eesti teaduste
akadeemia kirjastus
SINCE 1952
Proceeding cover
of the estonian academy of sciences
ISSN 1736-7530 (Electronic)
ISSN 1736-6046 (Print)
Impact Factor (2020): 1.045

Optimization of enterprise analysis model for KPI selection; pp. 371–375

Full article in PDF format |

Sergei Kaganski, Martin Eerme, Ernst Tungel


Nowadays, a number of methods and principles are available in literature for KPIs (key performance indicators) selection. However, they are facing with the same issue – lack of optimal procedures for selection of suitable metrics for particular company. The purpose of the study is to optimize enterprise analysis model (EAM) for KPIs selection and to reduce the time and resources necessary for the analysis of the enterprise. In the current study four outlier’s detection methods for eliminating “outliers” in the answers are utilized. Furthermore, the experts from production and academic institutions are participating in evaluation and analysis of questionnaires. The optimized EAM is going to help simplifying the choice of KPIs, reducing the amount of data and optimizing the data flow. The optimized set of questions in EAM and KPIs that could be used in companies for improving their productivity are determined. The research is focused on SMEs (small and medium enterprises) and intention is to increase their competence on the market. The general procedure for KPIs selection/optimization for SME is pointed out.


    1.  Raynard, P. and Forstater, M. Implications for small and medium enterprises in developing countries. UNIDO and the World Summit on Sustainable Development, Vienna, 2002.

    2.  Scholz-Reiter, B., Freitag, M., and Schmieder, A. A dynamical approach for modelling and control of production systems. AIP Conf. Proc., 2002, 622(1), 199–210.

    3.  Stricker, N., Micali, M., Dornfeld, D., and Lanza, G. Considering interdependencies of KPIs – possible resource efficiency and effectiveness emprovements. Procedia Manuf., 2017, 8, 300–307.

    4.  Snatkin, A., Eiskop, T., Karjust, K., and Majak, J. Production monitoring system development and modi­­fication. Proc. Est. Acad. Sci., 2015, 64, 567–580.

    5.  Paavel, M., Karjust, K., and Majak, J. Development of a product lifecycle management model based on the fuzzy analytic hierarchy process. Proc. Est. Acad. Sci., 2017, 66(3), 279−286.

    6.  Anggadwita, G. and Mustafid, Q. Y. Identification of factors influencing the performance of small medium enterprises (SMEs). ProcediaSocial Behav. Sci., 2014, 115, 415−423.

    7.  Venckeviciute, G. and Subaciene, R. European influence upon Lithuanian SME performance measurement. ProcediaSocial Behav. Sci., 2015, 213, 261−267.

    8.  Sahno, J., Shevtshenko, E., and Karaulova, T. Framework for continuous improvement of production processes. Eng. Econ., 2015, 26, 169–180.

    9.  Lehtimaki, A. Management of the innovation process in small companies in Finland. IEEE Trans. Eng. Manage., 1991, 38 (2), 120–126.

 10.  Amrina, E. and Vilsi, A. Key performance indicators for sustainable manufacturing evaluation in cement industry. Procedia CIRP, 2015, 26, 19–23.

 11.  Shahin, A. and Mahbod, M. A. Prioritization of key performance indicators. An integration of analytical hierarchy process and goal setting. IJPPM, 2015, 56, 226–240.

 12.  S. Kadarsah. Framework of measuring key performance indicators for decision support of higher education institution. J. Appl. Sci. Res., 2007, 3, 1689–1695.

 13.  Parmenter, D. Key Performance Indicators (KPI): Developing, Implementing and Using Winning KPIs. Second ed. John Wiley & Sons, Inc., New Jersey, 2010.

 14.  Yuan, J., Wang, C., Skibniewski, M. J., and Li, Q. Developing key performance indicators for public-private partnership projects: questionnaire survey and analysis. J. Manage. Eng., 2012, 28, 252–264.

 15.  Podgorski, D. Measuring operational performance of OSH management system. A demonstration of AHP-based selection of leading key performance indicators. Saf. Sci., 2015, 73, 146–166.

 16.  Kaganski, S. and Toompalu, S. Development of key performance selection index model. J. Achiev. Mater. Manuf. Eng., 2017, 82(1), 33−40.

 17.  Kaganski, S., Paavel, M., and Lavin, J. Selecting key performance indicators with support of enterprise analyze model. In Proceedings of the 9th International DAAAM Baltic Conference, Tallinn, Estonia, April 24–26, 2014, 97–102. Production%20Engineering%20and%20Management/Kaganski.pdf

 18.  Eckerson, W. W. Performance Management Strategies: How to Create and Deploy Effective Metrics. ftp: // analystreports/ar_peformance_mgmnt_strategies_how_to_create_and_deploy_effective_metrics.pdf

 19.  Paavel, M., Kaganski, S., Karjust, K., Lemmik, R., and Eiskop, T. Analysis model development to simplify PLM implementation. In Proceedings of the 10th International Conference of DAAAM Baltic, Industrial Engineering, Tallinn, Estonia, May 12–13, 2015 (Otto, T., ed.). DAAAM Baltic, Tallinn University of Technology, Tallinn, 2015, 69–74.

 20.  Majak, J., Pohlak, M., Eerme, M., and Velsker, T. Design of car frontal protection system using neural networks and genetic algorithm. Mechanika, 2012, 18(4), 453−460.

 21.  Karjust, K., Pohlak, M., and Majak, J. Technology route planning of large composite parts. Int. J. Mater. Form., 2010, 3, 631−634.

 22.  Lellep, J. and Majak, J. Nonlinear constitutive behavior of orthotropic materials. Mech. Compos. Mater., 2000, 36(4), 261−266.

 23.  Aruniit, A., Kers, J., Goljandin, D., Saarna, M., Tall, K., Majak, J., and Herranen, H. Particulate filled com­posite plastic materials from recycled glass fibre reinforced plastics. Mater. Sci. (Medžiagotyra), 2011, 17(3), 276−281.

 24.  Aggarwal, C. C. Outlier analysis. IBM T. J. Watson Research Center, Yorktown Heights, NY, 2013.

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