The efficiency of major industrial enterprises in Sichuan province of China: A super slacks-based measure analysis

Nan Zhu; Kai He

doi:10.3934/jimo.2021231

Article Contents

2023, Volume 19, Issue 2: 1328-1349. Doi: 10.3934/jimo.2021231

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The efficiency of major industrial enterprises in Sichuan province of China: A super slacks-based measure analysis

Nan Zhu^1, and
Kai He^2, ,

1.
Western Business School, Southwestern University of Finance and Economics, Chengdu, China
2.
School of Statistics, Southwestern University of Finance and Economics, Chengdu, China

^*Corresponding author: Kai He
^*Corresponding author: Kai He

Received: April 1, 2021

Revised: October 1, 2021

Early access: January 8, 2022

Published online: November 30, 2023

The first author is supported by the Fundamental Research Funds for the Central Universities grant 331510004007000002.

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Abstract

The main objective of this research is to measure the efficiency of 397 major industrial enterprises in Sichuan province of China in 2013.To this end, we employed DEA super slacks-based measure (Super-SBM) model for performance evaluation of 397 major manufacturing firms.The empirical results show that 21 of the 397 enterprises operate efficiently, and the average efficiency score of the analyzed enterprises is only 0.15. The enterprise with the highest efficiency score is 96.15% higher than the average score, which is the benchmark enterprise of operational efficiency. Among the selected sample enterprises, 5.29% of the industrial enterprises are highly efficient in operation. It was also noticed that the average efficiency score of pharmaceutical firms was the highest among all industrial firms with a mean score of 0.75, which is 80% higher than the overall average score of all industries. While the average efficiency of manufacturing of chemical raw materials and chemical products was the lowest with a mean score of 0.39. Results of sensitivity analysis show that profit has a great impact on the efficiency score of special equipment manufacturing firms, but a relatively weak impact on the firms which manufacture computers, communications, and other electronic equipment. The effect of export delivery value on efficiency score is not obvious.

Keywords:

Mathematics Subject Classification: Primary: 90B50, 90C46; Secondary: 90B90, 90-11.

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Figure 1. Proportion of industries in the sample population

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Figure 2. Proportion of industries in the sample population

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Figure 3. Proportion of industries in the sample population

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Figure 4. Proportion of industries in the sample population

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Table 1. Descriptive statistical results of variables

Variables	Mean	S.D.	Minimum	Maximum
Net asset ($ \yen $1000)	1228690.67	5267546.094	5915	64945892
Profit total ($ \yen $1000)	79682.71	603428.902	-3216012	10639417
Gross output value ($ \yen $1000)	1378248.21	7365880.709	59544	124326532
Export delivery value ($ \yen $1000)	679411.74	6679930.64	15	124326532
Employees(person)	977.82	4488.57	5	86303

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Table 2. Results of Pearson's correlation test

	Net asset	Profit total	Gross output value	Export delivery value
Net asset	1	-	-	-
Profit total	0.606**	1	-	-
Gross output value	0.857**	0.854**	1	-
Export delivery value	0.624**	0.895**	0.915**	1
Employees	0.660**	0.833**	0.841**	0.899**
**. Correlation is significant at the 0.01 level (2-tailed).

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Table 3. The industry classification of 397 enterprises and the corresponding number of enterprises. Note: For the convenience of description, the industry code will be used as a synonym for the industry, i.e., ind. x stands for industry x

NO.	The name of the industry	Number of companies
1	Agricultural and sideline food processing industry	16
2	Food manufacturing	26
3	Leather, furs, feathers and their products and footwear	26
4	Manufacturing of chemical raw materials and chemical products	60
5	Pharmaceutical manufacturing industry	25
6	Nonmetallic mineral products industry	19
7	Metal products industry	18
8	General Equipment manufacturing	42
9	Special Equipment manufacturing	44
10	Automobile manufacturing industry	31
11	Electrical machinery and equipment manufacturing	34
12	Manufacturing of computers, communications, and other electronic equipment	56
	Total	397

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Table 4. Mean and standard deviation of DEA efficiency score of sample population and different industries and corresponding number of effective enterprises

Industry	No. Of DMUs	Average of scores	SD	No. Of efficient DMUs
Overall	397	0.15	0.37	21
1	16	0.57	0.6	5
2	26	0.6	0.6	9
3	26	0.52	0.57	7
4	60	0.39	0.54	12
5	25	0.75	0.71	13
6	19	0.42	0.63	5
7	18	0.48	0.69	5
8	42	0.59	0.6	17
9	44	0.6	1.52	12
10	31	0.65	0.73	11
11	34	0.49	0.77	10
12	56	0.48	0.8	11

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Table 5. The list of efficient enterprises and their efficiency scores in the overall evaluation

Name of the entity	Efficiency	Industry
Hongfujin Precision Electronics (Chengdu)	3.9	12
Sichuan Jinglei Technology	2.51	7
Dell (Chengdu)	2.47	12
Sichuan Changhong	1.58	12
Compal Computer (Chengdu)	1.51	12
Chengdu Dixin Biotechnology Co	1.37	4
Sihai development industry	1.33	1
Intel Products (Chengdu)	1.3	12
Chengdu Lijun Industry	1.23	9
Fisher Kuian Transmission and Distribution Equipment (Chengdu)	1.2	8
Mianzhu Hanwang Inorganic salt chemical industry	1.16	4
Honghua Petroleum Equipment	1.11	9
Koren pharmaceutical Co.	1.1	5
Trida Industries	1.08	7
Baohe Taiyue communication cable	1.08	11
Yongxin Meat food	1.06	1
Jincheng petroleum Machinery	1.05	9
Xinzhonghao Chemical Co., LTD	1.02	4
Fly leather industry	1	3
Huatuo Optical Communications	1	12
Zyprexa technology	1	7

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Table 6. The input-output benchmark

	Net asset ($ \yen $1000)	Employees	Gross output value ($ \yen $1000)	Export delivery value ($ \yen $1000)	Profit total ($ \yen $1000)
Current
General analysis	1228691	978	1378248	679412	79683
ind.1	635946	469	1304598	35027	95323
ind.2	240166	496	559188	88369	35379
ind.3	175921	584	253902	159556	21333
ind.4	1085540	924	1109159	121324	75691
ind.5	1052549	435	581058	36860	85528
ind.6	471576	656	371023	59675	38883
ind.7	353214	911	647743	28144	57516
ind.8	1678640	1058	933981	133813	62761
ind.9	1303109	949	601445	190435	19052
ind.10	608704	777	637654	40284	12043
ind.11	980491	894	684541	98161	7254
ind.12	3226200	2085	5297026	4183804	287258
Suggestion after analyses
General analysis	926616	594	3974512	3198523	166636
ind.1	499935	225	1309227	102404	109285
ind.2	235144	349	646932	151758	38953
ind.3	163556	493	314933	308298	35525
ind.4	1011311	568	1432152	297795	177101
ind.5	1052549	374	581058	67545	85528
ind.6	320679	353	468506	163454	80670
ind.7	326686	801	906470	49946	104271
ind.8	1678640	813	933981	164141	70845
ind.9	998251	555	830374	467787	192450
ind.10	466446	542	637654	178296	31675
ind.11	763963	668	703241	248042	51609
ind.12	3226200	1981	5297026	4183804	287258

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Table 7. Input excesses and output shortfalls(Pct)

	Input excesses (%)		Output shortfalls (%)
	Net asset ($ \yen $1000)	Employees	Gross output value ($ \yen $1000)	Export delivery value($ \yen $1000)	Profit total ($ \yen $1000)
General analysis	-24.59%	-39.26%	188.37%	370.78%	109.12%
ind.1	-21.39%	-51.94%	0.35%	192.36%	14.65%
ind.2	-2.09%	-29.66%	15.69%	71.73%	10.10%
ind.3	-7.03%	-15.68%	24.04%	93.22%	66.53%
ind.4	-6.84%	-38.55%	29.12%	145.45%	133.98%
ind.5	0.00%	-14.02%	0.00%	83.25%	0.00%
ind.6	-32.00%	-46.29%	26.27%	173.91%	107.47%
ind.7	-7.51%	-12.07%	39.94%	77.47%	81.29%
ind.8	0.00%	-23.11%	0.00%	22.66%	12.88%
ind.9	-23.39%	-41.54%	38.06%	145.64%	910.14%
ind.10	-23.37%	-30.27%	0.00%	342.59%	163.01%
ind.11	-22.08%	-25.30%	2.73%	152.69%	611.42%
ind.12	0.00%	-5.01%	0.00%	0.00%	0.00%

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Table 8. Three combinations of input-output variables

Input-Output variables		All	ⅰ	ⅱ	ⅲ
I	Net asset	√	√	√	√
	Employees	√	√	√	√
	Gross output value	√	√	√	√
O	Export delivery value	√	√	-	-
	Profit total	√	-	√	-

√ means Selected

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Table 9. The efficiency results of different input-output variable combinations

Abbr. of enterprise name			Diff. combination
	All	ⅰ	ⅱ	ⅲ
Hongfujin Precision Electronics (Chengdu)	3.9	3.94	3.74	2.23
Sichuan Jinglei Technology	2.51	1.77	10.32	7.56
Dell (Chengdu)	2.47	3.92	1.67	2.39
Sichuan Changhong	1.58	1.5	2.22	3.01
Compal Computer (Chengdu)	1.51	2.04	1.34	2.01
Chengdu Dixin Biotechnology Co	1.37	0.94	1.35	0.82
Sihai development industry	1.33	0.02	1.59	0.98
Intel Products (Chengdu)	1.3	1.01	1.41	0.92
Chengdu Lijun Industry	1.23	0.01	1.39	0.17
Fisher Kuian Transmission and Distribution Equipment (Chengdu)	1.2	0.16	1.33	0.63
Mianzhu Hanwang Inorganic salt chemical industry	1.16	1.23	1.25	1.61
Honghua Petroleum Equipment	1.11	0.2	1.18	0.28
Koren pharmaceutical Co.	1.1	0	1.15	0.16
Trida Industries	1.08	0.01	1.13	0.48
Baohe Taiyue communication cable	1.08	0.05	1.13	0.34
Yongxin Meat food	1.06	0	1.1	0.42
Jincheng petroleum Machinery	1.05	0.04	1.08	0.38
Xinzhonghao Chemical Co., LTD	1.02	1.03	0.36	0.32
Fly leather industry	1	0.83	0.38	0.31
Huatuo Optical Communications	1	1	1	1
Zyprexa technology	1	1	1	1

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