In the early 2000’s a program which had improved manufacturing operations performance and reduce their costs was called Six Sigma. These efforts were focused on reducing the variability in manufactured products and to some extent services. By improving the quality of such products and services the company experienced superior financial performance. There were several conferences in the early 2000s in which black belt Six Sigma instructors worked on implementing Six Sigma principles in R&D.
Six Sigma is a highly disciplined quality process to systematically limit defective performance to 3.4 defects per million parts. What was found was that Six Sigma in R&D can either be a disaster or an excellent path towards increasing research capability. Early adopters of the methodology were ABB, AlliedSignal, American Express, Bombardier, Crane, DuPont, General Electric, Lockheed Martin, Motorola, Polaroid, Raytheon, Sony, and Texas Instruments.
However, because of the spotty performance of the initiative, the movement never became mainstream in R&D. What has been implemented however is the use of Six Sigma principles in the normal stage gate process. Successful use of Six Sigma principles relies on understanding during the stage and gate process the (1) impact of potential killer variables to commercialization, (2) the market’s key drivers, (3) the market size, (4) key customers, and (5) customer needs. To understand such variables Six Sigma practitioners focus on assessing the specifications and in particular validating the measurement system of key attributes so that the measurement system is capable of knowing whether or not the product design and manufacturing process will be able to meet the specifications. This means defining the performance objectives of both the product and manufacturing process in detail, documenting potential inputs, and analyzing sources of variability during the R&D development and scale up phases. It also means going the extra mile during scale up to determine the process capability, implementing process controls, and careful documentation of what is learned. The Six Sigma process makes full use of process maps and metrics, cause-and-effect matrices, capability analysis, design of experiments, multivariable analysis, hypothesis testing, failure mode and effects analysis, mistake-proofing, and control plans. See the reference section of this chapter for more details.
Design for Six Sigma improved the quality of thought during R&D gate reviews for manufactured products in particular. A key learning is that it is extremely useful in R&D organizations to include team members on R&D projects who have a excellent understanding of manufacturing statistical process control methodologies.