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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.
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.
Today, however, agility is seen more than ever as a critical success factor for companies. In times of an increasing degree of digital interconnection and minimum viable products, a mentality is entering the industrial service sector that has so far only been exemplified by Internet companies (e.g. Google): New products and especially digital services are developed in highly iterative processes. To this end, customers are involved in early test phases of development and provide feedback on individual functional modules, which – in contrast to the previous approach – are only gradually assembled into a market-ready “100 percent version”. But especially with the development of new digital services, companies must ensure more than ever that both the existing analog service business and the design of new digital services are geared to effectiveness and efficiency in order to meet the growing demands of customers and competitors.
To achieve this, companies must not only be familiar with the products currently on the market, but also master the entire product history, which in some cases goes back more than 30 years and varies greatly from one industry to another.
The rapid developments in information and communication technology enable new bus iness models that are based on digital platforms. Marketplaces such as Amazon or Airbnb have already adapted this business model to connect previously unconnected supply-side and demand-side to conduct a business transaction via a digital platform. Due to Industrie 4.0 and the rapid technological development that comes with it, digital platforms have entered the market within the area of the mechanical engineering. Different platform types exist, such as marketplaces for machine equipment or digital data platforms for connected machines. Although numerous companies claim to offer platform-based bus iness models, they often lack knowledge on individual business model components. To close this gap, this paper structures a variety of existing platforms based on their detail characteristics. Within this paper, existing typologies of digital platforms from other industry areas are analyzed. Case study research ofplatforms within the mechanical engineering is used to adjust these typologies and create a new one for digital platforms within the mechanical engineering.
This paper contributes to an assessment framework for valuing data as an asset. Particularly industrial manufacturers developing and delivering Smart Product Service Systems (Smart PSS) are comprehensively depended on the business value derived by processing data. However, there is a lack in a framework for capturing and comparing the Smart PSS data value with the purpose of increasing the accountability of data initiatives. Therefore a qualitative data value assessment approach was developed and specified on Smart PSS, based on an industrial case study research. [https://link.springer.com/chapter/10.1007/978-3-030-57997-5_39]
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.
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.
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.
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?