Performance of supermarkets in Santa Catarina: an analysis of small and medium-sized units

Silva Junior, Julio Cesar Araujo; Nodari, Douglas; Cavalheiro, Mariana de Oliveira; Victor, Fernanda Gomes

doi:10.1590/0104-530X4685-20

Julio Cesar Araujo Silva Junior

Universidade Federal de Viçosa – UFV, Programa de Mestrado em Economia, Departamento de Economia, Viçosa, MG, Brasil. E-mail: julio.econometria@gmail.com

Douglas Nodari

Universidade Comunitária da Região de Chapecó – UNOCHAPECÓ, Faculdade de Ciências Econômicas, Chapecó, SC, Brasil. E:mail: douglasn@unochapeco.edu.br

Mariana de Oliveira Cavalheiro

Universidade Federal do Rio Grande do Sul – UFRGS, Faculdade de Ciências Econômicas, Porto Alegre, RS, Brasil. E-mail: ; fernanda.victor@ufrgs.br

Fernanda Gomes Victor

Universidade Federal do Rio Grande do Sul – UFRGS, Faculdade de Ciências Econômicas, Porto Alegre, RS, Brasil. E-mail: ; fernanda.victor@ufrgs.br

http://orcid.org/0000-0002-2820-9506

Authorship (Year)	Main informations	DEA Features
Badin (1997)Badin, N. T. (1997). Avaliação da produtividade de supermercados e seu benchmarking (Dissertação de mestrado). Universidade Federal de Santa Catarina, Florianópolis.	Evaluated the efficiency of the supermarket sector through a strategy that involved Data Envelopment Analysis (DEA), Benchmarking and Production Function. Using data from Brazilian Association of Supermarkets ranking of 1996, it was observed that 467 supermarkets, among 600, could be considered inefficient.	Model: BCC, with input orientation and 600 DMUs.
		Variables: n⁰ of checkout’s, total store area, n⁰ of employees, n⁰ of stores and average income per capita (inputs) and revenues (output).
Barros & Alves (2003)Barros, C. P., & Alves, C. A. (2003). Hypermarket retail store efficiency in Portugal. International Journal of Retail & Distribution Management, 31(11), 549-560. http://dx.doi.org/10.1108/09590550310503285. http://dx.doi.org/10.1108/09590550310503...	They analyzed the efficiency of individual retail stores belonging to a chain of hypermarkets in Portugal. With information from the year 2000, they concluded that more than 63% of the sample was technically inefficient.	Models: CCR e BCC, with output orientation.
		Variables: sales and operational results (outputs); full and part time employees, labor cost, absenteeism, area, n⁰ of the points of sale, inventory e other costs (inputs). DMUs: 47.
Barros (2006)Barros, C. P. (2006). Efficiency measurement among hypermarkets and supermarkets and the identification of the efficiency drivers: a case study. International Journal of Retail & Distribution Management, 34(2), 135-154. http://dx.doi.org/10.1108/09590550610649795. http://dx.doi.org/10.1108/09590550610649...	Checked the efficiency of hypermarkets and supermarkets in Portugal. He adopted a two-step procedure with DEA and Tobit models. The findings showed that larger retail groups are more efficient than smaller retailers; and that national retailers are more efficient than regional retailers.	Models: CCR e BCC, with output orientation.
		Variables: n⁰ full time employees and value of assets (inputs); the outputs were: sales, operational results and added value. DMUs: 132 (1 observation by year, of 22 companies, between 1998-2003).
Sellers-Rubio & Mas-Ruiz (2006)Sellers-Rubio, R., & Mas-Ruiz, F. (2006). Economic efficiency in supermarkets: evidences in Spain. International Journal of Retail & Distribution Management, 34(2), 155-171. http://dx.doi.org/10.1108/09590550610649803. http://dx.doi.org/10.1108/09590550610649...	The authors estimated the economic efficiency of the supermarket chains of the Spanish retail industry using DEA. The sample consisted in 100 supermarket chains between 1995 and 2001. Their results indicated high levels of economic inefficiency in the Spanish retail sector.	Models: CCR e BCC, with output orientation.
		The inputs were: n⁰ de employees, points of sale and capital (own funds and debt). The outputs were sales and profits. DMUs: 100.
Didonet & Villavicencio (2008)Didonet, S. R., & Villavicencio, G. J. D. (2008). Orientación al mercado y eficiencia en los supermercados: evidencias empíricas. Revista de Administração da UFSM, 1(1), 134-152. http://dx.doi.org/10.5902/19834659579. http://dx.doi.org/10.5902/19834659579...	Didonet & Villavicencio verified the relationship between market orientation components and their efficiency in the supermarket segment of Belo Horizonte, using DEA and Tobit models. The results indicated that specific actions of generation and dissemination of intelligence have a positive influence on efficiency levels. And that only 17 among 67 supermarkets were efficient.	Model: BCC with output orientation.
		Variables: nº of employees, nº of checkout’s and area of sales (inputs); and average number of customers and average number of products in stock (outputs). DMUs: 67.
Moreno (2008)Moreno, J. J. (2008). Efficiency and regulation in Spanish hypermarket retail trade: a cross‐section approach. International Journal of Retail & Distribution Management, 36(1), 71-88. http://dx.doi.org/10.1108/09590550810847009. http://dx.doi.org/10.1108/09590550810847...	Analyzed the efficiency of hypermarkets in Spain focusing on state regulation of the sector. The results suggested that hypermarkets operating in areas with low regulatory constraints were more efficient than those located in areas of higher regulation.	Model: CCR with output orientation.
		Variables: inputs: employees and area m²; the output was sales. DMUs: 234.
Sonza & Ceretta (2008)Sonza, I. B., & Ceretta, P. S. (2008). Utilização do DEA para análise da eficiência nos supermercados brasileiros. In Anais do Congresso USP de Iniciação Científica em Contabilidade. São Paulo: USP.	With data from the Brazilian Association of Supermarkets ranking, the authors verified the relationship between efficiency and size of companies. They concluded that large companies were more efficient than others.	Models: CCR e BCC, with input and output orientation.
		Variables: sales area per store, checkout's and number of employees; gross sales and percentage variation of gross sales (outputs). DMUs: 500.
Figueiredo & Mello (2009)Figueiredo, D. S., & Mello, J. C. C. B. S. (2009). Índice híbrido de eficácia e eficiência para lojas de varejo. Gestão & Produção, 16(2), 286-300. http://dx.doi.org/10.1590/S0104-530X2009000200011. http://dx.doi.org/10.1590/S0104-530X2009...	Figueiredo & Mello have generated a hybrid index of efficiency and effectiveness with a retail application. The main contribution refers to the use of artificial DMUs in contexts where there are multiple inputs and multiple outputs.	Model: BBC with input orientation.
		Variables: sales area per store and n⁰ of employees (inputs); gross sales and number of served clients (outputs). DMUs: 39.
Ferreira et al. (2009)Ferreira, M. A. M., Venâncio, M. M., & Abrantes, L. A. (2009). Análise da eficiência do setor de supermercados no Brasil. Economia Aplicada, 13(2), 333-347. http://dx.doi.org/10.1590/S1413-80502009000200007. http://dx.doi.org/10.1590/S1413-80502009...	They analyzed the technical and scale efficiency of the Brazilian supermarket sector, using data from the ABRAS ranking of the year 2005. They found an association between the technical efficiency and the size of the supermarkets, in addition to a low efficiency in the sample analyzed.	Models: CCR e BCC, with output orientation.
		Variables: nº of checkouts, n⁰ of employees and total used area (inputs); gross annual sales of supermarkets (output). DMUs: 300.
Yu & Ramanathan (2009)Yu, W., & Ramanathan, R. (2009). An assessment of operational efficiency of retail firms in China. Journal of Retailing and Consumer Services, 16(2), 109-122. http://dx.doi.org/10.1016/j.jretconser.2008.11.009. http://dx.doi.org/10.1016/j.jretconser.2...	Yu & Ramanathan evaluated the operational efficiency of retail companies in China using three methodologies: DEA, Malmquist Productivity Index and Tobit model. The period under investigation comprised the years 2000 to 2003. Their results indicate a very small number of efficient companies and an improvement of 37% of the companies investigated during the period.	Models: CCR e BCC, with output orientation.
		Variables: n⁰ of employees and total area of sales (inputs) and turnover and profit before tax (outputs). DMUs: 61.
Souza et al. (2010)Souza, M. A. F., Macedo, M. A. S., & Ferreira, M. S. (2010). Desempenho organizacional no setor supermercadista brasileiro: uma análise apoiada em dea. Revista de Gestão, 17(2), 151-167. http://dx.doi.org/10.5700/rege392. http://dx.doi.org/10.5700/rege392...	They analyzed the performance of the 100 smallest supermarket companies among the 300 largest in 2005 in Brazil. The 10 largest supermarkets performed better than the 10 smaller ones. All with one store presented performance problems related to the scale, while those with more than one presented problems related to technical efficiency.	Models: CCR e BCC, with input orientation.
		Variables: n⁰ of stores, n⁰ of checkouts and n⁰ of employees (inputs); and gross sales (output). DMUs: 100.
Reiner et al. (2013)Reiner, G., Teller, C., & Kotzab, H. (2013). Analyzing the efficient execution of in-store logistics processes in grocery retailing: the case of dairy products. Production and Operations Management, 22(4), 924-939. http://dx.doi.org/10.1111/poms.12003. http://dx.doi.org/10.1111/poms.12003...	The researchers empirically evaluated and quantified the logistics processes of supermarkets and hypermarkets in a network in Europe. The results indicate different levels of efficiency for different store formats.	Model: BBC.
		Variables: space, employees and shelf measurements (inputs); turnover, service level and waste (outputs). DMUs: 202.
Ko et al. (2017)Ko, K., Chang, M., Bae, E., & Kim, D. (2017). Efficiency analysis of retail chain stores in Korea. Sustainability, 9(9), 1629. http://dx.doi.org/10.3390/su9091629. http://dx.doi.org/10.3390/su9091629...	They investigated the factors that affect the efficiency of chain stores in Korea, using DEA and Tobit models. They concluded that too many items can reduce efficiency and that competition helps improving efficiency (to the point of excessive competition).	Model: BBC with output orientation.
		Variables: store size, n⁰ of items, n⁰ of employees and rent cost (inputs); sales revenue and n⁰ of clients (outputs) DMUs: 32.
Sinik (2017)Sinik, N. (2017). Assessing organizational efficiency via data envelopment analysis in the Austrian supermarket sector. In Proceedings of the 8th Annual Global Business Conference. Dubrovnik: GBC.	Evaluated the efficiency of supermarkets in Austrian malls in the year 2015. The results pointed to a high level of efficiency (average of 91%) in the analyzed period.	Models: CCR e BCC.
		Variables: clients, transactions size, area, labor costs, works hours, productivity and loss of stock (inputs); turnover separated in two departments (output). DMUs: 28.
Melo et al. (2018)Melo, F. L. N., Sampaio, R. M. B., & Sampaio, L. M. B. (2018). Efficiency, productivity gains, and the size of Brazilian supermarkets. International Journal of Production Economics, 197, 99-111. http://dx.doi.org/10.1016/j.ijpe.2017.12.016. http://dx.doi.org/10.1016/j.ijpe.2017.12...	They evaluated the efficiency of the Brazilian supermarket sector and the changes in productivity from 2005 to 2012. They found that the supermarket chains have low levels of average efficiency and that there was an increase of productivity in the period. Small supermarket chains showed the lowest levels of efficiency in 2005 and 2012, but were the ones that achieved the highest levels of productivity growth in the period.	Model: Bootstrap Data Envelopment Analysis, with RCE and RVE.
		Variables: n⁰ of employees, n⁰ of checkout’s and area of store in m² (inputs); annual gross sales (output).
		DMUs: in 2005, 494.
		DMUs: in 2012, 701.

	2014				2015
	checkout’s	Employees	Area	Gross sales (R$)	checkout’s	Employees	Area	Gross sales (R$)
Mean	5.03	22.68	775.74	6,043,7	5.19	22.65	802.23	6,640,3
Mean	5.03	22.68	775.74	44.68	5.19	22.65	802.23	15.13
Standard deviation	4.73	32.55	591.62	6,883,9	4.98	28.45	615.70	7,704,5
Standard deviation	4.73	32.55	591.62	17.07	4.98	28.45	615.70	35.60
Coefficient of variation	93.98%	143.52%	76.26%	113.90%	95.94%	125.63%	76.75%	116.03%

	Model	DMU	DMU	DMU	DMU	DMU	DMU	DMU	DMU	DMU	Total	% of Model
2014	BCC	1	2	4	5	6	7	8	10	11	17	54.84%
	BCC	13	14	16	18	24	27	29	30		17	54.84%
	CCR	5	7	10	11	27					5	16.13%
	Scale	5	7	10	11	27					5	16.13%
2015	BCC	1	2	4	5	6	7	8	10	11	17	54.84%
	BCC	13	14	16	18	20	24	27	30		17	54.84%
	CCR	5	7	11	20	27					5	16.13%
	Scale	5	7	11	20	27	29				6	19.35%

DMU	2014			2015
DMU	CCR	BCC	Scale	CCR	BCC	Scale
DMU_01	0.6750	1.0000	0.6750	0.6684	1.0000	0.6684
DMU_02	0.6829	1.0000	0.6829	0.6264	1.0000	0.6264
DMU_03	0.6782	0.8756	0.7746	0.6692	0.8386	0.7980
DMU_04	0.8764	1.0000	0.8764	0.9227	1.0000	0.9227
DMU_05	1.0000	1.0000	1.0000	1.0000	1.0000	1.0000
DMU_06	0.4958	1.0000	0.4958	0.4485	1.0000	0.4485
DMU_07	1.0000	1.0000	1.0000	1.0000	1.0000	1.0000
DMU_08	0.6562	1.0000	0.6562	0.6290	1.0000	0.6290
DMU_09	0.8135	0.9392	0.8662	0.7770	0.9192	0.8453
DMU_10	1.0000	1.0000	1.0000	0.4552	1.0000	0.4552
DMU_11	1.0000	1.0000	1.0000	1.0000	1.0000	1.0000
DMU_12	0.5339	0.6979	0.7649	0.5477	0.7154	0.7656
DMU_13	0.8018	1.0000	0.8018	0.7943	1.0000	0.7943
DMU_14	0.6997	1.0000	0.6997	0.6360	1.0000	0.6360
DMU_15	0.5948	0.9202	0.6464	0.6328	0.9372	0.6752
DMU_16	0.6700	1.0000	0.6700	0.4383	1.0000	0.4383
DMU_17	0.7217	0.7506	0.9615	0.5429	0.8175	0.6642
DMU_18	0.9179	1.0000	0.9179	0.7955	1.0000	0.7955
DMU_19	0.5809	0.6351	0.9146	0.5769	0.6093	0.9469
DMU_20	0.8919	0.9614	0.9277	1.0000	1.0000	1.0000
DMU_21	0.5921	0.8768	0.6753	0.5996	0.8723	0.6874
DMU_22	0.5580	0.8142	0.6853	0.5576	0.8012	0.6959
DMU_23	0.7135	0.8904	0.8013	0.6918	0.8694	0.7957
DMU_24	0.5633	1.0000	0.5633	0.5328	1.0000	0.5328
DMU_25	0.6089	0.7673	0.7935	0.6665	0.7369	0.9044
DMU_26	0.8609	0.8853	0.9725	0.8868	0.9139	0.9704
DMU_27	1.0000	1.0000	1.0000	1.0000	1.0000	1.0000
DMU_28	0.6387	0.6975	0.9158	0.6152	0.6530	0.9421
DMU_29	0.7671	1.0000	0.7671	0.9536	0.8718	1.0000
DMU_30	0.6753	1.0000	0.6753	0.6281	1.0000	0.6281
DMU_31	0.4229	0.5515	0.7669	0.4120	0.5366	0.7677

2014/BCC				2015/BCC
inefficient DMU / value	Variables with slack			inefficient DMU / value	Variables with slack
inefficient DMU / value	Check	Empl	Area	inefficient DMU / value	Check	Empl	Area
DMU_03(0.875611)	0.00	0.00	10.05	DMU_03(0.838554)	0.00	0.00	85.17
DMU_09(0.939164)	0.00	0.00	0.00	DMU_09(0.919223)	0.00	0.00	0.00
DMU_12(0.697928)	0.00	0.00	2.77	DMU_12(0.715407)	0.00	0.00	66.64
DMU_15(0.920163)	0.00	3.17	73.69	DMU_15(0.937185)	0.00	2.36	86.29
DMU_17(0.750553)	0.00	0.00	0.00	DMU_17(0.817461)	0.00	4.71	0.00
DMU_19(0.635138)	0.00	0.00	0.00	DMU_19(0.609290)	0.00	0.00	0.00
DMU_20(0.961406)	0.00	0.00	147.52	DMU_21(0.872319)	0.00	0.00	68.51
DMU_21(0.876769)	0.00	0.29	56.56	DMU_22(0.801206)	0.00	2.30	0.00
DMU_22(0.814239)	0.00	3.58	0.00	DMU_23(0.869424)	0.00	0.00	29.99
DMU_23(0.890352)	0.00	0.00	0.00	DMU_25(0.736916)	0.00	5.09	0.00
DMU_25(0.767347)	0.00	3.60	0.00	DMU_26(0.913862)	0.00	7.73	0.00
DMU_26(0.885298)	0.00	3.07	0.00	DMU_28(0.653046)	0.00	0.00	0.00
DMU_28(0.697484)	0.00	0.00	0.00	DMU_29(0.871795)	0.00	1.38	0.00
DMU_31(0.551477)	0.00	0.00	5.06	DMU_31(0.536620)	0.00	0.00	53.92

\frac{M i n : θ_{i}}{\{v, u\}} = \frac{u_{1 i} q_{1 i} + u_{2 i} q_{2 i} + \dots + u_{s i} q_{s i}}{v_{1 i} x_{1 i} + v_{2 i} x_{2 i} + \dots + v_{m i} x_{m i}}

subject to \frac{u_{1 i} q_{1 j} + u_{2 i} q_{2 j} + \dots + u_{s i} q_{s j}}{v_{1 i} x_{1 j} + v_{2 i} x_{2 j} + \dots + v_{m i} x_{m j}} \leq 1, \forall j = 1, 2, \dots, n

u_{1 i}, u_{2 i}, \dots u_{s i} \geq 0 e u_{i} \neq \bar{0}

v_{1 i}, v_{2 i}, \dots v_{m i} \geq 0 e v_{i} \neq \bar{0}

[1] Source: Elaborated by authors.

Brasil

Brasil

Performance of supermarkets in Santa Catarina: an analysis of small and medium-sized units

Desempenho dos supermercados de Santa Catarina: uma análise de pequenas e médias unidades

Abstract: