The integration of human factors into a company's production design process

Abstract

Human factors (HF) considerations, integrated early in design of production assembly systems, can improve both worker health and business performance. A longitudinal case study using an action research style collaboration between researchers and a large electronics manufacturer was the platform for this investigation. The findings show “how” HF, previously outside engineering with HF specialists (HFS) performing reactive injury assessments, increasingly became integrated into each stage of the design process with HF adapted tools, enforceable targets, sign-off, and most HF work focused on proactive design alongside engineers. An operations research tool (cognitive mapping) was used to identify the HF perceptions of Senior Directors and link these to their strategic goals. As a result, HF specialists changed their focus from injury risk to reducing fatigue and improving worker performance and assembly quality. Several industrial engineering tools were also adapted for HF (eg. HF failure mode effects analysis, HF design-for-assembly) and used to quantitatively communicate HF concerns, drive continuous improvement, visibly demonstrate change, and lead to benchmarking. Qualitative data analyzed with a grounded theory methodology resulted in six constructs in the final “Design for Human Factors” theory. The theory propositions state that when: 1. HFS acclimate to the engineering process, language and tools; and 2. strategically align HF to the design and business goals, then HF becomes perceived as a means to improve business performance. This results in 3. HFS being pulled onto the engineering team, which increases HF application and engineers’ awareness of HF, and 4. Management hold engineers accountable for HF targets. Being on the engineering team leads to 5. Engineering tools adapted to include HF targets, and in combination results in 6. HF becoming embedded in the design process. Senior directors reported that increased HF application has improved the design of more recent assembly lines and made it easier for operators. The theory contributes an explanation about how HF can be integrated into design processes to inform researchers and practitioners and improve proactive HF application. Recommendations include increased education for HFS in engineering, and more collaborative research to develop tools that quantify and link worker performance to business metrics.

Adapting Engineering Design Tools to Include Human Factors

“…They were organizationally disconnected from engineering, within an occupational health and safety department, and most of their work was recommending workplace modifications in response to incidents of worker injury. Their recommendations were not communicated to engineering and therefore did not carry forward to subsequent assembly designs (Village, 2014). The overall goal of the collaboration, consistent with that of the HFS in the organization, was to find ways for the HFS to work proactively with engineers when designing the production assembly system.…”

“…The tools are company prototypes, customized for the type of assembly and design process within the case study organization. References are provided for those wanting to review the tools in more detail to customize them for other applications (Village, 2014). Table 1 summarizes, for each tool, the purpose, inputs, outputs, and tool application in the case study.…”

“…However, as the researcher notes below indicate, the HFS recognized that some sort of tool was needed to make their involvement sustainable: To satisfy this need, the HFS developed an HF-DFA tool, loosely based on the DFA by Boothroyd et al (2001), to evaluate the ease of manual assembly tasks and to prioritize tasks for improvement. The tool included 22 items known from the scientific literature and experience of engineers and HFS to contribute to concerns related to HF, ease of assembly, quality, and speed of assembly in hand-intensive assembly tasks (Village, 2014). A simple 0-2 scoring system was devised, where "0" represents an easy assembly, "1" moderate ease, and "2" a difficult assembly, for a maximum score of 44.…”

“…The final HF-DFF tool contains 12 items and, similarly to the HF-DFA, had a scoring system from "0" (low HF risk) to "2" (high HF risk). More information about the HF-DFF can be found in Village et al (2012) and Village (2014). It was also incorporated into a controlled engineering document with HF targets established within the DFF process.…”