Organizations are confronted with quality failures that must be addressed. Often, statistical methods can be helpful, but the organization lacks an internal statistician. This paper explores the use of a statistical problem‐solving team using Six Sigma professionals as problem‐solving team leaders, trainers, and coaches. A case study methodology is used. The actions and events from the implementation of a statistical problem‐solving team in a global manufacturing organization in the automotive industry through the first decade after implementation are described. Statistical problem‐solving bridges the gap between engineer and statistician using engineers trained to awareness‐level knowledge of statistics and supported by Six Sigma Master Black Belts. The statistical problem‐solving team members need to be highly trained in the three pillars of statistical problem‐solving: Key statistical concepts, key statistical analysis, and the key test method as well as a solid foundation in quality tools. This paper provides a unique view of a global problem‐solving team using both Six Sigma tools and methods and the 8D process as a problem‐solving methodology.

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Explanatory hypotheses are formed and evaluated in root cause analysis. However, prior to investigation, the hypotheses must be prioritized. Often, methods such as nominal group technique, multi‐voting, and simple voting are used to decide which to investigate first. This research seeks to provide concrete criteria for the prioritization of hypotheses using three levels of prioritization based on the strength of the available evidence. A quality leadership email distribution list was used to distribute a survey to quality departments. Respondents were asked to rate various scenarios as confirmed, strong, moderate, and weak evidence towards supporting a hypothesis. Only 2 of 13 scenarios did not have statistically significant results and these results can be used by quality departments to prioritize hypotheses to investigate during root cause analysis.

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The Kano Model is a well-researched approach to rating the attributes of products and services. However, much of the research available consists of research conducted to identify attributes for products and services and evaluations of the Kano Model with other approaches. This paper seeks to study the analysis of Kano Model results. This paper uses a case study of the analysis of Kano Model survey results from a survey conducted only to gather data for the analysis. The data was analysed using a spreadsheet with formulas to perform the necessary calculations. Attributes were rated using Kano Model criteria. Several results were tied and an attribute was rated as indifferent, even though many respondents viewed the attribute as must-be or attractive, indicating a need to refine Kano Model evaluation criteria.