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|Publication type:||Conference paper|
|Type of review:||Peer review (publication)|
|Title:||Quantitative modelling of the value of data for manufacturing SMEs in smart service provision|
Benedech, Rodolfo Andres
|Proceedings:||International Conference on Exploring Service Science (IESS 2.2)|
|Editors of the parent work:||Le Dinh, Thắng|
|Conference details:||12th International Conference on Exploring Service Science, Geneva (virtual), 16-18 February 2022|
|Series:||ITM Web of Conferences|
|Publisher / Ed. Institution:||EDP Sciences|
|Subjects:||Value; Data-driven service; Quantitative modelling; Simulation modelling|
|Subject (DDC):||658.403: Decision making, information management|
|Abstract:||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.|
|Fulltext version:||Published version|
|License (according to publishing contract):||CC BY 4.0: Attribution 4.0 International|
|Departement:||School of Engineering|
|Organisational Unit:||Institute of Data Analysis and Process Design (IDP)|
|Published as part of the ZHAW project:||Gestaltung der Interaktionen von Mensch und Maschine in autonomen digitalen Service Ecosystemen (sozio-technischen Systemen)|
Data Sharing Framework
|Appears in collections:||Publikationen School of Engineering|
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|2022_Meierhofer-etal_Quantitative-modelling-data-value-SME.pdf||1.61 MB||Adobe PDF|
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Meierhofer, J., Benedech, R. A., Schweiger, L., Barbieri, C., & Rapaccini, M. (2022). Quantitative modelling of the value of data for manufacturing SMEs in smart service provision [Conference paper]. In T. Le Dinh & M. Drăgoicea (Eds.), International Conference on Exploring Service Science (IESS 2.2) (p. 4001). EDP Sciences. https://doi.org/10.1051/itmconf/20224104001
Meierhofer, J. et al. (2022) ‘Quantitative modelling of the value of data for manufacturing SMEs in smart service provision’, in T. Le Dinh and M. Drăgoicea (eds) International Conference on Exploring Service Science (IESS 2.2). EDP Sciences, p. 04001. Available at: https://doi.org/10.1051/itmconf/20224104001.
J. Meierhofer, R. A. Benedech, L. Schweiger, C. Barbieri, and M. Rapaccini, “Quantitative modelling of the value of data for manufacturing SMEs in smart service provision,” in International Conference on Exploring Service Science (IESS 2.2), Feb. 2022, p. 04001. doi: 10.1051/itmconf/20224104001.
Meierhofer, Jürg, et al. “Quantitative Modelling of the Value of Data for Manufacturing SMEs in Smart Service Provision.” International Conference on Exploring Service Science (IESS 2.2), edited by Thắng Le Dinh and Monica Drăgoicea, EDP Sciences, 2022, p. 4001, https://doi.org/10.1051/itmconf/20224104001.
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