Felix Brambring

Approach for Reducing Data Inconsistencies in Production Control

Companies of the manufacturing industry gather a high number of different high-resolution data concerning production and manufacturing processes. Advanced Planning and Scheduling Systems use these data sets collected on the shop-floor in order to adjust their scheduling for near- and middle-term production orders. Usually, the quality of these production feedback data is reduced by inconsistencies and errors which impairs the results of the planning process. The consequence is a low adherence to planned delivery dates. In this paper typical data inconsistencies are analysed for their rate of occurrence and an approach for reducing data inconsistencies by applying association rule induction is proposed.

Cognition-Enhanced, Self-optimizing Production Networks

This research area focuses on the management systems and principles of a production system. It aims at controlling the complex interplay of heterogeneous processes in a highly dynamic environment, with special focus on individualized products in high-wage countries. The project addresses the comprehensive application of self-optimizing principles on all levels of the value chain. This implies the integration of self-optimizing control loops on cell level, with those addressing the production planning and control as well as supply chain and quality management aspects. A specific focus is on the consideration of human decisions during the production process. To establish socio-technical control loops, it is necessary to understand how human decisions are made in diffuse working processes as well as how cognitive and affective abilities form the human factor within production processes.

Self-Optimizing Decision-Making in Production Control

This paper deals with the concept for self-optimizing decision-making in production planning and control. The concept is based on a value stream that provides real-time production data. This data enables a qualified decision regarding production planning and control. Practice has shown that production systems with a high production process complexity—such as job shop production with low volume production—are difficult to control automatically. Therefore, employees have an important role to play but need to be supported regarding their decision-making. The goal is to highlight relevant decisions and put them into the correct context. An unconventional and interactive illustration that abandons classic numerical key performance indicators helps to derive the correct decisions. Varying levels of detail regarding the depicted data allow the user to “zoom” in or out of the state of his production system. By support of simulation and visualization tools, the aim of this paper is to present a concept for self-optimizing decision-making in production control in order to help user making the right decision.

Approach of Information Provision for High Resolution Production Control

Production information is essential for controlling a company’s production. The awareness and targeted use of data is decisive for constant and positive enhancements of a company. At the moment, many manufacturing companies are still at the very beginning of collecting and using data gathered during production processes. The cost-benefit ratio of the various steps on the way to a high resolution production control is not clear at all. To overcome the hesitation of many small and medium-sized companies in collecting and using more data from their production, migration paths with an explicit focus on the cost-benefit ratio of decisions have to be characterized. This approach will ease the way to gain a suitable information level of a production facility for different objectives. The aim of this paper is to present the targeted approach for the efficient provision of production information in order to enable a high resolution production control.

Investigating the impact of production structure on logistic targets using a dynamic simulation model

To achieve a high logistical performance, adjustments in production structure are inevitable. The impact of structural changes however remains unclear. To investigate the effects of changes concerning production structure on logistical targets, the use of simulation models is possible. In this paper, we propose an approach for dynamically generating simulation models of production structures using structural and parametrical input data. The resulting model can be used to investigate various aspects and problems regarding production structure which is exemplified by a case study evaluating the performance of changes in production structure. The results are displayed in company specific characteristic curves showing the degree of logistic target achievement and the total amount of costs depending on a structural indicator.

Training near-the-job

Kurzfassung Mitarbeiter produzierender Unternehmen sehen sich in Zeiten von Industrie 4.0 veränderten Aufgaben und Herausforderungen ausgesetzt. Um den Erhalt von Produktivität und Wettbewerbsvorteilen sicherzustellen, müssen Unternehmen ihre Mitarbeiter im Umgang mit neuen Technologien schulen. Dieser Beitrag stellt Möglichkeiten zur Gestaltung von Wissenstransfers im Produktionsumfeld vor und beschreibt ein methodisches Vorgehen zur Konzeption von Lernspielen. Durch einen strukturierten Leitfaden soll den Anwendern die Möglichkeit zur eigenen Gestaltung erfolgreicher Lernspiele gegeben werden.

Predicting the potential of production structure changes using characteristic curves

Competitive market environments force manufacturing companies to achieve a high adherence to logistical targets. As their improvement potential is limited by the production structure, projects to modify machine-segment allocation are usually initiated using variant generation and qualitative evaluation methods which are neither able to identify optimal solutions nor to give an overview of possible performances. Furthermore, these projects are normally initiated to address the urgent need of change instead of organized opportunity management. This article presents a methodology for a high-level evaluation of the entire spectrum of possible production structures resulting in characteristic curves to display the progression of costs and logistical performance. Based on usually available master data of manufacturing companies, this approach allows continuous monitoring. The approach, consisting of the steps production structure generation, simulation and visualization, is validated using a real industry case from the machinery and plant engineering industry.

Concept for the Analysis of Disruption Consequences in a Complex Job Shop Manufacturing

In this paper, an approach to quantify the effects of disruptions in a complex job shop manufacturing is introduced. Key figures within this approach are the effects of disruptions on logistic targets. With the help of discrete event simulation, the consequences of several disruptions are quantified and visualized.

Cyber-physische Produktionssteuerung

Kurzfassung Die Erreichung einer hohen Termintreue stellt für 67 Prozent aller Unternehmen aus dem Maschinen- und Anlagenbau die führende logistische Zielgröße dar. Ansätze, die im Zuge von Industrie 4.0 entwickelt werden, versprechen eine bessere Beherrschung der Produktionsplanung und -steuerung (PPS), welcher bei der Erreichung einer hohen Termintreue trotz kurzfristiger Änderungswünsche seitens des Kunden und anderer unvorhersehbarer Störungen eine herausragende Bedeutung zukommt. Der vorliegende Beitrag befasst sich mit den Auswirkungen von Industrie 4.0 auf die Produktionsplanung und -steuerung (PPS) in einem komplexen Produktionsumfeld. Im Rahmen des Forschungsprojekts ProSense werden Lösungsansätze erarbeitet, um Fertigungssteuerer in einem cyber-physischen Produktionssystem (CPPS) optimal unterstützten zu können.