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Institute
- Business Transformation (10)
- Dienstleistungsmanagement (67)
- FIR e. V. an der RWTH Aachen (232)
- Informationsmanagement (51)
- Produktionsmanagement (115)
Kennzahlen und Führungssysteme sind im Sinne des Performance-Managements ein zentraler Aspekt des Managements industrieller Dienstleistungen. Die Performancemessung bezieht dabei sowohl strategische Aspekte als auch Ergebnisse auf der operativen Ebene mit ein.
Die Immaterialität sowie die Integrativität von Dienstleistungen bedingen auch, dass mehr perspektivische Kennzahlen und Führungssysteme erforderlich sind, die neben monetären Kennzahlen auch die Erfassung und Auswertung von für Dienstleistungen spezifischen kunden- sowie kundenprozessbezogenen Kennzahlen ermöglichen. Im Beitrag werden ausgewählte Messansätze vorgestellt. Hierfür wird eine Gliederung in die Teilbereiche Kundengerichtete, Unternehmensgerichtete und Intern gerichtete Messansätze vorgenommen.
Within each of the three design fields numerous design elements exist (e.g. degree of centralization, number of warehouses etc. in the field network design). Hence, the interdependencies of all design elements have to be analyzed to allow optimal decisions for the design of an efficient and effective spare parts logistics. Nevertheless, the complexity among all interdependencies can hardly be understood. Therefore it is necessary to reduce the complexity of design decisions by focusing on the most important design elements according to the logistical requirements of different spare part categories. In order to achieve this goal, a classification of spare parts in terms of their key characteristics has been developed. For different spare part categories only a smaller set of design elements and their interdependencies has to be taken into account. The reduced number of key design elements per spare part category can be analyzed and understood in depth. Thus a Systems Dynamics approach is used to allow a better configuration of network design, cooperation concepts and inventory management in spare parts Supply Chains on the basis of specific logistics requirements of different spare part categories.
In dynamic markets flexible and efficient production systems are the main success factor for companies. The production system in this context includes all five phases of the SCOR-Model: Source, Make, Deliver, Plan and Return. In a subproject of the cluster of excellence "Integrative Production Technology for High-Wage Countries" at RWTH Aachen University, a configuration logic is being developed that enables companies to configure their production system according to the dynamic requirements of the market. As a major intermediate result, a holistic description model for production systems has been defined. In combination with numerous attributes in the sub-models, a detailed characterization of the production system is possible.
The sub-model for the design of the Supply Chain (mainly Deliver) will be depicted in detail in this paper. Representative for the design of a Supply Chain, spare parts logistics - as one of the most challenging tasks in logistics planning - is analyzed in depth. For this purpose spare parts logistics is divided into three design fields: network design, cooperation concepts (e.g. with logistics providers, customers, suppliers) and inventory management. Decisions in the design fields are highly interdependent, any spare parts logistics configuration has to take these interdependencies into account.
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.
The European Commission set out the goal of carbon neutrality by 2050, which shall be achieved by fostering the twin transition - sustainability through digitalization. A keystone in this transition is the implementation of a prospering Circular Economy (CE). However, product information required to establish a flourishing CE is hardly available or even accessible. The Digital Product Passport (DPP) offers a solution to that problem but in the current discussion, two separate topics are focused on: its architecture and its application on batteries. The content of the DPP has not been an essential part of the discussion, although access to high-quality data about a product's state, composition and ecological footprint is required to enable sustainable decision-making. Therefore, this paper presents a classification of product data for circularity in the manufacturing industry to emphasize the discussion about the DPP's content. Developed through a systematic literature review combined with a case-study-research based on common operational information systems, the classification comprises three levels with 62 data points in four main categories: (1) Product information, (2) Utilization information, (3) Value chain information and (4) Sustainability information. In this paper, the potential content structure of a DPP is demonstrated for a use case in the machinery sector. The contribution to the science and operations community is twofold: Building a guideline for DPP developers that require scientific input from available real-world data points as well as motivating manufacturers to share the presented data points enabling a circular product information management.
The almost boundless possibilities of realizing saving potentials and innovations drive manufacturing companies to implement Business Analytics as part of the digitalization roadmap. The increasing research within the field of algorithm design and the wide range of user-friendly tools simplify generating first insights from data also for non-professionals. However, small and medium sized companies struggle implementing Business Analytics company-wide due to the lack of competencies. Especially the customization of a multitude of analytic methods in order to match a superordinate, business-relevant question is not done easily. This paper enables researchers as well as practitioners to close the gap between business relevant questions and algorithms. From a practical point of view, this paper helps shortening the search time for a suitable algorithm. Out of a research perspective, it aims to help positioning new algorithms within a structured framework in order to enhance the communication of algorithms’ capabilities.
Wichtige Wachstumsmärkte werden zunehmend durch Handelshemmnisse abgeschottet. Während die Automobilindustrie bereits mit dem Completely Knocked Down (CKD)-Konzept reagiert, indem Erzeugnisse teilzerlegt exportiert und lokal endmontiert werden, fehlen für den Maschinen- und Anlagenbau geeignete Ansätze für die Übertragung. Der vorliegende Artikel leistet dazu einen Beitrag, in dem die relevanten Merkmale einer CKD-Baugruppe definiert und vor dem Hintergrund relevanter Wirkungszusammenhänge ein Vorgehen für die Ableitung idealer Baugruppentypen skizziert wird.
Störungen und Änderungen des Produktionssystems führen zu Kosten und Aufwänden, bieten jedoch auch die Chance zur kontinuierlichen Verbesserung.
Um Änderungsanfragen zu erfassen, können etablierte Ansätze genutzt werden. Diese vernachlässigen jedoch die Anforderungen, denen sich ein Produktionssystem im Zeitalter der Digitalisierung ausgesetzt sieht. Der vorliegende Beitrag stellt einen Ansatz zur standardisierten Erfassung von Änderungsanfragen vor, welcher die Ausgangsbasis für die Bewertung von Änderungsanfragen in bestehenden IT-Systemen bietet.
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.
Methods of machine learning (ML) are difficult for manufacturing companies to employ productively. Data science is not their core skill, and acquiring talent is expensive. Automated machine learning (Auto-ML) aims to alleviate this, democratizing machine learning by introducing elements such as low-code or no-code functionalities into its model creation process. Due to the dynamic vendor market of Auto-ML, it is difficult for manufacturing companies to successfully implement this technology. Different solutions as well as constantly changing requirements and functional scopes make a correct software selection difficult. This paper aims to alleviate said challenge by providing a longlist of requirements that companies should pay attention to when selecting a solution for their use case. The paper is part of a larger research effort, in which a structured selection process for Auto-ML solutions in manufacturing companies is designed. The longlist itself is the result of six case studies of different manufacturing companies, following the method of case study research by Eisenhardt. A total of 75 distinct requirements were identified, spanning the entire machine learning and modeling pipeline.
Industry 4.0 and Smart Maintenance represent a great opportunity to make manufacturing and maintenance more effective, safer, and reliable. However, they also represent massive change and corresponding challenges for industrial companies, as many different options and starting points have to be weighed and the individual right paths for achieving Smart Maintenance need to be identified. In our paper, we describe our approach to evaluating maintenance organizations in a case study for the oil and gas industry, developing a shared vision for the future, and deriving economical and effective measures. We will demonstrate our approach, by showcasing a specific example from the oil and gas industry, where a need for action on HSE-relevant critical flanges in the company's piping systems was identified. We describe the steps, that were taken to identify the need for action, the specifications of the project and the criticality analysis of the piping system. This resulted in the derivation of a digitalization measure for critical flanges, which was first commercially analyzed and then the flanges were equipped with a continuous monitoring solution. Finally, a conclusion is drawn on the performed procedure and the achieved improvements.