Personnel planning in industrial manufacturing is complex and heavily influenced by experience. Shift planning, job rotations, and the coordination of different qualifications must be carried out on a daily basis, taking into account production plans, material flows, travel distances, and short-term changes in operations. This detailed planning is time-consuming, difficult to understand, and can place a strain on both managers and employees if decisions are perceived as non-transparent or unfair.
This is precisely where the following practical example comes in. It was developed as part of the KARL Competence Center research project, in which CyberForum is involved as a consortium partner. BLANC & FISCHER Corporate Services was responsible for the technical management of the use case. The project partners adesso, EDI, Fraunhofer ISI, Karlsruhe University of Applied Sciences, ifab, and wbk were involved in the operational implementation.
motivation
The companies belonging to the BLANC & FISCHER family holding place the highest value on customer satisfaction for their products and therefore work continuously to make their own processes and material flows more efficient and quality-oriented.
Managers in BLANCO production plan personnel deployment for the individual shifts. Various parameters such as production planning, distances, production combinations, and employee skills are used for this planning. Managers combine these parameters based on their extensive experience to create a detailed deployment plan in order to carry out production as efficiently as possible. Furthermore, planning can change due to changing situational events during production and must then be adjusted accordingly by managers.
Creating and regularly adjusting the schedule is very complex and time-consuming. Furthermore, the planning is not always comprehensible to employees, which can lead to dissatisfaction among employees if the planning is subsequently deemed unfair. The AI-based assistant is therefore intended to relieve the burden of planning on production managers on the one hand and contribute to fair and comprehensible planning for employees on the other.
objective
The goal of the use case, led by BLANC & FISCHER Corporate Services, is to use AI as an assistance solution and support for managers. The AI will be trained on the basis of various complex parameters such as production planning, distances, production combinations, and employee skills using historical data in order to process this information in a structured manner. This provides managers with the best possible combinations for detailed resource planning as decision proposals and at the same time reduces their workload. They retain full control and decision-making authority over the current circumstances. Furthermore, the AI solution is intended to offer employees full transparency and traceability regarding the personnel planning process and increase satisfaction.
approach
First, an analysis of the current situation is used to develop an understanding of the processes and identify the current pain points. The pain points identified in this way are used to derive process goals with key performance indicators for the relevant stakeholders, which are then used as a basis for designing the target process together with the specialist department. After prioritizing requirements and creating user stories as part of the target process, the first implementation package for the development of a demonstrator can be put together with the partners. At the same time, an AI readiness check is developed and carried out with the scientific partners to identify the strategic and organizational fields of action. The results are then mapped out on a roadmap to initiate a possible pilot project in practice with the department.
added value
BLANC & FISCHER Corporate Services expects the successful implementation of this use case to facilitate job rotations and the training of new managers. The AI-supported assistance system will help to reduce the workload on managers and increase staff efficiency and satisfaction. Furthermore, machine utilization and production quality can be further optimized through suitable parameter combinations, and a feedback loop can be incorporated for better future production planning. This self-learning component of the AI system helps to ensure high-quality, efficient, and optimal intralogistics processes.
Note: The research and development project "Competence Center KARL" is funded by the Federal Ministry of Research, Technology, and Space (BMFTR) as part of the program "The Future of Value Creation – Research on Production, Services, and Work" (funding code: 02L19C250) and supervised by the Project Management Agency Karlsruhe (PTKA). The author is responsible for the content of this publication.
Are you facing similar challenges in industrial manufacturing or production planning?
Whether it's personnel planning, production control, intralogistics, or data management—many issues vary from company to company. However, the fundamental challenges are similar: increasing complexity, high demands on efficiency and quality, and the need for transparent and traceable decisions. We would be happy to show you which approaches and offerings may be useful for your company in the industrial environment.
Isabel Ernst: Let's find out together how AI can benefit your company in concrete terms.
- Project management AI Innovation Lab
- isabel.ernst@cyberforum.de
- 0721 602 897-717