Production Control

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.

In the project CrimpProd-S the integration of decentralized, self-learning production control systems in the context of Industry 4.0 in combination with additive manufacturing is investigated. The overall target is the economic evolutionary integration of lot size 1 in existing production facilities. Therefore a procedure model and customized methods are developed to analyze the potentials regarding the existing company’s strategy, value chain as well as the business model. This integrated procedure guarantees the efficient and customer-oriented transformation.

There is a variety of empirically validated methods for implementing pull-systems in the manufacturing industry, but pull-based replenishment for the process industry remains a research gap. This article describes the development of a model for implementing a pull-system for an intracompany production network in the process industry and its validation in a case company.

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.

The methods presented allow the splitting of classic monolithic numerical controls of industrial robots and machine tools into their functional units. The core functionalities can then be brought onto different computers in even separate places. Using techniques of augmented reality allows enriching a captured scene with additional information, as a virtual model of the industrial robot or the planned paths. Combining these approaches leads to a simplified programming task for industrial robots as the programs can be visualized in their context. This decreases setup time and improves quality.

Current control systems are limited from a technical viewpoint in areas such as scalability, reconfiguration time and computational complexity for algorithms. These limitations call for a new concept for control systems to address current and future requirements. One idea is that the physical location of the control system has to be moved of the machine to a cloud. In this way, the control system becomes scalable and can handle highly complex computational tasks while keeping the process expertise in the bay.

The volatile world urges companies to react more agile to fast changes. Monitoring is a major building block for corporate agility. A lack of understanding of causal interrelations between a multitude of volatility drivers demands a new monitoring approach. Big data is presented as an adequate tool and its agility support is discussed from a data and analytics point of view. Conclusively, guidelines for developing a monitoring system are discussed.

Volatility in market demand leads to temporary over- and under utilization of productive assets. Heijunka aims at de-coupling the production system 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 development of a Heijunka model for the process industry in order to close that gap.