Automated Prediction of Relevant Key Performance Indicators for Organizations

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

doi:10.1007/978-3-030-20485-3_22

Automated Prediction of Relevant Key Performance Indicators for Organizations

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

^*Corresponding author for this work

Vrije Universiteit Amsterdam

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-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 language	English
Title of host publication	Business Information Systems - 22nd International Conference, BIS 2019, Proceedings
Editors	Witold Abramowicz, Rafael Corchuelo
Publisher	Springer
Pages	283-299
Number of pages	17
ISBN (Electronic)	9783030204853
ISBN (Print)	9783030204846
DOIs	https://doi.org/10.1007/978-3-030-20485-3_22
Publication status	Published - 26 Jun 2019
Event	22nd International Conference on Business Information Systems, BIS 2019 - Seville, Spain Duration: 26 Jun 2019 → 28 Jun 2019

Publication series

Name	Lecture Notes in Business Information Processing
Volume	353
ISSN (Print)	1865-1348
ISSN (Electronic)	1865-1356

Conference

Conference	22nd International Conference on Business Information Systems, BIS 2019
Country/Territory	Spain
City	Seville
Period	26/06/19 → 28/06/19

Funding

Supported by the NWO AMUSE project (628.006.001): a collaboration between Vrije Universiteit Amsterdam, Utrecht University, and AFAS Software in the Netherlands. This work is a result of the AMUSE project. See amuse-project.org for more information.

Keywords

Key Performance Indicators
Prediction
Relevance

Access to Document

10.1007/978-3-030-20485-3_22

aksuFinal published version, 937 KBLicence: Taverne

Cite this

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 (pp. 283-299). (Lecture Notes in Business Information Processing; Vol. 353). Springer. https://doi.org/10.1007/978-3-030-20485-3_22

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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",

author = "{\"U}nal Aksu and Schunselaar, {Dennis M.M.} and Reijers, {Hajo A.}",

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Aksu, Ü, Schunselaar, DMM & Reijers, HA 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

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. / Witold Abramowicz; Rafael Corchuelo. Springer, 2019. p. 283-299 (Lecture Notes in Business Information Processing; Vol. 353).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

TY - GEN

T1 - Automated Prediction of Relevant Key Performance Indicators for Organizations

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AU - Schunselaar, Dennis M.M.

AU - Reijers, Hajo A.

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N2 - 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.

AB - 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.

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KW - Prediction

KW - Relevance

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Automated Prediction of Relevant Key Performance Indicators for Organizations

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