Thus, activities like commissioning, maintenance, and even operation will in the future demand a lot more technical knowledge of each employee. However, this development is contrasted by the decline of adequately qualified staff, owed in part to older employees entering the retirement phase, which results in a continuous loss of large volumes of knowledge accumulated through experience. Add to that the issues of demographic change and shortage of skilled labour, which are increasingly becoming a problem for companies as they result in shortfalls in new hires of qualified personnel. This enormous contradiction of an increase in complexity on the one side and the situation concerning the requisite workforce on the other requires that new forms of employee empowerment be studied. To illustrate the problem, please consider the following example:
During the night shift, a piece of equipment unexpectedly fails on a given company’s production line. The shopfloor employee responsible for the equipment notices the problem and leaves his post to check the system control panel and find out what caused the outage. Error code “#513”, as shown on the equipment display, is meaningless to him as is, so that he has to search the equipment manual for an explanation. Using the manual, the worker determines that the outage was caused by a component not being correctly detected. At the same time, he realises that he is unable to eliminate the problem himself because he has no experience with this error message. Accordingly, he attempts to call in the responsible maintenance employee. The latter, however, is busy working on another problem and is unavailable for the next 20 minutes. The prolonged downtime prevents the equipment operator from continuing his work and delays the production process. That, in turn, impacts upstream and downstream processes, as well. In the end, such a downtime event causes enormous financial losses. To prepare the equipment operator for dealing with such scenarios in the future, training courses are set up by corporate management. These courses, however, mean that the equipment operator cannot work productively during the time taken up by the training, so that additional financial losses will ensue.
One solution approach for the problem described is currently being developed within the framework of the APPsist research project for developing intelligent-adaptive assistance systems for knowledge and management support in smart production. The project is funded by the German Federal Ministry for Economic Affairs and Energy (BMWi). The research objective is an assistance system that will provide a customisable approach for supporting employees with a broad spectrum of different skillsets in the performance of activities such as the operation and maintenance of machinery and systems. For this purpose, the employees are equipped with different end devices (e.g. tablets, smartphones, smart watches, AR goggles). The APPsist system installed on the device then displays the requisite steps as each activity is being performed. Customised for each employee, these steps will be described in more or less detail and visualised with different content (videos, photos, text in different languages, virtual overlays, etc.). Thus, the employees are, in the end, empowered to perform activities that would go beyond their original skills. To supplement this assistance system, learning units will be provisioned to allow for workplace-based learning. In connection with the APPsist system, the scenario described above would play out as follows:
During the night shift, a piece of equipment unexpectedly fails on a given company’s production line. The shopfloor employee responsible for the equipment is immediately notified on his smart watch: “Attention! Component was not detected properly”. He relocates directly to the equipment, where his tablet automatically displays the instructions for eliminating the problem identified as “component not detected”.
After starting the assistance tool, he can – aided by a variety of supporting content (photos, videos, text, and virtual overlays) – eliminate the problem independently. Due to his limited experience with the problem, the descriptions are very detailed. As a result of this customised support, the equipment can be restarted after a mere 10 minutes and the productivity loss is thus minimised. As the repair job is performed, the APPsist system automatically detects the learning progress of the equipment operator and offers him specially adapted learning units for further in-depth study, which the employee can process without needing long periods of time, immediately after completing the repair. Thanks to this continuous further training, the learning process becomes sustainable and workplace-based, and costly time-consuming training courses are, for the most part, no longer required.
Essentially, APPsist is designed to implement the following degree of novelty and benefit:
Degree of novelty:
- Context-sensitive and intelligent-adaptive assistance system for employees on the shopfloor
- Operability in a very large bandwidth of activities on the shopfloor (operation, servicing, maintenance, commissioning of machinery and systems)
- Direct equipment interface for optimum system compatibility and an efficient utilisation of the APPsist system
- The increase in complexity of activities generated as the “Industry 4.0” development progresses becomes manageable.
- Employees on any level of pertinent expertise are empowered to perform activities that exceed their previously learnt skills.
- Significantly greater flexibility for assigning tasks to employees.
- Reduction of MTTR (Mean Time To Repair), increase in OEE (Overall Equipment Effectiveness)
- Employee knowledge is retained within the company
In combination with correlating learning units, sustainable learning at the workplace is ensured.
APPsist – intelligent-adaptive assistance systems for knowledge and management support in smart manufacturing – is a model project of the German Federal Ministry for Economic Affairs and Energy (BMWi) focusing on digitalisation and professional qualification on the shopfloor. A national consortium of representatives from science, manufacturing industry, IT industry, trade associations, and social partners researches potentials and feasibility of workplace-integrated assistance systems for providing real-time support to employees working on machinery and systems as well as “in situ” knowledge and qualification services to build a comprehensive understanding of the man-machine interactions on the shopfloor.