Overall Equipment Effectiveness within Counterfactual Impact Evaluation Concept

Žaneta Rylková, Karel Stelmach, Petr Vlček


Counterfactual impact evaluation (CIE) is a scientific quantitative approach mainly based on experiments and quasi experiments. CIE is trying to prove a causal relationship between outputs and outcomes. CIE does not take into account coherence of external incentives of companies with internal incentives that have or may have an impact on the behaviour of enterprises. The paper sets up internal evaluation indicators for businesses, counterfactuals useful for creating a more complex metrics evaluating businesses in the area of performance. The aim of the paper is to present model situation using the elementary principle of counterfactual impact evaluation based on “the Overall Equipment Effectiveness (OEE)”.


CIE; OEE; production process; business evaluation

JEL Codes

M11; M21; M29.

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Angrist, J. D., and Krueger, B., 2001. Instrumental Variables and the Search for Identification: From supply and Demand to Natural Experiments. The Journal of Economic Perspectives, 15(4), 69-85. doi: http://dx.doi.org/10.1257/jep.15.4.69

Angrist, J. D., and Pischke, J. S., 2009. Mostly Harmless Econometrics. Princenton: Princenton University Press.

Borovska, T., 2015. Optimization of agricultural enterprises based on the methodology of optimal aggregation. Paper presented at the X International Scientific and Technical Conference “Computer science and information technologies” CSIT’ 2015, Lvov, Ukraine.

Borovska, T., Severilov, P., Kolesnik, I., and Severilov, V., 2017. The Optimal Aggregation of Integrated Regional Systems "Production, Waste Recycling". In N. Shakhovska (Ed.), Advances in Intelligent Systems and Computing: Advances in Intelligent Systems and Computing. doi:http://dx.doi.org/10.1007/978-3-319-45991-2_11

Borovska, T. M., Severilov, P. V., and Khomyn, Y. P., 2014. Alternative models optimal development industrial systems under uncertainty. System Research and Information, 4, 121-136.

Borovska, T. N., 2014a. Optimal aggregation of production systems with parametric connections. Eastern-European Journal of Enterprise Technologies, 4(11(70)), 9-19. doi: http://dx.doi.org/10.15587/1729-4061.2014.26306

Borovska, T. N., 2014b. Optimal aggregation systems with stochastic functions of production. Paper presented at the Proceedings of the National University “Lviv Polytechnic”.

Borovska, T. N., Kolesnik, I. S., Severilov, V. A., and Severilov, P. V., 2014. Optimal models of innovation development production systems. Eastern-European Journal of Enterprise Technologies, 5(2(71)), 42-50. doi: http://dx.doi.org/10.15587/1729-4061.2014.28030

Duflo, E., Glennerster, R., and Kremer, M., 2007. Using Randomization in Development Economics Research: A Toolkit. Handbook of Development Economics, 4, 3895-3962.

Gehlof, M., 2016. OEE vs. MTBF: Ktera z nich je ta prava metoda mereni? Charleston: Allied Reliability Group.

Gertler, P. J., Martinez, S., Premand, P., Rawlings, L. B., and Vermeersch, C. H. M. J., 2011. Impact Evaluation in Practice. Washington: World Bank.

Gu, Y., Wu, Y., Xu, M., Wang, H., and Zuo, T., 2017. To realize better extended producer responsibility: Redesign of WEEE fund mode in China. Journal of Cleaner Production, 164, 347-356. doi: http://dx.doi.org/10.1016/j.jclepro.2017.06.168

Hora, O., Vyhlidal, J., Sirovatkova, T., and Suchanec, M., 2015.

Metodika ,,Nove postupy pro zpracovani a vyuziti databaze nezamestnanych k hodnoceni programu aktivni politiky zamestnanosti a dalsich opatreni k zacleneni na trhu prace. Prague: Research Institute of Labor and Social Affairs.

Imbens, G. W., and Wooldridge, J. M., 2009. Recent developments in the econometrics of program evaluation. Journal of Economic Literature, 47(1), 5-86. doi: http://dx.doi.org/10.1257/jel.47.1.5

Khandker, S. R., Koolwal, G. B., and Samad, H. A., 2010. Handbook on Impact Evaluation: Quantitative Methods and Practices. Washington: World Bank.

Morgan, S. L., and Winship, C., 2010. Counterfactuals and causal inference: methods and principles for social research. Cambridge: Cambridge University Press.

Mouque, D., 2012. What are counterfactual impact evaluations teaching us about enterprise and innovation support? Brussels: European Commision.

Potluka, O., 2014. Management dopadových evaluací. Prague IREAS center.

Potluka, O., Bruha, J., Pelucha, M., Kveton, V., Vrbova, L., Loun, J., and Spacek, M., 2013. Pilotní counterfactual impact evaluation OP LZZ, oblast podpory Prague: IREAS center.

Potluka, O., and Špacek, M., 2013. Postupy a metody kontrafaktuálních dopadových evaluací pro Operační program Zaměstnanost v období 2014 – 2020. Prague: Ministry of Labor and Social Affairs.

Synek, M., 2011. Manazerska ekonomika. Prague: Grada Publishing.

Wang, H., Gu, Y., Li, L., Wu, Y., and Zuo, T., 2017. Operating models and development trends in the extended producer responsibility system for waste electrical and electronic equipment. Resources, Conservation and Recycling, 127, 159-167. doi: http://dx.doi.org/j.resconrec.2017.09.002

Wooldridge, J. M., 2010. Econometric Analysis of Cross Section and Panel Data. Cambridge: MIT Press.

Zavřel, L., 2015. Kontrafaktuální evaluace dopadů ROP Jihovýchod na vymezených územích. Evaluační teorie a praxe, 3(1), 53-58.

DOI: http://dx.doi.org/10.1515/saeb-2017-0037


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