Due to the high number of tasks, the differentiation of requirements and the variety of machines, also amplified by the arrival of unexpected high priority tasks, there is an emerging challenge for automating production scheduling, using optimization methods.

Task Distribution Engine (TDE) is an application for task scheduling and assignment of human resources and machines, based on optimization methods. Supervisor is able to prioritize criteria for fine-tuning the automatically produced solution. The web-based interfaces for technicians aim at allowing them to update the status of their tasks, in order to update the supervisors and reconfigure the system. Two modes have been implemented, one for real-time operation and one for simulation. (Zikos et al., 2018)

Due to the wide number of participatory design (PD) methods available today, correct method selection from a layman is challenging, as wrong method selection can lead to inadequate results and subsequently to reduced acceptance of PD (Bojko et al., 2019). 

A software-based solution has been developed, simplifying the process of method selection. Based on user input, the algorithm determines/suggests the most suitable methods and hides the non-suitable ones.

The deliverable describes the pilots to be implemented.

The deliverable describes the results of the evaluation of the pilots.

In highly automated metal processing lines, intense customer training is needed. On-site training is not sufficiently efficient; thus, a virtual training solution is needed.

The proposed online training solution utilizes 3D models, videos and textual guidance to enable training technicians in realistic working scenarios. Each training task is followed by questions or learning exercises (quiz) for highlighting the most important matters of the topic.

Resolving unexpected failures can be very challenging as manuals do not include every case. Tacit knowledge of experienced workers is a cornerstone in such situations. Thus, knowledge sharing and communication among technicians can provide a viable solution.

The Social Media Platform (SoMeP) is a web-based application, aiming to increase interaction, communication and knowledge sharing among technicians. SoMeP integrates discussions to the manufacturing environment and real-time alarms, thus giving easy access to situationally relevant knowledge. Gamification features aim at increasing motivation and participation in knowledge sharing.

In the increasingly digitalized working environments, workers easily lose sight of what they achieve at work. However, positive feedback on work performance, as well as recognizing personal achievements and development at work are important for work motivation.

A web-based solution is proposed for providing data-driven, personal feedback to factory operators. The solution focuses on creating awareness, tracking daily accomplishments, demonstrate progress and development of work, generating feedback based on automatically collected data, e.g. an activity wristband etc. The feedback is only for the worker’s personal use, and not accessible to the employer. (Heikkilä et al., 2018).

Manual assembly of large size industrial products is a complex task, accompanied by paper-based instruction manuals. As a result, using such static documentation increases the cognitive load of workers and requires frequent travelling between the assembly unit and the tool magazine.

The ARAG solution, replaces paper-based instructions with holograms, which are displayed to the technician’s field of view (FOV) through a head-mounted display (HMD), overlaid on the real product. Further utilizing HMD’s capabilities, user is free to interact and manipulate the 3D objects, by altering their position, pose and scaling.

Manual assembly of highly customized large products requires skillful workers in varying levels. Henceforth, there is a need for a platform which renders expert knowledge on manufacturing operations available to anyone, anywhere, anytime efficiently enough to minimize waste of time and support upskilling novice technicians.

On-the-job learning is a web-based training tool allowing technicians have access to expert knowledge either on-line or off-line. A database containing videos of experts accomplishing relevant assembly tasks has been developed. Furthermore, the tool has been enhanced with an object detection algorithm, in order to retrieve the correct guidance video for the user.

The deliverable describes the pilots to be implemented.

The deliverable describes the results of the evaluation of the pilots.

This paper forms a review of the lessons learned from the point of view of five EU funded H2020 research projects (A4BLUE, Factory2Fit, INCLUSIVE, HUMAN and MANUWORK, 2016-2020), working in parallel and constituting the Human-Centred Factories (ACE Factories) cluster. Knowledge and technology providers, as well as industrial partners, have grouped together to deliver solutions that will bridge the gap to the factories of the future.

The purpose of this white paper is to share the ACE Factories cluster understanding of future human-centred factories and to provide recommendations on how to get this vision into industrial practice.  This is a key report of the ACE Factories cluster, which aims to support the replication of successful innovative technologies tested through the end-users of the ACE Factories cluster projects. The core target groups of this report are people in academia, industry practitioners and policy makers at the local, national and EU levels.