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Neuland Internet
(2015)
Today, maintenance exceeds this definition, it is significantly more.
In many companies, it plays the role of an incubator for development
and drives digital transformation forward. The very essence of
Industrie 4.0 is the optimisation of the flow of information within as
well as outside of a company to accelerate the adjustment of company
organisations in the context of increasing competitive pressure.
Because of the variety of interfaces, information and data that
is available as well as its service character, maintenance lends itself easily as the area of choice for a company to make Industrie 4.0 real. Whilst doing so, the aim is not to equip employees with the
latest “gimmick“ for order processment or to be the company with
the highest number of lighthouse projects. Instead, maintenance
ensures reliable and cost-efficient production and, consequently,
the primary creation of added value of the manufacturing company.
Those who were identified as top performers during the “Smart
Maintenance“ consortium benchmarking by FIR at RWTH Aachen
University gain particular useful ideas twice as often as other follower companies directly from staff, thus releasing the right potential.
Information and data help to reach these goals and transfer the
vision of smart maintenance into actual pratice. But what is smart
maintenance exactly and how far along are you in the development
of your individual smart maintenance concept?
Im Rahmen von Industrie 4.0 kommt der prognosebasierten bedarfsgerechten Instandhaltung eine besondere Bedeutung zu. Sie steigert die Wirtschaftlichkeit von Produktionsanlagen beispielsweise durch eine Erhöhung der Verfügbarkeit, Lebensdauer oder Leistung. Dafür sind verschiedene Bausteine notwendig, die in dem Vortrag erläutert werden.
KVD-Service-Studie 2016
(2016)
Welche Qualifikationsanforderungen an die Servicemitarbeiter sind heute und zukünftig von Bedeutung? Welche unterstützenden Technologien sind für den Service heute und in der Zukunft relevant? Welche Auswirkungen ergeben sich daraus für die Serviceorganisation? Um die aktuellen Trends der zukünftigen Arbeitswelten im Service zu analysieren, liegt der Schwerpunkt der diesjährigen Service-Studie, die vom KVD zusammen mit dem FIR durchgeführt wurde, auf dem Themenkomplex Mensch und Technologie – neue Herausforderungen im Kontext der Industrie 4.0.
Herr Müller ist wirklich sauer. Es ist bereits das vierte (!) Mal, dass
sein Wagen nicht zum vereinbarten Zeitpunkt abholbereit ist. Ne-
ben der lästigen Wartezeit hat die Unzuverlässigkeit der Werkstatt
weitere negative Konsequenzen, die nicht nur Herrn Müller selbst
betreffen: Dank der Verzögerung schafft es Herr Müller nun schon
wieder nicht, seine Tochter vom Ballett abzuholen und muss,
schon wieder, seine Frau darum bitten, für ihn einzuspringen. Die
wiederum hatte eigentlich schon andere Pläne für den Abend und
muss jetzt spontan umdisponieren.
This paper presents a simulation approach for service production processes on the basis of which an optimal operating point for service systems can be identified. The approach specifically takes into account the characteristics of human behavior. The simulation is based on a system theory approach to the service delivery process. A specific use case of the simulation approach is presented in detail to illustrate how characteristic curves are deduced and an optimal operating point is obtained.
Traditional manufacturing companies increasingly launch data-driven services (DDS) to enhance their digital service portfolio. Nonetheless, data-driven services fail more often than traditional industrial services or products within the first year on the market. In terms of market launch, their digital characteristics differ from traditional industrial services and thus need specific structures and actions, which companies currently lack. Therefore, a process guideline for a six-month market launch phase of DDS is developed. The guideline relies on analogies from product, service and software launches based on the latest literature from service marketing and successful practices from various industries. Finally, the guideline is evaluated within five industrial case studies. Thus, the guideline provides scientific research insights regarding the market launch process of DDS and adds to the research of service marketing. It provides practical guidance for manufacturing companies by serving as a reference process for the market launch and offering a collection of successful practices within this area.
Traditional manufacturing companies increasingly launch data-driven services (DDS) to enhance their digital service portfolio. Nonetheless, data-driven services fail more often than traditional industrial services or products within the first year on the market. In terms of market launch, their digital characteristics differ from traditional industrial services and thus need specific structures and actions, which companies currently lack. Therefore, a process guideline for a six-month market launch phase of DDS is developed. The guideline relies on analogies from product, service and software launches based on the latest literature from service marketing and successful practices from various industries. Finally, the guideline is evaluated within five industrial case studies. Thus, the guideline provides scientific research insights regarding the market launch process of DDS and adds to the research of service marketing. It provides practical guidance for manufacturing companies by serving as a reference process for the market launch and offering a collection of successful practices within this area. [https://link.springer.com/chapter/10.1007/978-3-030-00713-3_14]
Erfolgsprinzipien der Smart Maintenance – Was wir von den Besten aus der Praxis lernen können
(2019)
Der Industriestandort Deutschland befindet sich im Wandel. Neue, digitale Technologien ermöglichen es, Betriebs-, Zustands- und Ereignisdaten in stetig steigender Menge zu erfassen, aufzubereiten, zu analysieren und für die industrielle Anwendung nutzbar zu machen. Dieser Nutzen zeichnet sich durch die Beschleunigung der unternehmerischen Entscheidungs- und Anpassungsprozesse aus und stellen damit das eigentliche Potenzial von ‚Industrie 4.0‘ dar. An dieser Stelle stehen Unternehmen heute vor der Herausforderung, die Transformation zur Smart Maintenance effektiv und effizient zu gestalten. Zu häufig neigen Unternehmen dazu, „das Rad wiederkehrend neu zu erfinden“, da Praxiseinblicke über die eigenen Standort- und Unternehmensgrenzen hinweg fehlen. Um eben jene benötigten Praxiseinblicke und Erfahrungswerte liefern zu können, wurde am FIR an der RWTH Aachen gemeinsam mit sechs Industriepartnern sowie dem Fraunhofer IML aus Dortmund das „Konsortial-Benchmarking Smart Maintenance“ durchgeführt. Anhand eines eigenen Ordnungsrahmens wurden zentrale Fragestellungen der Smart Maintenance identifiziert und mittels Fragebogenstudie untersucht. Durch die angeknüpfte statistische Auswertung konnte zwischen sogenannten ‚Top-Performern‘ (TP) und ‚Followern‘ (FL) unterschieden werden. In diesem Beitrag werden ausgewählte Ergebnisse der Studie sowie der Interviews den Ebenen des Ordnungsrahmens folgend vorgestellt.
Operation and Maintenance (O&M) is a key value driver for offshore wind farms. Consequently, reducing O&M costs improves their profitability. This paper introduces different typologies of dispositioning maintenance tasks in offshore wind farms, in order to help design the strategies and organization of maintenance. Based on the special requirements of offshore wind farms regarding planning and controlling the O&M activities, a morphological analysis was developed. With this different disposition strategies for offshore wind farms could be generated. The consequences of choosing different characteristics are allegorized in an exemplary fashion. The work presented in the following is the foundation for designing a software-based dispositioning tool for usage in offshore wind farms, which will help to increase the effectiveness of the disposition in offshore wind farms by maximizing the number of accomplished tasks per day and minimizing the time technicians stay on the wind turbine and the ships.
The industrial food production is currently caught between the increas-ing demands of numerous stakeholders, economic profitability and the challenges of digitization. A solution to face these various challenges can be seen in the aggregation of data into higher-value, independent data products that can be of-fered and sold on a buyer's market. Large amounts of heterogeneous data are already available in the value chain of the industrial food production, e.g. throughout the data-driven harvesting of primary products, further processing by interconnected production facilities and the information-intensive product distri-bution to end consumers. However, the data is usually only evaluated and used locally for the optimization of internal processes or, at the most, within compre-hensive partnerships. The purpose of this paper is to identify new revenue oppor-tunities for current and future players in the industrial food production by using data as an independent economic good (data products). For this purpose, scenar-ios for the development and use of data products via Industrial Internet of Things platforms are developed for a food technical reference process, the industrial chocolate production and its value chain. On this basis, examples for different types of data products and their value propositions are derived. The results can not only serve food producers and relevant stakeholders but all industrial produc-ers as an input for the future, yield-increasing orientation of their business models.
Data-driven services play an important role in
innovative business models of successful manufacturing
companies: They hold great potential for the creation of unique
selling points and improve the differentiation of manufacturing
companies in highly competitive markets. However, the large
number of newly invented digital services that fail shortly after
launching implies that companies struggle with the invention and
implementation of data-driven service solutions, which ends in a
waste of resources. The following paper introduces guideline
principles for successful innovation processes for data-driven
services. The principles were identified during in-depth case
studies with manufacturing companies. They contribute to a
necessary paradigm change for manufacturing companies in
terms of data-driven services for machines. The six identified
principles emphasize new aspects regarding the new dimension of
data-driven solutions and improve the life cycle management of
products and services. They demonstrate how the rules of agile
development can lead to successful and more efficient service
innovations in the industrial sector.
Die digital vernetzte industrielle Produktion verspricht schnellere und effizientere Prozesse - in Entwicklung und Produktion wie auch in Service, Marketing und Vertrieb oder bei Anpassung ganzer Geschäftsmodelle. Agil zu handeln und in Echtzeit Veränderungen vorzunehmen, wird in der Industrie 4.0 zur strategischen Erfolgseigenschaft eines Unternehmens. Voraussetzung dafür ist der Aufbau einer immer breiteren Datenbasis. Ob deren Potenzial effektiv genutzt wird, hängt jedoch auch wesentlich von der Organisationsstruktur und Kultur eines Unternehmens ab.
Die vorliegende acatech STUDIE stellt ein neues Instrument vor, mit dem produzierende Unternehmen den Weg zum lernenden, agilen Unternehmen individuell gestalten können. Der acatech Industrie 4.0 Maturity Index ist als sechsstufiges Reifegradmodell aufgebaut und analysiert die in der digitalisierten Industrie benötigten unternehmerischen Fähigkeiten in den Gestaltungsfeldern Ressourcen, Informationssysteme, Kultur und Organisationsstruktur. Jede erreichte Entwicklungsstufe verspricht produzierenden Unternehmen einen konkreten Zuwachs an Nutzen. Das Modell wurde in der praktischen Anwendung in einem mittelständischen Betrieb validiert.
Digitally connected industrial production promises faster and more efficient processes - in development and production, services, marketing & sales and for adapting entire business models. Agility and the ability to make changes in real time are strategic chracteristics of successful companies in Industrie 4.0. To acquire these features, it is necessary to create a continuously expanding data base. However, a company's organisational structure and culture also play an important part in determining whether this data's potential is leveraged effectively.
This acatech STUDY describes a new tool for helping manufacturing enterprises to forge their own individual path towards becoming a learning, agile company. The acatech Industrie 4.0 Maturity Index is a six-stage maturity model that analyses the capabilities in the area of resources, information systems, culture and organisational structure that are required by companies operating in a digitalised industrial environment. The attainment of each development stage promises concrete additional benefits for manufacturing companies. The model's practical application was validated in a medium-sized company.
KVD-Service-Studie 2018
(2018)
Digitalisierung ist eine der zentralen Herausforderungen der heutigen Zeit. Aber die Digitalisierung rein technisch zu betrachten, ohne Augenmerk auf die Unternehmenskultur zu legen, wird dazu führen, dass Unternehmen die Potenziale der Digitalisierung nicht vollständig realisieren können. Wer weiterhin wettbewerbsfähig sein will, muss den Wert der Digitalisierung erkennen und sie für sein Unternehmen nutzbar machen. Aber welche Auswirkungen ergeben sich daraus für die Serviceorganisation? Um den aktuellen Stand der digitalen Kultur im Service zu erfassen, die Dimensionen zu verstehen und einen Weg für Unternehmen aufzuzeigen, liegt der Schwerpunkt der diesjährigen Service-Studie, die vom KVD zusammen mit dem FIR durchgeführt wurde, auf dem Themenkomplex Digitale Service-Kultur.
Industrial service is currently undergoing tremendous changes, largely driven by the development of new technologies, in particular the advancing digitalization. Never before have organizations had more comprehensive and insightful data assets - and never before have the opportunities to fully exploit this potential been better. However, most companies are unaware of how they can make use of this potential and which development steps are necessary to react to the current situation. To change this, a maturity-based approach was developed which describes four development stages of an industrial service company from a technological, organizational and cultural point of view. The maturity model makes it possible to develop a digital roadmap that is tailormade to each company, which helps to introduce Industrie 4.0 and transform industrial service companies into learning, agile organizations.
Industrie 4.0 ist in den Bilanzen deutscher Industrieunternehmen aktuell noch nicht angekommen. Seit der Einführung des Begriffs „Industrie 4.0“, als Bezeichnung für die massenhafte Verbindung von Informations- und Kommunikationstechnologien mit der industriellen Produktion, wird das Thema national wie international in Wirtschaft und Forschung in zahlreichen Initiativen und Projekten behandelt. Enorme wirtschaftliche Potenziale wurden und werden in diversen Studien beziffert, um den revolutionären Charakter dieser Entwicklung zu unterstreichen.
Industrial Smart Services: Types of Smart Service Business Models in the Digitalized Agriculture
(2019)
Due to lack of experience of companies with digital business models, agricultural machinery manufacturers and agricultural service companies are facing a positioning problem in their ecosystem. Smart services are getting more important for these companies and they have issues to define a matching business model for their newly developed smart services. The lack of a framework for smart service business models makes it even harder for companies to successfully develop new services. This paper contributes to a better understanding of business models for smart services and establishes a common morphological framework to define different types of business models for smart services. Six types of business models of industrial smart services were identified during the research based, which was based on a literature review and interviews with leading experts in the field of smart services. The validation of the developed types and its practical application was carried out as part of the German research project Smart-Farming-World and its four developed use cases. This paper gives a detailed description of the application of the framework on the use case nPotato.