RELATIVE RELIABILITY RISK INDEX FOR GREEN SUPPLY CHAIN MANAGEMENT

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1 International Journal of Mechanical Engineering and Technology (IJMET) Volume 9, Issue 13, December 2018, pp , Article ID: IJMET_09_13_128 Available online at ISSN Print: and ISSN Online: IAEME Publication Scopus Indexed RELATIVE RELIABILITY RISK INDEX FOR SUPPLY CHAIN MANAGEMENT *Mrs. A. S. Chavan Research Scholar, Bharati Vidyapeeth (Deemed to be University) College of Engineering, Pune-Satara Road, Pune, Dr. R. N. Patil I/C Principal, Bharati Vidyapeeth s College of Engineering, Lavale, Pune, Dr. S T. Chavan Professor, Maharashtra Institute of Technology, Kothrud, Pune Mr. Nitin Kulkarni Research Scholar, Sri Satya Sai University of Technology & Sciences, Sehore, M.P, India. Dr. Sachin. S. Chavan Professor, Bharati Vidyapeeth (Deemed to be University), College of Engineering, Pune-Satara Road, Pune, *corresponding author ABSTRACT Green Supply Chain Management (GSCM) has become one of most promising tool in reducing environmental hazards of manufacturing industries. Selection of indicators and its implementation needs good decision making. The Aim of Relative Reliability Risk Index (R 3 I) is to assess relatively the reliability of an industry wherein GSCM can be implemented and to screen out the ones that are judged to have unacceptable level of rank and hence risk. The relative measurement of indicators is done by Analytical Hierarchy method and weights are assigned through entropy method. Alternatives Functionality Graph is plotted to get to know the strength of each indicator. Relative Reliability Risk Index (R 3 I) is calculated and results obtained from the case study are compared. Key words: Reliability, Analytical Hierarchical Process (AHP), Relative Reliability Risk Index (R 3 I). Alternative Functionality Graphs (AFGs), Green Supply Chain (GSCM) Cite this Article: Mrs. A. S. Chavan, Dr. R. N. Patil, Dr. S T. Chavan, Mr. Nitin Kulkarni, Dr. Sachin. And S. Chavan, Relative Reliability Risk Index for Green Supply Chain Management, Journal of Mechanical Engineering and Technology, 9(13), 2018, pp editor@iaeme.com

2 Mrs. A. S. Chavan, Dr. R. N. Patil, Dr. S T. Chavan, Mr. Nitin Kulkarni, Dr. Sachin. And S. Chavan 1. INTRODUCTION Green Supply Chain plays an important role in an industry and has direct impact on its efficiency and effectiveness. Many multi-criteria decision making methods are available to prioritize and rank the indicators identified by the researchers, but knowing the most important indicator and its depth may consume time for any new industry, so to get to know the rank of the indicators and thereafter its implementation may enhance the profit of any industry. Therefore it is projected to relatively assess the reliability of indicators for an industry. Since reliability is a very important criterion, it is proposed to evaluate reliability and obtain ratings and ordinal ranking. Performance is a measure of reliability and the functionality index which refers to the performance of an industry after the implementation of GSCM indicators and sub-indicators. Therefore a relative approach is followed in calculating R 3 I using the Analytical Hierarchy Process. The idea of Alternative Functionality Graph (AFG) is introduced to enable an industry to look at the final outcome graphically and provide ease of making decisions. 2. LITERATURE Selection of vendors with the different performance factors is done by many researchers since 1960, Dickson [1], Weber et al. [2], but the solutions involve high degree of uncertainties and fuzziness. Bellman and Zadeh [3] proposed Fuzzy set theory for problem modelling and solution, introduced fuzzy set theory in Multi Attribute Decision Making as an approach to effectively deal with the inherent impression, vagueness and subjectiveness of the human decision making process. AHP based strategic sourcing model is developed for effective vendor selection by Bindu et al. [4]. Vendor selection as mentioned in the perspective by Bindu et al. [5] is having a direct impact on the efficiency and responsiveness of the entire supply chain. Although many multicriteria decision making methods, are available to select the final vendor from the set of available vendors, the data to calculate reliability of vendors may not be available, in case of new vendors. Therefore it is proposed to relatively assess the reliability of new alternative vendors and then screen out those that seem to have unacceptable level of rank. Performance is a measure of reliability and the functionality indication, as concluded by Weber et al. [2] refers to the performance of the vendor. Therefore a relative approach is followed in calculating R 3 I using the Analytical Hierarchy Process. Since 1960 many researchers worked on measuring the performance of GSCM and analysis of various criteria for its selection and implementation, Joseph Sarkis et al. [7]. In selection of various criteria from the main problem there exist a high degree of fuzziness and uncertainties, Stefan Seuring et.al.[8]. The selection and implementation of indicators and sub-indicators of Green supply chain Management for any industry should not only meet ecological requirements and in turn bring profit to any industry, but also help in fulfilling various criteria such as reduction in carbon footprint, reduction of scarp, and enhancing quality. It is needed to develop a systematic selection process for identifying and prioritizing important indicators and sub-indicators and to evaluate the tradeoffs between technical, economic and performance criteria, as brought out by Rao et al. [6]. The method should also reduce time in selection of various indicators, implementation of Green supply chain management (GSCM) practices and develop consensus decision making, A. S. Chavan et.al [14]. The Aim of introducing Relative Reliability Risk Index (R 3 I) is to assess relatively the reliability of an industry wherein Green supply Chain management can be implemented and then to screen out the ones that are judged to have unacceptable level of rank and hence risk. Further therein to optimize the operations so as to gain benefits in terms of profit and reduction in carbon footprints and thus attain effective GSCM implementation. The objective of introducing R 3 I is to editor@iaeme.com

3 Relative Reliability Risk Index for Green Supply Chain Management highlight the industries that are relatively stronger in terms of reliability. Table 1 shows the list of indicators and its abbreviations for R 3 I. 3. METHODOLOGY Seven industries are to be ranked with respect to seven indicators identified from multi-criteria decision making methods. The problem is to identify the best industry for the implementation of GSCM. The subjective assessments of the seven industries based on seven main indicators were the only available data from within but for new industry this data need to be captured progressively over a period of time and which will be subjective in nature. The subjective assessment of seven industries based on seven indicators as shown in Table 2. The methodology is used to decide the overall ranks of an industries alternative and validate the ranks with the qualitative and quantitative outcomes obtained from the case study. Table 1 List of Indicators and abbreviations for R 3 I [14] Sr. No. Indicators Abbreviations 1 Consideration of environmental issues in the process of production planning and control (Six Sigma/process capability, Product PPC Stewardship ) 2 Consideration of environmental issue during selection of manufacturing process (Industrial engineering tools: 5S, Kaizen, SMP Method Study, Work Measurement, Industrial Safety, Ergonomics) 3 People involvement initiative ( Quality circle) QC 4 Co-operation with customers for environmental packaging CEP 5 Application of Reverse logistics RE 6 Are you recovering your scrap through government certified agency? RSRM 7 Selection of Employee and Technology SET Table 2. Subjective assessment of industries based on indicators Sr. Green Green Green Green Green Green Green Abbreviations No PPC SMP QC CEP RE RSRM SET Total Rank To calculate Relative Reliability Risk Index (R 3 I) of the alternatives available, four tier method is used. The schematic representation of the various steps in four tier method is as shown in figure editor@iaeme.com

4 Mrs. A. S. Chavan, Dr. R. N. Patil, Dr. S T. Chavan, Mr. Nitin Kulkarni, Dr. Sachin. And S. Chavan Figure 1. Methodology of Four Tier Method The steps are as follows: In step 1, the function structure i.e. Industry Evaluation attributes is established. Main objective is the selection of an industry forms the highest level of the hierarchy. The lower level is represented by seven main indicators of Green Supply Chain. The bottommost hierarchy is occupied by the seven industry alternatives available as shown in Figure No. 2. In step 2, once the function structure is established, the Analytical Hierarchy Process developed by Saaty et al. [9-11], is applied so as to relatively rate the main indicators of the function structure as shown in figure 1. After the comparisons have been made, we obtain the priorities. Application of AHP is done using a Microsoft Excel to calculate consistency ratio. Any number of main indicators can be compared and a measure of inconsistencies during the comparisons is provided which gives a good measure of the relative ratings and provides a check whether the comparison should be performed again. However, prioritization among the main criteria is optional. In the method proposed here, we calculate the priorities of the alternatives with respect to the objective. This is because the criteria those are available with us are the main indicators from the function structure. It would be inadvisable to compare the indicators that are basic to the system using the pair wise comparison matrix, because all the main indicators may seem to be equally important. Instead, we use entropy method to calculate the weights of the indicators available with us. Figure 2. Structural Hierarchy for Industry Selection Therefore after establishing the level structure, comparison matrices are formed and comparisons of lower level criteria are made with respect to the property at the upper level. There are seven industries alternative. The indicators on which the selection depends are seven i. e. Consideration of environmental issues in the process of production planning and control (Six Sigma/process capability, Product Stewardship) (PPC), Consideration of environmental issues during selection of Manufacturing process (Industrial engineering tools: 5S, Kaizen, Method editor@iaeme.com

5 Relative Reliability Risk Index for Green Supply Chain Management Study, Work Measurement, Industrial Safety, Ergonomics) (SMP), People involvement initiative (Quality circle) (QC), Co-operation with customers for environmental packaging (CEP, )Application of Reverse logistics (RE), Are you recovering your scrap through government certified agency? (RSRM), and Selection of Employee and Technology (SET) as shown in Table 1. We compare all the seven alternative industries with respect to each criterion at the level above it. There will be seven comparison matrices for these comparisons. The information on the priorities obtained for all the indicators is tabulated in the form of Decision Matrix as shown in Table 5. In step 3, using these priorities, the Alternatives Functionality Graphs (AFGs) are drawn. AFG indicates the relative measure of functionality fulfilment with respect to each of the available alternatives. In step 4, Using entropy method, weights are assigned to the functions. This method is preferred because it does not require the decision maker to affix the weight; instead weights are calculated using the information obtained from the decision matrix after applying AHP. It rules out any chance of prejudice or manipulation to assign weights by the decision maker. Even if the weights have already been assigned by the decision maker, they can be combined with the weights obtained using this method. The application of AHP leads to normalized priorities in the decision matrix, which are then used to extract information for input to the entropy method. The weights of seven main indicators are obtained. Using these weights, the R 3 I of all the seven industry alternatives are calculated. Industry with highest R 3 I ranks the best industry for the implementation of GSCM practices. The steps for the developed methodology are as given below: i. Decide the priorities among the seven industries based on each main function using Analytical Hierarchy Process (AHP). AHP was developed in 1972 as a practical approach for solving relatively complex problems as depicted by Saaty [9-11]. It is used for multi criteria problems in a number of application domains. The general approach of AHP is to decompose the problem and make pair wise comparison of all the indicators at a given level with the related indicators in the level just above to which it belongs. For the qualitative analysis for selection among industry alternatives, Pair wise comparisons for each criterion is done on a scale of relative importance, 1 reflecting equal weightage and 9 reflecting absolute importance shown by Saaty [6]. Here the AHP method is used to select the best industry wherein GSCM can be effectively implemented. We compare the seven industry alternative with respect to seven main indicators at the level above it. The sample comparison matrices for PPC are as shown in Table 4. Performance Measure Table 3. Assessment Data of Industries based on Indicators PPC SMP QC CEP RE RSRM SET 01 Excellent Excellent Superb Excellent Excellent Superb Superb 02 Good Fair Excellent Good Excellent Excellent Excellent 03 Fair Poor Excellent Good Fair Fair Good 04 Good Excellent Fair Excellent Good Fair Good 05 Fair Good Excellent Excellent Fair Good Fair 06 Poor Poor Fair Fair Fair Poor Fair 07 Good Good Super Good Good Excellent Excellent editor@iaeme.com

6 Mrs. A. S. Chavan, Dr. R. N. Patil, Dr. S T. Chavan, Mr. Nitin Kulkarni, Dr. Sachin. And S. Chavan ii. Form a Decision Matrix with the priorities obtained for seven main indicators for all the seven alternatives. The comparison matrices are used to calculate the final priorities for the available alternatives. With each matrix, there is associated a consistency ratio (CR), which gives the measure of consistency in the comparisons made. CR should be less than or equal to 0.1 for the results to be acceptable, else the comparison should be undertaken again. PPC: For assessing the PPC aspect of all the seven industries, pairwise comparison weightages obtained by consensus of the experts, are entered in the (7 x 7) comparison matrix as shown in Table 4. After the AHP calculations, the functional priorities of all the seven industries are obtained which shows Industry Green 01 has obtained the highest ranking in the indicator PPC. PPC 01 Table 4. Comparison Matrix for Industry Alternatives w. r. t. PPC Priority Function (PF) / /3 1/2 1 1/ / / /3 1/2 1 1/ / /4 1/3/ 1/2 1/3 1/2 1 1/ /2 1/ Eigen Value: 7.098, Consistency Ratio: , Consistency Index: Similarly, Priority Function have been calculated for remaining indicators i.e SMP, QC, EP, RE, RSRM, and SET. A Decision Matrix is then attained by compiling the obtained functional priorities of all the seven industries on all the main criteria as shown in Table 5. Table 5. Decision Matrix Industry i = 1-7 A1 A2 A3 A4 A5 A6 A7 Criteria j = 1-7 GRE 01 GRE 02 GRE 03 GRE 04 GRE 05 GRE 06 GRE 07 V1 PPC V2 SMP V3 QC V4 CEP V5 RE V6 RSRM V7 SET iii. Alternatives Functionality Graphs (AFG). AFGs are graphs between the functional priorities calculated by AHP and the alternatives. This approach towards evaluating alternatives helps identify the strengths and weaknesses of all the alternatives, function wise. Unfortunately systematic methods are not always used in industries as stated by Chakraborty et. al. [12]. The AFG is shown in Figure 3. X axis represents seven number of industry alternatives editor@iaeme.com

7 Relative Reliability Risk Index for Green Supply Chain Management Figure 3. Alternatives Functionality Graph iv. Entropy Method After the application of AHP, the priority I Decision Matrix obtained is shown in Table 5. The weights for the seven indicators considered are calculated using the information from the decision matrix and the entropy method. The entropy method is Multi Attribute Decision Making Method, as stated by Hwang [13]. This method has been adopted as a part of calculating R 3 I, because it may be inappropriate for a decision maker to compare indicators relatively from the function structure. The information contents of the normalized values of the attributes can be measured using entropy values. The entropy Vj of the set of normalized outcomes of attribute j is given by, = ( ( )) For all j (j = 1 to k represents attributes) 1 And ( i = 1 to n represents alternatives) I ij = Normalized element of the Decision Matrix β = constant = (1 In (n)) 2 The smoothness (E j) of a function j = (1-V j) is calculated for all the seven functions. If there are no preferences available, the criteria weights are calculated using the equation, W j = (E j ( ) 3 Where, Ej = (1-VJ) If the decision maker has the weights available beforehand i. e. We, then it can be combined with the weights calculated above, resulting in new weights that are Wnew. The weights obtained after the application of the method are shown in the Table 13. W new = ((We W j) ( ( )) 4 Table 6. Weights for the criteria as obtained by Entropy Method Criteria Weights Obtained (%) (W j) PPC 9.95 (W 1) SMP (W 2) QC (W 3) CEP 6.77 (W 4) RE 9.71 (W 5) RSRM (W 6) SET (W 7) editor@iaeme.com

8 Mrs. A. S. Chavan, Dr. R. N. Patil, Dr. S T. Chavan, Mr. Nitin Kulkarni, Dr. Sachin. And S. Chavan Normalization of Decision Matrix is not required since the sum of the priorities for any attributes j is 1. Having calculated the weights and priorities, we obtain R 3 I, shown in Table 7 using the following equation. R 3 I i = ( ) for all i 5 Table 7. Results of R 3 I Ranking R 3 I of Industries R 3 I Rating RANK Green Green Green Green Green Green Green SAMPLE CALCULATIONS: Let = Normalized element of Decision matrix as shown in Table 5. N = Number of alternative industries, there are seven industry alternatives. (i = 1 to n and n = 7). K = Number of industry evaluation criteria. In this case there are seven criteria, viz. PPC, SMP, QC, CEP, RE, RSRM and SET. (j = 1 to k and k = 7). Then, Calculate Entropy of each criterion, Vj (j = 1-7) by the following formula, = ( ( )) (j = 1 to k represents attributes) (i = 1 to n represents alternatives) Calculate Smoothness of each function, Ej = 1- Vj (j = 1-7) Calculate the Weights for the indicator by using the following formula, Wj = (Ej ( ) ) Indicator Weights Obtained (%) (W j) PPC SMP QC 14.9 CEP 8.9 RE RSRM SET editor@iaeme.com

9 Relative Reliability Risk Index for Green Supply Chain Management Calculate the Relative Reliability Risk Index (R 3 I) for all the industries using the following formula, R 3 Ii = ( ) for all i By taking the values of l from Table 6 and the corresponding weights from Table 7, we calculate R 3 I of each industry as shown below, 4. RESULTS The results are as discussed. By arranging the above results in descending order, we get Table 8 Comparison between the industries ranking obtained by entropy method and Qualitative outcome and carbon footprints. Table 8. Results of R 3 I and comparison with Questionnaire and Carbon footprints R 3 I of Industries Point Scale Reduction in Carbon Questionnaire Outcome Foot prints (%) R 3 I Rating RANK Green Green Green Green Green Green Green CONCLUSION R 3 I thus used relative index reliabilities to compare the ranking obtained by questionnaire and Carbon footprint generation. The methodology involves application of the AHP to relatively compare the choices and the entropy method for obtaining the weights of the indicators considered. The ranks obtained by the industries observed to be same with all the methods. REFERENCES [1] Dickson, W., An analysis of vendor selection systems and decisions, Journal of Purchasing, Vol. 2, 1966, pp [2] Weber, C.A., Current, J.R., Benton, R, Vendor Selection Criteria and Methods, European Journal of Operational Research, Vol.50 (2), 1991,pp [3] Zadeh, L.A., Fuzzy sets, Information and Control, 1965, pp [4] Rupa Bindu, B. B. Ahuja., AHP Based Strategic Sourcing A Novel Integration for Vendor Selection, Proc. of the International conference on Issues and Challenges in Supply Chain Management, B.H.U., Varanasi, U.P [5] Rupa Bindu, Prashant More., SCM in India- A Perspective, Proc. of the International Conference on Manufacturing and Management, Vellore, India, [6] Rao, P. & Holt, D. (2005), Do Green Supply Chains Lead to Competitiveness and Economic Performance, International Journal of Operations & Production Management, 25(9), pp [7] Joseph Sarkis, Aref A. Hervani, Marilyn M. Helms, (2005), Performance measurement for green supply chain management, Benchmarking: An International Journal, Vol. 12 Issue: 4, pp [8] Stefan Seuring, (2012), A review of modelling approaches for sustainable supply chain management, Decision Support Systems, 54, pp editor@iaeme.com

10 Mrs. A. S. Chavan, Dr. R. N. Patil, Dr. S T. Chavan, Mr. Nitin Kulkarni, Dr. Sachin. And S. Chavan [9] T.L. Saaty, How to make a decision: the analytic hierarchy process, European Journal of Operational Research 48 (1) (1990) [10] Thomas L. Saaty, Yoram Wind, Marketing Applications of the Analytical Hierarchy Process, JSTOR (Journal of scientific tools in operation research), Management Science, Vol. 26, No.7, July 1980, pp [11] Thomas L. Saaty, Decision making for Leaders, 2001, Pittsburgh. [12] Dr. Shankar Chakraborty and Dr. Diganta Taha., A Supply Chain Management Module for Integrated functioning of a Manufacturing Organization, Manufacturing Technology Today, 2005, pp [13] Hwang, C.L., Yoon, K., Multiple Attributes Decision Making Methods and Applications, 1981, Springer Berlin, Heidelberg. [14] A. S. Chavan, Dr. R. N. Patil and Dr. S T. Chavan, Prioritization of Green Supply Chain Indicators with Analytic Hierarchy Process (AHP) Model, International Journal of Mechanical Engineering and Technology, 9(10), 2018, pp editor@iaeme.com