while the context that generated the question is still foremost in your mind. This has, I think, fundamentally changed our ability to think about complex problems, especially those which lead us out of our area of expertise - enough to qualify as a true phase-transition in our thinking and problem solving capabilities. The potential for a new idea to spread far and wide in a very short time, combined with the filtered-search capacity that allows us to quickly find a specific idea-needle in that enormous data-haystack, qualifies, in my mind, as a genuine advance in the scientific method. After all, it is simply an enhancement of the basic algorithm of the scientific method (evolution really): the generation and distribution of variant ideas combined with a strong selective filtering process. (CL)
Our M>W>D approach, I have realised was affecting what is encapsulated above.. in this sentence..
This capacity has so reduced the turnaround time for finding the answer to a question that may emerge in one's research or thinking, that you can get an answer while the context that generated the question is still foremost in your mind.
We literally worked on reducing the time from a CAD 'sketch' to a physical prototype from a month to a week to a day. This had the effect of enabling the project team to collaborate across disciplinary and organisational boundaries to improve the quality of decisions.
Imagine the logistics of getting a model round, say Europe, for feedback from a group that had met for a day, a month ago; they return to their day jobs and loose focus and context of that discussion that launched the prototyping activity. Now imagine getting to 17.00 pm after a hard days work in collaboration with the core team covering the eight sides of Design Space. The CAD modellers spend another two hours 'improving' the model... before joining the team for a well-earned dinner. The modelmaker takes the file converts it for the rapid prototyping machine sla or sls (or....) then overnight a model, or models with variations, are created. Meanwhile the core team go for that dinner. Next morning the models are put in the Design Space (project working studio) ready for a 9.00 am start. The models provoke an hour of tough discussions leading to a combined design with the best attributes brought together and approved for exposure to consumers (to ask "What do you think?"). By lunchtime the team are on their way to meetings elswhere or aeroplanes home. The CAD modeller hones the digital prototype and by the day's end the file is Filed for access by the prototyping network.. within 24-48 hours physical models will be growing on machines around the world... next week we will be getting consumer feedback in all the major markets that can be addressed by this product.
The Iterative Capital capability of rapid prototyping devices is very potent. Like any good capital manager we need to know what we have and how much of it. We also need to consider everybody and everything involved in an iteration, and how they might benefit from the IC.
Michael Schrage, in Hyperinnovation writes
"Tensions and Trade-offs
Consider this simple “thought experiment” to illustrate the tensions and trade-offs that digital modelling presents. A new rapid prototyping and seamless simulation infra-structure enables a manufacturing company to double the number of development cycles its product team can run. Under the old system, new product teams could perform 10 cycles during their 10-month development window. That is, the development team could do 10 iterations —or versions — of its product before the ship date. The new technologies now let the development team run up20 full iterations at virtually no extra cost. Think of those extra cycles as currency: Each additional cycle can “purchase” either a product improvement, a cost reduction, or a speed-up. Each cycle Is as valuable as any other cycle. Unspent cycles are monies saved. The hyper-innovation management challenge emerges. Just how should the team “spend” or “invest” those 10 extra cycles? What expenditure of this iterative capital will give the best returns? Should these innovation teams
:•Spend all their cycles on speed to come to market in half the time?
•Spend their cycles on improvements and come to market with a product that is 50 percent “better” after 10months?
•Spend their cycles on cost reduction to be able to cut prices by 30 percent?
•Spend all 10 cycles on the ideal blend of speed, price, and quality? Just what is that optimal blend? Why?
•Bet a couple of cycles on an intriguing but risky enhancement?
•Use a few cycles to test an alternate design approach?
•Save three cycles to keep the development costs down?
•Take those 10 cycles to develop an entirely new product concept?
There are no inherently right answers. Even worse, these hypothetical alternatives are far too simplistic. They lack the pain and menace that managers confront when hard organizational choices have to be made. Iterative capital investments, just like financial capital investments. There are no inherently right answers. Even worse, these hypothetical alternatives are far too simplistic. They lack the pain and menace that managers confront when hard organizational choices have to be made. Iterative capital investments, just like financial capital investments and human capital investments, create political and cultural conflicts for organizations."
"For example, the development team has to decide whether to use those extra iterative cycles to focus on particular product features or specific cost reductions. Allocating the new cycles can create rifts: Should design get three; manufacturing get three; marketing get three; and the remaining one be held in reserve for emergencies? Perhaps the product manager should “own” the cycles budget. Deciding when key customers and suppliers can be brought in to help spend cycles is unclear. Some innovation champions may want them there at the very beginning. A more conservative management may prefer to hold their participation until the end. It’s also possible that doubling the number of cycles will have no impact at all on the way the firm manages its design relationships with suppliers and customers."
It is the behaviour of the teams around tangible prototypes, digital or physical, that ultimately determines how innovatively and effectively they use their Iterative Capital; and how well they progress on the road to becoming a Dream Team. It seems facilitating the right sorts of conversations goes a long way to facilitating winning combinations of 'stuff' that delivers great experiences.