Cleaner production assessment of group company based on improved AHP and grey relational analysis

Abstract

Cleaner production assessment is a measure of the state and level of cleaner production, also a necessary method of promoting cleaner production in enterprises. For the purpose of the improvement of cleaner production of enterprises, improved Analytic Hierarchy Process (AHP) model and grey relational analysis (GRA) are used to assess the same nature of the three enterprises of one group with seven quantitative indicators concordant with the Cleaner Production Report. The results are consistent with the clean production reports from three enterprises, which show that the integrated methods are feasible and objective, and can be used as a tool for internal cleaner production assessment.

Keywords

Cleaner production assessment improved AHP grey relational analysis

1 Introduction

Cleaner production is not only a new environmental protection strategy and a new way of thinking, but the development model for the sustainable development of the industry in twenty-first Century [1]. China’s environment has caused many problems with the intensification of industrialization and urbanization [2, 3], the implementation of cleaner production is a good choice for improving the existing environmental problems of enterprises. Aiming at the quantitative indicators of the three enterprises, this paper tries to find a way to assess cleaner production within enterprises

At present, the domestic and foreign cleaner production assessment mainly focuses on the Fuzzy Analytic Hierarchy Process (FAHP), Delphi method, percentage method, grey relational analysis method etc. Wang Liping et al. [4] put forward the application of AHP method to determine the audit focus and implementation plan in the cleaner production audit. Jiangdong Bao et al. applied improved AHP model [5] to occupational disease assessment, and in this study, the improved AHP model is used to obtain the weight values of comparison sequences. On the basis of improved AHP and GRA methods, the result can meet the internal assessment requirements of cleaner production. In this paper, the technical route follows this way: after the introduction, selecting the assessment indicators, presenting the AHP and GRA models, taking three enterprises as examples based on the models, getting the results and giving the suggestions and conclusions.

2 The Assessment indicators of cleaner production

The selection of indicators mainly refers to the Cleaner Production Standard, Electroplating Industry/People’s Republic of China Environmental Protection Industry Standard (HJ/T314-2006) [6], Cleaner Production Assessment Indicator System in Machinery Industry [7], and Cleaner Production Report (2016) [8]. To keep in line with the actual values of Cleaner Production Report (2016), the qualitative indicators i.e. environmental management, production technology and equipment, and comprehensive utilization of resources aren’t selected in this paper, while seven quantitative indicators are included as shown in Table 1, and with unit of million yuan industrial added value (MYIAV). Additionally, the quantitative indicators i.e. SO₂ discharge of MYIAV and smoke dust discharge of MYIAV are included in the Cleaner Production Report (2016) without any measurement data. In views of the difficulty of data acquisition, the two indicators are deleted in this assessment.

Table 1
The assessment indicators of cleaner production

Quantitative Indicators

U₁  Petroleum discharge of MYIAV

U₂  Energy consumption of MYIAV

U₃  Fresh water consumption of MYIAV

U₄  Steel consumption of MYIAV

U₅  COD discharge of MYIAV

U₆  Waste residue discharge of MYIAV

U₇  Effluent discharge of MYIAV

Quantitative Indicators
U₁ Petroleum discharge of MYIAV
U₂ Energy consumption of MYIAV
U₃ Fresh water consumption of MYIAV
U₄ Steel consumption of MYIAV
U₅ COD discharge of MYIAV
U₆ Waste residue discharge of MYIAV
U₇ Effluent discharge of MYIAV

3 Indicator weight of cleaner production on the basis of improved AHP Model

3.1 Evaluation weight set

In this study, the importance of the indicators is related to experts to determine the weight value of every indicator factor in building a provision referring to 1∼9 Thomas Saaty [9, 10] scale method to determine the indicators values. If the parameter on the horizontal axis for each line is less important than the one on the vertical axis, it holds a value between 1 and 9. On the contrary, it holds the value between the reciprocals of the 1∼9. [11]. Emrouznejad, A., & Marra, M. [12] stated the literature review with a social network analysis of AHP. Aghdaie MH, Alimardani M. [13] applied AHP to a target market selection based on market segment evaluation. the traditional method of AHP explained the rationality of expert scoring of the decision matrix [14], while Wei. C.P.& Zhang. Z.M. put forward a method to keep the matrix consistency [15], and Shuang Chen et al. [16] used the improved method of AHP for the indicators sorting, Jiangdong Bao et al. [17] intergrated the improved AHP method with 2-tuple linguistic information in a mining industry to get more accurate expert scoring. This paper applies the same improved AHP method which is optimized as shown in Table 2.

Table 2
The experts scoring table of the importance among indicators

9/9-9/1 (Intensity of importance) 1–9 (Intensity of importance) Definition

9/9 1 Equal importance

9/8 2 Equal to moderate importance

9/7 3 Moderate importance

9/6 4 Moderate to strong importance

9/5 5 Strong importance

9/4 6 Strong to very strong importance

9/3 7 Very strong importance

9/2 8 Very to extremely strongly importance

9/1 9 Extreme importance

Reciprocals The judgement of parameter i compared with parameter j is a_ij, then the one of parameter j compared with parameter i is a_ji.

9/9-9/1 (Intensity of importance)	1–9 (Intensity of importance)	Definition
9/9	1	Equal importance
9/8	2	Equal to moderate importance
9/7	3	Moderate importance
9/6	4	Moderate to strong importance
9/5	5	Strong importance
9/4	6	Strong to very strong importance
9/3	7	Very strong importance
9/2	8	Very to extremely strongly importance
9/1	9	Extreme importance
Reciprocals		The judgement of parameter i compared with parameter j is a_ij, then the one of parameter j compared with parameter i is a_ji.

3.2 Consistency testing

Calculating the consistency indicators through the formula: $CI = \frac{λ_{max} - n}{n - 1}$ ;

Calculating the consistency ratio through the formula: $CR = \frac{CI}{RI}$ .

In the formulas, n stands for the order and λ stands for the maximal eigenvalue, RI stands for the random consistency indicator [18]. And the value of RI is listed in Table 3. If 0≤CR≤10%, the consistency of AHP model receives satisfactory answers, If CR > 10%, it receives an opposite one. According to the above procedures, the weight of the indicators can be calculated, and the consistency test should also be carried out. More importantly, the experts should be well trained for the scoring knowledge.

Table 3
RI value

n 1 2 3 4 5 6 7 8 9

RI 0 0 0.58 0.9 1.12 1.24 1.32 1.41 1.45

n	1	2	3	4	5	6	7	8	9
RI	0	0	0.58	0.9	1.12	1.24	1.32	1.41	1.45

4 Cleaner production grey relational analysis model

The grey system theory was created by the Chinese Professor Deng Julong in 1880s [19]. The Grey system theory has been successfully introduced to agricultural, industrial, economic and other science fields for over 20 years. A grey system is a system where some information is known and some is unknown. Grey relational analysis is an important part of grey system theory method is used to analyze the correlation degree of each factor of the system, to calculate the grey relation between the system characteristic variables and the variables of the data sequence and to analyze the advantages results and the evaluation results [20].

Lu et al. [21] introduced the calculation models about the grey relation between the sequences. Deng [22] put forward a method of Deng Relational Analysis in 1987. Zhao [23] gave a method of Grey Euclid Relation Grade in 1998. Li [24] used a method of Absolute Correlation Degree in 1995. Liu [25] raised the method of Generalized Degree of Grey Incidence in 1992. Tang [26] presented a method of T’s correlation Degree in 1995. The method of Deng Relational analysis is utilized in this study. Adem Acir et al. [27] identified the optimum parameters affecting the energy and exergy efficiencies for a novel design solar air heater (SAH) using GRA.

4.1 Determining the number of analysis sequence

Select reference series and let X = {x₀, x₁, ⋯, x_m} be grey relational factor set, where x₀ is a reference sequence, x_i is a comparison sequence, x₀ (k) is the K point number of x₀, and x_i (k) is the K point number of x_i as shown below.

$\begin{matrix} x_{0} & = & (x_{0} (1), x_{2} (2), \dots, x_{0} (n)) \\ x_{1} & = & (x_{1} (1), x_{1} (2), \dots, x_{1} (n)) \\ x_{2} & = & (x_{2} (1), x_{2} (2), \dots, x_{2} (n)) \\ \dots \dots \\ x_{m} & = & (x_{m} (1), x_{m} (2), \dots, x_{m} (n)) \end{matrix}$ (1)

4.2 Non dimensional variables

Various factors in the data in the column may be different due to the dimension, so it is not easy to get the correct conclusion when in comparison. The data is generally performed by non dimensional treatment when the grey relational analysis is carried out.

$\begin{matrix} x_{i}^{k} = & (1, \frac{x (2)}{x (1)}, \frac{x (3)}{x (1)}, \dots, \frac{x (k)}{x (1)}), \\ where k = 1, 2, . . . n; i = 1, 2, . . . m \end{matrix}$ (2)

4.3 Calculating the relational coefficient

The relational coefficient of x₀ (k) and x_i (k) is shown below [21]. $ζ_{i} (k) = \frac{\min_{i} \min_{k} | X_{0} (k) - X_{i} (k) | + ρ \max_{i} \max_{k} | X_{0} (k) - X_{i} (k) |}{| X_{0} (k) - X_{i} (k) | + ρ \max_{i} \max_{k} | X_{0} (k) - X_{i} (k) |}$ (3) $r (x_{0} (k), x_{i} (k)) = \frac{Δ_{min} + ρ Δ_{max}}{Δ_{0 i} (k) + ρ Δ_{max}}$ (4) In the Formula, Δ_0i (k) = |x₀ (k) - x_i (k) | is the absolute difference, $Δ_{min} = min_{i} min_{k} Δ_{0 i} (k)$ is the minimum difference between two poles, $Δ_{max} = max_{i} max_{k} Δ_{0 i} (k)$ is the maximum difference between two poles, ρ is the resolution ratio, ρ ∈ (0, 1) (remarks: ρ value normally equals 0.5 in actual calculation.), and ω_k is the weight of K point number which satisfies $0 ⩽ ω_{k} ⩽ 1, \sum_{k = 1}^{n} ω_{k} = 1$ .

4.4 Calculating grey relationa

Because the relational coefficient is the degree value at all times (that is, each point of the curve) between comparison sequence and reference sequence, and there is more than one, the information is too scattered for the overall comparison. Average value is treated as the degree value between comparison sequence and reference sequence, and r_i formula of the relational coefficient is as follows. $r_{i} = w_{k} \sum_{k = 1}^{n} ζ_{i} (k)$ (5)

4.5 Grey relaiona ranking

Normally, if r (x₀, x_i) > r (x₀, x_j), the relationa of x_i and x₀ is higher than that of x_j and x₀. That is to say, the influence degree of x_i on x₀ is higher than that of x_j on x₀.

The procedures above show how the grey relationa can be calculated and compared. In formula 8, the weight values can be calculated by improved AHP model instead of the average values of the indicators numbers.

5 Case study

In this paper, a large machinery industry group in Wuxi City, Jiangsu Province, China is taken as an example. The qualitative indicators are assigned by using 2-tuple linguistic information, then the indicators of cleaner production are assessed by using improved grey relational analysis model. The group mainly produces diesel nozzle and injector products that meet the requirements of national emission regulations. And there are about 8000 employees in the group with 11078.59 million yuan annual output value in 2016. Additionally, it has been certified by IATF16949:2016 Quality Management System, ISO14001:2015 Environment Management System, and OHSAS18001:2007 Occupational Health and Safety Management System.

5.1 The indicator weight and consistency testing

In this study, thirty experts with at least five working experience in the enterprises including two internal auditors were trained to give the score of the importance of the indicators with the improved AHP model. And according to the model calculation procedure of improved AHP, CR = CI/RI = (λ - n)/(n - 1)/1.32 = 0.00062 < 0.1, Hence, the value has passed the consistency testing. And the calculating results of the indicators are shown in Table 4.

Table 4
Calculating results of first-grade indicators

U₁ U₂ U₃ U₄ U₅ U₆ U₇ W_i

U₁ 9/9 4/9 7/9 6/9 9/9 9/6 8/9 0.126

U₂ 9/4 9/9 7/9 9/9 9/4 9/5 4/9 0.176

U₃ 9/7 7/9 9/9 8/9 6/9 9/3 8/9 0.160

U₄ 9/2 9/9 9/8 9/9 4/9 9/3 9/5 0.183

U₅ 9/9 4/9 6/9 9/4 9/9 6/9 5/9 0.123

U₆ 6/9 5/9 3/9 3/9 6/9 9/9 5/9 0.082

U₇ 9/8 4/9 9/8 5/9 9/5 9/5 9/9 0.150

1.000

	U₁	U₂	U₃	U₄	U₅	U₆	U₇	W_i
U₁	9/9	4/9	7/9	6/9	9/9	9/6	8/9	0.126
U₂	9/4	9/9	7/9	9/9	9/4	9/5	4/9	0.176
U₃	9/7	7/9	9/9	8/9	6/9	9/3	8/9	0.160
U₄	9/2	9/9	9/8	9/9	4/9	9/3	9/5	0.183
U₅	9/9	4/9	6/9	9/4	9/9	6/9	5/9	0.123
U₆	6/9	5/9	3/9	3/9	6/9	9/9	5/9	0.082
U₇	9/8	4/9	9/8	5/9	9/5	9/5	9/9	0.150
								1.000

5.2 Dimensionless of the second grade indicator

In order to obtain comparable data sequences, the method of averaging is used to make the original data non dimensional, and the values after averaging of the three enterprises c₁, c₂ and c₃ are shown in Table 5.

Table 5
The values after averaging of the indicators

U₁ U₂ U₃ U₄ U₅ U₆ U₇

c ₁ 0.429 0.965 1.042 0.750 0.666 0.706 0.840

c ₂ 1.500 1.054 1.091 1.333 1.187 1.324 1.088

c ₃ 1.071 0.981 0.868 0.917 1.147 0.971 1.072

	U₁	U₂	U₃	U₄	U₅	U₆	U₇
c ₁	0.429	0.965	1.042	0.750	0.666	0.706	0.840
c ₂	1.500	1.054	1.091	1.333	1.187	1.324	1.088
c ₃	1.071	0.981	0.868	0.917	1.147	0.971	1.072

The reference sequence is determined according to the relative optimization principle as shown below. U₀=(0.429,0.965,0.868,0.750,0.666,0.706,0.840)

According to Formula(3), Δ min = 0, Δ max = 1.547.

5.3 Calculating relationa coefficient

According to the formula (4), let ρ = 0.5, then the following values can be obtained as shown in Table 6:

Table 6
The values of relationa coefficient of the indicators

U1 U2 U3 U4 U5 U6 U7

ζ _c1 1.000 0.997 0.655 0.998 0.998 0.998 0.997

ζ _c2 1.000 0.333 0.374 0.474 0.504 0.503 0.386

ζ _c3 1.000 0.367 0.389 0.464 0.616 0.511 0.447

	U1	U2	U3	U4	U5	U6	U7
ζ _c1	1.000	0.997	0.655	0.998	0.998	0.998	0.997
ζ _c2	1.000	0.333	0.374	0.474	0.504	0.503	0.386
ζ _c3	1.000	0.367	0.389	0.464	0.616	0.511	0.447

5.4 Relation analyzing

Grey relational analysis does not require too many samples, according to the calculation formula of relationa $r_{i} = w_{k} \sum_{k = 1}^{n} ζ_{i} (k)$ , the weight is usually measured by the average value of the indicators numbers without considering the reality of the difference in the weight of the indicators. While the improved AHP method is just an effective solution to such problems. The grey relationa r_i of the comparative indicator u_i and reference indicator u₀ can be obtained as the following: $\begin{array}{l} r_{c 1} = w_{U_{1} - U_{7}} \sum_{k = 1}^{7} ξ_{c 1} (k) = 0.943; \\ r_{c 2} = w_{U_{1} - U_{7}} \sum_{k = 1}^{7} ξ_{c 2} (k) = 0.492; \\ r_{c 3} = w_{U_{1} - U_{7}} \sum_{k = 1}^{7} ξ_{c 3} (k) = 0.523; \end{array}$

Obviously, r_c₁ > r_{c
₃} > r_{c
₂}.

According to the calculation result, in the cleaner production assessment of the three enterprises of the group, c₁ has the highest level of cleaner production, followed by c₃ and lastly by c₂, which keeps in line with the cleaner production report from a third party. The result indicates that the group should pay more attention to c₂ and c₃ for further improvement of cleaner production.

6 Discussion and suggestion

Compared with AHP, Delphi method, percentage method and grey relational analysis, the grey relational analysis integrated with improved AHP method ensures reliability of the indicator weight. The weight of U₄ (Steel consumption of MYIAV) keeps the highest one (0.183) in all the indicators, which has the biggest impact on the overall assessment of cleaner production. On account of this, enterprises should pay more attention to the steel consumption, and raise the level of cleaner production from the source.

As the internal control tools of cleaner production, methods of AHP and GRA are simple and practical. While in practice, the subjectivity of expert scoring makes an inaccurate risk to the indicator weight, so in the future research, the AHP model still needs to be improved.

At present, the popular cleaner production assessment method is based on GaBi 5 series (one of life cycle assessment software), which is easy to get started with professional database complete. But the high price limitations make it hard to gain popularity.

7 Conclusions

The cleaner production audit report from the third party of 2016 shows that the cleaner production comprehensive audit assessment results of the three enterprises were 91.5 points, 80.3 points and 85.6 points, which is consistent with the assessment result of this paper. Therefore, this combination method is practical and can be used as a tool for internal cleaner production assessment and improvement in the enterprise.

Cleaner production is a process which needs continuous improvement with many factors affecting the implementation of cleaner production. From this point of view, it is not enough to consider the selection results of cleaner production assessment indicators, but also need to consider its personnel knowledge and ability, internal and external risks of enterprises, and the difficulty degree in implementation. Therefore, it needs to be further studied and improved.

Plans for continuous cleaner production improvement are needed to implement, e.g. the stainless steel channel used as chromium plated station spare parts is to be replaced by titanium alloy channel steel to increase corrosion resistance, staff training, the risk management of cleaner production, and environmental compliance obligation management should be strengthened. The cleaner production should be continuously identified, assessed and improved to promote economic and environmental benefits for a win-win result.

References

Xiong

W.Q.

, Yin

and Liu

Q.C.

, Research on Grey Correlation Analysis for Cleaner Production, Journal of Chongqing University32 (11) (2009), 1352–1356.

, Zhang

J.D.

, Jiang

, ., Environ Geochem Health39 (2017), 923–934.

, Zhang

J.D.

, Jiang

, ., Environ Sci Pollut Res23 (2016), 13100–13113.

Wang

L.P.

, Yu

X.L.

, Yang

J.D.

and Xu

J.J.

, Application of AHP-Fuzzy Comprehensive Evaluation in Cleaner Production Audit, Environmental Science and Management37 (12) (2012), 180–188.

Bao

J.D.

, Johansson

and Zhang

J.D.

, An Occupational Disease Assessment of the Mining Industry’s Occupational Health and Safety Management System Based on FMEA and an Improved AHP Model, Sustainability9 (2017), 94.

Cleaner Production Standard, Electroplating Industry. Available online: (accessed on 8 April 2017) http://www.bzxz.net/bzxz/137892.html.

Cleaner Production Assessment Indicator System in Machinery Industry. Available online: (accessed on 8 April 2017) http://www.mei.net.cn/jxgy/201409/576560.html.

Cleaner Production Report, Enterprise internal documents2016, 11.

Saaty

T.L.

, Making and validating complex decisions with the AHP/ANP, J Syst Sci Syst Eng14 (2005), 1–36.

10.

Saaty

T.L.

, About a hundred years of creativity in decision making. Int, J Anal Hierarchy Process7 (1) (2015), 138–143.

11.

Azadeh

and Zadeh

S.A.

, An integrated fuzzy analytic hierarchy process and fuzzy multiple-criteria decision-making simulation approach for maintenance policy selection, Simulation1 (2016), 3–18.

12.

Emrouznejad

and Marra

, The state of the art development of AHP (1979–2017): A literature review with a social network analysis, International Journal of Production Research2 (2017), 1–23.

13.

Aghdaie

M.H.

and Alimardani

, Target market selection based on market segment evaluation: A multiple attribute decision making approach, Int J Oper Res24 (3) (2015), 262–278.

14.

Bao

J.D.

, Zhang

J.D.

, Li

, Liu

C.Y.

and Shi

S.P.

, Social benefitsof the mine occupational health and safety management systems ofmines in China and Sweden based on a fuzzy analytic hierarchyprocess: A comparative study, Journal of Intelligent & FuzzySystems31(6) (2016), 3113–3120.

15.

Wei

C.P.

and Zhang

Z.M.

, An algorithm to improve the consistency of a comparison matrix, Syst Eng Theory Pract20 (2000), 62.

16.

Chen

and Yang

G.F.

, Eco-environmental quality appraisal of yellow river delta wetland using the improved AHP method. South-to-North Water Divers, Water Sci Technol9 (2011), 99–101.

17.

Bao

J.D.

, Johansson

and Zhang

J.D.

, Comprehensive Evaluation on Employee Satisfaction ofMine Occupational Health and Safety Management System Based on Improved AHP and 2-TupleLinguistic Information, Sustainability9 (1) (2017), 133.

18.

Le Nguyen

, Quyen

H.A.

and Nguyen

N.A.

, Application of fuzzy-analytic hierarchy process algorithm and fuzzy load profile for load shedding in power systems, Int J Electr Power Energy Syst77 (2016), 178–184.

19.

Deng

J.L.

, Control problems of grey systems, Systems & Control Letters1 (1982), 288–294.

20.

Anton

and Gurcan

, Short-term freeway traffic parameter prediction: Application of grey system theory models, Expert Systems with Applications62 (2016), 284–292.

21.

, Liu

and Liu

, The theory of gray relative analysis and it’s new research, Journal of Wuhan University of Technology22 (2) (2016), 41–43.

22.

Deng

J.L.

, Basic method of grey system, Huazhong University of Science and Technology Press, 1987.

23.

Zhao

Y.L.

, Wei

S.Y.

and Mei

Z.X.

, Grey Euclid relation grade, Journal of Guangxi University23(1) (1998), 10–13.

24.

X.Q.

, Research on the computation model of grey interconnect degree, Systems Engineering13 (1995), 58–61.

25.

Liu

S.F.

, Generalized degree of grey incidence: Information and systems, Dilian DMU Publishing House43 (1992), 113–116.

26.

Tang

W.X.

, The concept and the computation method of T’s correlation degree, Application of Statistics and Management14 (1) (1995), 34–37.

27.

Adem

, Mehmet

E.C.

, İsmail

and Ramazan

Ç.

, Parametric optimization of energy and exergy analyses of a novel solar air heater with grey relational analysis, Applied Thermal Engineering122 (2017), 330–338.

Cleaner production assessment of group company based on improved AHP and grey relational analysis

Abstract

Keywords

1 Introduction

2 The Assessment indicators of cleaner production

3.1 Evaluation weight set

Table 3 RI value n 1 2 3 4 5 6 7 8 9 RI 0 0 0.58 0.9 1.12 1.24 1.32 1.41 1.45

4.1 Determining the number of analysis sequence

5 Case study

5.1 The indicator weight and consistency testing

Table 5 The values after averaging of the indicators U1 U2 U3 U4 U5 U6 U7 c 1 0.429 0.965 1.042 0.750 0.666 0.706 0.840 c 2 1.500 1.054 1.091 1.333 1.187 1.324 1.088 c 3 1.071 0.981 0.868 0.917 1.147 0.971 1.072

Table 6 The values of relationa coefficient of the indicators U1 U2 U3 U4 U5 U6 U7 ζ c1 1.000 0.997 0.655 0.998 0.998 0.998 0.997 ζ c2 1.000 0.333 0.374 0.474 0.504 0.503 0.386 ζ c3 1.000 0.367 0.389 0.464 0.616 0.511 0.447

6 Discussion and suggestion

7 Conclusions

References

Table 3
RI value

n 1 2 3 4 5 6 7 8 9

RI 0 0 0.58 0.9 1.12 1.24 1.32 1.41 1.45

Table 5
The values after averaging of the indicators

U₁ U₂ U₃ U₄ U₅ U₆ U₇

c ₁ 0.429 0.965 1.042 0.750 0.666 0.706 0.840

c ₂ 1.500 1.054 1.091 1.333 1.187 1.324 1.088

c ₃ 1.071 0.981 0.868 0.917 1.147 0.971 1.072

Table 6
The values of relationa coefficient of the indicators

U1 U2 U3 U4 U5 U6 U7

ζ _c1 1.000 0.997 0.655 0.998 0.998 0.998 0.997

ζ _c2 1.000 0.333 0.374 0.474 0.504 0.503 0.386

ζ _c3 1.000 0.367 0.389 0.464 0.616 0.511 0.447