OCCUPATIONAL APPLICATIONS Worker well-being and overall system performance are important elements in the design of production lines. However, studies of industry practice show that current design tools are unable to consider concurrently both productivity aspects (e.g., line balancing and cycle time) and worker well-being related aspects (e.g., the risk of musculoskeletal disorders). Current practice also fails to account for anthropometric diversity in the workforce and does not use the potential of multi-objective simulation-based optimization techniques. Accordingly, a framework consisting of a workflow and a digital tool was designed to assist in the proactive design of workstations to accommodate worker well-being and productivity. This framework uses state-of-the-art optimization techniques to make it easier and quicker for designers to find successful workplace design solutions. A case study to demonstrate the framework is provided. Rationale: Simulation technologies are used widely in industry as they enable efficient creation, testing, and optimization of the design of products and production systems in virtual worlds. Simulations of productivity and ergonomics help companies to find optimized solutions that maintain profitability, output, quality, and worker well-being. However, these two types of simulations are typically carried out using separate tools, by persons with different roles, with different objectives. Silo effects can result, leading to slow development processes and suboptimal solutions. Purpose: This research is related to the realization of a framework that enables the concurrent optimization of worker well-being and productivity. The framework demonstrates how digital human modeling can contribute to Ergonomics 4.0 and support a human factors centered approach in Industry 4.0. The framework also facilitates consideration of anthropometric diversity in the user group. Methods: Design and creation methodology was used to create a framework that was applied to a case study, formulated together with industry partners, to demonstrate the functionality of the noted framework. Results: The framework workflow has three parts: (1) Problem definition and creation of the optimization model; (2) Optimization process; and (3) Presentation and selection of results. The case study shows how the framework was used to find a workstation design optimized for both productivity and worker well-being for a diverse group of workers. Conclusions: The framework presented allows for multi-objective optimizations of both worker well-being and productivity and was successfully applied in a welding gun use case.

Usually, optimizing productivity and optimizing worker well-being are separate tasks performed by engineers with different roles and goals using different tools. This results in a silo effect which can lead to a slow development process and suboptimal solutions, with one of the objectives, either productivity or worker well-being, being given precedence. Moreover, studies often focus on finding the best solutions for a particular use case, and once solutions have been identified and one has been implemented, the engineers move on to analyzing the next use case. However, the knowledge obtained from previous use cases could be used to find rules of thumb for similar use cases without needing to perform new optimizations. In this study, we employed the use of data mining methods to obtain knowledge from a real-world optimization dataset of multi-objective optimizations of worker well-being and productivity with the aim to identify actionable insights for the current and future optimization cases. Using different analysis and data mining methods on the database revealed rules, as well as the relative importance of the design variables of a workstation. The generated rules have been used to identify measures to improve the welding gun workstation design.

Engineers use modelling and simulation techniques to efficiently create, evaluate, and optimize design solutions. In an industrial production context, engineers often need to consider requirements related to both productivity and worker well-being in order to find successful design solutions. However, simulations related to productivity and worker well-being respectively, are typically carried out by different engineering roles, using different digital tools. This lack of integrated work procedure could lead to inefficient development processes and suboptimal design solutions. Additionally, since performing multi-objective optimizations is likely to be seen as a complicated task by engineers in areas such as design engineering, production engineering, and ergonomics, requiring specific knowledge and skills, such tasks are typically performed by engineers specialized on optimization. This paper presents the development and usability evaluation of a digital tool that supports engineers not specialized in optimization to define and perform simulation-based multi-objective optimizations of requirements related to both productivity and worker well-being in an automated and simultaneous manner. The digital tool is the result of research carried out over a period of four years, following an iterative development and assessment process by the means of use cases, done in close collaboration with potential users of the digital tool, i.e. engineers at several companies. The usability evaluation of the digital tool shows that potential users in the industry view the tool as a promising support for performing their engineering tasks in a more efficient and integrated manner.