Production Control

The SARS-CoV-2-crisis has brought many companies into existential distress. The sudden loss of suppliers and customers as well as short-term changes in regulations and relevant information sources pose challenges for companies. The linking and visualization of information from the corporate environment with internal capabilities remain an unsolved problem. This article shows how a situation picture can help companies to act in a goal-oriented manner.

A fast flow of information throughout the entire supply chain is unavoidable for risk minimization and is not subject of a discussion in volatile times or crisis situations. The flow of information within the supply chain is characterized by various forms of transmission: EDI, cloud applications or other system interfaces are manifold in the areas of value-added networks for digital risk monitoring and process efficiency increase. If corporate processes are examined more closely, one area remains digitally underrepresented at the moment: The digital twin of a production tool. The handling of these production tools must now be taken to a new level.

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.

Volatility in market demand leads to temporary over- and under-utilization of production assets and stocks. Levelling (heijunka) as a lean method aims at de-coupling production from market volatility. The production program is spread as even as possible over time. This achieves high asset utilization, short lead times, and low inventories. There are validated heijunka methods for the manufacturing industry, but for the process industry this remains a research gap. This article describes the Product Wheel and its validation at a building material manufacturer in order to close that gap.

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.

The digital transformation of production logistics processes promises great potentials for increasing their efficiency. The processes can thus be better controlled and existing capacities better utilized. For a successful digital transformation, the quality of the information that will be collected and processed is a key factor. This paper presents a method for a systematic digital transformation of production logistics processes, with a focus on information flows and their quality.

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.