Publications

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The early contributions of female researchers such as Marie Curie and Lisa Meitner to physics—and ultimately to the Manhattan Project—have been widely recognized and documented. In addition, numerous historical accounts have revealed the significant impacts of other female scientists, engineers, and technologists during the Manhattan Project. Despite the strong role of women in the Manhattan Project, the momentum has not continued into the present day, as reflected by the current demographics of the Department of Energy (DOE) National Laboratories. Although the overall U.S. workforce is about 50% female, the workforce at the DOE National Labs is only about 30% female. The statistics for technical management and research staff at the DOE National Labs are even more dire; women make up only about 18% of these ranks in contrast to the percentages of women in computer science (25%) and physical science (39%) in the U.S. workforce. These current statistics are not the desired state for the DOE National Labs and contrast sharply with the long history of accomplishments by women at the Labs. We believe the DOE National Labs should lead the charge on diversity and inclusion (D&I) and serve as a model enterprise for bringing women into our scientific and technical workforce.

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In high-consequence engineering organizations, such as Sandia, quality assurance may be heavily dependent on staff competency. Competency-dependent quality assurance models are at risk when the environment changes, as it has with increasing attrition rates, budget and schedule cuts, and competing program priorities. Risks in Sandia's competency-dependent culture can be mitigated through changes to hiring, training, and customer engagement approaches to manage people, partners, and products. Sandia's technical quality engineering organization has been able to mitigate corporate-level risks by driving changes that benefit all departments, and in doing so has assured Sandia's commitment to excellence in high-consequence engineering and national service.

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The current framework for quality scholarship in the United States ranges from the training and education of future quality engineers, managers, and professionals to focused and sustained research initiatives that, through academic institutions and other organizations, aim to improve the knowledge and application of quality across a variety of sectors. Numerous quality journals also provide a forum for professional dissemination of information.

Over the course of time, the profession of quality engineering has witnessed significant change, from its original emphasis on quality control and inspection to a more contemporary focus on upholding quality processes throughout the organization and its product realization activities. This paper describes the profession of quality engineering, exploring how todays quality engineers and quality professionals are certified individuals committed to upholding quality processes and principles while working with different dimensions of product development. It also discusses the future of the quality engineering profession and the future of the quality movement as a whole.

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Statistical concepts, methods, and tools are often used in the implementation of statistical thinking. Unfortunately, statistical tools are all too often misused by not applying them in the context of statistical thinking that focuses on processes, variation, and data. The consequences of this misuse may be ''data torturing'' or going beyond reasonable interpretation of the facts due to a misunderstanding of the processes creating the data or the misinterpretation of variability in the data. In the hope of averting future misuse and data torturing, examples are provided where the application of common statistical tools, in the absence of statistical thinking, provides deceptive results by not adequately representing the underlying process and variability. For each of the examples, a discussion is provided on how applying the concepts of statistical thinking may have prevented the data torturing. The lessons learned from these examples will provide an increased awareness of the potential for many statistical methods to mislead and a better understanding of how statistical thinking broadens and increases the effectiveness of statistical tools.