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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.
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]
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.