Agile production

Rarely does a manufacturing company operate as a self-contained production unit; most products are the result of a coordinated supply chain. Processes within individual companies therefore depend on the higher-level processes in the supply chain. This leads to disruptions throughout the supply chain if a single company fails to meet its delivery commitments. The department focuses on the development of methods for the strategic and tactical design of material and information flows in supply chains and value networks in various industries. Research focuses on the optimal network topology and flow rules to reduce the vulnerability of supply chains to disruptions while meeting production logistics objectives.

Engineer-to-Order (ETO) products are typically found in the tooling and special machinery industries. The development or design of the product is initiated only after a customer has placed an order. As a result, manufacturers lack reliable information to calculate and determine the required product resources (order throughput time and manufacturing costs) from this point onwards. To facilitate optimal resource planning and efficient order throughput in this scenario, the department is actively involved in the development of new planning methods. Emphasis is placed on the creation of polymorphic ETO representations and research into sub-contracting methods that can be integrated into existing planning systems.   

One of the primary objectives of production is to meet the company's production and supply targets while making the most efficient use of human, raw material, and energy resources. Environmental, social and governance (ESG) aspects are becoming increasingly important and require planning and control beyond purely economic considerations. The department is actively involved in developing methods for accurate production planning to minimize costs while considering industry-specific constraints on resource use.

Collaboration between manufacturing companies, whether in the form of a value network or a platform, relies on a common language that facilitates communication and ensures a consistent understanding of the information shared. This language can be used, for example, to exchange information about available machine capacity and demand for machine capacity. By matching available and requested machine capacity, temporary collaborations between companies can be established. Semantic approaches, such as knowledge graphs, are well suited to building this common language. The department is dedicated to the further development of semantics for both subtractive and additive technologies. It will also develop approaches to link supply and demand in the context of both centralized and decentralized collaborations.

Material flow simulations are often used to identify current or potential bottlenecks, to facilitate the determination of process limits, and to aid decision making in layout planning. Material flow simulation is a well-established tool for modelling existing or new production processes, considering both business and process interdependencies. This approach allows the evaluation and optimization of both overall system performance and individual process performance. Material flow simulation plays a critical role in uncovering opportunities for improvement within the production areas of manufacturing companies. The results highlight areas for optimization and provide a starting point for discussions to identify practical recommendations and guide further research.

The integration of assistance systems on the shop floor is becoming increasingly important for manufacturing companies. However, successful implementation can be undermined by a lack of employee acceptance of these systems. Concerns about complexity, control and monitoring or a lack of knowledge can lead to employees using assistance systems inadequately or incorrectly. In this context, the department focuses on formulating employee-centered qualification strategies for such assistance systems. In addition, manufacturing companies are given the opportunity to replicate real-world application scenarios using existing assistance systems in a laboratory environment. This allows for the identification of requirements, necessary qualification strategies and practical testing.