FIR e. V. an der RWTH Aachen
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Failure management in the production area has been intensely analyzed in the research community. Although several efficient methods have been developed and partially successfully implemented, producing companies still face a lot of challenges. The resulting main question is how manufacturers can be assisted by a sustainable approach enabling them to proactively detect and prevent failures before they occur. A high-resolution production system based on analyzed real-time data enables manufacturers to find an answer to the main question. In this context, Big Data technologies have gained importance since the critical success factor is not only to collect real-time data in the production but also to structure the data. Therefore, we present in this paper the implementation of Big Data technologies in the production area using the example of an actual research project. After the literature review, we describe a Big Data based approach to prevent failures in the production area. This approach mainly includes a real-time capable platform including complex event processing algorithms to define appropriate improvement measures.
Electricity generated by wind turbines (WT) is a mainstay of the transition to renewable energy. In order to economically utilize WT is, operating and maintenance costs, which account for 25% of total electricity generation costs in onshore WT’s, are a focus of cost reduction activities. Implementing a data-driven prescriptive maintenance approach is one way to achieve this. So far, various approaches for prescriptive maintenance for onshore WT’s have been suggested.
However, little research has addressed the practical implementation considering sociotechnical aspects. The aim of this paper is therefore to identify success factors for the successful implementation of such a maintenance strategy with clear and holistic guidance on how existing knowledge on prescriptive maintenance from science can be transferred to business practice. These recommendations are developed through case study research and classified in the four structural areas of Acatech’s Industry 4.0 Maturity Index: Resources, Information Systems, Organizational Structure and Culture.
With big data-technologies on the rise, new fields of application appear in terms of analyzing data to find new relationships for improving process under-standing and stability. Manufacturing companies oftentimes cope with a high number of deviations but struggle to solve them with less effort. The research project BigPro aims to develop a methodology for implementing counter measures to disturbances and deviations derived from big data. This paper proposes a methodology for practitioners to assess predefined counter measures. It consists of a morphology with several criterions that can have a certain characteristic. Those are then combined with a weighting factor to assess the feasibility of the counter measure for prioritization.
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.
Towards a Methodology to Determine Intersubjective Data Values in Industrial Business Activities
(2021)
This paper contributes to a valuation framework for valuing data as an intangible asset. Especially those industrial manufacturers developing and delivering holistic digital solutions are limited in calculating the true business value of data initiatives. Since the value of data is strongly dependent on the respective use case, a completely objective valuation is not possible. This complicates decision-making on the internal side regarding investments in digital transformation, and on the external side to communicate existing benefits to third parties via financial reporting. Therefore, the target is to design a valuation framework that allows industrial manufacturers to determine an intersubjective, i.e., traceable and transparent, data value. In order to develop a framework that can be applied in practice, the approach is based on industrial case study research.
Aufgrund kürzer werdender Produktzyklen und steigender Produktvielfalt werden produzierende Unternehmen mit einer zunehmenden Anzahl von Produktanläufen konfrontiert. Ziel aktueller Forschungsaktivitäten ist es daher, anlaufintensive Unternehmen zu befähigen, verlässliche Produktionsprogramme in kurzer Zeit zu erstellen. Lerneffekte sollen genutzt werden können ohne Diversifikationseffekte zu vernachlässigen. Zur Erreichung dieser Zielsetzung wird ein Modell für eine kybernetische PPP bei Produktanläufen entwickelt.
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.
Die Verschärfung des Wettbewerbsumfelds produzierender Unternehmen und die als Antwort hierauf in den Fokus rückenden agilen Methoden vergrößern die Bedeutung einer effizienten Handhabung von Änderungsprozessen. Am Beispiel des Maschinen- und Anlagenbauers Ortlinghaus zeigt der Beitrag, dass eine Kombination aus ungeeigneten Änderungsprozessen und mangelhaftem IT-Support in der Praxis oft die schnelle und gleichzeitig qualitätsgesicherte Durchführung von Änderungsprozessen verhindert. Der Zielkonflikt aus geringem Zeitbedarf und hoher Prozessqualität lässt sich durch Anpassungen in der IT-Unterstützung reduzieren. Hierdurch können Erfolgsfaktoren für ein effizientes Änderungsmanagement gehoben und die Problemfelder der Workflowunterstützung, Informationsverteilung und Datenhandhabung verbessert werden. Zentrales Hindernis zur Adressierung der Erfolgsfaktoren stellt die aktuell zur Abwicklung von Change Requests genutzte Arbeitsumgebung dar. Der Beitrag präsentiert hierfür als zentralen Lösungsansatz die Internet of Production Infrastruktur. Das Potenzial der Internet of Production Infrastruktur im Kontext des Änderungsmanagements wird anhand von drei Anwendungsbeispielen verdeutlicht. Abschließend wird der Migrationspfad für Unternehmen bei der Einführung eines effizienten Änderungsmanagements aufgezeigt.
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.
Increasing the energy efficiency and meanwhile avoiding unplanned maintenance breaks are keys for manufacturing companies to stay competitive in the future. This paper presents an energy saving and maintenance cost reducing approach for manufacturing environments. The approach describes first occurring types of energy wastage within manufacturing and characterizes them in more detail. Including additional external information, the significance of an identified on-going wastage can be determined. Based on the type of wastage and the significance; concrete recommendations for measures to prevent the wastage are delivered. The identified wastage facilitates detecting inefficient operating mode as well as wearing and malfunctioning at machines. By using complex event processing technologies realtime information can forwarded directly to the responsible persons to enable quick reactions to prevent energy wastage and unplanned downtimes. The paper presents an approach to identify detection and propose concepts for manufacturing enterprises. The information processing procedure is used for the implementation of two Use Cases.