Plane Thermoelastic Waves in Infinite Half-Space Caused
Decision Making: Applications in Management and Engineering
Vol. 3, Issue 2, 2020, pp. 162-189.
ISSN: 2560-6018
eISSN: 2620-0104
DOI: https://doi.org/10.31181/dmame2003162b
* Corresponding author.
E-mail addresses: sanjibb@acm.org (S. Biswas)
MEASURING PERFORMANCE OF HEALTHCARE SUPPLY
CHAINS IN INDIA: A COMPARATIVE ANALYSIS OF MULTI-
CRITERIA DECISION MAKING METHODS
Sanjib Biswas 1*
1 Calcutta Business School, West Bengal, India
Received: 2 August 2020;
Accepted: 1 October 2020;
Available online: 11 October 2020.
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.
Key words: Healthcare Supply Chain, Financial Metrics, PIPRECIA, MABAC,
CoCoSo, MARCOS.
1. 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
mailto:sanjibb@acm.org
Measuring performance of healthcare supply chains in India: A comparative analysis...
163
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 (Ellram et al., 2002; 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
Biswas/Decis. Mak. Appl. Manag. Eng. 3 (2) (2020) 162-189
164
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
Measuring performance of healthcare supply chains in India: A comparative analysis...
165
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.
2. 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
2.1. 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.
Table 1. List of companies under study
Company Name Code Company Name Code
Abbott India Ltd. A1 Glaxosmithkline
Pharmaceuticals Ltd.
A11
Alembic
Pharmaceuticals Ltd.
A2 Glenmark Pharmaceuticals
Ltd.
A12
Alkem Laboratories Ltd. A3 Ipca Laboratories Ltd. A13
Aurobindo Pharma Ltd. A4 Jubilant Life Sciences Ltd. A14
Biocon Ltd. A5 Pfizer Ltd. A15
Cadila Healthcare Ltd. A6 Piramal Enterprises Ltd. A16
Cipla Ltd. A7 Sanofi India Ltd. A17
Divi'S Laboratories Ltd. A8 Strides Pharma Science Ltd. A18
Dr. Reddy'S
Laboratories Ltd.
A9 Sun Pharmaceutical Inds.
Ltd.
A19
Fortis Healthcare Ltd. A10 Torrent Pharmaceuticals Ltd. A20
2.2. 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.
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166
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; Farris and 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
Measuring performance of healthcare supply chains in India: A comparative analysis...
167
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.
Table 2. List of criteria
Criteria Code UOM Definition Effect
Direction
SG C1 Times (Salest-Sales t-1)/ Sales t-1 (+)
RWC C2 Times Earnings before Interest,
Depreciation, Tax and
Amortization divided by Working
Capital
(+)
CCL C3 Times Cash and marketable
securities/current liabilities
(+)
ITR C4 Times Cost of goods sold/average
inventory
(+)
CCC C5 Days The average time elapsed
between cash disbursement and
collection
(-)
2.3. Methods
General Notations:
Ai = No. of alternative options (Healthcare companies in this paper); i =
1,2, … . m
Cj = No. of Criteria; j = 1,2, … . n
X = [Xij]m×n
; Decision Matrix
Xij = Performance value of ith alternative for jth criterion
𝑋𝑗
𝑚𝑎𝑥 = Maximum value for criterion j
𝑋𝑗
𝑚𝑖𝑛 = Minimum value for criterion j
𝑊j = Weight or importance level for the criterion j
R = [rij]m×n
; Normalized Decision Matrix
rij = Normalized performance value of ith alternative for jth criterion
2.3.1. PIPRECIA Method
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
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168
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 Cj is given by:
𝑆𝑗
𝑟 = {
> 1 when Cj ≻ Cj−1
1 when Cj = Cj−1
< 1 when Cj ≺ Cj−1
(1)
Here, ‘r’ denotes a particular respondent among all.
Step 4: Find out the coefficient 𝐾𝑗
𝑟
𝐾𝑗
𝑟 = {
1 when j = 1
2 − 𝑆𝑗
𝑟 when j > 1
(2)
Step 5: Determine the recalculated criteria weights
𝑄𝑗
𝑟 = {
1 when j = 1
𝑄𝑗−1
𝑟
𝐾𝑗
𝑟 when j > 1
(3)
Step 6: Calculate the relative criteria weights
𝑊𝑗
𝑟 =
𝑄𝑗
𝑟
∑ 𝑄𝑗
𝑟𝑛
𝑗=1
(4)
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:
𝑊𝑗
∗ = (∏ 𝑊𝑗
𝑟𝑅
𝑟=1 )
1/𝑅
(5)
Here, ‘R’ is the total number of respondents.
Final criteria weights are given by:
𝑊𝑗 =
𝑊𝑗
∗
∑ 𝑊𝑗
∗𝑛
𝑗=1
(6)
2.3.2. 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
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169
- 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 (Roy et al., 2018)
- 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:
rij =
(Xij− Xj
min)
(Xj
max− Xj
min)
; For beneficial criteria (7)
rij =
(Xij− Xj
max
)
(Xj
min− xj
max)
; For non-beneficial criteria (8)
Step 3: Construction of the weighted normalization matrix Y = [Yij]m×n
Where,
Yij = Wj(rij + 1) (9)
Step 4: Determination of the Border Approximation Area (BAA) represented as
T = [Tj]1×n
Where, Tj = (∏ Yij
m
i=1 )
1/m
(10)
Step 5: Derive Q Matrix related to the separation of the alternatives from BAA
Q = Y-T (11)
A particular alternative Ai is said to be belonging to the Upper Approximation Area
(UAA) i.e. T+ if Qij > 0 or Lower Approximation Area (LAA) i.e. T
− if Qij < 0 or BAA i.e.
T if Qij = 0.
The alternative Ai 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
Si = ∑ Qij
n
j=1 (12)
2.3.3. 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
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170
- 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:
rij =
Xij−Xj
min
Xj
max− Xj
min (For beneficial criteria) (13)
rij =
Xj
max
−Xij
Xj
max− Xj
min (For non-beneficial criteria) (14)
Step 3: Determine the aggregate of the weighted normalized performance values
as given by
Si = ∑ Wj
n
j=1 rij (15)
Step 4: Calculation of the aggregate of the power weight of comparability values
Pi = ∑ (rij)
Wjn
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
ki1 =
Pi+Si
∑ (Pi+Si)
m
i=1
(17)
ki2 =
Si
min
𝑖
Si
+
Pi
min
𝑖
Pi
(18)
ki3 =
𝛼(𝑆𝑖)+(1−𝛼)Pi
(𝛼 max
𝑖
Si+(1−𝛼) max
𝑖
Pi)
(19)
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 𝛼 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 ki value
(higher value implies more importance) which is given as:
ki = (ki1 ki2ki3)
1/3 +
1
3
(ki1 + ki2 + ki3) (20)
2.3.4. MARCOS Method
This method is a new addition to the portfolio of compromise solution based MCDA
(Stević et al., 2020). It has been used in solving complex research problems (Stević and
Brković, 2020; Stanković et al., 2020). The procedural steps are explained below:
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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.
𝐴𝐼𝑆 = min
𝑖
𝑥𝑖𝑗 𝑤ℎ𝑒𝑛 𝑗 ∈ 𝐽
+; max
𝑖
𝑥𝑖𝑗 𝑤ℎ𝑒𝑛 𝑗 ∈ 𝐽
− (21)
𝐼𝑆 = max
𝑖
𝑥𝑖𝑗 𝑤ℎ𝑒𝑛 𝑗 ∈ 𝐽
+; min
𝑖
𝑥𝑖𝑗 𝑤ℎ𝑒𝑛 𝑗 ∈ 𝐽
− (22)
Here, 𝐽+ represents a set of beneficial criteria (whose effect direction is +ve) and
𝐽− indicates a set of non-beneficial criteria (having –ve effect direction).
Step 2: Normalization
The normalized values are given by (using linear normalization
rij =
Xij−AIS
IS− AIS
(𝑤ℎ𝑒𝑛 𝑗 ∈ 𝐽+) (23)
rij = 1 −
Xij−AIS
IS− AIS
(𝑤ℎ𝑒𝑛 𝑗 ∈ 𝐽−) (24)
Step 3: Formation of weighted matrix
Vij = wjrij (25)
Step 4: Calculation of utility degrees alternatives with respect to IS and AIS
𝐾𝑖
− =
𝑆𝑖
𝑆𝐴𝐼𝑆
(26)
𝐾𝑖
+ =
𝑆𝑖
𝑆𝐼𝑆
(27)
Where,
𝑆𝑖 = ∑ Vij
𝑛
𝑗=1 (28)
Step 5: Determination of utility functions with respect to IS and AIS
f(𝐾𝑖
−) =
𝐾𝑖
+
𝐾𝑖
++ 𝐾𝑖
− (29)
f(𝐾𝑖
+) =
𝐾𝑖
−
𝐾𝑖
++ 𝐾𝑖
− (30)
Step 6: Calculation of the utility function values for the alternatives
f(𝐾𝑖 ) =
𝐾𝑖
++ 𝐾𝑖
−
1+
1− f(𝐾
𝑖
+)
f(𝐾
𝑖
+)
+
1− f(𝐾𝑖
−)
f(𝐾
𝑖
−)
(31)
Decision rule: The higher is the utility value, better is the alternative
3. Findings and Discussion
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).
Table 3. Experts’ profiles
Expert 1 2 3
Experience 10 years 18 yrs 25 years
Industry Healthcare Healthcare, FMCG Chemical, Healthcare
Biswas/Decis. Mak. Appl. Manag. Eng. 3 (2) (2020) 162-189
172
Table 4. Response of Expert 1 & Weights of the Criteria
Criteria Code Sj1 Kj1 Qj1 Wj1
SG C1 1.000 1.000 0.2247
RWC C2 1.15 0.850 1.176 0.2643
CCL C3 0.8 1.200 0.980 0.2203
ITR C4 0.4 1.600 0.613 0.1377
NWCC C5 1.1 0.900 0.681 0.1530
Table 5. Response of Expert 2 & Weights of the Criteria
Criteria Code Sj2 Kj2 Qj2 Wj2
SG C1 1.000 1.000 0.2180
RWC C2 1.25 0.750 1.333 0.2907
CCL C3 0.55 1.450 0.920 0.2005
ITR C4 0.8 1.200 0.766 0.1671
NWCC C5 0.65 1.350 0.568 0.1238
Table 6. Response of Expert 3 & Weights of the Criteria
Criteria Code Sj3 Kj3 Qj3 Wj3
SG C1 1.000 1.000 0.2787
RWC C2 0.8 1.200 0.833 0.2323
CCL C3 0.9 1.100 0.758 0.2111
ITR C4 0.75 1.250 0.606 0.1689
NWCC C5 0.45 1.550 0.391 0.1090
Now by applying eq. (5) and (6), we derive the final weights of the criteria (see table
7).
Table 7. Weights of the Criteria
Criteria Code Wj* Wj
SG C1 0.239 0.2401
RWC C2 0.261 0.2626
CCL C3 0.210 0.2115
ITR C4 0.157 0.1579
NWCC C5 0.127 0.1279
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.
Measuring performance of healthcare supply chains in India: A comparative analysis...
173
Table 8. Consistency check I
Group_Weight
Weight_Exp_1 .945*
Weight_Exp_2 .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.
Table 9. Decision matrix
Weight 0.2401 0.2626 0.2115 0.1579 0.1279
Criteria
C1 C2 C3 C4 C5
(+) (+) (+) (+) (-)
Company
A1 0.0973 0.4011 1.96 6.06 90.18
A2 0.2428 2.1991 0.12 4.11 101.15
A3 0.1063 1.2033 0.29 5.76 -5.31
A4 0.1897 0.8828 0.01 2.7 240.85
A5 -0.2638 0.4479 0.54 5.35 144.47
A6 0.1147 2.0538 0.04 4.9 195.12
A7 0.0857 0.5013 0.95 4.31 266.65
A8 0.2713 0.5612 1.69 2.79 242.32
A9 0.1353 0.5313 0.73 4.6 206.03
A10 -0.0084 -0.4166 0.02 15.46 -68.32
Weight 0.2401 0.2626 0.2115 0.1579 0.1279
Criteria
C1 C2 C3 C4 C5
(+) (+) (+) (+) (-)
Company
A11 0.0809 1.5779 0.41 6.43 37.95
A12 0.1372 0.8885 0.12 4.38 66.8
A13 0.1285 0.6983 0.35 3.52 209.58
A14 0.031 -1.8818 0.01 6.43 41.2
A15 0.051 0.4464 2.15 5.38 21.52
A16 0.0703 -0.0758 0.08 14.29 17.57
A17 0.1121 0.8037 0.69 5.74 83
A18 0.0684 1.4684 0.24 3.46 126.97
A19 0.1422 -0.8247 0.04 3.69 233.16
A20 0.3413 4.0801 0.19 3.96 237.09
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
Biswas/Decis. Mak. Appl. Manag. Eng. 3 (2) (2020) 162-189
174
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).
Table 10. Ranking result (MABAC)
Company Sum (Si) Rank_MABAC Company Sum (Si) Rank_MABAC
A1 0.1592 3 A11 0.0758 6
A2 0.0859 5 A12 0.0028 11
A3 0.0658 9 A13 -0.0515 16
A4 -0.0748 17 A14 -0.1371 18
A5 -0.1519 20 A15 0.1794 1
A6 -0.0053 13 A16 0.0713 7
A7 -0.0299 15 A17 0.0561 10
A8 0.1100 4 A18 -0.0215 14
A9 -0.0039 12 A19 -0.1507 19
A10 0.0664 8 A20 0.1610 2
Table 11. Ranking result (CoCoSo)
Company ki Rank_CoCoSo Company ki Rank_CoCoSo
A1 2.170174 3 A11 2.060398 6
A2 2.067633 4 A12 1.880885 11
A3 2.036865 8 A13 1.699495 15
A4 1.42355 18 A14 1.357961 19
A5 1.270518 20 A15 2.202375 1
A6 1.860046 12 A16 2.062095 5
A7 1.475917 16 A17 1.993071 9
A8 1.922084 10 A18 1.792973 14
A9 1.817654 13 A19 1.44915 17
A10 2.038985 7 A20 2.185628 2
Table 12. Ranking result (MARCOS)
Company Ki- Ki+ f(Ki-) f(Ki+) f(Ki) Rank
A1 4.211465 0.617644 0.12790 0.8721 0.606271 3
A2 3.704441 0.543284 0.12790 0.8721 0.533281 6
A3 2.911298 0.426964 0.12790 0.8721 0.419103 15
A4 3.282138 0.481351 0.12790 0.8721 0.472488 10
A5 2.103364 0.308474 0.12790 0.8721 0.302795 19
A6 3.552041 0.520934 0.12790 0.8721 0.511342 8
A7 3.787117 0.55541 0.12790 0.8721 0.545183 5
A8 4.735647 0.694519 0.12790 0.8721 0.681731 2
A9 3.62839 0.532131 0.12790 0.8721 0.522333 7
A10 2.539582 0.372449 0.12790 0.8721 0.365591 18
A11 3.248171 0.476369 0.12790 0.8721 0.467598 12
A12 2.849102 0.417843 0.12790 0.8721 0.410149 16
Measuring performance of healthcare supply chains in India: A comparative analysis...
175
Company Ki- Ki+ f(Ki-) f(Ki+) f(Ki) Rank
A13 3.277273 0.480637 0.12790 0.8721 0.471788 11
A14 1.602783 0.23506 0.12790 0.8721 0.230732 20
A15 3.959377 0.580673 0.12790 0.8721 0.569981 4
A16 3.090712 0.453276 0.12790 0.8721 0.444931 13
A17 3.36252 0.493139 0.12790 0.8721 0.484059 9
A18 3.018697 0.442715 0.12790 0.8721 0.434564 14
A19 2.642797 0.387586 0.12790 0.8721 0.38045 17
A20 5.103074 0.748405 0.12790 0.8721 0.734625 1
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.
Table 13. Ranking summary
Company Ranking Results Final Rank
(SAW) MABAC CoCoSo MARCOS
A1 3 3 3 3
A2 5 4 6 5
A3 9 8 15 9
A4 17 18 10 17
A5 20 20 19 19
A6 13 12 8 11
A7 15 16 5 16
A8 4 10 2 4
A9 12 13 7 12
A10 8 7 18 10
A11 6 6 12 6
A12 11 11 16 13
A13 16 15 11 15
A14 18 19 20 20
A15 1 1 4 2
A16 7 5 13 7
A17 10 9 9 8
A18 14 14 14 14
A19 19 17 17 18
A20 2 2 1 1
Biswas/Decis. Mak. Appl. Manag. Eng. 3 (2) (2020) 162-189
176
Table 14. Consistency test II
Rank
MABAC
Rank
CoCoSo
Rank
MARCOS
Final
Rank
Kendall's tau
Rank_MABAC 1
Rank_CoCoSo .884** 1
Rank_MARCOS .463** .368* 1
Final_Rank .895** .842** .526** 1
Spearman's
rho
Rank_MABAC 1
Rank_CoCoSo .959** 1
Rank_MARCOS .638** .528* 1
Final_Rank .980** .952** .690** 1
** Correlation is significant at the 0.01 level (2-tailed).
* Correlation is significant at the 0.05 level (2-tailed).
Table 15. Result of WSRT
Pair Significance Value* Decision
MABAC and CoCoSo 0.439 The null hypothesis is supported
MABAC and MARCOS 0.965 The null hypothesis is supported
MARCOS and CoCoSo 1.000 The null hypothesis is supported
* at the 0.05 level (Asymptotic significance, 2-sided)
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.
Table 17-22 demonstrates the results of sensitivity analysis for all the MCDM
frameworks.
Table 16. Experimental cases for sensitivity analysis
Criteria
Weights
Actual Case 1 Case 2 Case 3 Case 4
C1 0.2401 0.2626 0.2401 0.2401 0.2401
C2 0.2626 0.2401 0.2115 0.1579 0.1279
C3 0.2115 0.2115 0.2626 0.2115 0.2115
C4 0.1579 0.1579 0.1579 0.2626 0.1579
C5 0.1279 0.1279 0.1279 0.1279 0.2626
Measuring performance of healthcare supply chains in India: A comparative analysis...
177
Table 17. Result of sensitivity analysis (MABAC)
Company
Ranks under different cases
Actual Case 1 Case 2 Case 3 Case 4
A1 3 2 2 2 2
A2 5 5 8 10 9
A3 9 9 10 8 5
A4 17 17 17 18 18
A5 20 20 20 20 19
A6 13 13 14 13 15
A7 15 15 13 14 16
A8 4 4 3 5 8
A9 12 12 11 11 12
A10 8 8 7 3 3
Company
Ranks under different cases
Actual Case 1 Case 2 Case 3 Case 4
A11 6 7 6 7 6
A12 11 11 12 12 11
A13 16 16 16 16 17
A14 18 18 18 17 14
A15 1 1 1 1 1
A16 7 6 5 4 4
A17 10 10 9 9 7
A18 14 14 15 15 13
A19 19 19 19 19 20
A20 2 3 4 6 10
Table 18. Consistency check III (Among the rankings obtained through
sensitivity analysis for MABAC)
Actual Case1 Case2 Case3 Case4
Kendall's tau
Actual 1
Case1 .979** 1
Case2 .895** .916** 1
Case3 .821** .842** .884** 1
Case4 .705** .726** .726** .842** 1
Spearman's rho
Actual 1
Case1 .997** 1
Case2 .977** .982** 1
Case3 .935** .946** .971** 1
Case4 .863** .878** .892** .950** 1
** Correlation is significant at the 0.01 level (2-tailed).
Biswas/Decis. Mak. Appl. Manag. Eng. 3 (2) (2020) 162-189
178
Table 19. Result of sensitivity analysis (CoCoSo)
Company
Ranks under different cases
Actual Case 1 Case 2 Case 3 Case 4
A1 3 3 2 4 3
A2 4 4 7 9 8
A3 8 8 8 6 5
A4 18 18 18 23 19
A5 20 20 20 24 20
A6 12 12 13 13 14
A7 16 16 16 21 18
A8 10 10 10 10 11
A9 13 13 12 12 13
A10 7 7 5 1 2
Company
Ranks under different cases
Actual Case 1 Case 2 Case 3 Case 4
A11 6 6 6 5 6
A12 11 11 11 11 9
A13 15 15 15 19 15
A14 19 19 19 22 16
A15 1 1 1 3 1
A16 5 5 4 2 4
A17 9 9 9 8 7
A18 14 14 14 14 12
A19 17 17 17 20 17
A20 2 2 3 7 10
Table 20. Consistency check IV (Among the rankings obtained through
sensitivity analysis for CoCoSo)
Actual Case1 Case2 Case3 Case4
Kendall's tau
Actual 1
Case1 1.000** 1
Case2 .937** .937** 1
Case3 .800** .800** .863** 1
Case4 .737** .737** .800** .874** 1
Spearman's rho
Actual 1
Case1 1.000** 1
Case2 .986** .986** 1
Case3 .916** .916** .956** 1
Case4 .890** .890** .926** .970** 1
** Correlation is significant at the 0.01 level (2-tailed).
Measuring performance of healthcare supply chains in India: A comparative analysis...
179
Table 21. Result of sensitivity analysis (MARCOS)
Company
Ranks under different cases
Actual Case 1 Case 2 Case 3 Case 4
A1 3 3 3 3 4
A2 6 6 7 8 10
A3 15 15 15 15 17
A4 10 10 11 14 7
A5 19 19 19 19 18
A6 8 8 9 11 9
A7 5 5 5 6 3
A8 2 2 1 1 1
A9 7 7 6 7 5
A10 18 18 18 10 19
Company
Ranks under different cases
Actual Case 1 Case 2 Case 3 Case 4
A11 12 12 12 12 15
A12 16 16 16 17 16
A13 11 11 10 13 8
A14 20 20 20 20 20
A15 4 4 4 4 6
A16 13 13 13 5 13
A17 9 9 8 9 12
A18 14 14 14 16 14
A19 17 17 17 18 11
A20 1 1 2 2 2
Table 22. Consistency check V (Among the rankings obtained through
sensitivity analysis for MARCOS)
Actual Case1 Case2 Case3 Case4
Kendall's tau
Actual 1
Case1 1.000** 1
Case2 .958** .958** 1
Case3 .758** .758** .800** 1
Case4 .768** .768** .768** .589** 1
Spearman's rho
Actual 1
Case1 1.000** 1
Case2 .994** .994** 1
Case3 .872** .872** .880** 1
Case4 .917** .917** .920** .758** 1
** Correlation is significant at the 0.01 level (2-tailed).
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
Biswas/Decis. Mak. Appl. Manag. Eng. 3 (2) (2020) 162-189
180
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.
Figure 1. Sensitivity Analysis (MABAC)
Figure 2. Sensitivity Analysis (CoCoSo)
0
5
10
15
20
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
Actual
case 1
case 2
case 3
case 4
0
5
10
15
20
25
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
Actual
Case 1
Case 2
Case 4
Case 3
Measuring performance of healthcare supply chains in India: A comparative analysis...
181
Figure 3. Sensitivity Analysis (MARCOS)
4. 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
0
5
10
15
20
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
Actual
Case 1
case 2
case 3
case 4
Biswas/Decis. Mak. Appl. Manag. Eng. 3 (2) (2020) 162-189
182
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.
Acknowledgment: The author would like to express his sincere gratitude to all
anonymous reviewers whose remarks have helped to improve the quality of this work.
Further, the author extends sincere thanks to Ms. Priyanka Roy and Mr. Shouvik
Sarkar, PGDM graduate students at Calcutta Business School for their immense
support in data collection and formatting.
Author Contributions: The author takes public responsibility for appropriate
portions of the content.
Funding: This research received no external funding.
Conflicts of Interest: The author declare no conflicts of interest.
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