Financial Performance Measurement of Hungarian Retail Food Companies

• Autorzy: Veronika Fenyves , Zoltán Bács , Laura Karnai , Adrián Nagy , Tibor Tarnóczi
• Języki publikacji: EN

Abstrakty

EN

The comparison of company performances, i.e., benchmarking, is becoming more and more critical. Presently, companies mostly use traditional financial ratios to evaluate their financial performance. We also use financial ratios to measure and compare company performances, from which we create complex efficiency coefficients using Data Envelopment Analysis. Using Data Envelopment Analysis, we analyzed the efficiency of retail food companies in Hungary's Northern Great Plain region from 2009 to 2014 using their financial reports. To improve the result of the performance measurement, we used the bootstrap method, the Hamiltonian Monte Carlo simulation, and Bayesian statistics. We transformed the primarily deterministic DEA method into a stochastic DEA model. The primary target of this extension is to enhance statistical inference in DEA and to integrate it with a stochastic mechanism of Bayesian techniques. To develop the stochastic DEA model, we use Stan Stochastic Modelling Language within the framework of the R Statistics. Analyzing the results, we can state that the DEA method can be used for analyzing efficiency, and the additions shown can make the evaluation much more accurate. We can conclude that the best results were produced by the combined method, during a simultaneous application of the input orientation. (original abstract)

Słowa kluczowe

EN

Performance measurement; Data Envelopment Analysis (DEA); Principal Component Analysis (PCA); Bayesian statistics; Agri-food company; Bootstrap

PL

Pomiar dokonań; Metoda DEA (analiza obwiedni danych); Analizy głównych komponentów; Statystyka bayesowska; Przedsiębiorstwo rolno-spożywcze; Metody samowsporne

• Czasopismo: Contemporary Economics
• Rocznik: 2018
• Tom: 12
• Numer: nr 4 Digital Transformations and Value Creation in International Markets
• Strony: 459--471

Twórcy

autor

Veronika Fenyves

• University of Debrecen

autor

Zoltán Bács

• University of Debrecen

autor

Laura Karnai

• University of Debrecen, Hungary

autor

Adrián Nagy

• University of Debrecen, Hungary

autor

Tibor Tarnóczi

• University of Debrecen, Hungary

Bibliografia

• Adler, N., & Golany, B. (2017). PCA-DEA. In: W. Cook, & J. Zhu (Eds.), Modeling Problem Structure and Data Varieties using Data Envelopment Analysis: A Problem-Solving (pp. 139-153). Boston, MA: Springer.
• Andrejic, M., Bojovic, N., & Kilibarda, M. (2013). Benchmarking distribution centres using Principal Component Analysis and Data Envelopment Analysis: A case study of Serbia. Expert Systems with Applications, 40(10), 3926-3933.
• Bogetoft, P. & Otto, L. (2011). Benchmarking with DEA, SFA, and R. New York, NY: Springer Verlag.
• Bolstad, W. M. (2004). Introduction to Bayesian Statistics. Hoboken, NJ: John Wiley & Sons. Inc.
• Box, G.E.P. (1979, May). Robustness in the Strategy of Scientific Model Building. Defense (Technical Summary Report No. 1954). Mathematic Research Center. University of Wisconsin.
• Carpenter, B., Gelman, A., Hoffman, M. D., Lee, D., Goodrich, B., Betancourt,... Riddell, A. (2017). Stan: A probabilistic programming language. Journal of Statistical Software, 76(1), https://doi.org/10.18637/jss.v076.i01.
• Charnes, A., Cooper, W. W., Rhodes, E. (1978). Measuring the efficiency of decision making units. European Journal of Operational Research, 2, 429-444.
• Cirillo, A. (2016). RStudio for R Statistical Computing Cookbook. Birmingham, UK: Packt Publishing.
• Cook, W. D., & Zhu, J. (2005). Modeling performance measurement. Applications and implementation issues in DEA. New York, NY: Springer.
• Cooper, W. W., Seiford, L. M., & Tone, K. (2007). Data Envelopment Analysis: A Comprehensive Text with Models, Applications, References and DEASolver Software. New York, NY: Springer.
• Dong, F., Mitchell, P. D., Knuteson, D., Wyman, J., Bussan, A. J., & Conley S. (2016). Assessing sustainability and improvements in US Midwestern soybean production systems using a PCA-DEA approach. Renewable Agriculture and Food Systems, 31(6), 524-539.
• Emrouznejad, A., Parker, B. R., Tavares, G. (2008). Evaluation of research in efficiency and productivity: A survey and analysis of the first 30 years of scholarly literature in DEA. Journal of Socio-Economic Planning Sciences, 42(3), 151-157.
• Everitt. B., & Hothorn, T. (2011). An Introduction to Applied Multivariate Analysis with R. New York, NY: Springer.
• Faed, A., Chang, E., & Saberi, M. (2016). Intelligent customer complaint handling utilising principal component and data envelopment analysis (PDA). Applied Soft Computing, 47, 614-630.
• Farrell, M. (1957). The measurement of productive efficiency. Journal of Royal Statistical Society, Series A, 120(3), 253-281.
• Fu, H. P., Ou, J. R. (2013). Combining PCA With DEA to Improve the Evaluation of Project Performance Data: A Taiwanese Bureau of Energy Case Study. Project Management Journal, 44(1), 94-106.
• Hoffman, M. D., & Gelman, A. (2014). The No-U-Turn Sampler: Adaptively Setting Path Lengths in Hamiltonian Monte Carlo. Journal of Machine Learning Research 15, 1593-1623.
• Huang, Z., & Li, S. X. (2011). Stochastic DEA Models With Different Types of Input-Output Disturbances. Journal of Productivity Analysis, 15, 95-113.
• Jothimani, D., Shankar, R., & Yadav, S. S. (2017). A PCADEA framework for stock selection in Indian stock market. Journal of Modelling in Management, 12(3), 386-403.
• Jolliffe, I. T. (2002). Principal component analysis. New York, NY: Springer.
• Korner-Nievergelt, F., Roth, T., von Felten s, Guèlat, J., Almasi, B., Korner-Nievergelt, P. (2015). Bayesian Data Analysis in Ecology Using Linear Models with R, BUGS, and Stan. London, UK: Elsevier.
• Kruschke, J. K. (2010). Doing Bayesian Data Analysis: A Tutorial with R and BUGS. London, UK: Elsevier.
• Li, L., & Li, M. (2014), Evaluation of Energy Production Companies Efficiency Based on the Combination of Principal Component Analysis (PCA) and Data Envelopment Analysis (DEA). Environmental Engineering and Management Journal, 13(5), 1147-1154.
• Lunn, D., Thomas, A., Best, N., Spiegelhalter, D. (2000). Winbugs - a Bayesian modelling framework: concepts, structure, and extensibility. Statistics and Computing, 10, 325-337.
• Plummer, M. (2003, March 20-22). JAGS: A program for analysis of Bayesian graphical models using Gibbs sampling. In: K. Hornik, F. Leisch, & A. Zeileis (Eds.), Paper presented at the 3rd international workshop on distributed statistical computing (DSC 2003). Vienna, Austria: Technische Universität Wien. Retrieved from https://www.r-project.org/conferences/DSC-2003/Proceedings/Plummer.pdf
• Põldaru, R., & Roots, J. (2014). A PCA-DEA approach to measure the quality of life in Estonian counties. Socio-Economic Planning Sciences, 48, 65-73.
• Putri, E. P., Chetchotsak, D., & Jani, M. A. (2017). Performance Evaluation Using PCA and DEA: a Case Study of the Micro and Small Manufacturing Industries in Indonesia. ASR Chiang Mai University Journal of Social Sciences and Humanities, 4(1), 37-56.
• Sarkar, S. (2016). Application of PCA and DEA to recognize the true expertise of a firm: a case with primary schools. Benchmarking: An International Journal, 23(3), 740-751.
• Stan Development Team (2017). Stan Modeling Language: User's Guide and Reference Manual. Version 2.17.0. Available at http://mc-stan.org/users/documentation/index.html
• Tavares, G. (2002, January). A Bibliography of data envelopment analysis (1978-2001) (Research Report No. 01-02). Piscataway, NJ: Rutgers Center for Operations Research, Rutgers University.
• Unsal, M. G., & Orkcu, H. H. (2017), Ranking decision making units with the integration of the multidimensional scaling algorithm into PCA-DEA. Hacettepe Journal of Mathematics and Statistics, 46(6), 1187-1197.

Identyfikatory

DOI

10.5709/ce.1897-9254.290