Refine
Year of publication
Document Type
- Article (9)
- Book (4)
- Part of a Book (25)
- Conference Proceeding (86)
- Contribution to a Periodical (17)
- doctorallecture (1)
- Doctoral Thesis (1)
- Lecture (4)
- Internet Paper (4)
- Preprint (1)
Language
- German (64)
- English (95)
- Multiple languages (2)
Is part of the Bibliography
- no (161) (remove)
Keywords
- 02 (19)
- 03 (3)
- 3GPP (1)
- 5G (5)
- 5G mobile communication (2)
- 5G use case (1)
- 5G-Mobilfunk (1)
- 5G-Technology (1)
- AI (2)
- Aachener PPS-Modell (1)
Institute
- Business Transformation (14)
- Dienstleistungsmanagement (35)
- FIR e. V. an der RWTH Aachen (161)
- Informationsmanagement (43)
- Produktionsmanagement (72)
Künstliche Intelligenz (KI) hat sich über die letzten Jahre stetig zu einem Thema mit strategischer Priorität für Unternehmen entwickelt. Das zeigt sich nicht zuletzt in der gesteigerten Investitionsbereitschaft deutscher Unternehmen in KI-Projekte. Wirtschaftliche Akteure haben erkannt, dass durch eine sinnvolle Nutzung von KI-Technologien Wettbewerbsvorteile erzielt werden können. Die vorliegende Studie legt das Augenmerk auf den industriellen Einsatz einer KI-Technologie, die bereits heute von vielen Unternehmen erfolgreich genutzt wird: Die natürliche Sprachverarbeitung (engl. Natural Language Processing, kurz NLP). Die wirtschaftlichen Potenziale der Technologie liegen dabei in ihrer Fähigkeit, betriebliche Abläufe zu automatisieren und die Schnittstelle zwischen Mensch und Maschine zu verbessern und zu vereinfachen. Ziel der Studie ist es, die Potenziale der NLP-Technologie für Unternehmen nutzbar zu machen, indem konkrete Anwendungsfälle und allgemeine Handlungsempfehlungen sowie Nutzen und Risiken aufgezeigt werden.
Das Gegenteil von Theorie ist die Praxis. So sagt man landläufig und unterstellt damit oft, dass wissenschaftliche Erkenntnisse nicht immer für den Alltag taugen. Dass Theorie aber nicht gleich Theorie ist und Wissenschaft und Praxis trotz aller Unterschiedlichkeit aufeinander angewiesen sind, darauf weist das FIR an der RWTH Aachen schon mit der Auflösung seines Akronyms hin: „Forschung. Innovation. Realisierung.“ Hier zielen alle Forschungsaktivitäten darauf ab, Lösungen für reale Herausforderungen aus der Praxis zu schaffen, die am Ende auch umsetzbar sind. Eine nutzenbringende Verbindung zwischen den beiden scheinbar so unterschiedlichen Welten ist dafür unabdingbar und diese Lücke schließt das FIR mit Industriekooperationen, Wissens und Technologietransfer sowie Weiterbildungsangeboten auf vielen Ebenen. Nicht zuletzt positionierte sich das FIR als leitendes Institut des Clusters Smart Logistik auf dem RWTH Aachen Campus und füllt diese Rolle seit über 10 Jahren erfolgreich aus.
Schwerwiegende Gesundheits- und Wirtschaftskrisen wie die Weltfinanzkrise (ab 2007) oder die Covid-19-Pandemie im Jahr 2020 haben aufgezeigt, in welch turbulentem Umfeld sich die globalisierte Welt heutzutage befindet und wie schnell gewohnte Abläufe in Wertschöpfungsketten unterbrochen und angepasst werden müssen. Die alltäglichen Anforderungen in dem sich immer schneller wandelnden digitalen Zeitalter wachsen stetig und sind komplexer denn je.
Unternehmen sind angehalten, immer kurzzyklischer auf sich ändernde
Bedingungen und Störungen zu reagieren und strategische Entscheidüngen
zur Gestaltung von Wertschöpfungsketten zu treffen. Nur mithilfe
einer umfassenden Datengrundlage und -kommunikation kann die strategische Planung der Supply-Chain effektiv erfolgen und somit die benötigte Robustheit und Agilität ermöglicht werden.
Since data becomes more and more important in industrial context, the question arises on how data-driven added value can be measured consistently and comprehensively by manufacturing companies. Currently, attempts on data valuation are primarily taking place on internal company level and qualitative scale. This leads to inconclusive results and unused opportunities in data monetization. Existing approaches in theory to determine quantitative data value are seldom used and less sophisticated. Although quantitative valuation frameworks could enable entities to transfer data valuation from an internal to an external level to take account of progress in digital transformation into external reporting. This paper contributes to data value assessment by presenting a four-part valuation framework that specifies how to transfer internal, qualitative to external, quantitative data valuation. The proposed framework builds on insights derived from practice-oriented action research. The framework is finally tested with a machine tool manufacturer using a single case study approach. Placing value on data will contribute to management’s capability to manage data as well as to realize data-driven benefits and revenue. [https://link.springer.com/chapter/10.1007/978-3-030-85902-2_19]
Erfolgreiche Serviceinnovation im Zeitalter industrieller, datenbasierter Dienstleistungen unterscheidet sich deutlich von bisherigen Ansätzen der klassischen Dienstleistungsentwicklung. Diese Erkenntnis konnte aus einem breit angelegten Benchmarking in der deutschen Industrie gewonnen werden. Die Benchmarking-Studie identifizierte besonders erfolgreiche Unternehmen, deren Methoden und Ansätze zur Gestaltung innovativer Dienstleistungen in Form von Fallstudien im Detail untersucht wurden. Als Kernergebnis ergeben sich sechs Prinzipien, die erfolgreiche Serviceinnovation für datenbasierte Dienstleistungen auszeichnen.
Manufacturing companies are constantly increasing their efforts in the subscription business, also known as product-as-a-service business, offering usage and outcome based solutions (value-in-use) instead of transactional services and products (value-in-exchange). Customers are becoming contractual subscribers of the solution in return for recurring, performance-related payments. To address arising, inevitable challenges like (1) reducing customer churn, (2) increasing usage intensity and outcome quality, (3) ensuring the adoption of product and software releases as well as (4) fostering customer loyalty, leading manufacturing companies are setting up a new organizational, customer-facing unit, called Customer Success Management (CSM). This unit has its origins in the software-as-a-service business, operating next to established entities like sales, key account management and customer service. Since there are currently no holistic models for an end-to-end description of CSM-tasks in the manufacturing industry, this paper contributes to a taskoriented reference model, using a grounded theory approach, examining both manufacturing and software companies. Containing a reference framework with 8 main tasks, 17 basic tasks and 76 elementary tasks, the reference model supports manufacturing companies in adapting and customizing a company-specific CSM concept.
Electricity generated by wind turbines (WT) is a pillar of the transition to renewable energy [1]. In order to economically utilize WTs, operating and maintenance costs, which account for 25% of total electricity generation costs in onshore WTs, are a focus of cost reduction activities [2]. A prescriptive maintenance approach can support in achieving this goal. Prescriptive maintenance is a maintenance approach, where asset condition data is collected and analyzed to recommend specific actions to prevent breakdowns and reduce downtimes. However, the processing and analysis of data is quite complex. Especially unstructured data (such as comments of service technicians in free text fields) is often left unused, as companies, mostly SMEs lack the capacity to carry out these analyses. In this work we propose an approach to utilize the information from service reports, maintenance reports as well as status records from SCADA systems for the development of a prescriptive maintenance approach to onshore WTs. To achieve this, an ontology was utilized in this approach to codify implicit knowledge of service technicians and aid in making unstructured data usable for further analysis. The ontology was used to link historical service and maintenance reports with status codes, thus enabling automated analysis. In interviews with WT topic experts and through further research, damage mechanisms and corresponding maintenance measures were identified and a measure catalogue was developed to support service and maintenance activities. The recognition of the root cause of problems allows for a prescriptive maintenance approach that recommends targeted actions to reduce downtimes and optimize maintenance activities, it also allows to effectively control the outcome of maintenance activities and optimize their execution.
Pricing for Smart-Product-Service-Systems in Subscription Business Models for Production Industries
(2021)
In the production industry, subscription business models have the potential to create long-term relationships where a supplier provides a continuous value-oriented service to a customer based on digitalisation. Monetising this increase in value through pricing represents a central challenge for suppliers in subscription business. Unlike the current dominant transactional business, the focus of pricing is on the value-in-use of the customer (e.g. on the increase in output for the customer). In this regard, there is so far no pricing approach for practice that allows the linking of the performance data of the customer with the periodically charged price. However, in subscription businesses, such an approach is required to create win-win situations for the customer and supplier through continuous performance improvement. Therefore, this paper develops a novel process model for pricing of smart-product-service-systems in subscription business for production industries. This process can serve as basis for suppliers of subscriptions in the production industry to align pricing with the created value-in-use. In the long term, this allows companies to systematically develop their pricing to monetise the potential of digitalisation.
The Impact Of Manufacturing Execution Systems On The Digital Transformation Of Production Systems
(2021)
With the focus of manufacturing companies on the digital transformation, Manufacturing Execution Systems are market-ready, modular software solutions for manufacturing companies to integrate the value-adding and supporting processes horizontal and vertical in the company. Companies, especially small and mediumsized companies, face high internal and external costs for the implementation of the MES modules. An advantage of MES is the possibility to implement the systems in a continually, module-by-module approach, with the benefit of timely distributed investments. By realizing fast improvements, companies can use the benefits for further module implementations. This paper proposes a maturity model to measure the impact of an MES on the digital transformation of the company’s production systems. The model fulfils two purposes. The first, companies can measure the impact based on the difference between its current maturity index and the potential index of an implemented MES. The second is, the user can identify what impact an MES has in general on the digital transformation since the developed maturity model is derived from an established industry 4.0 maturity model. The development of the maturity model is based on the methodologies of AKKASOGLU and focuses on the further development of an established model. As an outlook, the application of the model will be described briefly. The proposed maturity model can directly be used by practitioners and offers implications for further development of MES functionalities.
Industrial practice shows a strong trend towards digitalization. It is not only economic crises, such as those triggered by Covid-19, that are reinforcing this trend. It is also the entrepreneurial urge to fulfill customer wishes in the best possible way and to adapt to new requirements as quickly as possible. Due to the advancing digitalization, the role of business application systems in manufacturing companies is therefore becoming increasingly important. The data processed in IT-Systems represent a great potential, especially for the evaluation of change requests in production. Through efficient change management, companies can record and process changes quickly. However, the necessary data basis to decide on existing change requests is still hardly used. Existing IT-Systems for change management coordinate the processing of change requests, but do not relate to data of operational application systems such as Enterprise-Resource-Planning. Therefore, a conceptual approach is required for the evaluation of change requests. This approach is based on an objective recording system that enables the transformation from the change description to an evaluation space. The paper presents an approach for the systematic transfer of requirement characteristics into the world of operational IT-Systems.