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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

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

Received  April 2021 Revised  October 2021 Early access January 2022

Fund Project: The first author is supported by the Fundamental Research Funds for the Central Universities grant 331510004007000002

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.

Citation: Nan Zhu, Kai He. The efficiency of major industrial enterprises in Sichuan province of China: A super slacks-based measure analysis. Journal of Industrial and Management Optimization, doi: 10.3934/jimo.2021231
References:
[1]

A. S. Aboumasoudi, S. Mirzamohammadi, A. Makui and J. Tamošaitienė, Development of network-ranking model to create the best production line value chain: A case study in textile industry, Economic Computation & Economic Cybernetics Studies & Research, 50.

[2]

P. Andersen and N. C. Petersen, A procedure for ranking efficient units in data envelopment analysis, Management Science, 39 (1993), 1261-1264.  doi: 10.1287/mnsc.39.10.1261.

[3]

N. K. Avkiran, The evidence on efficiency gains: The role of mergers and the benefits to the public, Journal of Banking & Finance, 23 (1999), 991-1013.  doi: 10.1016/S0378-4266(98)00129-0.

[4]

R. D. BankerA. Charnes and W. W. Cooper, Some models for estimating technical and scale inefficiencies in data envelopment analysis, Management Science, 30 (1984), 1078-1092.  doi: 10.1287/mnsc.30.9.1078.

[5]

G. BiY. LuoJ. Ding and L. Liang, Environmental performance analysis of Chinese industry from a slacks-based perspective, Ann. Oper. Res., 228 (2015), 65-80.  doi: 10.1007/s10479-012-1088-3.

[6]

H. CaiL. LiangJ. TangQ. WangL. Wei and J. Xie, An empirical study on the efficiency and influencing factors of the photovoltaic industry in china and an analysis of its influencing factors, Sustainability, 11 (2019), 6693.  doi: 10.3390/su11236693.

[7]

A. Canhoto and J. Dermine, A note on banking efficiency in portugal, new vs. old banks, Journal of Banking & Finance, 27 (2003), 2087-2098.  doi: 10.1016/S0378-4266(02)00316-3.

[8]

B. Casu and P. Molyneux, A comparative study of efficiency in european banking, Applied Economics, 35 (2003), 1865-1876.  doi: 10.1080/0003684032000158109.

[9]

P. ChandraW. W. CooperS. Li and A. Rahman, Using dea to evaluate 29 canadian textile companies-considering returns to scale, International Journal of Production Economics, 54 (1998), 129-141. 

[10]

K. Chapelle and P. Plane, Productive efficiency in the ivorian manufacturing sector: An exploratory study using a data envelopment analysis approach, The Developing Economies, 43 (2005), 450-471.  doi: 10.1111/j.1746-1049.2005.tb00954.x.

[11]

A. Charnes and W. W. Cooper, Programming with linear fractional functionals, Naval Res. Logist. Quart., 9 (1962), 181-186.  doi: 10.1002/nav.3800090303.

[12]

A. CharnesW. W. Cooper and E. Rhodes, Measuring the efficiency of decision making units, European J. Oper. Res., 2 (1978), 429-444.  doi: 10.1016/0377-2217(78)90138-8.

[13]

T. Charoenrat and C. Harvie, The performance of thai manufacturing smes: Data envelopment analysis (dea) approach, Global Business Review, 18 (2017), 1178-1198.  doi: 10.1177/0972150917710346.

[14]

T. J. Coelli, D. S. P. Rao, C. J. O'Donnell and G. E. Battese, An Introduction to Efficiency and Productivity Analysis, springer science & business media, 2005.

[15]

W. D. CookJ. HarrisonR. ImaniradP. Rouse and J. Zhu, Data envelopment analysis with nonhomogeneous DMUS, Oper. Res., 61 (2013), 666-676.  doi: 10.1287/opre.2013.1173.

[16]

W. D. Cook and L. M. Seiford, Data envelopment analysis (dea)–thirty years on, European J. Oper. Res., 192 (2009), 1-17.  doi: 10.1016/j.ejor.2008.01.032.

[17]

W. W. Cooper, L. M. Seiford and K. Tone, Data Envelopment analysis: A comprehensive Text with Models, Applications, References and DEA-Solver Software, Springer, 2007.

[18]

E. Düzakın and H. Düzakın, Measuring the performance of manufacturing firms with super slacks based model of data envelopment analysis: An application of 500 major industrial enterprises in turkey, European Journal of Operational Research, 182 (2007), 1412-1432. 

[19]

A. Ebrahimnejad and N. Amani, Fuzzy data envelopment analysis in the presence of undesirable outputs with ideal points, Complex & Intelligent Systems, 7 (2021), 379-400.  doi: 10.1007/s40747-020-00211-x.

[20]

A. Emrouznejad and G.-I. Yang, A survey and analysis of the first 40 years of scholarly literature in DEA: 1978–2016, Socio-Economic Planning sciences, 61 (2018), 4-8.  doi: 10.1016/j.seps.2017.01.008.

[21]

H. FangJ. Wu and C. Zeng, Comparative study on efficiency performance of listed coal mining companies in china and the us, Energy Policy, 37 (2009), 5140-5148.  doi: 10.1016/j.enpol.2009.07.027.

[22]

M. J. Farrell, The measurement of productive efficiency, Journal of the Royal Statistical Society: Series A (General), 120 (1957), 253-290.  doi: 10.2307/2343100.

[23]

L. Friedman and Z. Sinuany-Stern, Combining ranking scales and selecting variables in the dea context: The case of industrial branches, Computers & Operations Research, 25 (1998), 781-791.  doi: 10.1016/S0305-0548(97)00102-0.

[24]

Y. GongJ. ZhuY. Chen and W. D. Cook, DEA as a tool for auditing: Application to Chinese manufacturing industry with parallel network structures, Ann. Oper. Res., 263 (2018), 247-269.  doi: 10.1007/s10479-016-2197-1.

[25]

G. R. Jahanshahloo and M. Khodabakhshi, Suitable combination of inputs for improving outputs in dea with determining input congestion: Considering textile industry of china, Appl. Math. Comput., 151 (2004), 263-273.  doi: 10.1016/S0096-3003(03)00337-0.

[26]

M. KapelkoA. Oude Lansink and S. E. Stefanou, Investment age and dynamic productivity growth in the spanish food processing industry, American Journal of Agricultural Economics, 98 (2016), 946-961.  doi: 10.1093/ajae/aav063.

[27]

H.-S. Lee, An integrated model for sbm and super-sbm dea models, Journal of the Operational Research Society, 72 (2021), 1174-1182.  doi: 10.1080/01605682.2020.1755900.

[28]

H.-S. Lee, Slacks-based measures of efficiency and super-efficiency in presence of nonpositive data, Omega, 103 (2021), 102395.  doi: 10.1016/j.omega.2021.102395.

[29]

D. LiR. Hou and Q. Sun, The business performance evaluation index method for the high-tech enterprises based on the dea model, Journal of Intelligent & Fuzzy Systems, 38 (2020), 6853-6861.  doi: 10.3233/JIFS-179763.

[30]

S. C. Ray, The directional distance function and measurement of super-efficiency: An application to airlines data, Journal of the Operational Research Society, 59 (2008), 788-797.  doi: 10.1057/palgrave.jors.2602392.

[31]

L. M. Seifert and J. Zhu, Identifying excesses and deficits in chinese industrial productivity (1953–1990): A weighted data envelopment analysis approach, Omega, 26 (1998), 279-296.  doi: 10.1016/S0305-0483(98)00011-5.

[32]

K. Tone, A slacks-based measure of efficiency in data envelopment analysis, European J. Oper. Res., 130 (2001), 498-509.  doi: 10.1016/S0377-2217(99)00407-5.

[33]

K. Tone, A slacks-based measure of super-efficiency in data envelopment analysis, European Journal of Operational Research, 130 (2001), 498-509.  doi: 10.1016/S0377-2217(01)00324-1.

[34]

W. WuC. RenY. WangT. Liu and L. Li, Dea-based performance evaluation system for construction enterprises based on bim technology, Journal of Computing in Civil Engineering, 32 (2018), 04017081.  doi: 10.1061/(ASCE)CP.1943-5487.0000722.

[35]

A. ZhangY. Zhang and R. Zhao, Impact of ownership and competition on the productivity of chinese enterprises, Journal of Comparative Economics, 29 (2001), 327-346.  doi: 10.1006/jcec.2001.1714.

[36]

L. ZhangJ. WangH. WenZ. Fu and X. Li, Operating performance, industry agglomeration and its spatial characteristics of chinese photovoltaic industry, Renewable and Sustainable Energy Reviews, 65 (2016), 373-386.  doi: 10.1016/j.rser.2016.07.010.

[37]

J. Zhu, Multi-factor performance measure model with an application to fortune 500 companies, European journal of operational research, 123 (2000), 105-124.  doi: 10.1016/S0377-2217(99)00096-X.

show all references

References:
[1]

A. S. Aboumasoudi, S. Mirzamohammadi, A. Makui and J. Tamošaitienė, Development of network-ranking model to create the best production line value chain: A case study in textile industry, Economic Computation & Economic Cybernetics Studies & Research, 50.

[2]

P. Andersen and N. C. Petersen, A procedure for ranking efficient units in data envelopment analysis, Management Science, 39 (1993), 1261-1264.  doi: 10.1287/mnsc.39.10.1261.

[3]

N. K. Avkiran, The evidence on efficiency gains: The role of mergers and the benefits to the public, Journal of Banking & Finance, 23 (1999), 991-1013.  doi: 10.1016/S0378-4266(98)00129-0.

[4]

R. D. BankerA. Charnes and W. W. Cooper, Some models for estimating technical and scale inefficiencies in data envelopment analysis, Management Science, 30 (1984), 1078-1092.  doi: 10.1287/mnsc.30.9.1078.

[5]

G. BiY. LuoJ. Ding and L. Liang, Environmental performance analysis of Chinese industry from a slacks-based perspective, Ann. Oper. Res., 228 (2015), 65-80.  doi: 10.1007/s10479-012-1088-3.

[6]

H. CaiL. LiangJ. TangQ. WangL. Wei and J. Xie, An empirical study on the efficiency and influencing factors of the photovoltaic industry in china and an analysis of its influencing factors, Sustainability, 11 (2019), 6693.  doi: 10.3390/su11236693.

[7]

A. Canhoto and J. Dermine, A note on banking efficiency in portugal, new vs. old banks, Journal of Banking & Finance, 27 (2003), 2087-2098.  doi: 10.1016/S0378-4266(02)00316-3.

[8]

B. Casu and P. Molyneux, A comparative study of efficiency in european banking, Applied Economics, 35 (2003), 1865-1876.  doi: 10.1080/0003684032000158109.

[9]

P. ChandraW. W. CooperS. Li and A. Rahman, Using dea to evaluate 29 canadian textile companies-considering returns to scale, International Journal of Production Economics, 54 (1998), 129-141. 

[10]

K. Chapelle and P. Plane, Productive efficiency in the ivorian manufacturing sector: An exploratory study using a data envelopment analysis approach, The Developing Economies, 43 (2005), 450-471.  doi: 10.1111/j.1746-1049.2005.tb00954.x.

[11]

A. Charnes and W. W. Cooper, Programming with linear fractional functionals, Naval Res. Logist. Quart., 9 (1962), 181-186.  doi: 10.1002/nav.3800090303.

[12]

A. CharnesW. W. Cooper and E. Rhodes, Measuring the efficiency of decision making units, European J. Oper. Res., 2 (1978), 429-444.  doi: 10.1016/0377-2217(78)90138-8.

[13]

T. Charoenrat and C. Harvie, The performance of thai manufacturing smes: Data envelopment analysis (dea) approach, Global Business Review, 18 (2017), 1178-1198.  doi: 10.1177/0972150917710346.

[14]

T. J. Coelli, D. S. P. Rao, C. J. O'Donnell and G. E. Battese, An Introduction to Efficiency and Productivity Analysis, springer science & business media, 2005.

[15]

W. D. CookJ. HarrisonR. ImaniradP. Rouse and J. Zhu, Data envelopment analysis with nonhomogeneous DMUS, Oper. Res., 61 (2013), 666-676.  doi: 10.1287/opre.2013.1173.

[16]

W. D. Cook and L. M. Seiford, Data envelopment analysis (dea)–thirty years on, European J. Oper. Res., 192 (2009), 1-17.  doi: 10.1016/j.ejor.2008.01.032.

[17]

W. W. Cooper, L. M. Seiford and K. Tone, Data Envelopment analysis: A comprehensive Text with Models, Applications, References and DEA-Solver Software, Springer, 2007.

[18]

E. Düzakın and H. Düzakın, Measuring the performance of manufacturing firms with super slacks based model of data envelopment analysis: An application of 500 major industrial enterprises in turkey, European Journal of Operational Research, 182 (2007), 1412-1432. 

[19]

A. Ebrahimnejad and N. Amani, Fuzzy data envelopment analysis in the presence of undesirable outputs with ideal points, Complex & Intelligent Systems, 7 (2021), 379-400.  doi: 10.1007/s40747-020-00211-x.

[20]

A. Emrouznejad and G.-I. Yang, A survey and analysis of the first 40 years of scholarly literature in DEA: 1978–2016, Socio-Economic Planning sciences, 61 (2018), 4-8.  doi: 10.1016/j.seps.2017.01.008.

[21]

H. FangJ. Wu and C. Zeng, Comparative study on efficiency performance of listed coal mining companies in china and the us, Energy Policy, 37 (2009), 5140-5148.  doi: 10.1016/j.enpol.2009.07.027.

[22]

M. J. Farrell, The measurement of productive efficiency, Journal of the Royal Statistical Society: Series A (General), 120 (1957), 253-290.  doi: 10.2307/2343100.

[23]

L. Friedman and Z. Sinuany-Stern, Combining ranking scales and selecting variables in the dea context: The case of industrial branches, Computers & Operations Research, 25 (1998), 781-791.  doi: 10.1016/S0305-0548(97)00102-0.

[24]

Y. GongJ. ZhuY. Chen and W. D. Cook, DEA as a tool for auditing: Application to Chinese manufacturing industry with parallel network structures, Ann. Oper. Res., 263 (2018), 247-269.  doi: 10.1007/s10479-016-2197-1.

[25]

G. R. Jahanshahloo and M. Khodabakhshi, Suitable combination of inputs for improving outputs in dea with determining input congestion: Considering textile industry of china, Appl. Math. Comput., 151 (2004), 263-273.  doi: 10.1016/S0096-3003(03)00337-0.

[26]

M. KapelkoA. Oude Lansink and S. E. Stefanou, Investment age and dynamic productivity growth in the spanish food processing industry, American Journal of Agricultural Economics, 98 (2016), 946-961.  doi: 10.1093/ajae/aav063.

[27]

H.-S. Lee, An integrated model for sbm and super-sbm dea models, Journal of the Operational Research Society, 72 (2021), 1174-1182.  doi: 10.1080/01605682.2020.1755900.

[28]

H.-S. Lee, Slacks-based measures of efficiency and super-efficiency in presence of nonpositive data, Omega, 103 (2021), 102395.  doi: 10.1016/j.omega.2021.102395.

[29]

D. LiR. Hou and Q. Sun, The business performance evaluation index method for the high-tech enterprises based on the dea model, Journal of Intelligent & Fuzzy Systems, 38 (2020), 6853-6861.  doi: 10.3233/JIFS-179763.

[30]

S. C. Ray, The directional distance function and measurement of super-efficiency: An application to airlines data, Journal of the Operational Research Society, 59 (2008), 788-797.  doi: 10.1057/palgrave.jors.2602392.

[31]

L. M. Seifert and J. Zhu, Identifying excesses and deficits in chinese industrial productivity (1953–1990): A weighted data envelopment analysis approach, Omega, 26 (1998), 279-296.  doi: 10.1016/S0305-0483(98)00011-5.

[32]

K. Tone, A slacks-based measure of efficiency in data envelopment analysis, European J. Oper. Res., 130 (2001), 498-509.  doi: 10.1016/S0377-2217(99)00407-5.

[33]

K. Tone, A slacks-based measure of super-efficiency in data envelopment analysis, European Journal of Operational Research, 130 (2001), 498-509.  doi: 10.1016/S0377-2217(01)00324-1.

[34]

W. WuC. RenY. WangT. Liu and L. Li, Dea-based performance evaluation system for construction enterprises based on bim technology, Journal of Computing in Civil Engineering, 32 (2018), 04017081.  doi: 10.1061/(ASCE)CP.1943-5487.0000722.

[35]

A. ZhangY. Zhang and R. Zhao, Impact of ownership and competition on the productivity of chinese enterprises, Journal of Comparative Economics, 29 (2001), 327-346.  doi: 10.1006/jcec.2001.1714.

[36]

L. ZhangJ. WangH. WenZ. Fu and X. Li, Operating performance, industry agglomeration and its spatial characteristics of chinese photovoltaic industry, Renewable and Sustainable Energy Reviews, 65 (2016), 373-386.  doi: 10.1016/j.rser.2016.07.010.

[37]

J. Zhu, Multi-factor performance measure model with an application to fortune 500 companies, European journal of operational research, 123 (2000), 105-124.  doi: 10.1016/S0377-2217(99)00096-X.

Figure 1.  Proportion of industries in the sample population
Figure 2.  Proportion of industries in the sample population
Figure 3.  Proportion of industries in the sample population
Figure 4.  Proportion of industries in the sample population
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 $1000) 679411.74 6679930.64 15 124326532
Employees(person) 977.82 4488.57 5 86303
Variables Mean S.D. Minimum Maximum
Net asset ($ \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
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).
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).
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
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
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
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
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
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
Table 6.  The input-output benchmark
Net asset ($ \yen $1000) Employees Gross output value ($ \yen $1000) Export delivery value ($ \yen $1000) Profit total ($ \yen $1000)
Current
General analysis 1228691 978 1378248 679412 79683
ind.1 635946 469 1304598 35027 95323
ind.2 240166 496 559188 88369 35379
ind.3 175921 584 253902 159556 21333
ind.4 1085540 924 1109159 121324 75691
ind.5 1052549 435 581058 36860 85528
ind.6 471576 656 371023 59675 38883
ind.7 353214 911 647743 28144 57516
ind.8 1678640 1058 933981 133813 62761
ind.9 1303109 949 601445 190435 19052
ind.10 608704 777 637654 40284 12043
ind.11 980491 894 684541 98161 7254
ind.12 3226200 2085 5297026 4183804 287258
Suggestion after analyses
General analysis 926616 594 3974512 3198523 166636
ind.1 499935 225 1309227 102404 109285
ind.2 235144 349 646932 151758 38953
ind.3 163556 493 314933 308298 35525
ind.4 1011311 568 1432152 297795 177101
ind.5 1052549 374 581058 67545 85528
ind.6 320679 353 468506 163454 80670
ind.7 326686 801 906470 49946 104271
ind.8 1678640 813 933981 164141 70845
ind.9 998251 555 830374 467787 192450
ind.10 466446 542 637654 178296 31675
ind.11 763963 668 703241 248042 51609
ind.12 3226200 1981 5297026 4183804 287258
Net asset ($ \yen $1000) Employees Gross output value ($ \yen $1000) Export delivery value ($ \yen $1000) Profit total ($ \yen $1000)
Current
General analysis 1228691 978 1378248 679412 79683
ind.1 635946 469 1304598 35027 95323
ind.2 240166 496 559188 88369 35379
ind.3 175921 584 253902 159556 21333
ind.4 1085540 924 1109159 121324 75691
ind.5 1052549 435 581058 36860 85528
ind.6 471576 656 371023 59675 38883
ind.7 353214 911 647743 28144 57516
ind.8 1678640 1058 933981 133813 62761
ind.9 1303109 949 601445 190435 19052
ind.10 608704 777 637654 40284 12043
ind.11 980491 894 684541 98161 7254
ind.12 3226200 2085 5297026 4183804 287258
Suggestion after analyses
General analysis 926616 594 3974512 3198523 166636
ind.1 499935 225 1309227 102404 109285
ind.2 235144 349 646932 151758 38953
ind.3 163556 493 314933 308298 35525
ind.4 1011311 568 1432152 297795 177101
ind.5 1052549 374 581058 67545 85528
ind.6 320679 353 468506 163454 80670
ind.7 326686 801 906470 49946 104271
ind.8 1678640 813 933981 164141 70845
ind.9 998251 555 830374 467787 192450
ind.10 466446 542 637654 178296 31675
ind.11 763963 668 703241 248042 51609
ind.12 3226200 1981 5297026 4183804 287258
Table 7.  Input excesses and output shortfalls(Pct)
Input excesses (%) Output shortfalls (%)
Net asset ($ \yen $1000) Employees Gross output value ($ \yen $1000) Export delivery value($ \yen $1000) Profit total ($ \yen $1000)
General analysis -24.59% -39.26% 188.37% 370.78% 109.12%
ind.1 -21.39% -51.94% 0.35% 192.36% 14.65%
ind.2 -2.09% -29.66% 15.69% 71.73% 10.10%
ind.3 -7.03% -15.68% 24.04% 93.22% 66.53%
ind.4 -6.84% -38.55% 29.12% 145.45% 133.98%
ind.5 0.00% -14.02% 0.00% 83.25% 0.00%
ind.6 -32.00% -46.29% 26.27% 173.91% 107.47%
ind.7 -7.51% -12.07% 39.94% 77.47% 81.29%
ind.8 0.00% -23.11% 0.00% 22.66% 12.88%
ind.9 -23.39% -41.54% 38.06% 145.64% 910.14%
ind.10 -23.37% -30.27% 0.00% 342.59% 163.01%
ind.11 -22.08% -25.30% 2.73% 152.69% 611.42%
ind.12 0.00% -5.01% 0.00% 0.00% 0.00%
Input excesses (%) Output shortfalls (%)
Net asset ($ \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%
Table 8.  Three combinations of input-output variables
Input-Output variables All
I Net asset
Employees
Gross output value
O Export delivery value - -
Profit total - -
           
√ means Selected
Input-Output variables All
I Net asset
Employees
Gross output value
O Export delivery value - -
Profit total - -
           
√ means Selected
Table 9.  The efficiency results of different input-output variable combinations
Abbr. of enterprise name Diff. combination
All
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
Abbr. of enterprise name Diff. combination
All
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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