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In this paper, an approach towards energy management 4.0 will be presented. Energy management 4.0 is understood as an encompassing energy data based concept for manufacturing companies acting in an flexible energy grid of the future with the final goal of autonomous self-optimization Controlling, supervising and scheduling production and logistic steps based on a reliable communication infrastructure and real time data in accordance to achieve a maximum of profitability with regard to human factor is executed.
Guided by a four maturity levels of the "acatech Industrie 4.0 Maturity Index" developed by the German National Academy of Science and Engineering (acatech) different use cases are presented according to the steps of visibility, transparency, prognostic capacity and self-optimization. The basic idea of energy management 4.0 is described and an outlook of further steps that are needed to be evaluated for an implementation are presented.
Growing information systems (IS) often come along with growing IT complexity, because of emerging rag rug landscapes. This development causes rising IT costs and dependencies, which hinder the maintenance and expansion of the IS landscape. This article outlines the current research on published and presented methods to manage the rising IT complexity in a literature review. Because definitions of “IT complexity” vary a lot in literature, this paper also includes a definition of the term. In addition to that, it delivers a presentation of the used research methodology. Subsequently, it presents the findings in literature, highlights the research gap and – based on the literature analysis – presents, the steps that need to be taken. A discussion of the results and a summary complete the article.
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 Beitrages wird ein Modell für den Digitalen Schatten in der Auftragsabwicklung vorgestellt, welches die Basis für die Implementierung von Methoden der Datenanalytik darstellt.
Im Rahmen der vernetzten Digitalisierung stehen insbesondere kleine und mittlere IT-Organisationen und IT-Dienstleister vor der großen Herausforderung, in einem immer dynamischer werdenden Umfeld Leistungen in hoher Qualität zu liefern. Die Verknüpfung dieser Leistungen mit den zu unterstützenden Geschäftsprozessen und Geschäftsmodellen gestaltet sich schwierig und erfordert eine service- und prozessorientierte Denkweise.
Zur Bewältigung dieser Herausforderungen und der Umsetzung des "service- und prozessorientierten Denkens" bietet das IT-Service-Management (ITSM) Methoden und Maßnahmen zur kundenorientierten, prozessgesteuerten und transparenten Erbringung von IT-Services. Trotz bestehender ITSM-spezifischer Referenzmodelle und Regelwerke werden die beschriebenen Methoden von kleinen und mittleren IT-Organisationen und IT-Dienstleistern kaum genutzt. Der Grund hierfür liegt unter anderem in der hohen Komplexität der Regelwerke und dem damit verbundenen großen Implementierungsaufwand. Es fehlt ein Vorgehen, das die Fähigkeiten und Möglichkeiten von kleinen und mittleren Unternehmen (KMU) berücksichtigt, um IT-Prozesse eigenständig hinsichtlich der Serviceorientierung zu bewerten und zu optimieren.
Das Ergebnis des Forschungsvorhabens "GradeIT" ist eine Vorgehensweise, die KMU dabei unterstützt, relevante IT-Service-Prozesse für sich selbst zu identifizieren, um diese dann eigenständig zu bewerten und auf Basis transparent dargestellter Wirkungszusammenhänge zu spezifischen Einflussfaktoren erfolgversprechende Handlungsempfehlungen auszusprechen.
Due to the drastically increasing amount of data, decision making in companies heavily relies on having the right data available. Also because of an increasing complexity of structures and processes, quick and precise flows of information become more important.
This paper introduces a new approach for modelling information flows, creating a basis for an efficient information management. It can be used to structure the information requirements and identify gaps within the information processing.
To display its benefits, the proposed Information Logistics Notation (ILN) is applied to the information logistics of todays and future energy market and grid stability management, both processes of increasing complexity.
The manufacturing industry has to exploit trends like “Industrie 4.0” and digitization not only to design production more efficiently, but also to create and develop new and innovative business models. New business models ensure that even SMEs are able to open up new markets and canvass new customers. This means that in order to stay competitive, SMEs must transform their existing business models.
The creation of new business models require smart products. The required data base for new business models cannot be provided by SMEs alone, whereas smart products are able to provide a foundation, given the creation of smart data and smart services they enable. These services then expand functions and functionality of smart products and define new business models.
However, the development of smart products by small and medium-sized enterprises is still lined with obstacles. Regarding the product development process the inclusion of smart products means that new and SME-unknown domains diffuse during the process. Although there are many models regarding this process there appears to be a substantial lack of taking into account the competencies enabled by the implementation of digital technologies. Hence, several SME-supporting approaches fail to address the two major challenges these enterprises are faced with. This paper generally describes valid objectives containing relevant stakeholders and their allocation to the phases of the product life cycle.
Within each objective the potential benefit for customers and producers is analyzed. The model given in this paper helps SMEs in defining the initiation of a product development project more precisely and hence also eases project scoping and targeting for the smartification of an already existing product.
In order to introduce load management in the manufacturing industry, some obstacles need to be pointed out. This paper presents a feasible approach on how to implement load management measures in companies.
To this end, load management and energy management are explained and distinguished in a first step. Subsequently, the implementation method is introduced. Therefore, by means of this paper, companies will be enabled to use load management measures and significantly reduce their energy costs. In the second part of the paper, the introduced approach will be applied.
Hence, a use case of a manufacturing company is described. Alongside energy analyses with consumption data, specific measures are presented.
Nowadays, cyber physical systems support the improvement of efficiency in intralogistics by controlling and manipulating the production and logistic environment autonomously. Due to the complexity of the individual production processes, designing suitable cyber-physical systems based on their existing production environment is a challenge for companies.
This paper presents a new methodology on how to design cyber-physical systems conceptually to suit an individual production environment. Compared to existing design approaches, this methodology matches immediately the required functions to existing information and communication technology’s components insisting on the neutral assimilation of requirements.
Therefore, the requirement specification asks for needed functions in relating to offered functions of information and communication technology (ICT) components. The paper focusses the use case of implementing a cutting-edge mobile network technology into an existing tracking and tracing process.
In order to introduce load management in the manufacturing industry, some obstacles need to be pointed out. This paper presents a feasible approach on how to implement load management measures in companies. To do so, load management and energy management are explained and distinguished in a first step. Subsequently, the implementation method is introduced. Therefore, by using this paper, companies will be enabled to use load management measure and reduce their energy costs significantly.
Industry 4.0 is driven by Cyber-Physical Systems and Smart Products. Smart Products provide a value to both its users and its manufacturers in terms of a closer connection to the customer and his data as well as the provided smart services. However, many companies, especially SMEs, struggle with the transformation of their existing product portfolio into smart products. In order to facilitate this process, this paper presents a set of smart product use-cases from a manufacturer’s perspective. These use-cases can guide the definition of a smart product and be used during its architecture development and realization. Initially the paper gives an introduction in the field of smart products. After that the research results, based on case-study research, are presented. This includes the methodological approach, the case-study data collection and analysis. Finally, a set of use-cases, their definitions and components are presented and highlighted from the perspective of a smart product manufacturer.
Management of information and the IT systems it is stored in becomes a crucial capability for the industry. However, companies are struggling with the management of the various requirements and frequent changes of technology. Thus, IT complexity has become a major challenge for companies. At the same time, especially manufacturing companies are striving to implement Industrie 4.0 concepts. Many of these even have developed an Industrie 4.0 roadmap including various projects to change the company. Companies can develop such roadmaps by applying the Industrie 4.0 Maturity Index that gives a broad view on necessary capabilities for Industrie 4.0.
In our research, we analyzed data sets from over 10 manufacturing companies that have performed an Industrie 4.0 maturity assessment. Our hypothesis was that IT complexity challenges are hindering the implementation of Industrie 4.0 roadmaps significantly. We could prove this hypothesis at least for the companies analyzed and give insights on the specific challenges. Based on our analysis, we conclude our article by giving concrete recommendations on how to tackle IT complexity.
Digital technologies such as 5G, augmented reality, and artificial intelligence (AI) are currently being used in various ways by manufacturing companies. As the fourth industrial revolution progresses, it has become apparent that reckless use and inadequate regulation of these technologies have a detrimental effect on the environment in which they are utilized. Therefore, regulation of digital technologies is imperative today to ensure more responsible and sustainable use. While governments usually establish regulations, progress is not keeping pace with the demands and hazards of employing digital technologies. The European AI law serves as an example of the considerable distance yet to be covered before binding guidelines are established. Consequently, companies must take proactive measures today to ensure that they use digital technologies responsibly in their environments. In this context, identifying which digital technologies are pertinent to manufacturing companies in terms of regulation is crucial. Furthermore, a comprehensive approach is required to design compliance holistically for digital technologies and to systematically derive the corresponding guidelines. This paper introduces a set of models that not only determine the importance of
compliance in the application of different technologies but also present a framework for methodically designing compliance. Furthermore, the paper contributes to the development of an AI platform in the German research project PAIRS by investigating the compliance relevance of applications such as artificial intelligence.
The main challenge in all application areas of EV usage still is the energy storage within, as well as the energy transmission into an EV. However, this storage and transmission of energy also allows for synergies with a smart grid, if the information is adequately exchanged between roles in the energy and mobility sector. Since the energy transmission is a so called “fixed and intersection point” of E-Mobility, interoperability is required not only on an electrical (e.g. plugs), but also on an informational level. Standardization efforts are currently underway (e.g. IEC 15118), yet a comprehensive, consolidating view on the information system around energy transmission is missing. Therefore, this paper suggests a generic information system architecture for e-mobility (EM-ISA) derived from the Smart Grid Architecture Model (SGAM). EM-ISA shall be a base for companies to develop innovative services for their particular, ICT-enabled E-Mobility application area while at the same time stay at important points informational interoperable at the fixed and intersection point of energy transmission.
Der High-Resolution-Supply-Chain-Management-Ansatz erlaubt es Unternehmen, in Echtzeit auf dynamische Einflüsse des Marktes reagieren zu können.
Echtzeitfähige Planungs- und Regelungsprozesse können den Planungsaufwand reduzieren und gleichzeitig mit den zur Verfügung stehenden Echtzeitinformationen die Planungsqualität verbessern.
Planungsprozesse auf Basis von Echtzeitinformationen setzen voraus, dass ein konsistenter Informationsaustausch zwischen den unterschiedlichen Planungsebenen besteht sowie ein hoher Autonomiegrad innerhalb der einzelnen Planungsinstanzen.
PLM trifft ERP
(2013)
Das Management des Produktlebenszyklus ist eine komplexe Aufgabe, dessen volles Potenzial erst durch die Integration des gesamten Unternehmens erreicht wird. Um die Einbindung aller Fachabteilungen sicherzustellen, ist eine Potenzialuntersuchung notwendig, bei der Herausforderungen und mögliche Verbesserungen entlang des gesamten Produktlebenszyklus untersucht werden müssen. Der PLM-QuickCheck, den das FIR an der RWTH Aachen und das WZL der RWTH Aachen gemeinsam entwickeln, liefert hier einen möglichen Ansatz.
Der Begriff „Digitaler Schatten“ steht für ein hinreichend genaues, digitales Abbild der Prozesse, Information 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.
Digitale Technologien sind ein wesentlicher Bestandteil der Wertschöpfungskette in der industriellen Praxis geworden. Die Digitalisierung hat die Produktion und den modernen Arbeitsplatz in den vergangenen Jahrzehnten auf eine Art beeinflusst, die mit keiner anderen technischen Entwicklung vergleichbar ist, und die nun der vierten industriellen Revolution den Weg ebnet.
Die Essenz von Industrie 4.0 ist die Vernetzung von Produktionssystemen mithilfe von IT und dem Internet der Dinge, um prognosefähig zu sein und die Produktion effizienter und flexibler zu gestalten. Wesentliche Befähiger dieser Vision sind Daten aus Prozessen, Anlagen und Ressourcen, aus denen für das Unternehmen entscheidungskritische Informationen gewonnen werden. Hieraus lassen sich Erkenntnisse ableiten, die bisher verborgene Wirkungszusammenhänge zutage fördern.
Prognosemodelle errechnen auf der Basis dieser Erkenntnisse mögliche Zukunftsszenarien und belegen sie mit Wahrscheinlichkeitswerten bezüglich ihres Eintritts. Durch die Vernetzung der Informationen unterschiedlicher Aufgaben, Funktionen und Domänen lassen sich Handlungsempfehlungen fundieren, wobei eine unüberschaubare Anzahl relevanter Parameter berücksichtigt wird. Der Produktion wird ähnlich dem Rennsport eine Ideallinie aufgezeigt, an der sie sich orientieren kann, um in kürzester Zeit optimierte Ergebnisse zu erzielen.
In recent years supply chain participants are increasingly suffering the effects of disturbances in transportation supply chains. Both, dynamics in consumer demands and global supply chains lead to a growth in unplanned supply chain events. These can cause from rather manageable disturbances through to complete break-downs of transportation chains, resulting in high follow-up and penalty costs.
Consequently, concepts for an efficient supply chain disturbance management are needed, preferably with a real-time identification and reaction to disturbance events. Therefore in the following paper the research results of the German research project Smart Logistic Grids with the focus on designing an integrated model for the real-time disturbance management in transportation supply networks are presented. This includes the introduction of elaborated classification models for disturbances and action patterns as well as an associated costs and performance measurement system. Finally, a procedure model for the disturbance management is presented.
Systematization models for taylor-made sensor system applications and sensor data fit in production
(2015)
Industrial digitalization to realize smart factories is driven by an informatory base of high-resolution data provided by sensor systems on the shop-floor level. The challenge of technical availability of fitting measurement solutions nowadays turns in a struggle of finding the optimal solution for a specific task in an ever-growing sensor market. This paper analyzes and specifies necessary models to systematically derive and describe organizational, technical and informatory requirements for sensor system applications increasing the technological fit for faster integration and lower misinvestment rates.