Rigorous Viability Assessment of Machine Learning Projects : Example from the Domain of Predictive and Condition-Based Maintenance

Title data

Zipperling, Domenique ; Ott, Lorenz ; Vössing, Michael ; Kühl, Niklas:
Rigorous Viability Assessment of Machine Learning Projects : Example from the Domain of Predictive and Condition-Based Maintenance.
In: Business & Information Systems Engineering. (February 2026) .
ISSN 1867-0202
DOI: https://doi.org/10.1007/s12599-026-00986-2

Official URL:

Abstract in another language

Machine learning offers significant potential for organizations, yet transitioning models from development to deployment remains challenging. Frameworks such as CRISP-ML(Q) and MLOps emphasize the need to integrate business, economic, and machine learning perspectives. However, a systematic literature review reveals a lack of methods that link machine learning perspectives with business objectives. To address this gap, the authors introduce a metric – called profit-per-decision (ppd) – for binary classification that incorporates both model performance and economic impacts. Further, the Viability Assessment Framework is proposed, which utilizes the metric and enables organizations to assess viability at different project stages: pre-development, post-development, and post-deployment. The authors evaluate the framework through expert interviews and a scenario-based evaluation with experts from eleven different companies and develop an open-source web application to support interaction during the case studies. Results confirm the framework’s effectiveness in bridging technical and business perspectives, highlighting its industry relevance.

Further data

Item Type:	Article in a journal
Refereed:	Yes
Institutions of the University:	Faculties > Faculty of Law, Business and Economics > Department of Business Administration > Chair Business Informatics and Human-Centered Artificial Intelligence > Chair Business Informatics and Human-Centered Artificial Intelligence - Univ.-Prof. Dr.-Ing. Niklas Kühl Research Institutions > Central research institutes > Research Center for AI in Science and Society Research Institutions > Affiliated Institutes > Branch Business and Information Systems Engineering of Fraunhofer FIT Research Institutions > Affiliated Institutes > FIM Research Center for Information Management
Result of work at the UBT:	Yes
DDC Subjects:	000 Computer Science, information, general works > 004 Computer science 600 Technology, medicine, applied sciences > 650 Management, public relations
Date Deposited:	02 Mar 2026 06:37
Last Modified:	02 Mar 2026 06:37
URI:	https://eref.uni-bayreuth.de/id/eprint/96456