In Statistical Engineering, Problem-Solving Framework refers to the approach to performance improvement in which the root cause of an unusual process behavior is identified and remedied to regain normalcy (Hoerl & Snee, 2012). The approach can be employed when dealing with various project problems in a vast array of contemporary realms. In the education environment, for instance, the framework could be used in identifying and mitigating the cause of poor performance in a given study subject.
Example: Mitigating Poor Performance in Class
As mentioned above, the Problem-Solving Framework focuses on the unusual behavior, rather than the entire process or project (Hoerl & Snee, 2012). It, therefore, requires a very careful and detailed problem documentation. A case study would be the mitigation of poor performance amongst students for a given study subject. In such a set-up, the first step would be to document the problem and develop a questionnaire to help identify the root cause of the poor performance (Hoerl & Snee, 2012). The researcher would then choose and test the best solutions before measuring the results to determine whether or not the problem is solved. Check-sheets and flowcharts are some of the knowledge-based tools, which could come in handy for the them in the process (Hoerl & Snee, 2012).
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Questions to Ask to Identify the Root Cause
In the Problem-Solving Framework, the “5Whys” technique is always employed to reach to the root causes beginning from the symptoms (Hoerl & Snee, 2012). In the above study aimed at mitigating poor performance in class, for instance, the leading questions could be as detailed; why do students continually fail the subject? They never get well-prepared for the paper. Why don’t they get well prepared for the paper? They probably lack interest in the subject due to indiscipline. How can the indiscipline concern be handled? The above string of questions would suddenly lead to the identification and elimination of the root cause of the problem as coined in the Problem-Solving Framework.
References
Campbell, S. (2012). Flaws and fallacies in statistical thinking . New York, NY: Courier Corporation.
Hoerl, R. & Snee, R. (2012). Statistical thinking: Improving business performance . New York, NY: John Wiley & Sons.
