Högskolan i Skövde

In the digital transformation of manufacturing companies towards Industry 4.0, shop-floor operators of the future, Operator 4.0, will require digitalized presentation of information as cognitive support for their work. This paper explores five industrial cases where Information Support Technology have been conceptualized and developed. These cases have exemplified how digitalized presentation of information can be approached with considerations of operators with varying cognitive work situations and production characteristics. Furthermore, these new technical capabilities have increased the level of cognitive automation to support operators’ individual abilities to perform their work in an increasingly more complex production environment. In conclusion, Information Support Technology in the service of Operator 4.0 is intimately linked with digitalization strategies for transformation towards Industry 4.0.

Presented is a literature study into the importance of how information in assembly instructions in manual assembly is presented, more specifically how various factors such as the complexity of the assembly itself, the mental and physical workload of the worker, as well as the experience and skill level of the worker affect the requirements for information presentation. The requirements made by Industry 4.0 on flexibility in production lines and an increased number of variants produced causes increased demands on workers, which leads to more cognitive demands being made on assembly workers. Studies exist around assembly instruction modes, but have in many cases ignored factors such as worker skill level, mental workload, and task complexity and how these affect the requirements for information presentation, which is a major contribution of this study. The findings are that no single solution fits all requirements, but that the aforementioned factors should be taken into account.

Operator4.0 was coined in 2016 to create a research arena to understand how the physical, cognitive, and sensorial capabilities of an operator could be enhanced by automation. To create an interaction between operator and robots, there are important factors that needs to be defined. Two important factors are the task and function allocation. Without well-defined tasks it is hard to allocate the tasks between the robot and the human to create resource flexibility. Furthermore, it the tasks are knowledge-based rather than rule-based, the cycle time between operators can differ a lot. Two assumptions are discussed regarding knowledge-based tasks and automation. These are also tested in an experiment. Results show that it is a large variation of the cycle time for both humans (between 1,58 minutes up to 4,40 minutes) and robots (between 1,94 minutes up to 4,49 minutes) when it comes to knowledge-based and machine learning systems.