Lean Product Development stresses the importance of exploring the whole solution space, i.e., to become aware of every possible solution to our problem so that we can thoroughly investigate and compare them all. Using creative as well as systematic techniques, we can produce many, or even very many, possible solutions. One particularly efficient method is to break down the problem into functional steps, look for solutions to the sub-functions, and then systematically combine them. Even if some solutions to sub-functions are mutually incompatible for different reasons, the resulting number of solutions to the full problem is often surprisingly high. It can easily be several hundred thousand or even millions in even seemingly simple problems.

A large number of possible solutions ought to be positive to a designer, but that is not necessarily how everyone perceives the situation. On the contrary. In the process of identifying the best among several or many alternatives, quite a share of designers attempts to directly pick it, probably because it feels urgent to be able to quickly proceed to detailed design. And to have many candidate solutions is then a complication rather than an advantage. The instinct to try to choose the best option is very rational in our daily lives, when e.g. what to have for dinner on a particular day is not really important, and we save a lot of time by just selecting that which appeals to us at the time. But the same “beauty contest” approach in a product development process is unfortunately directly counterproductive. The reason is that the rationale for something to be “best” is that it is better overall than all alternatives. But this is very difficult to ascertain for a complex industrial product, which has many properties that need to be considered.

A much better procedure in product development is actually to do the exact opposite to try to pick a winner: Look for weaknesses and deficiencies in individual solutions and successively eliminate inferior ones. And do it by learning about them through early testing of alternatives and technologies, when it is simple and inexpensive.

There are several arguments for working in this inverse and probably, at least at first, counterintuitive manner:

1. It is comparatively simpler to find one weakness in a solution, which may be all it takes to eliminate it than to try to prove that a particular solution is superior to others.
2. Observed disadvantages allow the elimination of whole groups of solutions that share the same weaknesses.
3. You learn a lot by testing, and the knowledge gained can be documented, generalized, and visualized for reuse in future projects.
4. Testing is also better for comparison than using matrix methods, which are common in product development textbooks but do not encourage fact-based decisions. They inevitably also introduce uncertainty into the process of comparison through criteria weights, assessments of the degree of fulfillment of conditions, etc.
5. If you eliminate, you have Lady Luck on your side. Statistically, with 10 different solutions to choose between, your chance of identifying the best one among them at random is only 10 %. But you have a 90 % chance of eliminating a solution that is not the best, and it is not necessary to identify the very worst alternative until you are down to only two solutions. You only have to avoid excluding the best one among them.

As mentioned, using a knowledge-based process of successive elimination may feel awkward if you are not used to it. It takes some time to get acquainted with thinking in the opposite way, and one trick to speed that transition is to make a simultaneous adjustment of your vocabulary. What we say, write, hear, and read influence our thinking, and vice versa, so it helps to align terminology with thought. For this reason, try to avoid words and expressions like select, choose, superior, the best solution, winner, etc. and talk and think instead about eliminate, weakness, inferior, remaining/resulting solution, final concept, etc., and you may find it easier to implement and feel comfortable with this alternative and much more efficient approach to identifying the best solution to your problem.

Göran Gufstafsson is LPPDE Board member and Senior Lecturer in Mechanical Engineering in the Department of Industrial and Materials Science at Chalmers University of Technology.