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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.

Reducing wear of undercarriage track propulsion systems used in heavy construction equipment decreases the maintenance costs and increases the equipment's life. Therefore, understanding key factors that affect the wear rate is critical. This study is an attempt to predict undercarriage wear.

This research analyzes a sample of track-type dozers in the eastern half of North Carolina (NC), USA. Sand percentage in the soil, precipitation level, temperature, machine model, machine weight, elevation above sea level and work type code are considered as factors influencing the wear rate. Data are comprised of 353 machines. Machine model and work code data are categorical. Sand percentage, elevation, machine weight, average temperature and average precipitation are continuous. ANOVA is used to test the hypothesis.

The study found that only sand percentage has a significant impact on the wear rate. Consequently, a regression model is developed.

The regression model can be used to predict undercarriage wear and bushing life in soils with different sand percentages. This is demonstrated using a hypothetical scenario for a construction company.

This work is useful in managing maintenance intervals of undercarriage tracks and in bidding construction jobs while predicting machine operating expense for each specific job site soil makeup.

Based on the sociotechnical systems theory, we examined the human–technology interactions in the context of future works conditioned by machine learning (ML) and artificial intelligence (AI). Skills needed to support career sustainability and the future of the workforce, particularly for the middle-skilled workforce in the contemporary United States America (USA) context, were also studied.

We conducted a scenario analysis to demonstrate the potential roles that human resource professionals may perform to fill the skill gaps given their expertise in the shaping and skilling processes.

Assessing the success of the integration of AI and ML into the middle-skilled workforce requires a multi-faceted approach that considers performance metrics, cost-effectiveness, job satisfaction, environmental impact and innovation. Employees with AI skills can be more competitive in the workforce and forward to high-skilled positions.

Empirical research and related studies focusing on evaluations of reskilling and upskilling processes and outcomes would support career sustainability and the future development of middle-skilled workers.

Through a proactive strategic career development plan with AI integration, middle-skilled workers may enhance their career sustainability and be prepared for future higher-skilled work.

The economic downturn caused by technology-induced unemployment may be addressed by unleashing middle-skilled workforce potentials for future work created by AI and robotics and sustaining economic competitiveness.

This article offers important implications for human resource development theory-minded researchers and scholarly practitioners.