Högskolan i Skövde

Indroduction: As the manufacturing assembly industry advances, increased customizations and product variety results in operators’ executing more cognitively complex tasks. To bridge these cognitive challenges, the assessment of operators’ health and performance in relation to their tasks has become an increasingly important topic in the field of cognitive ergonomics.

Methods: This paper examines operators’ mental workload through an integrated approach by implementing measures covering different mental workload signals: physiological, performance-based, and subjective, while assembling a 3D-printed drone. In this study, four validated mental workload instruments were used and their correlation levels were evaluated: error rate, completion time, the Rating Scale Mental Effort (RSME), and Heart Rate Variability (HRV).

Results: The results indicate that three out of four mental workload measures significantly correlate and can effectively be used to support the assessment of mental workload. More specifically, error rate, completion time, and RSME.

Discussion: Since current literature has stressed the importance of developing a multidimensional mental workload assessment framework, this paper contributes with new findings applicable to the manufacturing assembly industry.

Digitalization and automation in industry can have both positive and negative effects on social sustainability. On one hand it can be a basis for monotonous, uncreative, and even dangerous workplaces and in some cases might even result in people losing their work. On the other hand, it can be a base for ergonomically sound and inclusive work, engaging everyone in improvements. This project aims for moving the focus on positive effects for social sustainability while still staying cost efficient and effective in economic and ecologic sustainability for digitalization and automation of work instructions and training in manual operations like assembly, machine operation & setup, maintenance, and material handling. The Industry 4.0 paradigm offers radically increased opportunities for doing just that. For example, increased digitization can create efficiency improvements through shorter lead times and reduced disruptions to production. New generations of technology and software as well as information dissemination can be accelerated and the traceability of products and materials in the industrial systems can be greatly increased. Digitization also provides opportunities to increase industrial resilience to challenges coming from elsewhere, such as demographic change and climate threats. Advanced application of digitization is seen by industries and decision-makers as the most important enabler for achieving the strategic sustainability goals and Agenda2030. A crucial factor for competitiveness is the human contribution. Here too, digitalisation is radically changing the conditions. In the last 20 years, work instructions have been transformed from printed text on paper into an increasingly digital representation. As knowledge increases about how work instructions for the manufacturing industry should be designed, they are rarely designed according to user conditions. At best, this results in a missed opportunity for performance improvements and at worst, it could potentially result in quality deficiencies, efficiency deficiencies and a lower degree of inclusion of staff groups. Digitization and automation permeate both society and industry more and more and there are many different technologies on the market. These can contribute to both increased efficiency and flexibility for the industry. However, there are a lot of challenges to both implement, design, and use instructions. Studies conducted in industry 2014–2018 show that operators and assembly workers only use instructions in 20–25% of cases in the operational phase when they are perceived as inefficient (Fast-Berglund & Stahre, 2013; Mattsson et al., 2018). Of course, this also increases the risks of, for example, assembly errors by not using instructions to the extent that they should be used. The corporate culture and standards are also an important part of how instructions are created and used. Depending on the structure and condition of the company and the production unit, for example, an assembly instruction at one company may include information about the product, process, and work environment, while an assembly instruction at another company includes completely different or only parts of this information. Of course, this is a natural consequence of sometimes far-inherited corporate cultures and traditions, but experience has also shown that it is to a very large extent the nature of work that defines the type of support system needed. In line with increased automation and increasing product variation as a result of increased customisation, operators’ tasks will require more creative work than before where the aim is to enable and handle the results of individual workers' creative thoughts about improvements in their own work situation, increasing cognitive load (Taylor et al., 2020). The development of digitalisation has created new opportunities for improved communication among employees in the manufacturing industry (Oesterreich & Teuteberg, 2016). Therefore, this technological development can and should support operators cognitively (Kaasinen et al., 2020; Mattsson et al., 2016). Although many new digital technologies are being developed and are available (Romero et al., 2016), it is still difficult to implement these so that people's cognitive work is supported. This is often due to the fact that the implementation does not take place in a way that people are comfortable with (Parasuraman & Riley, 1997). In many cases, humans are expected to adapt to technology and not the other way around (Thorvald et al., 2021). To implement better support for their operators, companies should focus on identifying the information needs that exist (Haghi et al., 2018) and then visualize it in a way that is useful to operators. The central aim for the project is to demonstrate how knowledge and systematic development of cognitive support and information design can increase quality and flexibility in future production and how this can be considered in the implementation of digital work instructions. In the industrial case studies, current state-of-practice in information presentation will be investigated and analysed together with state-of-the art knowledge and technology to map successful efforts in industry, identify what it is that makes them successful, or how a particularly challenging situation can be further improved through our knowledge of cognitive work in production.

The rapid progress of automation within the assembly industry have improved efficiency and productivity but introduced new challenges. Operators physical tasks have been replaced by more cognitively demanding tasks. With increased global distribution, it is almost standard to offer an increased product variety and customisations require more cognitively complex work. Assessment of operators mental workload has gained interest since it can be used to optimise work performance, diminish errors and poor decision-making, reduce risk of employee absenteeism, and monitor operators health. Assessments today focus on either, subjective, physiological, or performance-based parameters to examine mental workload. The rapid literature review aims to map the domain of mental workload assessments, explicitly in the assembling industry, by targeting its history, preferences, and future trends. It investigates which assessments that have been employed historically. Secondly, examines the body of literature and potential instrument preferences. At last, explores future trends of mental workload assessments.

We detail the results of an ongoing study into the preference of workers in 6 different industrial companies for assembly instruction display types and modalities for their tasks. This study is performed as a part of a project that aims to create a theoretical framework for understanding requirements for instruction presentation in industry, and providing guidance to the creators of assembly instructions. The study, as well as the project as a whole, aims to expand on approaches from the Industry 4.0 framework, with a particular focus on the more recent Operator 4.0 approach that adds a focus on more human-centric aspects of digitalisation in industry. The study being presented is comprised of facility visits to each partner company where the current state of practice was presented by each company, an examination of information presentation and ope- rating procedures by the authors, and in-depth interviews with assembly workers at each site. All companies examined deal with variants in production, and the comple- xity of assembly spans from low to extremely high. The companies involved mostly rely on experienced workers, with high training, and relatively long times to train new personnel. The interviews led to findings such as simplified images being strongly pre- ferred for both beginners and experienced workers, with an emphasis on the image matching the worker’s viewpoint to the product, and experienced workers preferring simplified images with highlighted markings for details that can be seen from where the task is performed, and more. The findings will be used in further work to create a theoretical framework around digital work instructions, as well as used directly to help partner companies better standardise their instructions to support the cognitive abilities and limitations of their assembly workers. The goal with this is to create safe, comfortable and profitable workplaces that fulfil goals of social sustainability in the long term.

The Operator 4.0 typology depicts the collaborative operator as one of eight operator working scenarios of operators in Industry 4.0. It signifies collaborative robot applications and the interaction between humans and robots working collaboratively or cooperatively towards a common goal. For this collaboration to run seamlessly and effortlessly, human-robot communication is essential. We briefly discuss what trust, predictability, and intentions are, before investigating the communicative features of both self-driving cars and collaborative robots. We found that although communicative external HMIs could arguably provide some benefits in both domains, an abundance of clues to what an autonomous car or a robot is about to do are easily accessible through the environment or could be created simply by understanding and designing legible motions.