Factory2Fit | Empowering and participatory adaptation of factory automation to fit for workers

A use case derived from Continental’s measurement lab has been used for validation, revealing the importance of task properties careful choice, time to familiarize employees to such system and assuring sensitive data security. 

Within Factory2Fit there were 2 use cases for the codesign process piloted at Continental plant Limbach-Oberfrohna. One pilot was carried out for the workplace design and one for the work process design. An evaluation of the method selection and execution showed that there was good acceptance among the workers who contributed to the design process. To reach positive results during the codesign process it is essential to assess the boundary conditions and the group structure very well. 
 

The developed tool could be extended to become a part of a bigger communication platform, between the equipment provider and their customers, aiming at strengthening their relationship. 
 

SoMeP was piloted at Prima Power, unveiling that the integration of production information and messaging is valuable and time-saving in getting guidance. Gamification can motivate workers to share knowledge (Zikos et al., 2019). The use of social media will require organizational policies e.g. in moderating the content (Aromaa et al., 2019). 

Worker Feedback Dashboard was piloted in three factories with ten workers. For user acceptance, it has been crucial that the workers participated in planning how to use the solution, and what kind of work practices were related to its use. The pilot evaluation results indicate that there are potential lead users for the Worker Feedback Dashboard. Introducing the solution would facilitate showing the impacts and could then encourage those who may be more doubtful to join. 

ARAG solution was piloted in a factory of United Technologies Corporation (UTC). The validation results reflected the potential of the solution, technicians’ acceptability to solutions specifically designed for supporting them in complex operations. Recent studies have shown that gamification tools can be utilized in industrial AR solutions for reducing technicians’ learning curve and increasing their cognition (Tsourma et al., 2019). 
 

On-the-job learning tool was piloted in a UTC factory producing air handling units. What is learned, is that in order to display the content more understandable, users must be able to interact with it, by viewing the components CAD files and make or read remarks.

TDE offers the generation of optimized production plans while the waiting time for the high-priority tasks has been reduced. Moreover, workplace is more user-friendly, and supervisors have a detailed overview of the running and planned work.

The method selection tool can also be used by non-experts as there is no need of vast know-how for the utilization of the offered tool. Therefore, the layman can easily select the right method out of a wide range. Codesign itself strengthens the companies as the workers can include their tacit knowledge into the workplace - or work process design and therefore have a higher commitment to their job once their ideas are implemented.

With the proposed solution, online training can be scheduled optimally, shortening training time. Moreover, training supports understanding and dealing with exceptional situations such as disturbances in production. Increased productivity and job satisfaction are expected, lowering the threshold of operators to start using the manufacturing line independently.

SoMeP emphasizes in improving collaboration and communication between technicians and promotes knowledge sharing, and practices. In the long-term SoMeP can be used both as a communication channel and information exchange hub, but also as a valuable knowledge repository as well as an educational system.

The proposed solution aims at raising spirits at work, by helping employees recognize their strengths as well as their development needs. In the long term, the application can assist employees to develop their working habits.

The ARAG solution reduces the cognitive load, and increases the technician’s satisfaction, while the precise guidance assures optimality and correctness in task execution. Furthermore, with the proposed solution training time is minimized as well as inspection by experts, also reducing manufacturing times and maximizing resource utilization.

With the proposed solution, access to expert knowledge is granted to anyone who is registered to the platform. What is more, supervisors are able to assign training courses to specific technicians for improving their skills.