MEASURING PERFORMANCE OF HEALTHCARE SUPPLY CHAINS IN INDIA: A COMPARATIVE ANALYSIS OF MULTI- CRITERIA DECISION MAKING METHODS

Original scientific paper Abstract: The supply chain forms the backbone of any organization. However, the effectiveness and efficiency of every activity get manifested in the financial outcome. Hence, measuring supply chain performance using financial metrics carries significance. The purpose of this paper is to carry out a comparative analysis of supply chain performances of leading healthcare organizations in India. In this regard, this paper presents an integrated multi-criteria decision making (MCDM) framework wherein we derive the weights of the criteria based on experts’ opinions using PIvot Pairwise RElative Criteria Importance Assessment (PIPRECIA) method. We then apply three distinct frameworks such as Multi-Attributive Border Approximation area Comparison (MABAC), Combined Compromise Solution (CoCoSo) and Measurement of alternatives and ranking according to COmpromise solution (MARCOS) for ranking purpose. In this context, this paper presents a comparative analysis of the results obtained from these approaches. The results show that large cap firms do not necessarily perform well. Further, the results of three MCDM frameworks demonstrates consistency.


Introduction
With rapid development in information technology and communication technology (ICT), consumers' nature and requirements have changed to a great extent in order to win the battle at the market place and, more specifically, to survive. Organizations are increasingly putting primary emphasis on strengthening the supply chains. The performance of the supply chain stands as a critical deciding factor for ensuring business sustainability. Hence, Supply Chain Management (SCM) encompasses all related decisions to strike a balance between demand and supply, linked with the financial outcome (Huang et al., 2008). In other words, SCM addresses the issue of economic sustainability (Al-Hussaini, 2019). Supply chain decision-makers must adequately consider two interdependent objectives such as reduction of cost and improvement of service levels for contributing to the overall profitability of the organization (Parasuraman et al., 1991;Mentzer et al., 1999;Ray et al., 2004;Johnson and Templar, 2011). The basic premise of the supply chain concept is built on horizontal integration and development, shifting away from the functional brilliance (Lester, 1999). Hence, all the activities across the supply chain must be performed in sync and directed towards attaining the overall business objectives of meeting the needs and requirements of the customers and fulfilling the stakeholders' expectations. In effect, supremacy in supply chain performance contributes in achieving overall organizational excellence D'Avanzo et al., 2004;Christopher, 2005) which is beyond the local scope of cost optimization (Lambert and Cooper, 2000;Ellram and Liu, 2002;Farris and Hutchison, 2002). Supply chain practitioners need to connect operational efficiency with financial investment outcomes (LaLonde, 2000). Though the explicit linkage of supply chain performance with financial performance is quite complex to realize (Frohlich and Westbrook, 2001), Christopher (1998) mentioned three dimensions of financial performance: profitability, liquidity, and productivity or asset utilization in which the contributions of supply chain performance can be gauged. Therefore, it is understood that there is a need to bridge the gap between the supply chain operational framework consisting of performance criteria and the financial metrics for assessing business outcomes. Financial metrics help the supply chain decision-makers and executors to understand the impact of the operational decisions and efficiency on the overall profitability of the business unit (Tan, 1999;Ketzenberg et al., 2006;Kremers, 2010;Kancharla and Hegde, 2016). Also, the measurement of supply chain performance in financial terms enables the organizations to get an outlook on future earnings, which would value the shareholders (Krause et al., 2009). In this regard, Wisner (2011) demonstrated the impact of the supply chain's performance on the organization's financial results.
Over the years, several researchers have made significant contributions in developing comprehensive performance assessment frameworks for supply chains. One famous framework, such as the SCOR (Supply Chain Operations Reference) model integrates the primary processes (plan, source, make, deliver and return) of supply chain operation with the overall strategy of the organization (Kocaoğlu et al., 2013;Askariazad and Wanous, 2009;Parkan and Wang, 2007;Lockamy and McCormack, 2004). The SCOR model enables to interconnect the process efficiency with the business effectiveness reflected in, both the financial (e.g., supply chain cost, cost of goods sold or COGS, return on assets, return on working capital) and the operational (e.g., order fulfillment time, supply chain flexibility, supplier relationship, % yield, delivery efficiency, supply chain adaptability, distribution planning, network design) outcomes. In this regard, Elgazzar et al. (2012) identified the firm's financial strategy's priorities and put forth a framework to link the SCOR model-based supply chain performance measures with financial metrics (identified through Du-Pont ratio analysis). In tune with this work, in recent times, many researchers have put their efforts into establishing the relationship of supply chain operational performance and financial outcome of the organization across the industry (Zhu and Sarkis, 2004;Li et al., 2006;Wagner et al., 2012). Innovation is also given due importance for improving supply chain performance (Chithambaranathan et al., 2015). As we see that supply chain performance depends on several parameters, MCDM methods have been used by the researchers. In literature we notice applications of various MCDM frameworks related to supply chain performance measurement (For example, Bhagwat and Sharma, 2007;Wong and Wong, 2007;Varma et al., 2008;Yang, 2009;Najmi and Makui, 2010;Pramod and Banwet, 2011;Elgazzar et al., 2012;Bhattacharya et al., 2014;Jothimani and Sarmah, 2014;Rouyendegh et al., 2014;Shafiee et al., 2014;Tyagi et al., 2014;Tseng et al., 2014;Dey et al., 2016;Uygun and Dede, 2016;Ghosh and Biswas, 2016;Moharamkhani et al., 2017;Govindan et al., 2017;Janaki et al., 2018;Sufiyan et al., 2019;Grida et al., 2020).
In this paper we focus on the healthcare sector in India. In India, healthcare is one of the most talked-about and promising sectors in terms of customers' attachment and emotion (Schneller and Smeltzer, 2006), complexity, growth, revenue generation and employment potential. The expected business is around INR 8.6 trillion by 2022. The sector has been emphasized by the Govt. of India (GOI) as the plan is to spend 2.5 percent of the country's GDP in public health by 2025 (Source: IFBE Report). Already several initiatives (e.g., Ayushman Bharat) have been conceptualized and implemented by the GOI. A healthcare supply chain is said to be inefficient at utilizing invested capital, which eventually increases operating costs (Kwon et al., 2016). Hence, as compared to supply chain of the other industries (i.e., commercial supply chains) there is enough scope for improving the performance (de Vries and Huijsman, 2011) to bring down the operating through effective utilization of resources, provide quality service to the users at an affordable price while maximizing shareholders' returns. This paper intends to carry out a comparative assessment of supply chain performance of leading organizations belonging to the healthcare sector in India. Financial metrics are used as criteria for assessment. From the methodological point of view, in this paper we consider three recently developed MCDM algorithms such as MABAC, CoCoSo, and MARCOS. We are interested to examine the competitive positions of the sample firms using the lens of these three different algorithms. We aim to compare the results obtained from the applied MCDM methods. We see that most of the past research considered the methods like Analytic Hierarchy Process (AHP), Analytic Network Process (ANP), Decision Making Trial and Evaluation Laboratory (DEMATEL), Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) for measuring supply chain performance. Our work uses a combined framework of both outranking and compromise solution algorithms. This paper adds value to the growing literature in the following way. First, it addresses the issue of performance measurement of healthcare organizations in India. In the literature, there are evidences of linking supply chain performance with financial performance. However, measuring comparative supply chain performances based on financial metrics, particularly for health care supply chains in India seems to be rare. Second, in this paper we use a combined subjective and objective methodology. We apply PIPRECIA method to prioritize the criteria based on the opinions of experts in the stated field. We then apply three distinct frameworks such as Multi-Attributive Border Approximation area Comparison (MABAC), Combined Compromise Solution (CoCoSo) and Measurement of alternatives and ranking according to COmpromise solution (MARCOS) for for comparing supply chain performance using published financial data. We capture expert opinions for understanding relative priorities of the criteria to infuse practitioners' views which provides a basis for comparing the results obtained from three distinct algorithms. In this paper, we use a combination of similarity based and compromise solution oriented methods. In order to compare supply chain performance, it is not only required to find closeness to average standard, but also trading off or compromising on performances subject to different attributes assume practical relevance. This is required as financial metrics do not reveal a comprehensive view of operational performance. Further, to arrive at the conclusion, we use Simple Additive Weighting (SAW) method which uses the score values as calculated by MABAC, CoCoSo and MARCOS. To our best knowledge, there has not been any previous work which have attempted to compare the performance of the MCDM algorithms used in this paper.
The rest of the paper is organized as follows. In the section 2, the detailed methodology is presented while section 3 encapsulates the findings and includes a brief discussion of the findings. Section 4 concludes the paper while pointing out some of the implications of this study and future research agenda.

Data and Methodology
In this paper the following steps are followed for carrying out the research work.
Step 1: Selection of sample Step 2: Identification of the criteria Step 3: Determination of criteria weights using expert opinion based PIPRECIA method Step 4: Comparative ranking based on supply chain performance using MABAC, CoCoSo, and MARCOS algorithms Step 5: Comparison of ranking results as obtained by using three distinct methods and arrive at a combined final ranking

Sample
In this study, leading Indian healthcare organizations listed in BSE, India are considered. In the selection of sample organizations, the size of the company is taken as the classifier. Accordingly, top 20 companies (Source: the database of the Centre for Monitoring Indian Economy Pvt. Ltd., CMIE Prowess IQ) are included under the consideration of this study. Table 1 provides the list of such companies.

Criteria Selection
In this paper we focus on the following abilities of the supply chains such as customer attractiveness through its products and services, profitable utilization of the working capital, efficient management of the working capital and inventory, and liquidity. Accordingly, we select five criteria for comparing relative supply chain performances of the sample organizations.
Sales Growth (SG) is a manifest of acceptance of the firm's products and services in the marketplace. SG is an indication of improved product and service quality, timeliness in delivery, flexibility, and responsiveness, which increase revenue. Hence, SG represents the operational efficiency of the activities carried out across the supply chain and holds a positive linkage with supply chain performance (Brewer and Speh, 2000). It is measured in terms of an incremental difference in sales value over two consecutive years. Return on Working Capital (RWC) is an important criterion for assessing the performance of supply chain as it entails the asset management efficiency of the firms (Okumuş et al., 2019). Cash to Current Liabilities (CCL) or cash ratio reflects the liquidity position of an organization. This ratio is one of the indicators that the creditors look at before taking loan related decision. CCL shows the ability of supply chains to generate cash for meeting short-term requirement such as debt repayment. Inventory not only is a cost element for any organization but also it is a part of the total asset (Shah and Shin, 2006). An effective inventory management adds to the overall profitability of the firm and hence, Inventory Turnover Ratio (ITR) needs to be optimized (Ganesan et al., 2009). ITR indicates the ability of the organization to effectively roll out its inventory. Finally, effective management of Cash Conversion Cycle (CCC) increases productivity, revenue generation, and results in a reduction in operating costs (Okumuş et al., 2019). Gunasekaran et al. (2004) reflected on the significance of converting the materials into cash through sales to ensure the return on investment for the shareholders. CCC stands on three components: cash receivables from the customer end, cash payables to the suppliers, and cash held up in the form of inventories (Richards and Laughlin, 1980). CCC is, therefore, an indicator of the efficiency of operations and effectiveness of the operational decisions about working capital management (Özbayraka and Akgün, 2006;Bagchi et al., 2012) which significantly impacts the profitability of the firms (Jose et al., 1996;Padachi, 2006;Lazaridis and Tryfonidis, 2006;Garcia-Teruel and Martinez-Solano, 2007;Falope and Ajilore, 2009;Okumuş et al., 2019). Lesser value of CCC signifies better profitability (Raheman and Nasr, 2007;Uyar, 2009) and lesser opportunity cost. Researchers (Churchill and Mullins, 2001;Hutchinson, 2002, 2003;Bauer, 2007) have pointed out that shorter the period of DSO, better it is for the firms to utilize the amount in different activities of the supply chain including sales promotion which has a positive impact on the financial performance. Moreover, the higher DSO cycle often leads to credit risk. The organizations usually offer discounts against early payments to encourage customers and maintain a mutual relationship (Moran, 2011). The nature of DIO in this context posits a challenge to the firms. Shah and Shin (2007) opined that drawing relationships between inventory holding and firm performance. The decisions on inventory management stand a bit complex. On one side, a higher inventory level ensures the timely availability of products. It enables the organizations to combat the effect of surge demand while on the other side, holding an additional inventory shot up the carrying costs and other potential hidden losses and bear a negative impact on the firm's liquidity. Keeping excess inventory results in forecasting error and becomes a potential cause of the Bullwhip effect (Tangsucheeva and Prabhu, 2013). Overall lower the DIO better is the performance of the supply chain (Chen et al., 2005;Singhal, 2005;Swamidass, 2007;Koumanakos, 2008;Capkun et al., 2009). On the other hand, more is the value of DPO, better will it be for the firms as the liquidity position is improved (Stewart, 1995). However, here lies a situation of tradeoff. Extending the payment cycle has a significant negative impact on the relationship between the firm and the suppliers (Fawcett et al., 2010). Modern SCM concepts believe in an end-to-end seamless operation, which demands integration among different chain members and mutual development. Higher DPO often generates a strangulated effect as many suppliers face a liquidity crisis, often results in reduced service level (Raghavan and Mishra, 2011;Timme and Wanberg, 2011). It is evident from the literature that the researchers are of double way opinions. For example, Farris and Hutchison (2002) advocated for longer DPO, while Deloof (2003) and Garcia-Teruel and MartinezSolano (2007) observed evidence of better performance with shorter DPO. In general, within a toleration level as set by the nature of the relationship among the suppliers and users, type of supply, and terms and conditions of the service level agreements, it is a common notion to consider higher DPO for better functioning of the organizations within the CCC. Some of the recent studies also have reported the use of financial metrics for comparing supply chain performances (Avelar-Sosa et al., 2019;Fekpe and Delaporte, 2019;Tripathi et al., 2019). Table 2 summarizes the criteria considered for this study. PIPRECIA is an extension of the widely used group decision making approach such as Stepwise Weight Assessment Ratio Analysis (SWARA) method as developed by Kersuliene et al. (2010). Most often in real-life situations, it is very difficult for reaching a consensus while a considerably larger set of decision makers attempts to find out the expected importance of a set of criteria and order them. The computational steps of PIPRECIA are quite similar to SWARA, but it provides a freedom not to put emphasis on sorting out the criteria based on expected significance in a group decision making environment (Biswas and Pamucar, 2020;Stanujkic et al., 2017;Keršulienė and Turskis, 2011). The computational steps as described by Stanujkic et al. (2017) are as follows: Step 1: Selection of a set of relevant criteria for evaluating the alternatives.
Step 2: (Optional) Sort the criteria according to their expected significances as rated by the decision makers in descending order. For a small number of experts, it works well; however, for a large group of respondents, it is very difficult to arrive at a bias-free group consensus. Hence, in that case this step is not required.
Step 3: Determination of the relative importance of the criteria. Starting from the second criterion, the relative importance or significance of any criterion C j is given by: Here, 'r' denotes a particular respondent among all.
Step 4: Find out the coefficient Step 5: Determine the recalculated criteria weights Step 6: Calculate the relative criteria weights In a group decision making environment for each decision maker, the above steps need to be carried out. Finally, for obtaining the group weight calculation, in a simple sense, geometric mean (GM) of individual weights is calculated (Stanujkic et al., 2017) as given by: * = (∏ =1 ) 1/ (5) Here, 'R' is the total number of respondents. Final criteria weights are given by:

MABAC Method
MABAC uses the distance of the alternatives from the boundary approximation area (Upper Approximation Area or UAA for ideal or desirable solutions and Lower Approximation Area or LAA for non-ideal or non-desirable solutions along with Border Approximation Area or BAA) based the performance values under the influence of the criteria (Pamučar and Ćirović, 2015). This method is a widely used approach (Debnath et al., 2017) as -It produces a stable solution as compared to TOPSIS and VIKOR (Pamučar and Ćirović, 2015) -It works with qualitative and quantitative data to classify the best, the worst, and borderline solutions ) -It is based on a comprehensive, rational and sensible algorithm (Xue et al., 2016) -It compares the alternatives on relative strength and weakness dimensions under the effect of the criteria (Roy et al., 2016).
This method has been applied in solving several social science related decision making problems (Yu et al., 2017;Sharma et al., 2018;Vesković et al., 2018;Roy et al., 2018;Biswas et al., 2019). The methodological steps (in brief) are as under: Step 1: Formation of the decision matrix X Step 2: Formation of the normalized decision matrix R Normalization: ; For beneficial criteria (7) ; For non-beneficial criteria (8) Step 3: Construction of the weighted normalization matrix Y = [Y ij ] m×n Where, Y ij = W j (r ij + 1) (9) Step 4: Determination of the Border Approximation Area (BAA) represented as Step 5: Derive Q Matrix related to the separation of the alternatives from BAA Q = Y-T (11) A particular alternative A i is said to be belonging to the Upper Approximation Area (UAA) i.e. T + if Q ij > 0 or Lower Approximation Area (LAA) i.e. T − if Q ij < 0 or BAA i.e. T if Q ij = 0.
The alternative A i is considered to be the best among the others if more numbers of criteria pertaining to it possibly belong to T + .
Step 6: Ranking of the alternatives in descending order based on the final appraisal score given by S i = ∑ Q ij n j=1 (12)

CoCoSo Method
The primary objective of any MCDM framework is to determine the best feasible solution among the available options under the influence of the set of relevant criteria. Now, in real-life situations, many times these criteria are characterized by non-commensurable and conflicting nature. Under this circumstance, there is no alternative which can satisfy the requirements of all the criteria to a considerable extent. Hence, decision makers need to accept a tradeoff or compromise solution subject to the criteria considered. Looking into it, researchers have attempted to develop such models which can deal with multiple criteria (conflicting nature) based compromise solution. The popular techniques such as TOPSIS, COPRAS and VIKOR have been used extensively in this regard. However, these techniques suffer from following issues -TOPSIS and VIKOR consider the negative ideal solution while calculating the Euclidean distance of each alternative -The traditional COPRAS and TOPSIS methods do not provide meaningful solutions when work with mixed data and suffer fro Bagchi m rank reversal phenomena (Aouadni et al., 2017) Under this situation, CoCoSo (Yazdani et al., 2018) works with weight aggregation process based on grey relational generation (which enables to cope up with conflicts) and incorporates the following features: -It uses the power of weights for aggregation. As a result, it provides relatively stronger distance measurement for modelling purposes. -For validation of ranking result (i.e., index) it uses three different aggregation strategies to generate the cumulative score. Therefore, it gives a complete ranking index taking compromising and conflicting situations into account. -In a nutshell, this method is an integration of simple additive weighting and exponentially weighted product models. The methodological steps can be described as follows (Yazdani et al., 2018): Step 1: Formation of the decision matrix X Step 2: Derive the normalized decision matrix R CoCoSo follows a normalization process suggested by Zeleny (1973). Accordingly, the normalized values are obtained as: Step 3: Determine the aggregate of the weighted normalized performance values as given by S i = ∑ W j n j=1 r ij (15) Step 4: Calculation of the aggregate of the power weight of comparability values P i = ∑ (r ij ) W j n j=1 (16) Step 5: Calculations of the relative weights of the alternatives For this step, in CoCoSo method the relative weights are calculated in three ways such as Here, these three strategies consider weighted arithmetic average; relative scores based on sum and product of performance values and allow the decision makers to flexibly select the values which can vary from 0 to 1 (the usual value being 0.5).
Step 6: Find out the final ranking score The final ranking of the alternatives is done depending on the overall k i value (higher value implies more importance) which is given as:

MARCOS Method
This method is a new addition to the portfolio of compromise solution based MCDA . It has been used in solving complex research problems (Stević and Brković, 2020;Stanković et al., 2020). The procedural steps are explained below: Step 1: Formation of the extended decision matrix (EDM) In the EDM the first row is occupied by the anti-ideal solution (AIS) values and the last row indicates the ideal solution (IS) values.
AIS indicates the most pessimistic choice whereas IS is the most optimistic selection. The values are calculated as follows.
Step 2: Normalization The normalized values are given by (using linear normalization  Table 4-7 present the step by step derivation of the criteria weights using PIPRECIA method. For this purpose, we have approached to three experts who have substantial experience in the stated field. Table 3 provides a summary of experts' profiles. In the tables 4-6, their responses are summarized and subsequently, values of the parameters are calculated using eq. (2), (3), and (4).  Now by applying eq. (5) and (6), we derive the final weights of the criteria (see table  7). It is important to ensure harmony in a typical group decision making format. For this purpose, we check the consistency of each individual expert's rating with the aggregated final weight. We calculate Spearman's ρ using IBM SPSS (version 24) software. Spearman's ρ measures the degree of interrelation in terms of the correlation coefficient among the variables compared. Table 8 shows that individual decisions are in sync with the group opinion. .945* Weight_Exp_2

Findings and Discussion
.951* Weight_Exp_3 .910* * Correlation is significant at the 0.05 level (2-tailed). Now, we move to rank the sample organizations based on their comparative supply chain performance. Table 9 exhibits performance values of the alternatives (organizations) under different criteria as considered here. Next, we carry out the comparative analysis of the organizations under study using the MCDM algorithms as used here. First, we apply MABAC method. Table 10 shows the final rankings based on appraisal scores, obtained using eq. 7-12. Proceeding further, we compare the performances of sample organizations using the compromise solution approach CoCoSo. Using the eq. 13-20 we derive the competitive positions of the alternatives (see table 11). Finally, table 12 highlights the findings in terms of ranking of the organizations derived as per the procedural steps (eq. 21-31) of the latest compromise solution based MCDM methodology such as MARCOS. In order to check the consistency among the results obtained from three distinct algorithms, We calculate Kendall's τ and Spearman's ρ using IBM SPSS (version 24) software. Spearman's ρ measures the degree of interrelation in terms of the correlation coefficient among the variables compared while Kendall's τ measures the probability of concordance and discordance among them (Nelsen, 1992). For identifying top and worst performers, the geometric mean of the year wise ranks is calculated for each organization. In literature, there are instances where researchers (Basak and Saaty, 1993;Barzilai and Lootsma, 1997;Stanujkic et al., 2015) have mentioned the use of the geometric mean in finding out the synthesized view to reach group consensus in case different opinions or methodologies are adopted. In these cases, the geometric mean is a useful measure for averaging (Fleming and Wallace, 1986). But, in this paper, for more objective evaluation, we use Simple Additive Weighting (SAW) method to compare the results obtained from three algorithms used here. We take the score values of the alternatives obtained from each algorithm and apply SAW method (Simanaviciene and Ustinovichius, 2010) using linear max-min normalization. We assign equal priorities to all three algorithms. Table  13 provides the summary of rankings and table 14 shows the result of the consistency test. For further investigation we perform related sample Wilcoxon Signed Rank Test (WSRT). Table 15 indicates the test result. The null hypothesis for WSRT states that the median of differences between rankings of any two algorithms is equal to zero.  .980** .952** .690** 1 ** Correlation is significant at the 0.01 level (2-tailed). * Correlation is significant at the 0.05 level (2-tailed). In order to check the stability of the results obtained by using these three algorithms we carry out the sensitivity analysis. Sensitivity analysis is useful for achieving a rational and reliable results while reducing subjectivity and bias (Mukhametzyanov and Pamucar, 2018;Pamučar et al., 2016). For carrying out the sensitivity analysis, we perform four experients wherein we replace the weights of the criteria other than that holds the highest weight. It means in each experiement, the weight of a particular criterion (not having the highest weight) gets replaced with the highest value while keeping the priorities of other criteria same. Accordingly, we rank the companies under each circumstance applying all three methods as mentioned here. Table 16 describes the experiments done for carrying out the sensitivity analysis.    This study reveals a number of observations. First, we observe that there is a variation in the comparative positions of the sample organizations as derived by using three MCDM frameworks. Top five positions are occupied by a same group of companies with some variations within the group. However, we see the bottom five group shows considerable changes in the positions as we apply three methods. Second, if we analyze specifically, MABAC based ranking shows highest correlation with the aggregate final result. Among the methods, the results obtained from MABAC, CoCoSo, and MARCOS are statistically consistent with each other as it gets revealed from table 14. Here, MABAC and CoCoSo show more consistency between the results obtained by using these algorithms. Third, considering overall, we find that the large cap firms have not done well as far as supply chain performance is concerned. Fourth, we observe that all methods responds more or less in a similar fashion to the sensitivity analysis. However, looking at the values of correlation coefficients, one can infer that CoCoSo performs slightly better under different situations. Figure 1-3 graphically present the result of sensitivity analysis for all three methods.

Conclusion
In this study, we attempt to compare a number of leading healthcare companies based on their supply chain performances measured in financial terms. For this purpose, we present a comparative analysis of three distinct algorithms such as MABAC, CoCoSo and MARCOS. We find that the large cap firms do not perform well. The results obtained from three MCDM frameworks show consistency while CoCoSo appears to be comparatively better. The present study has a number of managerial and social implications. First, with the effects of the factors like increasing population and pollution level, transformation in the climate, and changes in the lifestyle, healthcare operations have become critical and delicate in nature particularly in the diverse country like India. In addition, as the level of competition has got amplified to a large extent, the pressure of reducing prices for providing requisite service invokes a focused approach by the service providers. SCM is one of the key areas which can provide a competitive edge to the organizations. Hence, measuring supply chain performances following a multiple criteria based holistic framework linked with financial outcomes enables the organizations to take appropriate strategic and operational decisions. This study provides such a framework. Second, understanding relative performances of the focused organization and its competitors help the decision makers to take the appropriate futuristic course of actions. Third, most often policy makers need to know the nature of the industry and performances of the key players for formulating policies for the sector. The findings show significant variations across the organizations which might help the policy makers and industry analysts to intervene and formulate contemporary policies. Fourth, many a times the price for the offered services is decided from a cost plus margin point of view. Heath care is a typical sector where this approach often creates a disconnect between the service provider and the service users (i.e., the patients). This eventually impacts on the long-term business growth and brand value of the organization. Measuring supply chain performance and delving into its impact on the profitability of the organization helps the decision makers to come up with innovative and robust service offerings at an affordable price. Finally, the comparative analysis of MCDM algorithms. However, in the present study, the opinions of a few experts have been sought for measuring financial performances of the healthcare supply chains. In the future study, a larger group of experts and consumers can be approached to identify critical success factors for the healthcare supply chains and based on that a comparative assessment may be carried out. Nevertheless, we believe that this limitation does not necessarily dilute the usefulness and relevance of this work.