ITM Web Conf.
Volume 41, 2022International Conference on Exploring Service Science (IESS 2.2)
|Number of page(s)||14|
|Section||Smart Services’ Innovation Design|
|Published online||08 February 2022|
Quantitative Modelling of the Value of Data for Manufacturing SMEs in Smart Service Provision
1 School of Engineering, ZHAW Zurich University of Applied Sciences, Switzerland
2 School of Engineering, University of Florence
* Corresponding author: email@example.com
The provision of advanced services becomes a relevant differentiation for manufacturing companies, in particular for SMEs (small and medium-sized enterprises). These services, also referred to as smart services, require the collection and processing of data from equipment, customers, and processes, as well as the development of analytics models and the interpretation of their results for improved service value propositions. These steps require significant engagement of the firms in terms of technical and human resources, skills, and new types of value creation processes, which is a major hurdle especially for SMEs. As the value that can be achieved when leveraging the information inherent in the data is not known a priori, the enterprises are not sufficiently informed for taking the decision to engage. Consequently, they are missing out on relevant business opportunities due to a lack of quantitative models for assessing the value of data. In this paper, we discuss the existing literature on data valuation models and explore the state of practice through an interview-based field study. We develop a model for the utility-based valuation of data that helps companies expand their fund of knowledge and skills about the value of their data and thus make better-informed investment decisions. A simulation-based model is developed to support companies in this assessment by providing quantitative insights in the value potential of the data in various use cases. This model opens a series of new research questions for the further elaboration of the data valuation models.
© The Authors, published by EDP Sciences, 2022
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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