An analysis of critical success factors towards sustainable supply chain management – in the context of an engine manufacturing industry

ABSTRACT

Appropriate consideration and selection of practices, success factors are of utmost importance of the best use of resources, time and money to facilitate Sustainable Supply Chain Management (SSCM) development. This study presents an integrated framework based on Analytic Hierarchy Process (AHP) and Interpretive Structural Modelling (ISM) to evaluate potential alternatives for the SSCM. The criteria used for the selection of various enhancers of sustainability, which are developed along with the categories are environmental, economic, social, technical, organisational and business. An Indian-based engine manufacturing industry is taken to build up the model. AHP is employed to select the best criteria from the various Critical Success Factors (CSFs) listed under the categories based on the calculated weights. ISM and Matrice d’impacts croisés multiplication appliquée á un classment (MICMAC) analysis are employed to model the relationship among the various CSFs and to select the best from the developed matrix. The result indicates ‘support from senior management for environmental activities’, ‘environmental teamwork’ and ‘government support’ are the main CSFs for the achievement of sustainability of the supply chain.

KEYWORDS:

• Sustainable Supply Chain Management (SSCM)
• Interpretive Structural Modelling (ISM)
• Analytic Hierarchy Process (AHP)
• Engine Manufacturing Industry
• Critical Success Factors (CSFs)

1. Introduction

Nowadays, sustainability is a well-known term for industry. In 1987, the World Commission on Environment and Development described sustainable development as ‘development that meets the needs of the present generation without compromising the ability of future generations to meet their needs’ (Raut, Narkhede, and Gardas 2017). Social, environmental and economic come together to achieve sustainability in the industry. Industries are trying to implement a sustainable supply chain as the government pressure and competitions from the rival companies are increasing day by day (Prasad et al. 2018).

The manufacturing industries consume a great amount of electricity. As India is the third-largest Green House Gas (GHG) emitter, it is trying to reduce the emission intensity by 33–35% by 2030 with respect to the year 2005 (Timperley 2019). India’s manufacturing industries are also following this and try to understand the Critical Success Factors (CSFs) for their sustainability growth. CSFs are the success factors which helps the industry to not only survive in the market and also provides a way to success (Habidin et al. 2018). The manufacturing industry does not only need to identify the CSFs for the production of goods but also in the overall supply chain. Sustainable supply chain management (SSCM) is the new focus area for the industry, as it helps to maximise the profit and social well-being by minimising the environmental impacts (Prasad et al. 2018). It is not possible to focus on all factors at a time, therefore CSFs was developed in 1981 and several studies have adapted this (Aquilani et al. 2017; Moeuf et al. 2020; Li, Barrueta Pinto, and Diabat 2020). CSFs has used in different industry in the literature, like manufacturing industry (Toke and Kalpande 2019), steel manufacturing industry (Prasad et al. 2018), automotive industry (Luthra et al. 2018; Habidin et al. 2018), oil and gas industry (Raut, Narkhede, and Gardas 2017), textile industry (Li, Barrueta Pinto, and Diabat 2020) and freight logistics industry (Orji, Kusi-Sarpong, and Gupta 2020). This article investigates the CSFs for an engine manufacturing industry in India whose target is to achieve the sustainable future in the next ten years. That industry manufactures low, medium and heavy-duty engines and sells them to companies in India. As per our knowledge, this is the first paper which investigates the engine manufacturing industry based on business-to-business sales. Most of the previous literature focused on economic and environmental pillars (Kumar et al. 2018; Bai et al. 2019). So, there is a scope to consider the social pillar and other related aspects like technical and political, etc., in the field of the engine manufacturing industry. The main objective of this article is to identify the most important CSFs and make the structural model of those CSFs. The contributions of this article are as follows:

1. Identification of CSFs of SSCM in the manufacturing industry and divide them into six categories like environment, social, economical, technical, organisational and business to business, which are the different aspects to achieve the sustainable environment. This is done with the help of literature and experts’ opinions.

2. Finding out the most important CSFs for the engine manufacturing industry using the Analytic Hierarchy Process (AHP) technique. Then apply the Interpretive structural modelling (ISM) method and do the Matrice d’impacts croisés multiplication appliquée á un classment (MICMAC) analysis to identify the dependent CSFs and driving CSFs and make the relationship diagram between the important CSFs.

The arrangement of this paper is as follows: the literature review is discussed in section 2, followed by the proposed methodology in section 3. Then section 4 discusses about a case example and then discussions of findings are explained in section 5. Finally, section 6 concludes the study along with the future research direction.

2. Literature review

To date, various studies have been proposed to achieve the SSCM. Jabbour et al. (2015) researched the eco-innovations in more sustainable supply chains for a low carbon economy i.e. multiple case studies of human critical success factors in Brazilian leading companies. This study qualitatively analysed three cases of low carbon eco-innovation discussing the necessary Human Critical Success Factors (HCSF). Luthra, Garg, and Haleem (2015) used ISM to make a structure model of CSFs and used that in the Indian Mining industry. Luthra, Garg, and Haleem (2016) yet again used the ISM method to research the impacts of CSFs for implementing green supply chain management towards sustainability in the Indian automobile industry and found out that ‘Regulatory’ CSF plays the most important role in promoting the green practices along with ‘Internal Management’ and ‘Competitiveness’ CSFs playing a crucial role towards the achievement of expected performance outcomes. Gopal and Thakkar (2016) also analysed CSFs to implement sustainable supply chain practices in the Indian automobile industry and found out the different CSFs belongings to the four quadrants using ISM method. Liang et al. (2016) went on with identification of CSF for Sustainable Development of Biofuel Industry in China based on Grey DEMATEL and found out that the three factors, including government support degree, competitiveness, and local acceptability are identified as the most critical factors for promoting the sustainable development, but they are all effects rather than the origins of the problems existed. In contrast, maturity, safety and reliability, complexity, conversion efficiency, and investment cost are the most important causes, and they are also the origins of the problems that existed in that industry. Song, Ming, and Liu (2017) identified critical risk factors of SSCM using rough strength-relation analysis method and the results show that failure to select the right suppliers is the most prominent risk factor for SSCM, because supplier selection plays an important role in achieving the social, environmental, and economic benefits of SSCM. Jabbour, Mauricio, and Jabbour (2017) analysed the relationship between CSFs and the adoption of Green Supply Chain Management (GSCM) practices for some Brazil-based manufacturers of automotive batteries considered focal in their supply chains. The research results showed that companies with more proactive GSCM tend to have a higher level of effectively managed of CSFs (especially competence for greener products and processes) and support from green human resource management; on the other hand, companies with lower levels of GSCM proactivity tend to have fewer effectively managed CSFs and low support from GHRM aspects. This work also found CSFs that already are being well managed and others that deserve managerial attention i.e. information management, measurement, competence for greener products and processes, training and total involvement of employees. Raut, Narkhede, and Gardas (2017) studied the critical success factors involved in the context of oil and gas industries and used ISM method to argue that competitive advantage and brand image and market share were found to play the least influential role among 32 others. Luthra et al. (2018) identified the CSFs in the context of the Indian automotive industry’s SSCM and used DEMATEL to identify the influential factors. An integrated fuzzy DEMATEL and fuzzy interference system based framework was developed by Pourjavad and Shahin (2018) for performance evaluation of SSCM. CSFs in the Malaysian automotive industry had studied by Habidin et al. (2018) using reliability analysis. Prasad et al. (2018) studied the Indian steel companies SSCM and identified top three CSFs are environmental standards, safety and health focus and top leadership commitment using principal component analysis and exploratory factor analysis. Toke and Kalpande (2019) used the AHP method to find out the most CSFs in the Indian manufacturing Industry. Saeed and Kersten (2019) identified and analyses the drivers of SSCM that influence or encourage organisations to undertake sustainability initiatives and implement sustainable solutions throughout their supply chains the results revealed that regulatory and market pressures, concerning the number of citations, are the most prevailing drivers of SSCM for the implementation of sustainability practices. Ghafourian and Shirouyehzad (2019) used ISM and identified the CSFs. Pourjavad and Shahin (2020) used different multi-criteria decision-making approaches to select the green supplier in the fuzzy environment. Shahin and Razavi (2020) did gap analysis in the healthcare industry for suppliers sustainable development. Li, Barrueta Pinto, and Diabat (2020) discoursed about CSFs of corporate social responsibility for the textile industry.

From the above literature and with the help of experts, 50 CSFs are identified and divided into six categories as shown in Table 1. The main three dimensions to achieve sustainability are environmental, economic and social (Gopal and Thakkar 2016). Those are called as main three pillars for sustainable achievement. With this, technical, organisation and business environmental aspects are also included in the category section as those can affect the three pillars of sustainability (Liang et al. 2016; Raut, Narkhede, and Gardas 2017).

Table 1. Category-wise CSFs with references

Sl No	Category	CSF	References
1	Environmental	Support from senior management for environmental activities	Li, Barrueta Pinto, and Diabat (2020); Orji, Kusi-Sarpong, and Gupta (2020); Ghafourian and Shirouyehzad (2019); Prasad et al. (2018); Luthra et al. (2018); Jabbour et al. (2015)
2		Energy Consumption	Prasad et al. (2018); Luthra et al. (2018); Raut, Narkhede, and Gardas (2017)
3		Reverse Logistics Practice	Toke and Kalpande (2019); Prasad et al. (2018); Luthra et al. (2018); Raut, Narkhede, and Gardas (2017)
4		Green Product Development	Toke and Kalpande (2019); Luthra et al. (2018)
5		Empowerment of employees applied to environmental issues	Jabbour et al. (2015); Raut, Narkhede, and Gardas (2017)
6		Environmental teamwork	Jabbour et al. (2015)
7		Performance evaluation and rewards based on environmental criteria	Jabbour et al. (2015)
8		Employee engagement supporting environmental management	Luthra, Garg, and Haleem (2016); Jabbour et al. (2015)
9		Green Purchasing	Prasad et al. (2018); Luthra et al. (2018); Raut, Narkhede, and Gardas (2017)
10		Environmental Management System	Li, Barrueta Pinto, and Diabat (2020); Toke and Kalpande (2019);
11		Reduction of GHG emissions	Liang et al. (2016)
12		Land use change and Biodiversity	Liang et al. (2016)
1	Economical	Investment	Luthra et al. (2018); Liang et al. (2016)
2		Operation and maintenance cost	Liang et al. (2016)
3		Cost of production	Liang et al. (2016)
4		Competitiveness	Toke and Kalpande (2019); Luthra et al. (2018); Liang et al. (2016); Luthra, Garg, and Haleem (2015)
5		Financial incentives	Ghafourian and Shirouyehzad (2019); Prasad et al. (2018); Raut, Narkhede, and Gardas (2017)
6		Inflation and currency exchange rates	Song, Ming, and Liu (2017)
7		Market Share	Raut, Narkhede, and Gardas (2017); Song, Ming and Liu (2017)
1	Social	Buyer–supplier relationships to improve performance	Gopal and Thakkar (2016)
2		Local acceptability	Liang et al. (2016)
3		Regional development contribution	Liang et al. (2016)
4		Government support	Orji, Kusi-Sarpong, and Gupta (2020); Liang et al. (2016)
5		Working conditions	Liang et al. (2016)
6		Security	Orji, Kusi-Sarpong, and Gupta (2020); Liang et al. (2016)
7		Employment	Liang et al. (2016)
8		Customers awareness	Raut, Narkhede, and Gardas (2017); Luthra, Garg, and Haleem (2016); Gopal and Thakkar (2016)
1	Technical	Maturity	Liang et al. (2016)
2		Complexity	Liang et al. (2016)
3		Conversion efficiency	Liang et al. (2016)
4		Safety and reliability	Liang et al. (2016)
5		Sustainability Education	Raut, Narkhede, and Gardas (2017)
6		Availability of information	Jabbour, Mauricio, and Jabbour (2017); Gopal and Thakkar (2016); Luthra, Garg, and Haleem (2016); Luthra, Garg, and Haleem (2015)
7		ISO certifications	Raut, Narkhede, and Gardas (2017); Gopal and Thakkar (2016)
8		Quality of sustainability information	Luthra, Garg, and Haleem (2015); Luthra, Garg, and Haleem (2016)
1	Organisational	Pressures from governments	Toke and Kalpande (2019); Prasad et al. (2018); Gopal and Thakkar (2016)
2		Support from consumers	Luthra, Garg, and Haleem (2016); Luthra, Garg, and Haleem (2015)
3		Imitation	Gopal and Thakkar (2016)
4		Organisational learning	Li, Barrueta Pinto, and Diabat (2020); Jabbour et al. (2015); Gopal and Thakkar (2016)
5		Organisational Structure	Orji, Kusi-Sarpong, and Gupta (2020)
6		Positive Attitude towards social media	Orji, Kusi-Sarpong, and Gupta (2020)
7		Health and safety	Raut, Narkhede, and Gardas (2017)
8		Organisational capabilities and efforts	Li, Barrueta Pinto, and Diabat (2020); Raut, Narkhede, and Gardas (2017)
9		Organisational Culture	Orji, Kusi-Sarpong, and Gupta (2020); Li, Barrueta Pinto, and Diabat (2020); Prasad et al. (2018)
1	Business to Business	Stakeholder influences on sustainability implementation	Gopal and Thakkar (2016); Ghafourian and Shirouyehzad (2019)
2		Role of stakeholders	Gopal and Thakkar (2016); Luthra, Garg, and Haleem (2015)
3		Treating partners as a stakeholder of focal company	Gopal and Thakkar (2016)
4		Global marketing	Raut, Narkhede, and Gardas (2017)
5		Business to business pressure	Raut, Narkhede, and Gardas (2017)
6		Collaboration with suppliers	Raut, Narkhede, and Gardas (2017)

From the above literature, it is clear that many previous researchers applied sustainability in the different industries such as the automotive industry, agri-food industry, manufacturing industry, steel manufacturing industry, oil and gas industry, textile industry and freight logistics industry. Although, there is a lot of potentials still left in the full embodiment of SSCM. The challenges that the companies are facing to implement should be talked more in details to provide more intricate technicalities of the topic. In the engine manufacturing industry, there has been a lot of research on increasing the quality and the efficiency but the one field which has been derailed is the adoption of sustainable management in the industry. This article focuses on the identification of the important CSFs for the engine manufacturing industry from the overall CSFs in the manufacturing industry and makes the inter-relationship between the CSFs to enforce a sustainable supply chain.

3. Proposed methodology

This section details the proposed approach to identify the most important CSFs and identify the relation between them. Figure 1 shows the overall block diagram of the proposed approach.

Figure 1. Methodology Adopted

This article identifies the CSFs for the SSCM from the literature and divided them into six categories as shown in Table 1. From the literature and experts’ opinion, 50 CSFs are identified which is applicable in the manufacturing industry for the SSCM. As industry cannot focus on all the 50 CSFs at the beginning, this article is selected the CSFs which affect that specific industry. Now, to identify the important CSFs for the engine manufacturing industry, AHP technique is applied. The data is collected from both industry and academic experts by interview. After applying the AHP, if the Consistency Ratio (CR) is beyond a threshold (0.1), the data is modified based on the experts’ opinion. Using AHP technique, the important CSFs are identified based on the weights of the CSFs in each category. After that, the most important 20 CSFs are selected for SSCM in the field of the engine manufacturing industry. Those CSFs whose weight is less than a fixed benchmark (0.1) are eliminated. After that, the second round of data collection is conducted with the same supply chain experts from the engine manufacturing industry for the ISM application. The transitivity checks are also done in between the ISM process. Finally, the relations between the CSFs are identified. The detailed procedure of the AHP technique and ISM are mentioned in the following sections.

3.1. Analytic hierarchy process (AHP)

Satty first proposed the AHP method to deal with decision-making problems (Patil and Kant 2014). AHP helps us to make give the priority among many alternatives. Pairwise comparison is done and data is collected in Satty scales. AHP is a very simple and effective tool for a decision-maker as it checks the consistency of the evaluation, which reduces the bias and the weight calculation procedure of AHP is mentioned in the literature (Chen 2006)

The steps for selecting the most important CSFs for the engine manufacturing industry using AHP technique as follows:

Step 1: To compute the weights for the different CSF, the AHP starts creating a pairwise comparison matrix. A square matrix is created based on the number of CSFs in each category. Six different comparison matrixes are created.

Step 2: Then, formulate a normalised matrix which is done by using the related product of all the values in the row raised to the power $1/n$ ($n$ is the number of criteria) and finally the sum of the values obtained in the matrix.

Step 3: Then compute the ratio matrix where the ratio of each value obtained in each CSF raw is taken with the total of the column values in the previous matrix. Ratio value is the weight of that particular CSF.

Step 4: Check the consistency ratio as$CR=CI/RI$, where $RI$ is the random inconsistency index based on the number of criteria. $CI$ is the consistency index. Usually, a CR of 0.1 or less is considered acceptable, which reflects an unbiased judgement of the decision-maker.

Step 5: Finally, all the weights above a fixed benchmark (0.1) are taken into consideration as the most important CSFs for the engine manufacturing industry.

3.2. Interpretive structural modelling (ISM)

ISM is a methodology that helps us to identify the relationship among specific factors of a problem. It creates a structured model which helps us to identify the relationship between those factors.

Step 1: An inter-relationship of selected 20 CSF’s is established and listed.

Step 2: A Structural Self-Interpretive Matrix (SSIM) is formed which consists of the pair-wise relationship of the CSFs.

Step 3: An initial reachability matrix is formed with the help of SSIM.

Step 4: Transitivity of the initial reachability matrix is checked. It refers that if driver ‘A’ is related to ’B’ and ‘B’ is related to ‘C’, then “A’ is related to ‘C’. This will form the final reachability matrix.

Step 5: A level-partition matrix is drawn based on the final reachability matrix.

Step 6: The matrix is used to make the ISM model, which can be shown by either the element’s number or its name.

Step 7: MICMAC analysis is done based on CSFs dependence and driving powers. In this analysis, factors are divided into four clusters concerning the driving power and dependence power. These clusters are:

Cluster I: Autonomous Factors – factors that are relatively cut off from the system and have weak or no dependence on other factors;

Cluster II: Dependent Factors – factors that are primarily dependent on other factors;

Cluster III: Linkage factors – factors that are unstable and most influence others; and

Cluster IV: Independent Factors – factors that have weak influence from other factors and have to be paid most attention due to the strong key factors.

4. Case study

Consider a company ABC, which is a leading manufacturer of diesel and natural gas-powered engines in India and sells it to various industries includes heavy and medium-duty trucks, public and private vehicles. They are planning to apply a different strategy to achieve sustainability in their industry. They are making different plans for the next 10 and 30 years to get a sustainable future. This study is trying to focus on identifying the most influential factors and the relation between them. This time is very crucial to identify the most CSFs and focus on them in the coming years so that they can achieve their 10 and 30 years targets. For this, 50 CSFs are identified with the help of the literature and expert’s opinion. Then identify the important CSFs for that specific industry using AHP and judge interrelationship between them using ISM as mentioned in section 3. Expert’s opinions are taken through telephonic conversation and physical interaction. Data was collected from different experts as shown in Table 2.

Table 2. Experts List for Data Collection

Expert	Experience	Data Collected for
Academic	More than15 years	Initial CSFs selection and AHP
Academic-Industry	3 years in academic and more than 20 years in industry	AHP
Industry	More than 15 years in the manufacturing industry	Initial CSFs selection
Industry	More than 3 years in Engine manufacturing Industry	AHP and ISM
Industry	More than 20 years in the Engine manufacturing Industry	AHP and ISM

4.1. Identify the important CSFs using AHP

50 identified CSFs are divided into six categories as mentioned in Table 1. AHP ratings are taken in Satty’s one to nine scales, where one indicates that two compared CSFs are equally important and nine indicates one factor is more important than the other. Data is collected from the academic experts and industry experts and obtained the rating. Then AHP technique is used to get the weight of each CSF. Table 3 shows the pairwise comparison matrix of 12 CSFs under the environmental category (as mentioned in Table 1). The last column indicates the weights obtained from AHP procedure as mentioned in section 3.1. With the weight of 0.26, ‘environmental teamwork’ is the most important factor and ‘empowerment of employees applied to environmental issues’ is the least important with the weight of 0.02. The importance wise order of the CSFs based on the weight is as follows, ‘environmental teamwork’ > ‘performance evaluation and rewards based on environmental criteria’ > ‘support from senior management for environmental activities’ > ‘energy consumption’ = ‘employee engagement supporting environmental management’ > ‘green purchasing’ > ‘environmental management system’ > ‘reverse logistics practice’ = ‘green product development’ > ‘reduction of GHG emissions’ = ‘land use change and biodiversity’. The CR is 0.06 which is good enough to prove the consistency of the data. In the same way, this technique is applied to each category.

Table 3. Pairwise Comparison Matrix for Environmental Category

CSF	1	2	3	4	5	6	7	8	9	10	11	12	Weight
1	1.00	2.00	3.00	3.00	4.00	0.33	0.50	0.50	3.00	3.00	4.00	4.00	0.11
2	0.50	1.00	1.00	4.00	4.00	0.20	0.50	0.50	3.00	3.00	4.00	4.00	0.09
3	0.33	1.00	1.00	0.50	3.00	0.17	0.33	0.33	0.50	0.50	2.00	2.00	0.04
4	0.33	0.25	2.00	1.00	4.00	0.17	0.33	0.33	0.50	0.50	2.00	2.00	0.04
5	0.25	0.25	0.33	0.25	1.00	0.14	0.25	0.25	0.33	0.33	0.50	0.50	0.02
6	3.00	5.00	6.00	6.00	7.00	1.00	2.00	3.00	5.00	5.00	7.00	7.00	0.26
7	2.00	2.00	3.00	3.00	4.00	0.50	1.00	4.00	4.00	4.00	6.00	6.00	0.17
8	2.00	2.00	3.00	3.00	4.00	0.33	0.25	1.00	0.50	0.50	5.00	5.00	0.09
9	0.33	0.33	2.00	2.00	3.00	0.20	0.25	2.00	1.00	3.00	3.00	3.00	0.07
10	0.33	0.33	2.00	2.00	3.00	0.20	0.25	2.00	0.33	1.00	3.00	3.00	0.06
11	0.25	0.25	0.50	0.50	2.00	0.14	0.17	0.20	0.33	0.33	1.00	1.00	0.02
12	0.25	0.25	0.50	0.50	2.00	0.14	0.17	0.20	0.33	0.33	1.00	1.00	0.02

Table 4 is showing the pairwise comparison matrix and weight of CSFs under the economical category. The most important CSF is ‘market share’ (0.44) and least is ‘investment’ (0.2). 0.1 is the CR which proves the consistency of the matrix.

Table 4. Pairwise Comparison Matrix for Economical Category

CSF	1	2	3	4	5	6	7	Weight
1	1.00	0.50	0.33	0.25	0.20	0.13	0.11	0.02
2	2.00	1.00	0.25	0.17	0.50	0.13	0.13	0.03
3	3.00	4.00	1.00	0.20	0.25	0.14	0.13	0.05
4	4.00	6.00	5.00	1.00	1.00	0.20	0.13	0.10
5	5.00	2.00	4.00	1.00	1.00	0.50	0.25	0.11
6	8.00	8.00	7.00	5.00	2.00	1.00	0.25	0.24
7	9.00	8.00	8.00	8.00	4.00	4.00	1.00	0.44

Pairwise Comparison Matrix for Social Category is shown in Table 5. The most important CSF is ‘working condition’ (0.32). Following that ‘government support’ (0.30) and ‘regional development contribution’ (0.14) are ranked second and third respectively.

Table 5. Pairwise Comparison Matrix for Social Category

CSF	1	2	3	4	5	6	7	8	Weight
1	1.00	0.50	0.25	0.14	0.17	4.00	4.00	3.00	0.06
2	2.00	1.00	0.33	0.14	0.20	3.00	2.00	5.00	0.08
3	4.00	3.00	1.00	0.20	0.33	5.00	4.00	5.00	0.14
4	7.00	7.00	5.00	1.00	0.33	7.00	6.00	9.00	0.30
5	6.00	5.00	3.00	3.00	1.00	7.00	4.00	7.00	0.32
6	0.25	0.33	0.20	0.14	0.14	1.00	0.25	3.00	0.03
7	0.25	0.50	0.25	0.17	0.25	4.00	1.00	3.00	0.05
8	0.33	0.20	0.20	0.11	0.14	0.33	0.33	1.00	0.02

Table 6 is showing the pairwise comparison matrix for CSFs under the technical category. ‘quality of sustainability information’ (0.39) is the most important CSF. ‘Maturity’ (0.2) and ‘complexity’ (0.2) are the least important CSFs because both of them having the least weight among the eight CSFs.

Table 6. Pairwise Comparison Matrix for Technical Category

CSF	1	2	3	4	5	6	7	8	Weight
1	1.00	1.00	0.20	0.33	0.25	0.20	0.14	0.11	0.02
2	1.00	1.00	0.33	0.25	0.20	0.14	0.14	0.13	0.02
3	5.00	3.00	1.00	0.50	0.33	0.20	0.17	0.13	0.04
4	3.00	4.00	2.00	1.00	0.25	0.25	0.14	0.11	0.05
5	4.00	5.00	3.00	4.00	1.00	0.17	0.20	0.11	0.07
6	5.00	7.00	5.00	4.00	6.00	1.00	0.25	0.25	0.15
7	7.00	7.00	6.00	7.00	5.00	4.00	1.00	0.33	0.25
8	9.00	8.00	8.00	9.00	9.00	4.00	3.00	1.00	0.39

The pairwise comparison matrix and weights of the CSFs for the organisational category are presented in Table 7. ‘Organisational learning’ (0.25) is the most weighted criteria and ‘pressure from government’ (0.24) is ranked second. Table 8 is listed the pairwise comparison of the CSFs under the business category and ‘business to business pressure’ (0.40) is the important one according to weight. The minimum CR is maintained in all the AHP calculation to provide consistency in the results.

Table 7. Pairwise Comparison Matrix for Organisational Category

CSF	1	2	3	4	5	6	7	8	9	Weight
1	1.00	9.00	6.00	0.50	7.00	5.00	4.00	3.00	2.00	0.24
2	0.11	1.00	1.00	0.33	0.50	0.25	0.50	0.25	0.25	0.03
3	0.17	1.00	1.00	0.17	0.33	0.33	0.25	0.17	0.25	0.03
4	2.00	3.00	6.00	1.00	4.00	4.00	4.00	3.00	4.00	0.25
5	0.14	2.00	3.00	0.25	1.00	4.00	3.00	0.20	0.33	0.07
6	0.20	4.00	3.00	0.25	0.25	1.00	3.00	0.20	0.17	0.05
7	0.25	2.00	4.00	0.25	0.33	0.33	1.00	0.20	0.33	0.04
8	0.33	4.00	6.00	0.33	5.00	5.00	5.00	1.00	3.00	0.17
9	0.50	4.00	4.00	0.25	3.00	6.00	3.00	0.33	1.00	0.12

Table 8. Pairwise Comparison Matrix for Business Category

CSF	1	2	3	4	5	6	Weight
1	1.00	3.00	3.00	0.17	0.13	0.50	0.07
2	0.33	1.00	0.50	0.11	0.25	0.20	0.03
3	0.33	2.00	1.00	0.14	0.17	0.14	0.04
4	6.00	9.00	7.00	1.00	0.33	3.00	0.30
5	8.00	4.00	6.00	3.00	1.00	4.00	0.40
6	2.00	5.00	7.00	0.33	0.25	1.00	0.15

After applying AHP in all categories, based on the expert opinion, the CSF whose weight is beyond the threshold (0.1) is selected. Table 9 indicates the list of the most important 20 CSFs selected from Table 3 to 8 based on the mentioned threshold.

Table 9. 20 most important CSFs for SSCM in engine manufacturing industries

SL. NO.	CSFs	Category
C1	Support from senior management for environmental activities	ENVIRONMENTAL
C2	Environmental teamwork
C3	Performance evaluation and rewards based on environmental criteria
C4	Competitiveness	ECONOMICAL
C5	Financial incentives
C6	Inflation and currency exchange rates
C7	Market share
C8	Regional development contribution	SOCIAL
C9	Government support
C10	Working conditions
C11	Availability of information	TECHNICAL
C12	ISO certifications
C13	Quality of sustainability information
C14	Pressures from governments	ORGANISATIONAL
C15	Organisational learning
C16	Organisational capabilities and efforts
C17	Organisational Culture
C18	Global marketing	BUSINESS
C19	Business to business pressure
C20	Collaboration with suppliers

4.2. Computation of interrelationship between 20 CSFs using ISM

4.2.1. Developing SSIM matrix

The inter-relationship of 20 CSFs is presented in a matrix format with the help of four symbols i.e. ‘V’, ‘A’, ‘X’ and ‘O’ as mentioned in Table 10. ‘V’, ‘A’, ‘X’ and ‘O’ indicate that CSF i helps to achieve CSF j, ‘CSF j helps to achieve CSF i, CSFs i and j help to achieve each other and CSFs i and j are not related to each other respectively.

Table 10. The Structural Self Interaction Matrix (SSIM) of CSFs

C	20	19	18	17	16	15	14	13	12	11	10	9	8	7	6	5	4	3	2	1
1	V	V	V	V	V	V	V	V	V	V	V	A	V	V	V	V	V	V	V	X
2	V	A	V	V	V	V	V	V	O	V	V	V	V	O	O	V	O	V	X
3	A	A	A	V	V	V	O	O	O	O	V	O	V	O	O	O	X	X
4	A	A	A	A	O	O	O	O	A	O	A	O	A	A	O	A	X
5	V	A	V	O	O	O	A	V	O	V	X	A	V	V	V	X
6	A	A	A	O	O	O	O	A	O	A	O	A	A	X	X
7	V	A	V	O	O	O	O	V	X	O	O	A	A	X
8	A	V	V	X	A	X	A	X	V	O	X	A	X
9	O	O	O	V	V	V	O	O	V	O	V	X
10	V	A	A	A	A	V	A	A	V	O	X
11	A	A	V	V	V	V	A	X	X	X
12	V	V	V	O	O	O	O	O	X
13	V	A	V	O	O	O	O	X
14	V	A	V	O	O	O	X
15	A	A	V	X	V	X
16	V	A	V	X	X
17	V	V	V	X
18	A	A	X
19	V	X
20	X

4.2.2 Reachability matrix (RM)

From the SSIM matrix the initial reachability matrix is developed as shown in Table 11. The following rules are considered.

• SSIM (i, j) = (V) -> (RM (i, j) = 1) ∀ (RM (j, i) = 0))

• SSIM (i, j) = (A) -> (RM (i, j) = 0) ∀ (RM (j, i) = 1))

• SSIM (i, j) = (X) -> (RM (i, j) = 1) ∀ (RM (j, i) = 1))

• SSIM (i, j) = (O) -> (RM (i, j) = 0) ∀ (RM (j, i) = 0))

Table 11. The initial reachability matrix for the sustainable CSFs

C	20	19	18	17	16	15	14	13	12	11	10	9	8	7	6	5	4	3	2	1
1	1	1	1	1	1	1	1	1	1	1	1	0	1	1	1	1	1	1	1	1
2	1	0	1	1	1	1	1	1	0	1	1	1	1	0	0	1	0	1	1	0
3	0	0	0	1	1	1	0	0	0	0	1	0	1	0	0	0	1	1	0	0
4	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	1	0	0
5	1	0	1	0	0	0	0	1	0	1	1	0	1	1	1	1	1	0	0	0
6	0	0	0	0	0	0	0	0	0	0	0	0	0	1	1	0	0	0	0	0
7	1	0	1	0	0	0	0	1	1	0	0	0	0	1	1	0	1	0	0	0
8	0	1	1	1	0	1	0	1	1	0	1	0	1	1	1	0	1	0	0	0
9	0	0	0	1	1	1	0	0	1	0	1	1	1	1	1	1	0	0	0	1
10	1	0	0	0	0	1	0	0	1	0	1	0	1	0	0	1	1	0	0	0
11	0	0	1	1	1	1	0	1	1	1	0	0	0	0	1	0	0	0	0	0
12	1	1	1	0	0	0	0	0	1	1	0	0	0	1	0	0	1	0	0	0
13	1	0	1	0	0	0	0	1	0	1	1	0	1	0	1	0	0	0	0	0
14	1	0	1	0	0	0	1	0	0	1	1	0	1	0	0	1	0	0	0	0
15	0	0	1	1	1	1	0	0	0	0	0	0	1	0	0	0	0	0	0	0
16	1	0	1	1	1	0	0	0	0	0	1	0	1	0	0	0	0	0	0	0
17	1	1	1	1	1	1	0	0	0	0	1	0	1	0	0	0	1	0	0	0
18	0	0	1	0	0	0	0	0	0	0	1	0	0	0	1	0	1	1	0	0
19	1	1	1	0	1	1	1	1	0	1	1	0	0	1	1	1	1	1	1	0
20	1	0	1	0	0	1	0	0	0	1	0	0	1	0	1	0	1	1	0	0

Here SSIM (i, j) and RM (i, j) represents the (i, j)-th entry in the SSIM and RM respectively. RM (j, i) represents the (j, i)-th entry in the RM matrix. Then transitivity is checked as mentioned in section 3.2 on initial RM to obtain the final RM as shown in Table 12.

Table 12. The final reachability matrix for sustainable CSFs

C	20	19	18	17	16	15	14	13	12	11	10	9	8	7	6	5	4	3	2	1	DRIVING POWER
1	1	1	1	1	1	1	1	1	1	1	1	1*	1	1	1	1	1	1	1	1	20
2	1	1*	1	1	1	1	1	1	1*	1	1	1	1	1*	1*	1	1*	1	1	1*	20
3	1*	1*	1*	1*	1	1	0	1*	1*	0	1	0	1	1*	1*	1*	1	1	0	0	15
4	0	0	0	1*	1*	1*	0	0	0	0	1*	0	1*	0	0	0	1	1	0	0	7
5	1	1*	1	1*	0	1*	0	1	1*	1	1	0	1	1	1	1	1	1*	0	0	15
6	1*	0	1*	0	0	0	0	1*	1*	0	0	0	0	1	1	0	1*	0	0	0	7
7	1	1*	1	0	0	1*	0	1	1	1*	1*	0	1*	1	1	0	1	1*	0	0	13
8	1*	1	1	1	1*	1	1*	1	1	1*	1	0	1	1	1	1*	1	1*	0	0	17
9	1*	1*	1*	1	1	1	1*	1*	1	1*	1	1	1	1	1	1	1*	1*	1*	1	20
10	1	1*	1*	1*	1*	1	0	1*	1	1*	1	0	1	1*	1*	1	1	1*	0	0	16
11	1*	1*	1	1	1	1	0	1	1	1	1*	0	1*	1*	1	0	1*	1*	0	0	15
12	1	1	1	1*	1*	1*	1*	1*	1	1	1*	0	1*	1	1*	1*	1	1*	1*	0	18
13	1	1*	1	1*	1*	1*	0	1	1*	1	1	0	1	1*	1	1*	1*	0	0	0	15
14	1	1*	1	1*	1*	1*	1	1*	1*	1	1	0	1	1*	1*	1	1*	1*	1*	0	18
15	1*	1*	1	1	1	1	0	1*	1*	0	1*	0	1	1*	1*	0	1*	1*	0	0	14
16	1	1*	1	1	1	1*	0	1*	1*	1*	1	0	1	1*	1*	1*	1*	1*	0	0	16
17	1	1	1	1	1	1	1*	1*	1*	1*	1	0	1	1*	1*	1*	1	1*	1*	0	18
18	1*	0	1	0	0	1*	0	0	1*	0	1	0	1*	1*	1	1*	1	1	0	0	11
19	1	1	1	1*	1	1	1	1	1*	1	1	1*	1*	1	1	1	1	1	1	0	19
20	1	0	1	1*	1*	1	0	1*	1*	1	1*	0	1	1*	1	0	1	1	0	0	14
DEPENDENCE POWER	19	16	19	17	16	19	8	18	19	15	19	4	19	19	19	14	20	18	7	3

4.2.3 Level partitions

Using the final RM, the CSFs are placed in the reachability and antecedent set. The reachability set indicates that the CSF has a direct impact on both itself and other CSFs, whereas the antecedent set indicates that the CSF will be affected by both itself and other CSFs. Then, the intersection of the reachability and antecedent set is derived. The CSF whose reachability set and intersection sets are the same occupy the top level of hierarchy in the ISM model. The top-level factor does not lead to impact other parameters above its own level, once a level is identified; the CSF is removed from further consideration to make other levels. The same process is done in iterations until all the levels are identified. The level-partition matrix is shown in Table 13. Six iterations are required for the 20 CSFs to get identified and put into the levels.

Table 13. Level-partition matrix for reachability matrix

C	Reachability Set	Antecedent Set	Intersection	Level
1	1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20	1, 2, 9	1, 2, 9	VI
2	1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20	1, 2, 9, 12, 14, 17, 19	1, 2, 9, 12, 14, 17, 19	VI
3	3, 4, 5, 6, 7, 8, 10, 12, 13, 15, 16, 17, 18, 19, 20	1, 2, 3, 4, 5, 7, 8, 9, 10, 11, 12, 14, 15, 16, 17, 18, 19, 20	3, 4, 5, 7, 8, 10, 12, 15, 16, 17, 18, 19, 20	IV
4	3, 4, 8, 10, 15, 16, 17	1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20	3, 4, 8, 10, 15, 16, 17	I
5	3, 4, 5, 6, 7, 8, 10, 11, 12, 13, 15, 17, 18, 19, 20	1, 2, 3, 5, 8, 9, 10, 12, 13, 14, 16, 17, 18, 19	3, 5, 8, 10, 12, 13, 17, 18, 19	IV
6	4, 6, 7, 12, 13, 18, 20	1, 2, 3, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20	6, 7, 12, 13, 18, 20	II
7	3, 4, 6, 7, 8, 10, 11, 12, 13, 15, 18, 19, 20	1, 2, 3, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20	3, 6, 7, 8, 10, 11, 12, 13, 15, 18, 19, 20	II
8	3, 4, 5, 6, 7, 8, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20	1, 2, 3, 4, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20	3, 4, 5, 7, 8, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20	III
9	1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20	1, 2, 9, 19	1, 2, 9, 19	VI
10	3, 4, 5, 6, 7, 8, 10, 11, 12, 13, 15, 16, 17, 18, 19, 20	1, 2, 3, 4, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20	3, 4, 5, 7, 8, 10, 11, 12, 13, 15, 16, 17, 18, 19, 20	III
11	3, 4, 6, 7, 8, 10, 11, 12, 13, 15, 16, 17, 18, 19, 20	1, 2, 5, 7, 8, 9, 10, 11, 12, 13, 14, 16, 17, 19, 20	7, 8, 10, 11, 12, 13, 16, 17, 19, 20	IV
12	2, 3, 4, 5, 6, 7, 8, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20	1, 2, 3, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20	2, 3, 5, 6, 7, 8, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20	II
13	4, 5, 6, 7, 8, 10, 11, 12, 13, 15, 16, 17, 18, 19, 20	1, 2, 3, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 19, 20	5, 6, 7, 8, 10, 11, 12, 13, 15, 16, 17, 19, 20	III
14	2, 3, 4, 5, 6, 7, 8, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20	1, 2, 8, 9, 12, 14, 17, 19	2, 8, 12, 14, 17, 19	V
15	3, 4, 6, 7, 8, 10, 12, 13, 15, 16, 17, 18, 19, 20	1, 2, 3, 4, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20	3, 4, 7, 8, 10, 12, 13, 15, 16, 17, 18, 19, 20	III
16	3, 4, 5, 6, 7, 8, 10, 11, 12, 13, 15, 16, 17, 18, 19, 20	1, 2, 3, 4, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 19, 20	3, 4, 8, 10, 11, 12, 13, 15, 16, 17, 19, 20	IV
17	2, 3, 4, 5, 6, 7, 8, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20	1, 2, 3, 4, 5, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 19, 20	2, 3, 4, 5, 8, 10, 11, 12, 13, 14, 15, 16, 17, 19, 20	III
18	3, 4, 5, 6, 7, 8, 10, 12, 15, 18, 20	1, 2, 3, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20	3, 5, 6, 7, 8, 10, 12, 15, 18, 20	II
19	2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20	1, 2, 3, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 19	2, 3, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 19	III
20	3, 4, 6, 7, 8, 10, 11, 12, 13, 15, 16, 17, 18, 20	1, 2, 3, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20	3, 6, 7, 8, 10, 11, 12, 13, 15, 16, 17, 18, 20	II

4.2.4 ISM model

From the final reachability matrix, a structural model was developed using the level of the CSF as mentioned in Table 13. The relationship between the CSFs i and j is indicated by using the arrow-headed line as illustrated in Figure 2. Here single arrow-headed line (→) indicates unidirectional relationship whereas double arrow-headed line (↔) indicates bidirectional relationship between CSFs. For example, C18 is dependent on C20 and influences C4 whereas C12 and C20 both are influencing each other as illustrated in Figure 2.

Figure 2. ISM model

4.2.5 MICMAC analysis

The 20 CSFs are divided into four clusters using MICMAC analysis as mentioned in section 3.2 (see Figure 3). For example, as illustrated in Figure 3, driving CSFs are those whose dependence power is less than 11 and driving power is more than 10. In the same way, dependence CSFs are those whose dependency power is more than 10 and driving power is less than 11.

Figure 3. The cluster of sustainable or green manufacturing CSFs

5. Discussions of findings

In the present study, 50 CSFs has been identified using the literature survey and the experts’ point of view. As these CSFs had been collected based on the manufacturing industry, so all the factors are not crucial for the engine manufacturing industry. Also, the industry can’t consider all the 50 CSFs. To overcome this difficulty, the AHP method was applied and the most important 20 CSFs are identified. Based on the threshold value (0.1) three CSFs have considered from each of the environmental, social, technical and business category. And from the rest two categories, four CSFs has been chosen as their weights are more than the threshold value. The detailed results have been shown in section 4.

After listed down the 20 CSFs, an ISM model has developed using the final CSFs for implementing sustainability in the engine manufacturing industry. 20 CSFs have interrelated into six levels using final RM and built the model as shown in Figure 2.

In the ISM model, it can be noted that from the first iteration, competitiveness (C4) has found as the least influential role among the rest of the CSFs and it belongs to level 1. It does not affect the implementation of SSCM in the industry, so negligible concentration should be applied to that CSFs.

On moving down to the ISM model, in level two, there have five CSF’s namely, inflation and currency exchange rates (C6), market share (C7), ISO certifications (C12), global marketing (C18) and collaboration with suppliers (C20). We can conclude to a fair degree that the factors that go into this level are less influential than those which are lower in the model, which means that they do not have a direct influence on the drivers further down in the model. This is the main reason for the less attention given to these factors.

Ten CSFs have fallen in the third and fourth levels. Regional development contribution (C8), working conditions (C10), quality of sustainability information (C13), organisational learning (C15), organisational culture (C17) and business to business pressure (C19) have arrived in the third level and the fourth level has consisted of performance evaluation and rewards based on environmental criteria (C3), organisational capabilities and efforts (C16), availability of information (C11) and financial incentives (C5). They have fallen into the intermediate portion of the structural model hierarchy and they may be just underscored in this condition. They motivate and inspire the implementation of sustainable practices in manufacturing industries. pressures from governments (C14) fell into the fifth level and this factor can influence the above levels but cannot influence the lower level.

The factors from the sixth level need the most attention and focus as it has influenced both the top and intermediate layers of the ISM hierarchy. Support from senior management for environmental activities (C1), environmental teamwork (C2) and government support (C9) were the three CSFs that belong to the last level and were the most significant factors.

Figure 3 has shown the MICMAC analysis, which has generated using the final RM. CSFs driving power and dependence power can be identified using this analysis. Cluster 1 is blank which means none of the factors has low dependence as well as driving power. On the other hand, only two factors (C4 and C6) have low driving power but, high dependence power which is on the second cluster. 14 of the 20 factors (C3, C5, C7, C8, C10, C11, C12, C13, C15, C16, C17, C18, C19, C20) have high driving as well as dependency powers which lies in the third cluster. Cluster 4 consists of four factors (C1, C2, C9 and C14), which has low dependency power but high driving powers. So, from MICMAC analysis, we can observe that C1, C2, C9 and C14 are the factors that require maximum attention from the engine manufacturing industry. From the ISM model and MICMAC analysis, it can be concluded that the driving CSFs occupied the highest level and the dependent CSFs occupied the lowest level.

5.1. Theoretical implications

This study has shown six levels in the ISM model. Last level i.e. sixth level is the most important one as that level is the most driving power and least dependency power. This study has identified that in the ISM model C1, C2 and C9 were in the last level. These three CSFs are the ‘support from the management’, ‘team work’ and ‘government support’ to achieve sustainability. The organisation needs management and government support as well as support from their business group partners. Internal management support (Luthra, Garg, and Haleem 2016) is very much helpful to achieve sustainability. Top management support has identified the most influential CSF in the literature (Orji, Kusi-Sarpong, and Gupta 2020). Government initiatives and supports (Li, Barrueta Pinto, and Diabat 2020) are also having a major impact on this and team work promotes the circular economy which also helps to achieve sustainability. Toke and Kalpande (2019) has also identified that competitiveness is important for the Indian manufacturing industry. Indian Government has taken the initiatives and helping different industries to improve sustainability (Nukala 2016). So, from this result, it can be concluded that with the help of each other the industry can accomplish the sustainability goal. Adopting sustainability in the organisation helps in gaining a competitive advantage over other firms in the long run as competitiveness has the most dependence power as shown in MICMAC analysis.

5.2. Managerial implications

In the modern scenario, environmental regulations, sustainability and accountability of the environment have been growing in organisations. So, the need to implement sustainability in the manufacturing industries is building up to merge all the pillars in sustainability. Several areas within manufacturing can be benefitted greatly by adopting sustainable practices.

This paper was developed based on a real-life scenario and considered the case study from the engine manufacturing industry which is the original equipment manufacturer (OEM) and do the business-to-business sales. So, this solution is also applicable in a similar kind of manufacturing industry. The manager can focus more on the last level CSFs to achieve sustainability.

6. Conclusions

This study has conducted on the diesel and natural gas-engine manufacturing industry in India. Both academic and industry expert opinions have taken into consideration to do the research. First, this article searched the CSFs from the literature and with the helped of experts, listed down 50 CSFs and divided them into six categories. Then AHP technique has applied and selected the most important 20 CSFs across all the categories to implement SSCM in the engine manufacturing industry as 50 CSFs are too big to handle for any industry. After the 20 CSFs selection is made, the ISM method has used and made the ISM model to see the inter-relationship between the CSFs. Finally, MICMAC analysis has conducted to see the dependence power and driving power of the CSFs. Competitiveness has the least driving power CSFs and dependency has most than the other CSFs. It has been identified that the support from senior management has the most driving power followed by government support, environmental teamwork and pressure from the government have the driving power and most crucial CSFs for the industry as these four CSFs are belong to the last two levels in the ISM model.

In future work, an integrated Analytic Network Process (ANP) and ISM can use to check the relationship between the CSFs and compare the results with this article. Also, other multi-criteria decision-making approaches can be used in the fuzzy environment. Future research can also check the applicability of this study in other industries to implement the SSCM.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Notes on contributors

Nikkhil Pandey

Nikkhil Pandey is a graduate of Vellore Institute of Technology, Vellore, having earned his Bachelor of Technology in Mechanical Engineering. His research interests are supply chain management, lean engineering and operations management. He did his internship at Seceon Inc (a cybersecurity company). He is currently working as a Graduate Engineer Trainee at Becton and Dickinson in the vascular access department.

Manas Bhatnagar

Manas Bhatnagar is a graduate of Vellore Institute of Technology, Vellore, having earned his  Bachelor of Technology in Mechanical Engineering. Among his research interests are supply chain analysis, business analysis, and business strategy. His internship experience includes operations excellence intern at Cummins Turbo Technologies and Business Associate at SmartServ (Seattle, Washington). He currently works as a business analyst with a management consultancy in Mumbai, Maharashtra.

Dibyajyoti Ghosh

Dibyajyoti Ghosh is working as an Assistant Professor in the Operations area at the Department of Production, Operations, Systems and Quantitative Methods, VIT Business School, Vellore, India. He has completed his PhD from the Indian Institute of Technology, Dhanbad. His research interests are supply chain management, multi-criteria decision making and optimization.