Abstract:

The introduction of digital tools is an opportunity to support operator tasks of production in manufacturing industry, with Operator 4.0 to support human capabilities. Operators today are however challenged when they must handle unexpected stops caused by machine failures and the following error recovery process of automated production systems. Typical errors in production are when machine tool breaks, and assembly parts are accidentally dropped or when pieces get stuck. Short, long, and unplanned production stops are expensive, unavoidable, and hard to predict in advance which affect operator and maintenance personnel. With complex digital tools and integrated production systems the error recovery process becomes complex because there is no one-size-fits-all solution and a lack of intelligent and automated restart systems. Altogether it puts high pressure on operators’ knowledge and skills of restarting machines and systems caused by errors. They require a high level of competence and skills, as well as availability to skilled maintenance personnel. The operator role is changing and there is a need to both enhance the operator skills and to intertwine expertise from maintenance personnel.

Even if there are defined routines for industrial work and structures for managing digital technologies, it is not adapted to the individuals’ cognitive processes neither to their workplace learning. Hence, there is a complexity of identifying the roles of future operators and maintenance personnel and how they work and learn individually and intertwined, when coping with daily machine failures and error recovery. E.g., who does what, what mandate each role includes, and to what extent digital technology can be designed to support the respective role. Given this, the aim is to explore and define operators and maintenance personnel cognition and skills and how their roles vary and connect, to design for future digital support. We take a holistic socio-technical and user-centered design approach and apply a situation awareness model to capture cognition and learning. The model includes task/system factors, perception and decision making, and individual factors.

In an on-going case we have studied two industrial companies that produce similar components but are working differently with production and maintenance. 16 interviews were performed to investigate the differences between the companies, their current work practices, and future changes. Company A has a digital maintenance system while producing components in a more manual way than company B. Whereas, company B has installed an automated cell and changed their restart process routines, but still reports errors manually.

Results indicate that operators mainly work through intuition meanwhile maintenance personnel are reasoning during daily work. Maintenance personnel aim to work preventive meanwhile operators perform instant recovery work if they are skilled. Hence, the connection between maintenance personnel and operators needs to be tighter, but the roles and responsibilities also need to be clearly defined to clarify boundaries of effective work and to enhance work-integrated learning for both roles. Future work includes documentation of instructions and digitalization of the entire error recovery process. The overall results give insights in how to work further on a strategy for changing roles, defined work routines for designing and implementing a digital maintenance support system.

Keywords:

Digital technology, Maintenance, Production, Skills, Cognition.