Automated Prediction of Relevant Key Performance Indicators for Organizations
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Publication date
2019-06-26
Authors
Aksu, Ünal
Schunselaar, Dennis M.M.
Reijers, H.A.
Editors
Abramowicz, Witold
Corchuelo, Rafael
Advisors
Supervisors
Document Type
Part of book
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taverne
Abstract
Organizations utilize Key Performance Indicators (KPIs) to monitor whether they attain their goals. For this, software vendors offer predefined KPIs in their enterprise software. However, the predefined KPIs will not be relevant for all organizations due to the varying needs of them. Therefore, software vendors spend significant efforts on offering relevant KPIs. That relevance determination process is time-consuming and costly. We show that the relevance of KPIs may be tied to the specific properties of organizations, e.g., domain and size. In this context, we present our novel approach for the automated prediction of which KPIs are relevant for organizations. We implemented our approach and evaluated its prediction quality in an industrial setting.
Keywords
Key Performance Indicators, Prediction, Relevance, Taverne, Management Information Systems, Control and Systems Engineering, Business and International Management, Information Systems, Modelling and Simulation, Information Systems and Management
Citation
Aksu, Ü, Schunselaar, D M M & Reijers, H A 2019, Automated Prediction of Relevant Key Performance Indicators for Organizations. in W Abramowicz & R Corchuelo (eds), Business Information Systems - 22nd International Conference, BIS 2019, Proceedings. Lecture Notes in Business Information Processing, vol. 353, Springer, pp. 283-299, 22nd International Conference on Business Information Systems, BIS 2019, Seville, Spain, 26/06/19. https://doi.org/10.1007/978-3-030-20485-3_22, conference