Toyota Motor Corporation is an industry leader in product development lead time while using fewer engineers than its U.S. competitors. It has also shown remarkable consistency in market share growth and profit per vehicle, which led to cash reserves of $21 billion, exceeding those of the “Big Three” automakers combined.1 The Toyota Production System (TPS), dubbed “lean manufacturing,” has been critical in these accomplishments,2 but we believe that Toyota’s product design and development system is also an important contributor.3

While Taiichi Ohno and others have meticulously described the TPS, the Toyota development system has not been well documented.4 Indeed, Toyota does not use many of the practices often considered critical to successful concurrent engineering and associated with Japanese manufacturers. Its development teams are not colocated. Personnel, with the exception of the chief engineer and his staff, are not dedicated to one vehicle program. Cross-functional job rotation is unusual for the first ten to twenty years of an engineer’s career. Engineering and test functions rarely use quality function deployment (QFD) and Taguchi methods. Toyota excels at value engineering (VE) and value analysis (VA), yet Toyota engineers say they do not use any of the text-book tools and matrices for VE or VA. And there is nothing remarkable about Toyota’s CAD or CAE systems. These practices, then, do not explain Toyota’s effectiveness in developing new vehicles.

In a previous article, we called Toyota’s product development system the “second Toyota paradox.”5 TPS was the first; its features seem wasteful but result in a more efficient overall system, such as changing over manufacturing processes more frequently (presumably inefficient) in order to create short manufacturing lead times. The second paradox can be summarized in this way: Toyota considers a broader range of possible designs and delays certain decisions longer than other automotive companies do, yet has what may be the fastest and most efficient vehicle development cycles in the industry.

Traditional design practice, whether concurrent or not, tends to quickly converge on a solution, a point in the solution space, and then modify that solution until it meets the design objectives. This seems an effective approach unless one picks the wrong starting point; subsequent iterations to refine that solution can be very time consuming and lead to a suboptimal design.6

By contrast, what we call “set-based concurrent