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Monetizing Industry 4.0: Design Principles for Subscription Business in the Manufacturing Industry
(2019)
Subscription business models have a major role for monetizing products and services for manufacturing companies in the age of Industry 4.0. As the manufacturing industry has difficulties generating revenues through digitalization, the implementation of innovative business models are essential to remain successful. Physical assets are often capital-intensive and require a more complex manufacturing process than subscription business models. Moreover, subscription models can focus on the individual customer benefit and a consistent service transformation, constituting a unique selling proposition and a competitive advantage. Hence, the following paper provides a management model that enables manufacturing companies to successfully realize the transformation towards a subscription business model. The management model presents four major fields of action, each matched with one design principle that must be considered when dealing with subscription models in the manufacturing industry. These principles were determined by an in-depth case study analysis among various manufacturing companies. Opportunities, challenges and recommendations for action were then systematically derived and integrated into the management model.
Operating and maintenance costs, which account for 25% of total costs, are a powerful lever in reducing the electricity generation costs of onshore wind turbines (WT). These costs can be reduced by a condition-orientated maintenance approach. A condition-oriented maintenance strategy optimizes maintenance tasks by executing them with varying levels of detail and focus depending on the system and life cycle phase. OEMs evaluate operating data and structured data from the maintenance history for this purpose, but SMEs lack the capacity for this evaluation. In particular, the unstructured descriptive comments in the maintenance reports generated by service technicians remain unused. In this work, we propose a framework to incorporate this information from the maintenance reports along with the status records from the SCADA system. For this purpose, a mechanism has to be developed to make the contents of the service reports machine-evaluable. The mechanism used in this approach is an ontology, which enables the codification of implicit knowledge such as the experience knowledge of the service technicians. The ontology’s purpose is to link status codes of onshore WT with historical maintenance reports and thereby enabling an automated evaluation. Using an API (application programming interface), the ontology can be integrated into an algorithm to analyse status data and maintenance documents. In this manner, recommendations for actions can be derived and maintenance tasks can be optimized.
Many industrial companies face their digital transformation. In addition to an existing portfolio of products and services, new digital services are being developed to offer a portfolio of smart product service systems (Smart PSS). While the development of new digital services is rarely a problem for the companies, the organization of sales and distribution of Smart PSS in particular is a key issue. The sales of Smart PSS differs considerably from the sales of only products or services and must therefore be designed differently in order to meet customer requirements and successfully commercialize the developed Smart PSS. This paper therefore describes how the sales organization of Smart PSS should be designed successfully in various forms. The network thinking methodology is used in combination with a case study research approach to describe the connection between the offered portfolio, the customer requirements and the different elements of a sales organization. Furthermore, four different types of a sales organization for Smart PSS are described. This paper gives a recommendation for companies on a design of their sales organizations on which practical implications may be developed.
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.
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.
The additive manufacturing technique of "Selective Laser Melting" (SLM) provides the basis for a fundamental paradigm shift in industrial spare part manufacturing, affecting both technological and organizational company prac-tices. To harness the full potential of SLM-technology, considering agility and customizability, decentralized additive production networks need to be estab-lished. According to the principles just in time, just in place and just enough, a global online platform, which efficiently distributes construction orders to local manufacturing hubs could empower the market participants to utilize production capacities at optimal costs and minimal efforts. This work evaluates and selects key factors and creates scenarios for the development of platform-based networks for additive, SLM-based, spare part production. For this purpose, the selected key factors (e. g. material expenses, quality and process management and platform-based business models) are projected into the future, forming the three major scenarios "New distribution of roles in the SLM value chain", "SLM-technology for high wage countries" and "Individualization instead of mass production". These scenarios not only allow estimating the potential of an online network for additive spare part production, but also enable market participants to react pur-posively and agilely to unexpected market developments, and to foster the suc-cess of a platform-based additive spare part production.
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.
Smart Service Engineering
(2019)
In our digitalized economy, many traditional service engineering models lack flexibility, efficiency and adaptability. As today’s market differs significantly from the market of the late 20th century, service engineering models must meet different requirements today than they had to meet in the past. The present paper starts off by providing an overview of the requirements that modern service engineering models need to fulfill in order to succeed in today’s economic environment. Afterwards, three promising models that meet several of these requirements will be introduced.
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.