Automated Prediction of Relevant Key Performance Indicators for Organizations

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

*Corresponding author for this work

    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
    EditorsWitold Abramowicz, Rafael Corchuelo
    PublisherSpringer
    Pages283-299
    Number of pages17
    ISBN (Electronic)9783030204853
    ISBN (Print)9783030204846
    DOIs
    Publication statusPublished - 26 Jun 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
    ISSN (Electronic)1865-1356

    Conference

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

    Keywords

    • Key Performance Indicators
    • Prediction
    • Relevance

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