production control

Intelligent digital twins (IDT), as the next evolutionary stage of digital twins, have the potential to accelerate and optimize processes within companies. The intelligent twin presented here independently analyzes 3D CAD data and automatically conducts a physical simulation of the assembly. Utilizing the IDT optimized assembly, reduces production costs and accelerates the production planning process. This specific use case illustrates the broader possibilities and advantages of IDTs, offering valuable insights for their transferability.

Short delivery times are essential in competitive markets. In addition to product selection and quality requirements, customers are also demanding more and more from logistics. However, high product variance complicates the situation, as it involves complex material flows and therefore leads to long throughput times. However, a four-step process analysis and modeling can help to reduce throughput times and strengthen the competitiveness of companies from within.

This paper examines various forecasting models used to predict business crises in the automotive and electronic manufacturing industries, with a focus on German companies. By comparing the performance of these models, we aim to identify the best approach for each industry. We also discuss real-world business case scenarios to demonstrate the practical implications of our findings, including the role of risk management in supply chain and procurement departments. Our results show that the most effective model for forecasting crises in the automotive industry is the VAR model, while the EWS model is best suited for the electronic manufacturing industry. Furthermore, we identify key risk factors that supply chain and procurement departments must consider enhancing their resilience in the face of crises.

While the digital world has already made its way into many other production areas, the bakery industry has so far benefited little from these technological developments. This is because many baking processes are manually controlled and rely on expert knowledge. In addition, the process data from the machines is still not automatically captured via sensors. This paper describes a procedure for digitizing baking processes by developing an IoT framework consisting of an IoT device including measurement methods, an edge gateway, and a simulation-based solution for process optimization.

The food production process and raw material specific parameters significantly influence the quality of the final product. Furthermore, for products with several ingredients, complex parameter effects occur. This complexity causes the desire for a production control that considers these effects and orientates on final product quality. This article describes the state of the art and derives requirements form a case study in a delicatessen food production system. In summary, this paper presents a concept for production control with special consideration of the final product quality for the production of delicatessen salads.

The complexity of production and logistics systems generates the demand for industrial transformation: with sensor technology that enables efficient, flexible and reliable process monitoring and control using a 5G communication-infrastructure. In the “5GSensPRO” project, the Fraunhofer IPT in Aachen is developing a modularly expandable sensor cloud system for existing machines. Within a unique research environment, the world’s first 5G mobile radio network provides the opportunity to investigate and implement applications of 5G in production engineering.

Today, manufacturing facilities are designed for mass-producing identical goods. Although they often have a certain flexibility, they are not fully changeable. Changes are associated with a high cost. Changeability allows producers to react more quickly to changing demand situations and to efficiently produce small quantities. The Digital Twin is a key concept for implementing the required changeability. In the reference project BaSys 4.0, funded by the German Federal Ministry of Education and Research (BMBF), we have developed a manufacturing concept that enables changeable production. Our open source middleware Eclipse BaSyx provides a reference implementation of the BaSys 4.0 concepts.

In terms of Industrie 4.0, especially SMEs are facing the challenge of integrating data both vertically and horizontally. To achieve this task, common solutions such as ERP are increasingly replaced by manufacturing executions systems (MES). Due to the direct connection in production, MES allow a production control and serve as bridge between planning and manufacturing level. Data integration is furthermore the basis for an automated risk management in production. The research project quadrika develops an MES module that predictively recognizes risks and thus prevents faults.

The positive benefits of decentralized decisionmaking structures in production systems were already discussed in the 1990s. But it is only in recent years that the technologies required for implementation have reached sufficient market maturity to be able to implement corresponding concepts efficiently. In this way, the units involved can be enabled to participate “intelligently” in processes by means of autonomous technologies. The question of the actors actively involved in decentralised decisionmaking and implementation as well as the concrete design of decentralised production structures is of great importance. This article illustrates the importance of autonomy for decentralised production control and shows which performance actors involved in the process have the necessary capabilities to act autonomously.

The network analysis is a promising approach for assessing the behavior of material flow systems based on large data volumes. Previous studies focus mainly on static network analysis. This means that all the events that occur in a specific time period are aggregated to a single material flow network. As material flow systems are rapidly changing systems, a static view is not sufficient. The aim of this paper is to present existing concepts to obtain such structural changes and to assess their suitability for material flow networks by using real data.