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Previous studies have found general differences between non‐lean and lean production systems, but none of them have identified the major factors in supporting operations such as maintenance which are important in shifting the production from a non‐lean to a lean system. The purpose of this paper is to determine the major factors and parameters of maintenance operations that are most effective in enhancing production to a lean system.

A questionnaire is constructed and a direct mail survey is conducted in the greater Cincinnati/tri‐state region. The data collected are analyzed with SAS software using contingency tables with Fisher's exact test and a logistic regression analysis method.

The results show strong correlations between a lean production system and some of the major maintenance variables and parameters such as annual costs of maintenance personnel, parts/materials, and training.

The results of this study can be used as a guideline for engineers, experts and managers in order to monitor the maintenance operation during the transition process from a non‐lean to a lean production system.

The purpose of this paper is to develop an optimal model of an integrated quality and safety management system (QSMS).

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.

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.

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.

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.