Part 1 of 2: Define, Measure and Analyze

DMAIC and Throughput Efficiency

After one facilitates and reviews hundreds of Lean Six Sigma projects, certain commonalities become evident. Just like the surgeon that specializes in the same procedure, repetition and inherent variability afford opportunities to see things not apparent to the infrequent operator. This rule applies everywhere, including Lean Six Sigma (LSS) projects. Regardless of the training methods and program certifications, first-time and infrequent appliers of the LSS Body of Knowledge share common shortfalls in the application of LSS tools and techniques in the real world.

DMAIC is a process, and like any other process, it converts an opportunity into an output over a period of time. This conversion is measured as  “Throughput Efficiency”. If the starting point is poorly measured or vague, the value of the output will also be uncertain. By dividing DMAIC into a series of specific deliverables, or checkpoints, the conversion of an opportunity, or gap, into a benefit can be controlled and maximized.

Following is a step by step summary of common shortfalls by teams using the DMAIC method when applied to organizational-level process and system issues:

Define: The key objective here is to quantify the reason for improvement both in terms of performance and financial impact. While leaders may be interested in the process performance gap between actual and required, the Cost of Poor Quality (COPQ) elevates the sense of urgency. Teams often risk strong leadership support for their projects by conducting a cursory analysis of the COPQ or omitting it altogether. Other common issues include poorly defined stakeholders’ needs and the incorrect measure of performance.

Measure: Data collection and stratification in search of the most significant problem is key. Shortfalls include a poorly developed process flow chart, data collection limited to information readily available, and not paying attention to the type of data to be analyzed-continuous or discrete. Finally, once a specific, significant problem is selected, teams frequently fail to properly assess how much the chosen problem has on the performance gap and the COPQ previously presented in the Define step.

Analyze: Contrary to popular belief, the Fishbone Diagram does not confirm root causes, although it is helpful for identifying potential suspects. Statistical tools can show the likelihood of a causal relationship, which may be adequate in certain circumstances. But with a little extra effort, teams can do controlled experiments and verify the absence of the problem (effect) when the suspected cause is also removed. Finally, failure to assess the impacts of the verified causes on the problem selected in the Measure step is one of the weakest areas for most teams.

By quantifying the issue upfront-both in process performance and financial terms, stratifying so as to identify the most significant problem, and ensuring root causes selected are responsible for all or most of the gap, teams are on their way to maximize project benefits. We will conclude our discussion in Part 2 with the Improve and Control steps.

Note: The terms “Throughput”, “Efficiency”, and “Throughput Efficiency” have many definitions depending on the industries in which they are used. We choose to use Throughput Efficiency as the measure of opportunity conversion ratio within a desired time.

Bob Seemer has been a Master Black Belt since 1991 and typically trains and facilitates more than 300 Green Belt candidates on 70 projects per year in all sectors.