Skip to main content
SpringerLink
Log in
Book cover

International Conference on Business Information Systems

BIS 2019: Business Information Systems pp 283–299Cite as

Automated Prediction of Relevant Key Performance Indicators for Organizations

  • Conference paper
  • First Online:

Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 353))

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.

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.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   69.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   89.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Notes

  1. 1.

    The implementation of our Automated Relevant KPI Determination Approach is available at http://amuse-project.org/software/.

References

  1. Aksu, Ü., Schunselaar, D.M.M., Reijers, H.A.: A cross-organizational process mining framework for obtaining insights from software products: accurate comparison challenges. In: 18th IEEE Conference on Business Informatics (2016)

    Google Scholar 

  2. Aksu, Ü., Schunselaar, D.M.M., Reijers, H.A.: An approach for automatically deriving key performance indicators from ontological enterprise models. In: 7th International Symposium on Data-driven Process Discovery and Analysis (2017)

    Google Scholar 

  3. Carlucci, D.: Evaluating and selecting key performance indicators: an ANP-based model. Measuring Bus. Excellence 14(2), 66–76 (2010)

    Article  Google Scholar 

  4. del-Río-Ortega, A., Resinas, M., Cabanillas, C., Cortés, A.R.: On the definition and design-time analysis of process performance indicators. Inf. Syst. 38(4), 470–490 (2013)

    Article  Google Scholar 

  5. Eisenberg, M., Hu, X.: Dichotomous relevance judgments and the evaluation of information systems. Proc. Am. Soc. Inform. Sci. 24, 66–69 (1987)

    Google Scholar 

  6. Elliot, C., Mcullagh, C., Brydon, M., Zwi, K.: Developing key performance indicators for a tertiary children’s hospital network. Aust. Health Rev. 42(5), 491–500 (2018)

    Article  Google Scholar 

  7. Eshtaiwi, M., Badi, I., Abdulshahed, A., Erkan, T.E.: Determination of key performance indicators for measuring airport success: a case study in Libya. J. Air Transp. Manag. 68, 28–34 (2018)

    Article  Google Scholar 

  8. Frost, J.: Making predictions with regression analysis, March 2019. https://statisticsbyjim.com/regression/predictions-regression

  9. García, V., Mollineda, R.A., Sánchez, J.S.: Index of balanced accuracy: a performance measure for skewed class distributions. In: Araujo, H., Mendonça, A.M., Pinho, A.J., Torres, M.I. (eds.) IbPRIA 2009. LNCS, vol. 5524, pp. 441–448. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-02172-5_57

    Chapter  Google Scholar 

  10. Gluck, M.: Exploring the relationship between user satisfaction and relevance in information systems. Inf. Process. Manage. 32(1), 89–104 (1996)

    Article  Google Scholar 

  11. Granberg, T.A., Munoz, A.O.: Developing key performance indicators for airports. In: 3rd ENRI International Workshop on ATM/CNS (2013)

    Google Scholar 

  12. Gravetter, F.J., Wallnau, L.B.: Essentials of statistics for the behavioral sciences (2013)

    Google Scholar 

  13. Ioan, B., Nestian, A.S., Tita, S.M.: Relevance of key performance indicators (KPIs) in a hospital performance management model. J. Eastern Europe Res. Bus. Econ. 2012, 15 (2012)

    Google Scholar 

  14. Kachitvichyanukul, V., Luong, H., Pitakaso, R.: A hybrid MCDM approach to KPI selection of the coordination problems of production and sales departments-an empirical study of iron and steel industry of China and Taiwan. In: 13th Asia Pacific Industrial Engineering and Management Systems Conference (2012)

    Google Scholar 

  15. Kaganski, S., Paavel, M., Lavin, J.: Selecting key performance indicators with support of enterprise analyse model. In: 9th International DAAAM Baltic Conference “Industrial Engineering” (2014)

    Google Scholar 

  16. Kaganski, S., Snatkin, A., Paavel, M., Karjust, K.: Selecting the right KPIs for smes production with the support of PMS and PLM. Int. J. Res. Soc. Sci. 1(3), 69–76 (2013)

    Google Scholar 

  17. Kucukaltan, B., Irani, Z., Aktas, E.: A decision support model for identification and prioritization of key performance indicators in the logistics industry. Comput. Hum. Behav. 65, 346–358 (2016)

    Article  Google Scholar 

  18. Parsons, V.L.: Stratified Sampling (2017)

    Google Scholar 

  19. Padmaja, D.L., Vishnuvardhan, B.: Comparative study of feature subset selection methods for dimensionality reduction on scientific data. In: IEEE 6th International Conference on Advanced Computing (2016)

    Google Scholar 

  20. Parmenter, D.: Key Performance Indicators: Developing, Implementing, and Using Winning KPIs. Wiley, New York (2015)

    Google Scholar 

  21. Peral, J., Maté, A., Marco, M.: Application of data mining techniques to identify relevant key performance indicators. Comput. Stand. Interfaces 54, 76–85 (2017)

    Article  Google Scholar 

  22. Pinna, C., Demartini, M., Tonelli, F., Terzi, S.: How soft drink supply chains drive sustainability: key performance indicators (KPIs) identification. In: 51st CIRP Conference on Manufacturing Systems (2018)

    Google Scholar 

  23. Saitta, S.: What is a good classification accuracy in data mining? March 2019. http://www.dataminingblog.com/what-is-a-good-classification-accuracy-in-data-mining

  24. Schmidt, M., Schwöbel, J., Lienkamp, M., et al.: Developing key performance indicators for variant management of complex product families (2018)

    Google Scholar 

  25. Sokolova, M., Lapalme, G.: A systematic analysis of performance measures for classification tasks. Inf. Process. Manage. 45(4), 427–437 (2009)

    Article  Google Scholar 

  26. Steyerberg, E.W., et al.: Assessing the performance of prediction models: a framework for some traditional and novel measures. Epidemiology 21(1), 128 (2010)

    Article  Google Scholar 

  27. Tang, R., Shaw Jr., W.M., Vevea, J.L.: Towards the identification of the optimal number of relevance categories. J. Am. Soc. Inform. Sci. 50(3), 254–264 (1999)

    Article  Google Scholar 

  28. Van Horenbeek, A., Pintelon, L.: Development of a maintenance performance measurement framework-using the analytic network process (ANP) for maintenance performance indicator selection. Omega 42(1), 33–46 (2014)

    Article  Google Scholar 

  29. Zhou, Z.H.: Ensemble Methods: Foundations and Algorithms. Chapman and Hall/CRC, Boca Raton (2012)

    Book  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Utrecht University, Utrecht, The Netherlands

    Ünal Aksu & Hajo A. Reijers

  2. Vrije Universiteit Amsterdam, Amsterdam, The Netherlands

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

Authors
  1. Ünal Aksu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Dennis M. M. Schunselaar
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Hajo A. Reijers
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ünal Aksu .

Editor information

Editors and Affiliations

  1. Poznań University of Economics and Business, Poznań, Poland

    Witold Abramowicz

  2. ETSI Informática, University of Seville, Seville, Spain

    Rafael Corchuelo

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Aksu, Ü., Schunselaar, D.M.M., Reijers, H.A. (2019). Automated Prediction of Relevant Key Performance Indicators for Organizations. In: Abramowicz, W., Corchuelo, R. (eds) Business Information Systems. BIS 2019. Lecture Notes in Business Information Processing, vol 353. Springer, Cham. https://doi.org/10.1007/978-3-030-20485-3_22

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-20485-3_22

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-20484-6

  • Online ISBN: 978-3-030-20485-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation