In the wake of the recent 3M story around Six Sigma and innovation, many people have been asking if efficiency programs and innovation programs are fundamentally at odds. While I have already posted on the specific 3M story, I have received some emails asking for further insights on the potential for synergy between Six Sigma and innovation best practices.
The observation many have made is that Six Sigma is all about driving down costs and that this focus doesn’t seem compatible with the goals of product innovation. So where is the room for innovation in Six Sigma? To begin this brief exploration, let’s begin with some history.
Six Sigma was developed in the 1980’s as a response to the recognized cost of poor quality. The idea was that customers feel the variance, not the mean. Thus, by reducing variability the client experience would be made more reliable yielding the benefits of: elimination of waste, rework, and mistakes; increasing customer satisfaction; and enhancing profitability and competitiveness. After some time, the system we recognize as Six Sigma evolved. There are some variants; here we will focus on DMAIC (Define, Measure, Analyze, Improve, Control). But first, let me say a quick word on DFSS (Design For Six Sigma).
DFSS is distinctly different from classic Six Sigma. Whereas classic Six Sigma is a methodology focused on the optimization of the manufacturing process, DFSS is a newer method that encompasses the entire product development cycle. This necessarily includes the front end product definition, design, and ideation activities. While DFSS does not formally include specific tools for innovation, the DFSS process does contain a natural insertion point for innovation best practices to fill the innovate-here step. As this is well understood by DFSS practitioners, here we are not addressing DFSS and instead we focus on DMAIC where the points of innovation synergy may not be so obvious.
To understand how innovation best practices can be used to augment a classic Six Sigma program, it is useful to consider the methods (or tools) that are in common use to implement Six Sigma. Some of these include: Voice of the Customer, Quality Function Deployment, Root Cause Analysis, Process Mapping, Failure Mode Effects Analysis, XY Matrix, PUGH Matrix selection, Value Engineering Analysis, TRIZ (Theory of Inventive Problem Solving), Robust Design, Design of Experiment, and Mistake Proofing. Each of these specific methods can be enhanced with the use of innovation best practices. Let’s think about how this could map to the phases of DMAIC.
In this first phase, the project goals and customer (internal and external) deliverables are clearly articulated. Innovation best practices for problem definition using product and process analysis technique can be used to help identify and refine CTQs (Critical to Quality factors) as well as XY Matrix elements. Root cause analysis is also helpful to qualify the issues being considered and validate that the right issues are addressed. House of Quality elements can be mapped to Value Engineering Analysis metrics to help drive down stream analysis. Best practices for concept identification can be use to better identify risk mitigation strategies.
In this phase, the process is measured to baseline current performance. Product and process analysis methods can be used to identify the parameters and performance aspects of the system to be measured. Concept generation disciplines may be used to identify novel strategies to for measurement of factors that are not easily measured.
The Analyze phase is used to determine the root causes of the system defects. While some traditional DMAIC environments will use only statistical tools for this analysis, some more progressive environments will perform CTQ selection via parameter analysis and value engineering analysis.
Once the targeted defect is understood, it is time to improve the system by eliminating the defects. In many cases this tuning is done through incremental tuning of the system. However at times, there may exist the opportunity to implement a more fundamental change to the system. In these cases, concept generation methods can be fruitfully applied to identify known solution strategies for parameter optimization.
The final phase of DMAIC is about ensuring future process performance continues to conform to requirements. Here, innovation best practices can be employed to predict and prevent future failures and system non-conformances.
In short, while classical Six Sigma is primarily a statistically driven approach to system performance optimization, there are opportunities at every phase within the methodology to augment the process with innovation best practices.