Karen Schmahl

Professor of Practice in Engineering Management, Florida International University, Weston, FL, USA

Karen E. Schmahl, Ph.D., is currently a Professor of Practice in the Engineering Management Program at Florida International University. Teaching and research areas focus on quality engineering/management and engineering economic analysis.Her degrees are in Industrial Engineering with bachelor’s and master’s from Texas A&M and Ph.D. from the University of Cincinnati. She shares over 20 years of industry experience with her students. Over ten years were spent in the aerospace industry, most notably at GE Aircraft Engines with positions supporting manufacturing and technology modernization projects. Prior to joining FIU in 2013, Dr. Schmahl spent 10 years as an independent consultant focusing on continuous improvement projects, providing training in quality engineering and blackbelt statistics, and performing AS9100 system assessments.She has held registrations/certifications as a Professional Engineer (Ohio), ASQ Certified Six Sigma Black Belt, ASQ Certified Quality Engineer and ASQ Certified Manager of Organizational Excellence. Professional service activity includes serving as a member of the board of examiners for the Malcolm Baldrige National Quality Award and as a judge for the ASQ International Team Excellence Awards. Dr. Schmahl served several years as an evaluator of university industrial engineering programs for the Accreditation Board for Engineering and Technology (ABET).Dr. Schmahl has received numerous awards including an ASEE Outstanding Teaching Award and an Ohio Society of Professional Engineers Achievement Award. She is a charter member of the Epsilon Mu Eta, The Engineering Management Honor Society. She holds two patents and has had numerous publications.

The Power of Pareto Analysis in Quality Improvement Efforts

Pareto charts are a basic but powerful quality tool used to highlight the most significant contributors to problems. While most quality professionals know the principle behind Pareto charts and the mechanics of creating one, many practitioners are not aware of common pitfalls and techniques for more effective use of the tool. The Pareto Principle highlights that the relationship between inputs/sources and outputs/consequences is not balanced. Joseph Juran generalized the concept that a “vital few” sources impact outputs while “trivial many” have little significance on overall outcomes. A Pareto Chart is a frequency distribution bar chart where the bars for input categories are ordered from highest to lowest impact on a metric representing on output.One of the most common pitfalls comes in collection of the data itself. Where categories for analysis are not uniquely defined the analysis can be misleading. The default output metric is typically frequency, but this is not always the best metric to highlight the most significant issues.Often practitioners stop too soon in the analysis. With thoughtful data collection of additional categorical variables, Pareto Analysis can be performed of the same output data from multiple viewpoints and issues identified at high levels can be further segmented to help target specific causes.Practitioners also commonly err in comparing data before and after improvement initiatives are undertaken. For example, a common Pareto Chart in continuous improvement efforts shows the frequency of different types of defects. Direct comparison of such charts before and after improvement projects are not valid in determining the magnitude of improvements made. The metric must be “normalized” using an additional piece of information relative to total production volumes during the data collection time frame.Avoiding the pitfalls and effective techniques for use of Pareto Charts in continuous improvement efforts are illustrated using examples from manufacturing, healthcare, and logistics.