Human factors: spanning the gap between OM and HRM

Abstract

This paper examines the claim that the application of human factors (HF) knowledge can improve both human well-being and operations system performance.Methodology: A systematic review was conducted using a general and two specialist databases to identify empirical studies addressing both human effects and operations system effects in examining manufacturing operations system design aspects. Findings:We found 45 empirical studies addressing both the human effects and system effects of operations system (re)design. Of those studies providing clear directional effects, 95% showed a convergence between human effects and system effects (+,+ or -,-), 5% showed a divergence of human and system effects (+,-or -,+). System effects included quality, productivity, implementation performance of new technologies, and also more 'intangible' effects in terms of improved communication and co-operation. Human effects included employee health, attitudes, physical workload, and 'quality of working life'. Research limitations/implications:Future research should attend to both human and system outcomes in trying to determine optimal configurations for operations systems as this appears to be a complex relationship with potential long-term impact on operational performance. Practical implications:The application of HF in operations system design can support improvement in both employee well-being and system performance in a number of manufacturing domains.Originality/value: This paper outlines and documents a research and practice gap between the fields of HF and OM research that has not been previously discussed in the management literature. This gap may be inhibiting the design of operations systems with superior long term performance.

Emotional Status and Productivity: Evidence from the Special Economic Zone in Laos

“…Productivity analyses are an often-studied area within the field of production and operational management. Previous studies have examined workers' productivity from the viewpoint of job-related issues [10][11][12][13], technology [15,18], worker demographics and socioeconomic features [7,19,22,31], and worker health conditions [34][35][36][42][43][44][45]55,56]. Although previous research has explored several factors contributing to workers' productivity, few studies have examined the link between workers' on-job emotional states and productivity.…”

“…Studies have also emphasized how an employee's health and wellbeing impact productivity. Neumann and Dul [42], as well as Ødegaard and Roos [43], found that healthier employees are more productive. Gubler, Larkin, and Pierce [44] provided evidence that implementing a corporate wellness program improved employee productivity.…”

Modelling workload to quality using system dynamics in manufacturing and healthcare

“…The literature includes numerous case studies that show HF interventions resulting in improved quality. On the other hand, lack of HF design results in poor work environments and degraded quality (Neumann & Dul, 2010). Kolus et.…”

De-Icing of aircraft: Incorporating business risks and occupational health and safety

“…et al [41], Tixier et al [42]) in laboratory experiments (Dempsey et al [43]) or using digital human modeling (Illmann et al [44], Demirel and Duffy [3], Fritzsche et al [45], Neumann and Medbo [46], Cimino et al [47]) have been used to complete the comprehension, formalizing and modeling of the decisional subsystem of the system under study. Quantitative or qualitative characterization of such a model can be achieved using Bayesian methods (Knegtering and Pasman [48], Hu et al [49]) or multi-criteria methods such as AHP (analytical hierarchy process) (Saaty, [98]) or ELECTRE, when specific events can be associated with risks and probabilities of their occurrence can be estimated. When probabilities of occurrence cannot be estimated, consideration must be given to using combinatory or continuous optimization algorithms, depending on the nature of the system under study.…”

“…Combinatory optimization can be achieved using exact methods (e.g. Markov chains) or approximate methods such as meta-heuristics or techniques of artificial intelligence (Hu et al [49]). In all cases, the qualitative characterization of a model will necessarily involve elicitation of experts (Knegtering and Pasman [48], Zahra et al [41]).…”