Automated Prediction of Relevant Key Performance Indicators for Organizations

Ünal Aksu, Dennis M.M. Schunselaar, Hajo A. Reijers

Research output: Chapter in Book / Report / Conference proceedingConference contributionAcademicpeer-review

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.

Original languageEnglish
Title of host publicationBusiness Information Systems - 22nd International Conference, BIS 2019, Proceedings
EditorsRafael Corchuelo, Witold Abramowicz
PublisherSpringer Verlag
Pages283-299
Number of pages17
ISBN (Print)9783030204846
DOIs
Publication statusPublished - 1 Jan 2019
Event22nd International Conference on Business Information Systems, BIS 2019 - Seville, Spain
Duration: 26 Jun 201928 Jun 2019

Publication series

NameLecture Notes in Business Information Processing
Volume353
ISSN (Print)1865-1348

Conference

Conference22nd International Conference on Business Information Systems, BIS 2019
CountrySpain
CitySeville
Period26/06/1928/06/19

Fingerprint

Performance Indicators
Prediction
Enterprise software
Software
Key performance indicators
Monitor

Keywords

  • Key Performance Indicators
  • Prediction
  • Relevance

Cite this

Aksu, Ü., Schunselaar, D. M. M., & Reijers, H. A. (2019). Automated Prediction of Relevant Key Performance Indicators for Organizations. In R. Corchuelo, & W. Abramowicz (Eds.), Business Information Systems - 22nd International Conference, BIS 2019, Proceedings (pp. 283-299). (Lecture Notes in Business Information Processing; Vol. 353). Springer Verlag. https://doi.org/10.1007/978-3-030-20485-3_22
Aksu, Ünal ; Schunselaar, Dennis M.M. ; Reijers, Hajo A. / Automated Prediction of Relevant Key Performance Indicators for Organizations. Business Information Systems - 22nd International Conference, BIS 2019, Proceedings. editor / Rafael Corchuelo ; Witold Abramowicz. Springer Verlag, 2019. pp. 283-299 (Lecture Notes in Business Information Processing).
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Aksu, Ü, Schunselaar, DMM & Reijers, HA 2019, Automated Prediction of Relevant Key Performance Indicators for Organizations. in R Corchuelo & W Abramowicz (eds), Business Information Systems - 22nd International Conference, BIS 2019, Proceedings. Lecture Notes in Business Information Processing, vol. 353, Springer Verlag, 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

Automated Prediction of Relevant Key Performance Indicators for Organizations. / Aksu, Ünal; Schunselaar, Dennis M.M.; Reijers, Hajo A.

Business Information Systems - 22nd International Conference, BIS 2019, Proceedings. ed. / Rafael Corchuelo; Witold Abramowicz. Springer Verlag, 2019. p. 283-299 (Lecture Notes in Business Information Processing; Vol. 353).

Research output: Chapter in Book / Report / Conference proceedingConference contributionAcademicpeer-review

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Aksu Ü, Schunselaar DMM, Reijers HA. Automated Prediction of Relevant Key Performance Indicators for Organizations. In Corchuelo R, Abramowicz W, editors, Business Information Systems - 22nd International Conference, BIS 2019, Proceedings. Springer Verlag. 2019. p. 283-299. (Lecture Notes in Business Information Processing). https://doi.org/10.1007/978-3-030-20485-3_22