Matthias Blum

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.

Design of a data structure for the order processing as a basis for data analytics methods

Today, manufacturing companies are facing the influences of a dynamic environment and the continuously increasing planning complexity. Using advanced data analytics methods, processes can be improved by analyzing historical data, detecting patterns and deriving measures to counteract the issues. The basis of such approaches builds a virtual representation of a product - called the digital twin or digital shadow. Although, applied IT systems provide reliable feedback data of the processes on the shop-floor, they lack on a data structure which represents real-time data series of a product. This paper presents an approach for a data structure for the order processing which overcomes the described issue and provides a virtual representation of a product. Based on the data structure deviations between the production schedule and the real situation on the shop-floor can be identified in real time and measures to reschedule operations can be identified.

Der Digitale Schatten in der Auftragsabwicklung

Kurzfassung Im Kontext Industrie 4.0 kommt der Erfassung der anfallenden Daten in der Produktion und deren Nutzung eine zentrale Bedeutung zu. Analysen betrieblicher Daten, welche auf verschiedenen Ebenen generiert werden, lassen Rückschlüsse und Erkenntnisse zur besseren Entscheidungsfindung zu. Die Basis für den Einsatz von Verfahren der Datenanalyse und -auswertung stellt ein hinreichend genaues Abbild der relevanten Daten – der Digitale Schatten – in der Auftragsabwicklung, Produktion, Entwicklung oder angrenzenden Bereichen dar. Im Rahmen des vorliegenden Beitrags wird ein Modell für den Digitalen Schatten in der Auftragsabwicklung vorgestellt, welches die Basis für die Implementierung von Methoden der Datenanalytik darstellt.

Keine Industrie 4.0 ohne den Digitalen Schatten

Kurzfassung Der Begriff “Digitaler Schatten“ steht für ein hinreichend genaues, digitales Abbild der Prozesse, Informationen und Daten eines Unternehmens. Dieses Abbild wird benötigt, um eine echtzeitfähige Auswertebasis aller relevanten Daten zu schaffen, um hieraus letztendlich Handlungsempfehlungen abzuleiten. Die Bildung des Digitalen Schattens ist damit ein zentrales Handlungsfeld von Industrie 4.0 und stellt die Grundlage für alle weitergehenden Aktivitäten dar.

Cyber Physical Production Control

Currently the control of constantly increasing market dynamics and the simultaneously increasing individualization of process chains represent the central challenges for manufacturing companies. These challenges are caused by a lack of transparency in production planning, non-real-time processing of data as well as poor communication between the planning and control level. The research project ProSense addresses this problem and intends to eliminate the current problems in production by developing a high-resolution, adaptive production control based on cybernetic support systems and intelligent sensors. Through the development of a cyber-physical production control as one part of the project, which forms the basis for an innovative self-optimizing advanced planning system, ProSense provides a contribution to accomplish the goals of industry 4.0.

A Cybernetic Reference Model for Production Systems Using the Viable System Model

Designing viable and integrative production systems is challenging for big companies. Researchers often fail to holistically consider the production system. Thus, the aim of this paper is to propose a holistic approach how the supply chain, the production and shop floor planning intermesh. Hereby a Viable System Model was applied. Standardized communication channels were able to be defined among three entities. In conclusion this newly proposed approach enables companies to reduce necessary stocks, production lead times and manpower allocation. This proposed approach boosts the efficiency of all production planning processes. This in turn translates to decreased stocks, shorter lead times and to more efficient manpower allocation. This holistic approach is a key to success for companies, in particular, in high-wage countries.

Supply Chain 4.0: Logistikdienstleister im Kontext der vierten industriellen Revolution

Der Digitalisierungsprozess in der Industrie eroffnet der Wirtschaft ein riesiges Innovationspotenzial, mit der sie ihre Wettschopfungs- und Leistungsfahigkeit deutlich verbessern kann. Im Mittelpunkt des Zukunftskonzepts Industrie 4.0 steht zurzeit die Effizient der Produktionsprozesse. Die Verfugbarkeit von Echtzeitdaten an samtlichen Schnittstellen der Supply Chain eroffnet auch Logistikdienstleistern Moglichkeiten, an aktuellen Entwicklungen zu partizipieren und ihr Leistungsportfolio optimal zu gestalten. Der FIR e. V. an der RWTH Aachen schafft mit dem Begriff „Enterprise-Integration“ einen Ordnungsrahmen fur Logistikdienstleister, der drei interdisziplinare Forschungsfelder vereint und auf eine vollstandig integrative Gestaltung komplexer Wertschopfungssysteme fokussiert, um zukunftige Herausforderungen im Kontext Industrie 4.0 zu meistern.

Self-learning Production Control Using Algorithms of Artificial Intelligence

Manufacturing companies are facing an increasingly turbulent market – a market defined by products growing in complexity and shrinking product life cycles. This leads to a boost in planning complexity accompanied by higher error sensitivity. In practice, IT systems and sensors integrated into the shop floor in the context of Industry 4.0 are used to deal with these challenges. However, while existing research provides solutions in the field of pattern recognition or recommended actions, a combination of the two approaches is neglected. This leads to an overwhelming amount of data without contributing to an improvement of processes. To address this problem, this study presents a new platform-based concept to collect and analyze the high-resolution data with the use of self-learning algorithms. Herby, patterns can be identified and reproduced, allowing an exact prediction of the future system behavior. Artificial intelligence maximizes the automation of the reduction and compensation of disruptive factors.

A Simulation Based Approach to Investigate the Procurement Process and Its Effect on the Performance of Supply Chains

Influenced by the high dynamic of the markets the optimization of supply chains gains more importance. However, analyzing different procurement strategies and the influence of various production parameters is difficult to achieve in industrial practice. Therefore, simulations of supply chains are used in order to improve the production process. The objective of this research is to evaluate different procurement strategies in a four-stage supply chain. Besides, this research aims to identify main influencing factors on the supply chain’s performance. The performance of the supply chain is measured by means of back orders (backlog). A scenario analysis of different customer demands and a Design of Experiments analysis enhance the significance of the simulation results.

Die Wahl der richtigen Beschaffungsstrategie

Kurzfassung Im Zuge der Globalisierung und einer steigenden Marktdynamik gewinnt die Optimierung der Lieferketten zunehmend an Bedeutung. Die Untersuchung unterschiedlicher Beschaffungsstrategien im Wertschöpfungsnetzwerk unter Berücksichtigung des Einflusses verschiedener Produktionsparameter fällt in der unternehmerischen Praxis zunehmend schwer. Hierbei können Simulationen von Lieferketten Abhilfe schaffen, um Wertschöpfungsstrukturen entlang der Supply Chain zu analysieren und zu verbessern.