Refine
Document Type
- Conference Proceeding (7)
- Contribution to a Periodical (4)
- Book (3)
- Part of a Book (2)
- Internet Paper (1)
- Report (1)
Language
- English (8)
- German (7)
- Multiple languages (3)
Is part of the Bibliography
- no (18)
Keywords
- 02 (1)
- 03 (2)
- BMWK (1)
- Business Model (1)
- DLR (1)
- Data Ecosystems (1)
- Data Governance (1)
- Data Products (1)
- Datenmonetarisierung (1)
- Datenschutz (1)
Institute
More and more manufacturing companies are starting to transform the transaction-based business model into a customer value-based subscription business to monetize the potential of digitization in times of saturated markets. However, historically evolved, linear acquisition processes, focusing the transactionoriented product sales, prevent this development substantially. Elemental features of the subscription business such as recurring payments, short-term release cycles, data-driven learning, and a focus on customer success are not considered in this approach. Since existing transactional-driven acquisition approaches are not successfully applicable to the subscription business, a systematic approach to an acquisition cycle of the subscription business in the manufacturing industry is presented, aiming at a long-term participative business. Applying a grounded theory approach, a task-oriented model for themanufacturing industry was developed.
The model consisting of five main tasks and 14 basis tasks serves as best practice to support manufacturing companies in adapting or redesigning acquisition activities for their subscription business models.
In the food industry, a very large potential of data ecosystems is seen, in which data is understood, exchanged and monetized as an economic asset. However, despite the enormous economic potential, companies in the food industry continue to rely on traditional, product-oriented business models. Existing data in the value chain of industrial food production, e.g., in harvesting, logistics, and production processes, is primarily used for internal optimization and is not monetized in the form of data products. Especially the pricing of data products is a key challenge for data-based business models due to their special characteristics compared to conventional, analog offerings and multiple design options. The goal of this work is therefore to solve this issue by developing a framework that allows the identification of pricing models for data products in the industrial food production. For this purpose, following the procedure of typology formation, essential design parameters and the respective characteristics are derived. Furthermore, three types for pricing models of data products are shown. The results will serve not only stakeholders in the food industry but also manufacturing companies in general as input for an orientation of their databased business models.
Im Benchmarking zum Themenfeld „Monetizing Smart Products“ können Konzepte für Smarte Produkte und deren Vermarktung mit dem anderer Unternehmen verglichen werden, um wichtige Impulse für die Weiterentwicklung des digitalen Produktportfolios zu erhalten. Ziel des Benchmarkings ist die Identifikation von Unternehmen, die besonders erfolgreich Ansätze im Bereich der Monetarisierung Smarter Produkte umsetzen.
Um vertriebliche Herausforderung systematisch angehen zu können, hat das FIR das Innovationsprojekt „Vertriebsexzellenz für digitale Produkte & Services“ ins Leben gerufen. Im Rahmen dieses Innovationsprojekts werden gemeinsam mit einem branchen-übergreifenden Konsortium von Industriepartnern Strategien, Maßnahmen und Methoden abgeleitet, um einen messbaren Erfolg in der Vermarktung digitaler Produkte und Services zu erzielen.
Big data are collected along the entire food industry value chain, but remain mostly unused. Data sharing in data ecosystems could lead to efficiency gains and new revenue streams. We investigate data sharing within food industry and derive challenges and opportunities for data sharing in this context. We conducted interviews with ten qualified experts from the German food industry. The results reveal that mainly trust, usefulness and value influence users’ attitude towards data sharing. Our results confirm social exchange theory in conjunction with technology acceptance model as relevant underlying IS theories of data sharing.
To monetize the potential of digitalization in times of saturated markets, increased machinery and plant engineering companies are starting to transform the transaction-based business model into a customer- and service-oriented subscription business. Even though subscription offerings can create win-win situations for providers and customers, companies encounter significant difficulties in acquiring customers for this innovative business model. Historically linear acquisition processes focused on transactional product sales impede success. To identify key challenges and targeted coping strategies for customer acquisition we conducted in-depth interviews with 18 subscription managers and sales representatives from seven machinery and plant engineering case studies. In our research we uncovered four challenge dimensions: (1) lack of motivation, (2) missing skills and competences, (3) insufficient customer confidence and (4) transaction-oriented sales approach. Beyond that we derived four appropriate coping strategies (1) steering mechanisms, (2) human resource management, (3) trust building instruments and (4) systematic methodology to address them. These insights highlight the key challenges at the management level for customer acquisition that companies face when trying to initiate and sustain the transition from a purely transactional product and service business to subscription-oriented growth. Furthermore, they provide guidance how to cope with these challenges.
Trotz des enormen wirtschaftlichen Potenzials, das durch datenbasierte Geschäftsmodelle beziffert wird, fokussieren Unternehmen der Lebensmittelindustrie weiterhin traditionelle, produktzen- trierte Geschäftsmodelle. Die Digitalisierung wird lediglich als Möglichkeit zur internen Optimierung von Produktions- und Serviceprozessen gesehen. Große Mengen heute verfügbarer Daten entlang der gesamten Wertschöpfungskette bieten jedoch über die reine Effizienzsteigerung hinaus die Chance, eine mehrwertstiftende Datenökonomie in der Lebensmittelindustrie zu schaffen. Ziele des Forschungsprojekts ‚EVAREST‘ sind die Entwicklung und Verwertung von Datenprodukten im Ökosystem der Lebensmittelproduktion. Auf Basis einer herstellerübergreifenden, offenen Datenplattform so- wie begleitend entwickelter ökonomischer und rechtlicher Nutzungskonzepte werden die (rechts-) sichere Verwertung von Daten als Wirtschaftsgut und die Bereitstellung nutzerspezifischer Smart Services für verschiedene Anspruchsgruppen angestrebt. Das Verbundprojekt ‚EVAREST‘ wird durch das Bundesministerium für Wirtschaft und Energie (BMWi) mit dem Kennzeichen 01MT19003A gefördert und vom Projektträger DLR betreut.
Digitale Plattformen verfügen über das Potenzial, ganze Branchen in kürzester Zeit grundlegend zu verändern und bislang profitable Geschäftspraktiken abzulösen. Dieses Phänomen aus dem Business-to-Consumer(B2C)-Bereich konfrontiert zunehmend auch Unternehmen aus dem Business-to Business(B2B)-Bereich mit einem Paradigmenwechsel. Große Technologiekonzerne wie Siemens oder Bosch haben mit Mindsphere und der Bosch IoT-Suite Plattformen am Markt etabliert, welche diese neuen Wege der Wertschöpfung vorgeben. Kleine und mittlere Unternehmen (KMU) des Maschinen- und Anlagenbaus sind jedoch dem Risiko ausgesetzt, ohne eine eigene Plattformstrategie im Wettbewerb verdrängt zu werden. Deshalb ist das Verständnis von plattformbasierten Geschäftsmodellen und deren Umsetzung für sie elementar. Im Rahmen des Forschungsprojekts ‚PlattformHybrid‘ wurde erforscht, wie Unternehmen des Maschinen- und Anlagenbaus ihren individuellen Weg in die Plattformökonomie beschreiten können.
This chapter presents key challenges of digital pricing: selling value propositions, data-driven quantification of value, the design of value-driven pricing models, and the definition of subscription-based price metrics. To structure the pricing for smart-product-service offerings promisingly, a framework with four specific elements has been developed. To address the value propositions properly, this chapter presents four archetypes for offering smart-product-service systems. The chapter concludes by presenting an approach to quantify customer value for digital products and services.