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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.
Production in high-wage countries can be made more efficient, cost-effective, and flexible by solving the conflict between planning and value orientation. A promising approach is to focus on planning and decision-making processes (production planning and control, design of production processes and machinery, etc.) and to aim to maximize overall planning efficiency. Planning efficiency can be expressed as the ratio between the benefit generated by preparing detailed process instructions to produce the parts or components and the corresponding planning efforts. Industrial companies wanting to gain a competitive advantage in dynamic global markets have to identify a set of non-dominated solutions with the most favorable effort–benefit ratio rather than a single solution. The optimum between detailed planning and the immediate implementation of value-adding activities (process steps) in the process chain needs to be found dynamically for each product.
Today’s manufacturers are facing numerous challenges such as highly entangled and interconnected supply chains, shortening product lifecycles and growing product complexity. They thus feel the need to adjust and adapt faster on all levels of value creation. Self-optimization as a basic principle appears a promising approach to handle complexity and unforeseen disturbances within supply chains, machines and processes. Therefore it will improve the resilience and competitiveness of manufacturing companies.
This paper gives an introduction to the concept of self-optimizing production systems. After a short historical review, the different levels of value creation from supply chain design and management to manufacturing and assembly are analyzed considering their specific demands and needs for self-optimization. Examples from each of these levels are used to illustrate the concept of self-optimization as well as to outline its potential for flexibility and productivity. This paper closes with an outlook on the current scientific work and promising new fields of action.
Im Zuge der Globalisierung und einer steigenden Marktdynamik ge-
winnt die Optimierung der Lieferketten zunehmend an Bedeutung.
Die Untersuchung unterschiedlicher Beschaffungsstrategien im Wert-
schöpfungsnetzwerk unter Berücksichtigung des Einflusses verschie-
dener 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.
Industry 4.0 and the consequent necessity of digitalization has also impli-cations to the field of procurement, resulting in the so-called term of Procurement 4.0. Digitalization can be a valuable tool to increase the efficiency of the procurement organization and to exploit new opportunities of growth. A mandatory requirement to perform the digital transformation is an increased transparency along the procurement process chain. This paper aims to conceptualize a digital shadow for the procurement process in manufacturing industry as a basis for advanced data analytics procedures. The term digital shadow stands for a sufficiently accurate, digital image of a compa-ny's processes, information and data. This image is needed to create a real-time eval-uable basis of all relevant data in order to finally derive recommendations for action. The formation of the Digital Shadow is thus a central field of action for Industrie 4.0 and forms the basis for all further activities.
Individualisierung in Kombination mit dem Kundenwunsch nach immer kürzeren Lieferzeiten führt zu einer steigenden Komplexität und Dynamik auf Produktionsebene. Um weiterhin das Einhalten der logistischen Zielgrößen zu ermöglichen, müssen die zurzeit vorhandenen Unternehmensstrukturen und deren Prozesse vorbereitet werden.
Eine Möglichkeit, dem turbulenten Markt zu begegnen, ist ein adaptives Abweichungsmanagement in der Fertigungssteuerung, das Unternehmen einen adäquaten Umgang mit Abweichungen ermöglicht. Klassische Methoden der Fertigungssteuerung reichen nicht mehr aus, um mit der jetzigen Entwicklung umzugehen.
Das hier beschriebene Zielmodell und die internen sowie externen Einflussfaktoren sollen bei der Analyse der Zusammenhänge in der Fertigungssteuerung helfen. Das vorgestellte Vorgehensmodell zeigt, wie ein adaptives Abweichungsmanagement aufgebaut werden sollte, um die systematische, differenzierte und kategorisierte Betrachtung und Bewältigung von Abweichungssituationen zu ermöglichen.
Durch den vereinfachten Umgang mit Abweichungen wird sowohl eine tiefgreifende Analyse der Wirkungszusammenhänge als auch eine automatisierte Beruhigung der Produktion ermöglicht. Dies führt zu einer Reduktion von wiederkehrenden Abweichungen durch die Implementierung einer geschlossenen kaskadierten
Informationsrückführung.
In today´s turbulent market, the way data are used in production is one of the key aspects to maintain or increase a manufacturing company´s ability to compete. Even though most companies are aware of the advantages of collecting, analyzing and using data, the majority of them do not exploit these fully. Thus, IT systems and sensors are integrated into the shop floor in order to deal with the current challenges, leading to an overwhelming amount of data without contributing to an improvement of production control. Because of developments like digitization and Industry 4.0, there is an innumerable amount of existing research focusing on data analytics, artificial intelligence and pattern recognition. However, research on collaborative platforms in traditional production control still needs improvement. Therefore, the main goal of this paper is to present a platform based closed loop production control and to discuss the relevant data. The collaborative platform represents the basis for a future analysis of high-resolution data using cognitive systems in order for companies to maximize the automation of their production. A use case at the end of the paper shows the potential implementation of the findings in practice.
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.
Industrie 4.0 ist in Politik, Medien, Wissenschaft und Wirtschaft derzeit omnipräsent. Intelligenter, individueller, effizienter, schneller, vernetzter – so lauten nur einige Versprechen dieses neuen industriellen Zeitalters. Tatsächlich sind die Potenziale gerade für den deutschen Maschinen- und Anlagenbau gewaltig: Sowohl für Anbieter als auch für Anwender von Technologien rund um das Thema Industrie 4.0. Aber noch existieren viele ungelöste Fragen, Unsicherheiten und Aufgaben. Hier wollen wir mit unserer Readiness-Studie ansetzen und Hilfestellung leisten. Denn ein Selbstläufer wird Industrie 4.0 nicht. Mit der vorliegenden Studie soll die große Vision näher an die betriebliche Realität gebracht werden. Auch zeigen wir die anspruchsvollen Wegmarken auf, die für viele Unternehmen hinsichtlich ihrer Industrie 4.0-Fähigkeit noch zu passieren sind. Die Studie untersucht, an welcher Stelle der Maschinen- und Anlagenbau aktuell bei der Umsetzung steht. Motivation und Hemmnisse der Unternehmen werden ebenso in den Blick genommen wie die Unterschiede, die sich zwischen Mittelstand und großen Unternehmen ergeben. Im Ergebnis ist es erstmals möglich, die „Industrie 4.0-Readiness“ der Maschinenbau-Industrie detailliert und systematisch abzubilden.
Industrie 4.0 is all around us today: in politics, in the media, and on the agendas of researchers and entrepreneurs. Smarter, faster, more personalized, more efficient, more integrated – those are just some of the promises of this new industrial era. The potential, especially for Germany ́s mechanical
engineering industry and plant engineering sector, is indeed great, both for providers and for users of technologies across the spectrum of Industrie 4.0.
But there are still many unresolved questions, uncertainties, and challenges. Our readiness study seeks to address this need and offer insight. Because Industrie 4.0 will not happen on its own.
This study is intended to bring the grand vision closer to the business reality. We also highlight the challenging milestones that many companies must still pass on the road to Industrie 4.0 readiness.
The study examines where companies in the fields of mechanical and plant engineering currently stand, focusing on what motivates them and what holds them back, and on the differences that emerge between small and medium enterprises on the one hand and large enterprises on the other.
The results make it possible for the first time to develop a detailed, systematic picture of Industrie 4.0 readiness in the engineering sector.
The study concludes with recommendations for action in the business community, complementing the diverse suite of programs and activities offered by VDMA’s Forum Industrie 4.0. We would like to take this opportunity to thank the two sponsors of this project from the VDMA Forum, Dietmar Goericke and Dr. Christian Mosch, whose efforts played a critical role in making this study a success.
We are convinced that Industrie 4.0 can become a success story for Germany’s engineering sector. May our “Industrie 4.0 Readiness” study do its part in this effort.