Purpose The purpose of this paper is to investigate the possibility of including the cost consequence of failure in the a priori risk assessment methodology known as failure mode and effect analysis (FMEA). Design/methodology/approach A model of the standard costs that are incurred when an electronic control module in an automotive application fails in service was developed. These costs were related to the Design FMEA ranking of the level of severity of the failure mode and the probability of its occurrence. Monte Carlo simulations were conducted to establish the average costs expected for each level of severity at each level of occurrence. The results were aggregated using fuzzy utility sets into a nine-point ordinal scale of cost consequence. The criterion validity of this scale was assessed with warranty cost data derived from a case study. Findings It was found that the model slightly underestimated the warranty costs that accrued, but the fit could be improved with adjustments dictated by actual usage conditions. Research limitations/implications Cost data used in the simulations were derived from government and academic surveys, analyses, and estimates of the manufacturing cost structure; and nominal costs for various quality issues experienced by Tier 2 automotive electronics supplier. Specificity is lacking. The sample size and the type of the failure modes used to validate the model are constrained by the number and type of products which have had demonstrable performance concerns over the past three years, with cost data available to the authors. The power of the validation is limited. The validation is considered a screening assessment. Practical implications This work relates the characterization of risk with its potential cost and develops a scaling instrument to allow the incorporation of cost consequence into an FMEA. Originality/value A ranking scale was developed that related severity and occurrence rank scores to a cost consequence rank that keys to a cost of quality figure (given as percent of sales) that would accompany a realization of the failure mode.
Purpose The purpose of this paper is to develop an optimal model of an integrated quality and safety management system (QSMS). Design/methodology/approach Keywords related with these systems were identified from international standards and subsequently mined from a selection of peer reviewed articles that discuss and propose varying forms of integrated models for both systems. Cluster analysis was used to establish the degree to which integrated models, as described in the articles were quality dominant vs safety dominant. Word counts were utilized for establishing content and attributes for each category. An optimal integrated model was developed from the final cluster analysis and substantiated by a one-way analysis of variance. Experts from industry were consulted to validate and fine-tune the model. Findings It was determined that characteristics of an optimal integrated model include the keywords “risk,” “safety,” “incident,” “injury,” “hazards,” as well as “preventive action,” “corrective action,” “rework,” “repair,” and “scrap.” It also combines elements of quality function deployment as well as hazard and operability analysis meshed into a plan-do-check-act type work-flow. Research limitations/implications Given the vast array of clustering algorithms available, the clusters that resulted were dependent upon the algorithm deployed and may differ from clusters resulting for divergent algorithms. Originality/value The optimized model is a hybrid that consists of a quality management system as the superordinate strategic element with safety management system deployed as the supporting tactical element. The model was implemented as a case study, and resulted in 13 percent labor-hour saving.
Lean Six Sigma is a hybrid continuous improvement methodology that has various definitions, from those that are Lean dominant to those that are Six Sigma dominant. Text mining and cluster analysis based research has helped to illuminate the degree to which Lean Six Sigma models, as described in articles published in the International Journal of Lean Six Sigma, are Lean dominant versus Six Sigma dominant. The iterative cluster analysis was used to identify clusters of articles that were interpretable. The research found that some Lean dominant Lean Six Sigma articles ascertain Lean as the dominant philosophy and Six Sigma as a subordinate tool used in achieving the Lean objectives. The findings of this research as well extrapolation of the literature informed a recommended Lean Six Sigma model as described in this article. The recommended model is Lean dominant and consists of two subordinate methods-Six Sigma and statistical process control. The three synergistic approaches not only each serve in their own way to manifest process improvements, they also all contribute to organizational learning, which is considered a chief contributor to competitive advantage.
While this article can stand alone, the reader will be better served to also read the two companion pieces that preceded this one in previous journal issues. They are “Building a Model for Technology Studies,” Volume 22, Number 2, Summer/Fall 1996, and “Universal Performance Behaviors: What Ought to Be in the Technologist’s Toolkit,“ Volume 23, Number 1, Summer/Fall 1997. The first article described the model and the underlying rationale that guided its development. The second in the series articulated and identified behaviors that ought to be part of the array of competencies, or behaviors, of all technologists, regardless of their specific professional assignment. The body of thought presented in these three articles has been influenced by the author’s work as director of the Center for Quality, Measurement, and Automation (CQMA) in the College of Technology at Bowling Green (Ohio) State University. CQMA’s experiences with a number of technology transfer projects for business and industrial clients have contributed much to the refinement of the ideas presented here. Also, participation in the development of an Applied Quality Science bachelor’s degree program further tested, refined, and demonstrated the efficacy of the model as did more recent experiences with a research project conducted for the American Society for Quality Control to define core knowledge for technologists. Details and reports of these experiences may be obtained from the author. JS
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