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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.
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.
Ongoing digitalization and Industry 4.0 enable the development of new business models due to the increase in available data and digital connected products. A promising business model type for the machinery and plant engineering industry are subscription models, consisting of products and services offered in return for continuous payments. However, subscription-based business models are associated with extensive changes in the traditional machinery and plant engineering industry, in particular, for small and medium-sized companies (SMEs). Established concepts for the development of value propositions and business models neglect important aspects, such as the integrated development and optimization of products and services across the entire life cycle or the data infrastructure. This paper presents a concept for a methodology to support SMEs developing value propositions within subscription models. Therefore, the systematic identification of customer benefits, the determination and prioritization of subscription relevant functionalities as well as the design of product and service elements addressing those functionalities are the main aspects on which the focus is placed on. The result is a subscription value proposition canvas for SMEs to address the impact of subscription models on products and services.
In Germany’s transition to a more sustainable industrial landscape, electricity generated by wind turbines (WT) remains a mainstay of the energy mix. Operating and maintenance costs, which account for roughly 25% of electricity generation costs in onshore WTs make improvements of maintenance activities a key lever in the economic operation of WTs. Prescriptive maintenance is a possible approach for improved maintenance activities. It is a concept where asset condition data is used to recommend specific actions and has great potential for the operation of wind parks. However, especially small, but also large wind park operators, and maintenance service providers often struggle with the implementation of such a new maintenance approach. As a part of the research project ReStroK, a learning game has been developed to support the training and familiarization of maintenance technicians with the concepts and underlying principles of this maintenance approach. In this paper, the concept for the development of a learning game will be presented. Multiple scenarios for its usage and their corresponding requirements will be discussed and an overview over the game will be given.
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.
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.
Progress in the development of small electric and hybrid aircraft promises business opportunities for thin-haul air mobility services. In order to develop demand-oriented flight plan scenarios for Germany, this paper presents a model to estimate the marked volume of thin-haul air mobility. To quantify the potential demand, our model includes the steps of trip generation, trip distribution and mode choice. Trip generation and distribution takes place between 412 geographic subdivisions of Germany and is based on calibrated traffic forecast data for the year 2030. For the first time the five relevant modes of transport, namely: car, intercity train, intercity bus, commercial aircraft and thin-haul air mobility services, have been included in one model. The step of choosing the transport mode is implemented via a generalized cost approach, taking into account travel costs and travel time. Additionally, route modeling of all transport modes is enhanced by real market data using large-scale data readouts of web interfaces. As primary result we predict a market share of 6 % or 81 million trips per year for thin-haul air mobility services. The demand concentrates on a small number of airports: 30 % of the trips are estimated to be between only 20 airports. Hubs and main routes are identified to offer the potential for scheduled air services.
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.
Service Engineering Models
(2019)
Since the field of service engineering emerged in the late 20th century, the service industry has undergone drastic changes. Among the reasons for these changes is the increasing digitalization, which has made it difficult for companies to successfully develop new service offerings. While numerous service engineering models are available to provide guidance during the design of new services, many of them cannot keep up with the requirements of today’s economic environment. The present paper examines the requirements that service engineering models need to meet in order to be suitable guidelines for the digital age. To this end, the introduction illustrates how digitalization has changed the service industry. Afterwards, selected service engineering models and related norms are presented. Finally, a set of requirements for modern service engineering models derived from best practices from recent years is introduced.
Competitive differentiation in the manufacturing sector is no longer based on product and service innovations alone but on the ability to monetize the usage phase of products and services. To this end, manufacturers are increasingly looking at so-called subscription business models as a way of supplementing the traditional sale of products and services. Since supplier success in the subscription business is directly dependent on customer success, the setup and expansion of a so-called Customer Success Management (CSM) is required. While CSM has already been established in the software industry for several years, companies in the manufacturing sector are often still in the conceptual phase of a CSM, parallel to the setup and expansion of their subscription business. Therefore, this paper aims to support the set-up of a CSM by providing a reference data model, based on case study research, that can be used to support the organizational or daily CSM tasks and to serve as a blueprint for conceptualizing CSM-specific IT systems.