The present paper aimed at proposing new strategies for evaluating the green supply chain management for enhancing the priority to environmental factors in cement manufacturing life cycle analysis, there by reducing the carbon foot prints. These strategies help in producing eco-friendly products there striking the balance between economy and environment. Initially green supply chain priorities are defined by using grey relational analysis (GRA). The priority weights obtained by GRA method is used to determine the weight for each indicator selected in the present study and then GRA is combined with TOPSIS methodology to obtain the priority for level-II measurement indicators used in the present study. These strategies will influence the decision making priorities during cement manufacturing. Keywords - Green Supply Chain Management (GSCM), strategy prioritization, Grey Relational Analysis (GRA), TOPSIS, Life Cycle Analysis (LCA

Muralidhar, P

Ravindranath, K

Srihari, V

English

ExcelingTech Publishing Company (E-Journals)

Int. J Sup. Chain. Mgt                                                                                                                                                                                    Vol.2, No.1, March 2013 49 The Influence of GRA and TOPSIS for Assortment of Green Supply Chain Management Strategies in Cement Industry P. Muralidhar* Dr. K Ravindranath#   Dr. V Srihari* *National Institute of Construction Management and Research  NAC Campus, Kondapur Post Hyderabad-500084, India pmuralidhar17@gmail.com #Dept of Mechanical Engineering, SVU College of Engineering, TIRUPATI, India Kravi1949@yahoo.com  Abstract- The present paper aimed at proposing new strategies for evaluating the green supply chain management for enhancing the priority to environmental factors in cement manufacturing life cycle analysis, there by reducing the carbon foot prints. These strategies help in producing eco-friendly products there striking the balance between economy and environment. Initially green supply chain priorities are defined by using grey relational analysis (GRA). The priority weights obtained by GRA method is used to determine the weight for each indicator selected in the present study and then GRA is combined with TOPSIS methodology to obtain the priority for level-II measurement indicators used in the present study. These strategies will influence the decision making priorities during cement manufacturing. Keywords- Green Supply Chain Management (GSCM), strategy prioritization, Grey Relational Analysis (GRA), TOPSIS, Life Cycle Analysis (LCA).  1. Introduction  Nowadays, among various supply chain-related activities, the procurement of goods and services is playing vital role as a result of the globalization of the economy. Purchasing expenses towards material procurement can consume as much as 60% or more of ‘business’ revenues.  In the area of manufacturing arena, supplier selection is a crucial strategic decision that has long-term impacts on a company’s profitability and efficiency [1], [2].The present research paper aimed at using GRA and TOPSIS methodologies to develop the green supply chain strategies for cement industry to improve the profitability and efficiency. Based on the priority weights obtained using grey relational analysis(GRA) and technique for order preference similar to ideal solution (TOPSIS) methodologies, the life cycle of the cement manufacturing is designed with the cement manufacturing and distribution [3], [4], [5].  1.1 Case study  The current research study is aimed at cement manufacturing process. The cement industry is producing mainly three types of cements namely Ordinary Portland Cement (OPC), Portland Pozzalona Cement (PPC) and Portland Slag Cement (PSG). The PSG based cement is fly-ash based cement which produces least carbon emission rate compared to other cements. PSG based cement production helps in reducing carbon foot prints into the atmosphere during the life cycle because of the recycled raw materials like fly ash is used in PSG based cement manufacturing. The fly ash used here is the waste product from thermal power plant which reduces carbon footprints, helps in manufacturing energy efficient and environment friendly cement. To define the  GSCM strategies for  cement manufacturing four level-II measurement indicators i.e B1, B2, B3, B4, are defined which covers the entire life cycle of the cement manufacturing. The hierarchy structure is shown in Fig.1. with three levels [6], [7], [8]. ______________________________________________________________ International Journal of Supply Chain Management IJSCM, ISSN: 2050-7399 (Online), 2051-3771 (Print) Copyright © ExcelingTech, Pub. UK (http://excelingtech.co.uk/)  Int. J Sup. Chain. Mgt                                                                                                                                                                                    Vol.2, No.1, March 2013 50  Fig.1.Strategiesfor green Supply Chain Measurement Indicators   The organization of the paper is done as follows Section 2 presents state of art of strategy prioritization/selection of problem using GRA.  Section 3 presents problem description and proposed TOPSIS approach.  Section 4 illustrates some of the experimental results.  Section 5 concludes the paper with some comparison of results obtained by using GRA and TOPSIS.  2. Prioritization of GSM strategies using GRA   2.1. Grey Relational Analysis (GRA)  The grey system theory was developed by Deng (1982), which has been widely used for data analysis in different areas [9, 10, and 11]. Grey Relational Analysis method is quite useful to determine the strategies with little data or uncertain information. Using GRA weights are obtained for level-II indicators as shown in Fig.1. Then we used the weights obtained by GRA for determining the strategies of GSCM strategies using TOPSIS methodologies. The steps involved in GRA are as follows. Step 1.Determine the problem and generate the data in analytical sequence   Xo = (do1, do2, do3, do4 …. dom) Where m is the number of respondents and Xo is reference data and m represents the responses favorable to the situation. Step 2: Determine the comparison data related to the problem  Xi and presented in Table 1.  Xi =(di1, di2, di3, di4 …. dim)  where  i =1,… K and K is the size of the    scale from 1 to 5. So the data series consists of m values with k comparisons. Step 3: The difference of the data for series is calculated i and presented Table 2. Step 4: Then obtain the Global maxima value   max and Global minima value min    difference for the series of data.  Table 1: Rank Allocation Criteria     Indicators GP GM CS EM X1/ op1 X2/op2 X3/op3 X4/op4 C1 2 4 3 2 C2 3 3 4 1 C3 2 4 2 4 C4 3 4 1 3 C5 3 3 3 4 C6 2 4 2 1 C7 3 3 1 2 C8 2 4 5 3 C9 3 4 1 3 C10 2 4 2 4 C11 3 3 2 1 C12 3 3 3 2 C13 3 3 4 3 C14 3 4 3 2 C15 2 3 2 1 C16 4 4 1 4 i=(|do1- di1|, |do2- di2|, …,|dom-dim|) Int. J Sup. Chain. Mgt                                                                                                                                                                                    Vol.2, No.1, March 2013 51   max = imax(max i) Table 2: The difference data Indicators    min =  imin(min i) Step 5: The variation in the data is calculated for overall indicators of data and grey relational coefficient is established. Here, the grey relational coefficient i(j), indicates the difference of the jth data series of ith data point, then i(j) =      Where i(j) represents the jth  value in I in  difference data series,  value lies between 0 and 1. The coefficient  is used to compensate the effect of max should be extreme value in the data series. In general the value of  can be set to 0.5. Step 6: The grey relational coefficient is calculated for entire difference data series of dataset and i represents the grey relational grade for the  ith scale item and believed that the data point in the series are of the same weights. Then    i  =  The magnitude   reveals the overall standardized deviance of ith original data series from the base data series. Normally a developed item with higher values of  indicates higher degree of ranking by the experts on the specific item. Step 7: Sort  values into either descending order or ascending order to facilitate the managerial interpretation of results. 3.0. TOPSIS Approach  The TOPSIS (Technique for Order Performance by similarity to Ideal Solution) is very much useful in determining the ranks [12, 13, 14].Using TOPSIS the best alternative would be the one that is nearest to the positive ideal solution (PIS) and farthest from the negative ideal solution (NIS). The PIS is a solution that maximizes the benefit criteria and minimizes the cost criteria and the NIS is vice versa. This paper combines TOPSIS with GRA to evaluate the strategies of green supply chain management [15, 16].   Table 3.a: Global Weight Priority Matrix Indicators GP GM CS EM         C1 0.4 0.67 0.5 0.4 C2 0.5 0.5 0.67 0.34 C3 0.4 0.67 0.4 0.67 C4 0.5 0.67 0.34 0.5 C5 0.5 0.5 0.5 0.67 C6 0.4 0.67 0.4 0.34 C7 0.5 0.5 0.34 0.4 C8 0.4 0.67 1 0.5 C9 0.5 0.67 0.34 0.5 Indicators GP GM CS EM ∆1/ OP1 ∆2/ OP2 ∆3/ OP3 ∆4/ OP4 C1 3 1 2 3 C2 2 2 1 4 C3 3 1 3 1 C4 2 1 4 2 C5 2 2 2 1 C6 3 1 3 4 C7 2 2 4 3 C8 3 1 0 2 C9 2 1 4 2 C10 3 1 3 1 C11 2 2 3 4 C12 2 2 2 3 C13 2 2 1 2 C14 2 1 2 3 C15 3 2 3 4 C16 1 1 4 1 Int. J Sup. Chain. Mgt                                                                                                                                                                                    Vol.2, No.1, March 2013 52 C10 0.4 0.67 0.4 0.67 C11 0.5 0.5 0.4 0.34 C12 0.5 0.5 0.5 0.4 C13 0.5 0.5 0.67 0.5 C14 0.5 0.67 0.5 0.4 C15 0.4 0.5 0.4 0.34 C16 0.67 0.67 0.34 0.67 AVG 0.4856 0.57 0.41 0.477       The priority weights obtained using GRA for is as follows.  Table 3.b Final weights table using GRA  SNO Name of the indicator Weight   obtained 1 Green Purchasing(GP) 0.4856 2 Green manufacturing(GM) 057 3 Customer service(CS) 0.41 4 Environment management(EM) 0.477  3.1. TOPSIS Methodology   The TOPSIS approach is based on   • Qualitative attributes benefits • Quantitative attributes benefits • Cost attributes criteria   In  TOPSIS methodology  it’s assumed that we have m alternatives (options) and n attributes, we have the score of each option with respect to each criterion Step 1: Construct the normalized decision matrix a dn normalized scores are as follows rij = Xij/ (Σxij)½ for i = 1, …, m; j = 1, …, n and  for each indicator weights are taken from experts for a scale of 10 scores are  potred in the Table 4. Step 2: Construct the weighted normalized decision matrix.  Obtain the weights Wj where j=1…n from GRA method and global weights (Wj) are    calculated they are (48.56%,57%, 41%, 47.7%) for GP, GM , CS, EM respectively.            Then calculate the Uij= Wj rij. The weights are calculated by using GRA and results are tabulated in Table 5.a and Table 5.b respectively. Table 4: Weight Criteria Allocation for measurement Indicators Indicators GP GM CS EM C1 7 6 7 8 C2 6 7 6 7 C3 8 5 7 8 C4 6 7 6 8 C5 7 6 6 8 C6 8 7 7 8 C7 6 8 6 7 C8 7 7 7 8 C9 6 7 7 7 C10 6 8 7 8 C11 7 8 7 8 C12 5 7 6 7 C13 8 7 8 9 C14 7 7 8 7 C15 8 6 8 8 C16 7 6 8 8 (∑Xij2)  755  753  779  966  (∑Xij 2) 1/2   27.477  27.441  27.911  31.081   Step 3: Determine the positive ideal and negative ideal solution from Table 5.a and 5.b. Ideal solution P* = { U1* , …, Un*}, where              Uj*  ={ max (Uij) if j ∈ J ;  min (Uij) if  j ∈ J' }   The positive ideal solution is and               Negative Ideal solution               P'   = { U1' , …,Un' }, where  Int. J Sup. Chain. Mgt                                                                                                                                                                                    Vol.2, No.1, March 2013 53   U' = { min (Uij) if j ∈ J ;  max (Uij) if  j ∈ J’} Step 4: Calculate separate measures for each alternative and results are presented in the Table 5.b  =  [ Σ (Uj*– Uij)2 ] ½  i = 1, …, m   There fore   Positive Ideal Solution  Pi *   = (0.141, 0.166, 0.118, 0.123) Similarly the separation from the negative ideal solution          =  [ Σ (Uj' – Uij)2 ] ½     i = 1, …, m i   There fore   Negative Ideal Solution   P'i = (0.088, 0.125, 0.088, 0.046) Step 5: Calculate the relative closeness to the ideal solution Ci* and tabulated in Table 6.           Ci*= Pi' / (Pi* + Pi'),          0 <  Ci* < 1                  Then select the option closeness to 1   4. Results:  The integrated GRA-TOPSIS method of present research study portrayed priority weights,   ranks   are obtained for various Green Supply chain Strategies, priority weight for Green manufacturing is shows 89%, priority weight for Green Purchasing shows 88% and results are tabulated in Table 6.           Table 5.a Calculation Uij values Weight(Wj) 0.485 0.57 0.41 0.477 Indicators GP GM CS EM C1 0.255 0.219 0.251 0.257 C2 0.218 0.255 0.215 0.225 C3 0.291 0.182 0.251 0.257 C4 0.218 0.255 0.215 0.257 C5 0.255 0.219 0.215 0.257 C6 0.291 0.255 0.251 0.257 C7 0.218 0.292 0.215 0.225 C8 0.255 0.255 0.251 0.257 C9 0.218 0.255 0.251 0.225 C10 0.218 0.292 0.251 0.257 C11 0.255 0.292 0.251 0.257 C12 0.182 0.255 0.215 0.225 C13 0.291 0.255 0.287 0.290 C14 0.255 0.255 0.287 0.225 C15 0.291 0.219 0.287 0.257 C16 0.255 0.219 0.287 0.257  Table 5.b Determination of NIS and PIS Indicators GP GM CS EM C1 0.124 0.125 0.103 0.123 C2 0.106 0.145 0.088 0.107 C3 0.141 0.104 0.103 0.123 C4 0.106 0.145 0.088 0.123 C5 0.124 0.125 0.088 0.123 C6 0.141 0.145 0.103 0.046 C7 0.106 0.166 0.088 0.107 C8 0.124 0.145 0.103 0.123 C9 0.106 0.145 0.103 0.107 C10 0.106 0.166 0.103 0.123 C11 0.124 0.166 0.103 0.123 C12 0.088 0.145 0.088 0.107 C13 0.141 0.145 0.118 0.138 C14 0.124 0.145 0.118 0.107 C15 0.141 0.125 0.118 0.123 C16 0.124 0.125 0.118 0.123  Int. J Sup. Chain. Mgt                                                                                                                                                                                    Vol.2, No.1, March 2013 54 Table 6: Final ranking strategy from GRA -TOPSIS approach GS CM Strategy Results from Positive  Ideal Solution (1) Results from Negative  Ideal Solution (2) Relative closeness Determination (3= 2/(1+2)) Rank  (4) GP 0.011086 0.085715 0.885472 2 GM 0.011209 0.099156 0.898435 1 CS 0.011335 0.033571 0.747584 3 EM 0.011463 0.02245 0.661979 4  5. Conclusions  The present paper aimed at enhancing the priority for environmental related activities in life cycle of Cement manufacturing. The results obtained in the study portrayed that the environmental priority will help in reducing the carbon foot prints in cement manufacturing supply chain. It is strongly recommended that if fly ash is used as primary raw material in cement manufacturing system, it will significantly reduce carbon foot prints and load on the environment. The ranking strategies obtained in the present research paper using GRA-TOPSIS also will help in making the life cycle of cement manufacturing more greener.  References [1] Srivastava, S.,“Green supply state of art literature review” International journal of management review, Vol 9(1), pp. 53-80, 2007. [2] Muralidhar,P., Ravindranath, K.,  Srihari,V., “Perspective patterns of Environmental Green  supply chain management”  IJ-ETA-ETS Vol 3(2), pp. 233-237, 2010. [3] Ninlawan, C., Seksan, P., Tossapol, K., and Pilada, W.,The Implementation of Green Supply chain Management Practices in Electronic Industry, Proceedings of International Multy Conference of Engineers and Computer scientists,   Vol. III, March (2010) 17-19, 2010   Hong Kong. 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[10] Deng J Basis of Grey Theory, Huazhong University of Science and Technology press Wuhan.,2002. [11] Venkata Rao,R., Singh,D.,. A hybrid multiple attribute decision making method for Solving Problems of industrial environment, International Journal of Industrial Engineering Computations,(2), pp. 631–644. 2011. [12] Wanj,YJ., lee H S., and Lin,K., Fuzzy TOPSIS for Multi criteria decision- making” International Mathematical Journal Vol .3(4),  pp. 367-379, 2003. [13] Wanj Y J  and lee H S., “Generalizing the TOPSIS  for Fuzzy for Multi criteria decision making” Journal of Computers and Mathematics with applications.,Vol 53(11), pp.1762-1772,  2007. [14] Akram Zouggari and Lyes Benyoucef  Multi-criteria Group Decision Supplier Selection Problem using Fuzzy TOPSIS Based Approch EUSFLAT-LFA: Published by Atlantis Press. pp. 628-635,2011. [15] Tie-chin wang, hsien-da lee, Developing Fuzzy Topsis approach based on subjective weights and objective  weights [J] expert systems with applications, (36) pp8980-8985. 2009. [16] Sum Xiaodon., The Grey relational TOPSIS method and its applications based on the combined weights. Journal of Industrial Engineering and Management (1), pp. 62-66. 2006.  

The Influence of GRA and TOPSIS for Assortment of Green Supply Chain Management Strategies in Cement Industry

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The Influence of GRA and TOPSIS for Assortment of Green Supply Chain Management Strategies in Cement Industry

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