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AI-based industrial full-service offerings : A model for payment structure selection considering predictive power

Title data

Häckel, Björn ; Karnebogen, Philip ; Ritter, Christian:
AI-based industrial full-service offerings : A model for payment structure selection considering predictive power.
In: Decision Support Systems. Vol. 152 (2022) . - No. 113653.
ISSN 1873-5797
DOI: https://doi.org/10.1016/j.dss.2021.113653

Project information

Project title:
Project's official titleProject's id
Projektgruppe WI Digital FinanceNo information
Projektgruppe WI Künstliche IntelligenzNo information

Abstract in another language

Artificial Intelligence and servitization reshape the way that manufacturing companies derive value. Aim-ing to sustain competitive advantage and intensify customer loyalty, full-service providers offer the use of their products as a service to achieve continuous revenues. For this purpose, companies implement AI classification algorithms to enable high levels of service at controllable costs. However, traditional asset sellers who become service providers require previously atypical payment structures, as classic payment methods involving a one-time fee for production costs and profit margins are unsuitable. In addition, a low predictive power of the implemented classification algorithm can lead to misclassifications, which diminish the achievable level of service and the intended net present value of the resultant service. While previous works focus solely on the costs of such misclassifications, our decision model highlights impli-cations for payment structures, service levels, and – ultimately – the net present value of such data-driven service offerings. Our research suggests that predictive power can be a major factor in selecting a suitable payment structure and the overall design of service level agreements. Therefore, we compare common payment structures for data-driven services and investigate their relationship to predictive power. We de-velop our model using a design science methodology and iteratively evaluate our results using a four-step approach that includes interviews with industry experts and the application of our model to a real-world use case. In summary, our research extends the existing knowledge of servitization and data-driven ser-vices in the manufacturing industry through a quantitative decision model.

Further data

Item Type: Article in a journal
Refereed: Yes
Keywords: Artificial Intelligence; Servitization; Predictive Power; Payment Structures; Full-Service Provision
Institutions of the University: Faculties > Faculty of Law, Business and Economics > Department of Business Administration
Research Institutions
Research Institutions > Affiliated Institutes
Research Institutions > Affiliated Institutes > Fraunhofer Project Group Business and Information Systems Engineering
Research Institutions > Affiliated Institutes > FIM Research Center Finance & Information Management
Faculties
Faculties > Faculty of Law, Business and Economics
Result of work at the UBT: No
DDC Subjects: 000 Computer Science, information, general works > 004 Computer science
300 Social sciences > 330 Economics
Date Deposited: 10 Sep 2021 08:39
Last Modified: 25 Nov 2021 13:15
URI: https://eref.uni-bayreuth.de/id/eprint/66995