Arne Buthmann

When calculating the business case for a Six Sigma project, the cost of poor quality (COPQ), which is the cost caused by producing defects, is a commonly used concept. Within the total amount of quality cost, however, COPQ represents only a certain proportion. Costs do not result from only producing and fixing failures; […]

When comparing the average of two or more groups with the help of hypothesis tests, the assumption is that the data is a sample from a normally distributed population. That is why hypothesis tests such as the t-test, paired t-test, and analysis of variance (ANOVA) are also called parametric tests. Nonparametric tests do […]

With its DMAIC (Define, Measure, Analyze, Improve, Control) roadmap, the Six Sigma methodology provides a structured and systematic approach to solving business and process problems. The related toolkit is a selection of proven tools and methods that– correctly applied – help to determine, analyze, and improve a problem. All is well and good, however, […]

Normally distributed data is needed to use several statistical analysis tools, such as individual control charts, Cp/Cpk analysis, t-tests, and analysis of variance (ANOVA). When data is not normally distributed, the cause for non-normality should be determined and appropriate remedial actions should be taken. (An introduction to remedial actions for non-normal data can […]

What is non-normal data? Normally distributed data is a commonly misunderstood concept in Six Sigma. Some people believe that all data collected and used for analysis must be distributed normally. But normal distribution does not happen as often as people think, and it is not a main objective. Normal distribution is a means […]

A stakeholder of a Six Sigma project is anyone associated with the project either directly or indirectly. Examples of stakeholders are supervisors providing resources, team members, department heads and employees involved in the process. An up-front analysis of who the stakeholders are and how and when to involve them in the project can prevent potential […]

Every project faces a number of elements that risk its success. For instance, a lack of team-member availability, qualified resources, customer information, data, proven technologies, a clear scope – or deficiencies in a number of these areas – represents a risk. To prevent risks like these from happening, or at least to be prepared when […]

The following case study illustrates how a pharmaceutical company applied selected DFSS (Design for Six Sigma) tools to develop a new recruiting process for sales representatives. Tools and activities are described along the IDOV (Identify, Design, Optimize, Verify) phases, which served as a guiding roadmap through this process design project. Identify The need to completely […]

Green Belt and Black Belt coaching has for many organizations become an integral part of Lean Six Sigma implementation and critical to the success of improvement projects. However, standards and best practices of effective coaching are only rarely established. Some may ask, “Why coaching for someone who has gone through training?” While training is essential […]

Beyond technical know-how and project execution, outstanding Six Sigma Black Belts know how to get in the “flow” – that level of performance that comes when competence and challenge are aligned.

Most of the difficult situations Black Belts face in their day-to-day work are not related to the application of a specific Six Sigma tool, but rather to change or project management issues. For example, issues such as:  How to involve a stakeholder that is very negative about the project. How to get commitment from the […]

Integrating Design for Six Sigma (DFSS), IDOV (identify, design, optimize, validate) roadmap and selected DFSS tools in the information technology (IT) system development methodology can strengthen the business focus of IT system delivery. Adding additional steps at the beginning and end of the traditional system development cycle for DFSS can support the better understanding of […]

The paired t-test is used to check whether the average differences between two samples are significant or due only to random chance. In contrast with the “normal” t-test, the samples from the two groups are paired, which means that there is a dependency between them. The following example illustrates the difference between the regular t-test […]

When conducting the 2-sample t-test to compare the average of two groups, the data in both groups must be sampled from a normally distributed population. If that assumption does not hold, the nonparametric Mann-Whitney test is a better safeguard against drawing wrong conclusions. The Mann-Whitney test compares the medians from two populations and works when […]

One of the key success factors of Six Sigma is the ability to set up and run an interdisciplinary, multi-skilled and management-supported process improvement team. A Six Sigma project team – like any small team of workers asked to effect change in their organization – is expected to help promote a culture of innovation and spontaneity […]