Sustainability in supply networks: finding the most influential green interventions using interpretive structural modeling technique

ABSTRACT

Manufacturing organisations adopt various kinds of green interventions to make their operations more environment friendly. However not all interventions have a similar impact on enhancing the environmental efficiency of supply networks. The present study aims to identify the most effective green interventions for effective green supply chain management (GSCM) using the interpretive structural modelling (ISM) technique. GSCM includes the implementation of green interventions to enhance the environmental viability of their products and processes. First, a set of green interventions is identified through literature and expert opinions, and then these are analysed using the ISM technique. The green interventions ‘developing environmental strategies, policies and procedures’ and ‘performance review and long term action plan’ are identified as the most influential green interventions as they have low dependence and high driving power, which means that it is important to give emphasis on these interventions for effective green supply chain management. Additionally, the study identifies that ‘adoption of cutting edge technology’, ‘cleaner production’, and others are the interventions with high dependence and high driving power. Among all the interventions analysed none has an independent character. Sustainability managers can use this technique and the results in improving sustainability in their SCM.

KEYWORDS:

• Green interventions
• sustainability in supply networks
• interpretive structural modelling
• sustainability implementation

1. Introduction

The concept of sustainability aims to bring about sustainable development that makes a sustainable use of earth’s resources (Kuhlman and Farrington 2010). It refers to development that meets the need of the present without compromising the needs of the future generations (WCED 1987). Sustainability in supply networks refers to making their supply network environmentally friendly. Companies wanting to lower their environmental footprint have realised that it is possible only, if they are able to manage their complex supplier network. Implementation of sustainability in supply networks being one of the recent issues faced by organisations having complex external and internal connections, has led to an increase in implementation of ISO 14,001 environmental management systems by the companies, which in turn has increased the role of the supply networks in environmental management (Sarkis 2003). The concept of green supply chain management entails inclusion of environmental thinking into supply network management (Chin, Tat, and Sulaiman 2015). Green supply chain management (GSCM) is an organisational philosophy that helps the companies gain economic advantages by lowering the environmental risks and in parallel also improving the environmental efficiency of their operations (Zhu, Sarkis, and Lai 2008). As an acknowledgement of the increasing environmental consciousness, green supply chain management has appeared as a concept that takes into consideration the elements of sustainability and environmental thinking while carrying out inter and intra firm management of upstream and downstream supply chain network. Green supply chain management entails numerous green practices or interventions that help the companies improve the quality of their products, along with reducing their environmental footprint (Darnall, Jolley, and Handfield 2006). GSCM employs particular activities to keep track of and manage the environmental impacts through the life cycle of a product (Sellitto et al. 2019). The approach followed by companies towards environmental management has changed from a reactive one to a proactive one, and they are including sustainability into their decision makings and company culture (De Oliveira et al. 2018). GSCM encompasses the design, production and recycling aspects (Sahar, Afifudin, and Indah 2020).

Green interventions can be defined as certain practices that companies employ to reduce the environmental impact originating out of their product supply networks. Green supply chain management practices include activities like green purchasing, life cycle management concept integration into supply chain, and reverse logistics (Azevedo, Carvalho, and Cruz Machado 2011). There exists a positive correlation between economic performance and environmental commitment of a company(Al-Sheyadi, Muyldermans, and Kauppi 2019).

2. Objective of this research

Manufacturing organisations are making increased efforts to reduce the environmental footprint arising out of their operations. Implementing green interventions is a significant part of such efforts. Companies apply green interventions to implement sustainability in their operations. However before applying, the environmental managers need to know what are the most influential green interventions among a set of green interventions that they plan to implement. Some green interventions tend to be more influential than other green interventions. By focusing on the influential green interventions environmental managers can influence the implementation of other green interventions as well. There are various kinds of coercive and non-coercive sustainability drivers that impact the supply chain performance in companies (Nasrollahi et al. 2020).

To find the influential green interventions among a set of green interventions we need to study the interrelationships among the various green interventions. Interpretive structural modelling (ISM) technique has the potential to identify interrelationships among a set of criteria as well as assist in bringing direction and order to the intricacies of relationships among a system’s elements. Therefore, in this study we aim to utilise an ISM technique based framework to achieve the objective of finding the most influential green interventions for effective green supply chain management. The study aims to structure the green interventions adopted by the companies using ISM methodology.

The research paper is structured as follows. We begin with introduction in section 1, followed by the research objectives in section 2. Then section 3 presents the literature review followed by section 4 that discusses the research methods. In the section 5 we present the proposed framework to carry out the present study. The application of the proposed framework is presented in section 6. This is followed by the discussion of the results in section 7. Managerial implications are presented in section 8. Finally, section 9 presents the conclusions, limitations and future scope of the study.

3. Literature review

Literature review was carried out to explore the contemporary present knowledge and spot the knowledge gap. Examples of keywords used for search include, ‘Sustainability’, ‘Manufacturing organisations’, ‘Supply networks’, ‘Green interventions’. Companies adopt green interventions to improve their environmental performance. The following paragraphs discuss the major green interventions identified from the literature that are applied in companies.

Adoption of cutting edge technology

Contemporary technological progress has raised the potential of the technologies to greatly impact the sustainability of supply chains (Treiblmaier 2019). Physical internet is one such technological advancement based on a network of physical components, which exchange information to enhance the efficiency, effectiveness and sustainability of supply chain operations (Treiblmaier 2019). Mendoza-Fong et al. (2018) concluded that using homogenised and latest ICT and Green supply chain management implementation can lead to creation of economic, environmental and productive gains.

Green supplier selection

Green supplier selection is also an important green intervention adopted by companies. It involves considering sustainability criteria while selecting suppliers, working closely with the suppliers starting from product design phase till the production of the products by the supplier, and providing incentives to the suppliers for green technology adoption (Zsidisin and Siferd 2001). The organisations must focus beyond their factory gates to check the sustainability performance of their suppliers, and hence green supplier selection is important (Gupta, Soni, and Kumar 2019). Green supplier selection also reduces the environmental risks that may be associated with the supply networks (Darnall, Jolley, and Handfield 2006).

Supply chain mapping

To improve sustainability supply chain managers must be aware of the complexity of their supply networks (Jayaratne, Styger, and Perera 2012). For example a supply chain map may help the managers to better understand the intricacies of their supply network. Supply chain map is a map exhibiting linkages of the company with its suppliers and customers (Jayaratne, Styger, and Perera 2012). Supply network mapping helps remove the bottlenecks in the supply chain (Jayaratne, Styger, and Perera 2012) which may also lead to improvement in supply chain sustainability performance.

Joining industry level initiatives

There are certain industry level initiatives also, where companies belonging to a particular sector may adopt certain standards or some initiatives to improve the sustainability performance of the companies.

Reverse logistics

Reverse logistics is a major green intervention that includes the transportation of a used product from the user back to the manufacturer and then transformation of that product into a reusable product by the manufacturer (Fleischmann et al. 1997). It has become a matter of strategic priority for companies which they make decisions related to supply chain design and development (Rubio and Jiménez-Parra 2014). Reverse logistic has helped companies generate good return on investment as well improved market competitiveness (Sharma et al. 2011). Reverse logistics is an indivisible component of circular economy is inherently related to the fulfilment of circular economy (Alkahtani et al. 2021).

Cleaner production

Cleaner production leads to a better use of materials, lessened consumption of energy, and lesser levels of emission (Kjaerheim 2005) and is an effective green intervention. Cleaner production results in prevention of emissions at source and beginning of regular preventive improvement of the sustainability performance of the firms (Fresner 1998) and thus leads to supply chain sustainability. Cleaner production initiatives lead to development of new and smart technologies and new methods of organising supply networks (Giannetti et al. 2020). Successful implementation of cleaner production practices instils an environmentally conscious culture among the employees of an organisation (Oliveira Neto et al. 2020).

Green distribution

Many organisations follow green distribution strategies which leads to waste and energy use reduction along the downstream of the supply chain (Abdul Rehman Khan, 2018). Green buildings are also bringing about a change in the way the firms look at their facility assets and the companies are well aware of their environmental benefits that these building bring in improving the sustainability of their operations (Von Paumgartten 2003).

Effective transportation management

Effective transportation management like technological integration with suppliers as well as customers is affirmatively connected to the environmental monitoring and environmental collaboration (Singh, Singh, and Bhardwaj 2011). Decisions related to the choice of transportation modes (use of green vehicles), loading and unloading of vehicles, well planned vehicle routes influence the green supply network (Trivellas, Malindretos, and Reklitis 2020).

Eco-design

The practice of eco design includes designing the products for a reduced consumption of resources for reuse and recycle and prevention of the use of toxic substances in making the products (Green et al. 2012). Such steps lead to reduction in impacts along the supply chain. The process of eco design has a particular focus on the environmental footprint along the life cycle to have a more environment friendly production (Monteiro et al. 2019). Eco design process integrates environmental considerations during design and development phase of products, along with acting as a driver for innovations (Marconi and Favi 2020).

Developing environmental strategies, policies and procedures

Proactive environmental strategies are the practices that surpass the regulatory obligations, for example as regards to waste reduction and pollution prevention (Aragón-Correa 2003). Environmental strategy of a firm is said to be proactive if it displays a regular style of environmental practices, and is not just done to satisfy the environmental obligations (Murillo-Luna, Garcés-Ayerbe, and Rivera-Torres 2011).

Performance review and long term action plan

As the sustainability managers execute new strategies like investing in newer technology to enhance their sustainability performance, they should plainly express the targets and goals and collate these to real performance (Epstein and Roy 2001; Schaltegger, Burritt, and Stefan Schaltegger, Prof Roger Burritt 2014).

Based on the literature search the green interventions identified are summarised in the Table 1.

Table 1. Green interventions adopted by companies

Sr. No.	Green intervention	References
1	Adoption of cutting edge technology	(Mendoza-Fong et al. 2018; Treiblmaier 2019)
2	Green supplier selection	(Gupta, Soni, and Kumar 2019; D. Kannan, Govindan, and Rajendran 2014)
3	Supply chain mapping	(Jayaratne, Styger, and Perera 2012)
4	Joining industry level initiatives	(Giovannucci and Ponte 2005)
5	Reverse logistics	(Alkahtani et al. 2021; Rubio and Jiménez-Parra 2014)
6	Cleaner production	(Giannetti et al. 2020; Oliveira Neto et al. 2020)
7	Green distribution	(Abdul Rehman Khan, 2018; Danloup et al. 2015)
8	Effective transportation management	(Trivellas, Malindretos, and Reklitis 2020)
9	Green buildings	(Von Paumgartten 2003)
10	Eco design	(Marconi and Favi 2020; Monteiro et al. 2019)
11	Developing environmental strategies, policies and procedures	(Aragón-Correa 2003; Murillo-Luna, Garcés-Ayerbe, and Rivera-Torres 2011)
12	Performance review and long term action plan	(Epstein and Roy 2001; Schaltegger, Burritt, and Stefan Schaltegger, Prof Roger Burritt 2014)

3.1. Use of ISM methodology in literature

ISM methodology has been used in various studies in literature (see Appendix 1 for details of the ISM method). This section discusses the implementation of ISM methodology in literature. Zhu, Sarkis, and Lai (2008) tested two green supply chain management practices implementation measurement models and compared with the confirmatory factor analysis. Wu, Ding, and Chen (2012) evaluated the consequences of the institutional pressures and the green supply chain management drivers on the sustainable practices adopted in Taiwan’s textile and apparel industry. Zhu and Sarkis (2004) studied the relationship between the green practices and performance of the Chinese manufacturing organisations implementing green supply chain management. Azevedo, Carvalho, and Cruz Machado (2011) presented a conceptual model to understand the effect of green practices on the performance of supply chain. Kumar, Luthra, and Haleem (2013) presented an ISM based to analyse the customer involvement in sustainable supply chain management. Biswal et al. (2018) proposed an ISM based model to analyse the enablers of sustainable supply chain management. Yang et al. (2017) utilised ISM methodology to study the success factors that lead to effective implementation of supply chain management. Jayant and Azhar (2014) analysed the barriers that hamper sustainability adoption utilising interpretive structural modelling. Mathiyazhagan et al. (2013) presented an ISM based model to analyse the barriers to effective sustainability implementation in supply chain and understand the mutual influences among the barriers. ISM methodology was used in conjunction with DEMATEL method to understand the interaction among the various green supplier selection criteria by (Mehregan et al. 2014). Luthra et al. (2011) developed a structural model based on ISM to interpret the interdependence among the barriers. Khaksar et al. (2015) used ISM methodology to prioritise the green supply chain practices. Dube and Gawande (2016) analysed the green supply chain practices using interpretive structural modelling. Chakraborty, Das, and Satapathy (2015) employed ISM methodology to understand the sustainability issues in tea supply chain. Rehman and Shrivastava (2011) developed an approach to understand interrelationships among drivers to effective sustainable supply chain management. Anass and Said (2017) utilised an integrated ISM and fuzzy MICMAC approach to analyse the barriers to sustainability implementation. Nigam (2014) presented an interpretive structural modelling of the barriers that affect sustainability implementation in organisations. ISM methodology was utilised by (Shrimali et al. 2018) to analyse the barriers to lean implementation. Pujotomo, Sriyanto, and Widyawati (2018) employed ISM methodology to analyse barriers to implementation of cleaner production. Interpretive structural modelling was used by (Tooranloo and Rahimi 2018) to analyse the barriers to green supply chain management in healthcare centres. Kausar, Garg, and Luthra (2017) utilised the ISM methodology to understand the interrelationships among the enablers to green supply chain management and then used MICMAC technique to analyse the identified drivers corresponding to their driving power and dependence power. Kumar and Rahman (2017) utilised ISM methodology in conjunction with fuzzy AHP method to study enablers to sustainability implementation. Yang and Lin (2020) analysed the effects that supply chain collaboration has on green innovation performance utilising interpretive structural modelling. Shanker and Barve (2021) analysed sustainability concerns in diamond supply chain utilising a fuzzy ISM-MICMAC and DEMATEL approach. Jamwal et al. (2020) presented a review on multi criteria decision making methods, highlighting how the manufacturing industries can benefit from such techniques.

Research gaps: From the literature search we find that there is a significant research gap regarding the identification of influential green interventions. Green interventions have not been analysed on the basis of how dominant they are. Literature search reveals no clear framework to rank the influential green interventions. There is a scope of developing a framework using ISM methodology to prioritise the green interventions. Utilising this framework the sustainability managers can focus on the most dominant/influential green interventions to remove environmental impact hotspots in their supply networks. Additionally, various green interventions are present in literature but are dispersed and there is a need to compile these interventions at a single place. Structuring and analysing these interventions will benefit and help the decision makers. The manuscript is also different from the previous studies in that it analyses a new set of sustainability interventions.

4. Research methods

The technique used here is interpretive structural modelling (ISM) methodology. ISM is chosen for the present study as it is a widely accepted technique to identify the interrelationships among ‘certain items’ that define an issue (Watson 1978). In the present study ‘certain items’ are the green initiatives, and the study aims to find the most influential green interventions among a set of green interventions. The research aims to furnish a structured model that shows the influence and effectiveness of a green intervention in a graphical representation. ISM method is systematic, efficient, and furnishes a structured model/graphical representation of a problem enabling it to be expressed in an efficacious manner (Attri, Dev, and Sharma 2013) and is hence chosen for the present research. In ISM method a set of disparate directly or indirectly connected elements are structured into a thorough systematic model (Dewangan, Agrawal, and Sharma 2015; G. Kannan, Pokharel, and Sasi Kumar 2009; Mandal and Deshmukh 1994). Other techniques like Decision-making trial and evaluation laboratory (DEMATEL) and Analytic network process (ANP) or the hybridisation of two different methods are time consuming and complex. ANP method is not suitable to study interactive relationships (Shakeri and Khalilzadeh 2020; W.-W. Wu 2008), while DEMATEL method is used to envisage the structure of intricate causal relationships and can segregate the concerned criteria of a system into the cause and effect groups to streamline the operation of decision making (Amiri et al. 2011), while here we aim to find the level of influence green interventions have among a set. ISM method is more precise in in bringing direction and order to the intricacies of relationships among a system’s elements. Moreover ISM is most effective when the number of factors to be analysed are around 10–12, while methods like DEMATEL are preferred when a large number of factors are to be analysed (Hsu et al. 2013; A. Kumar and Dixit 2018).

• Proposed study: To Identify the influential green interventions among a set of available green interventions

The study proposed here is aimed to identify the influential green interventions among a set of available green interventions. To identify this we propose a framework based on ISM technique. Figure 1 presents the proposed framework. The study is planned to be done in two stages.

Figure 1. Schematic illustration of the proposed model for prioritising the green interventions for effective green supply chain management

Stage 1: Identification of the green interventions for effective green supply chain management and defining a decision making group

This is to be done using an extensive literature survey and expert feedback. A decision making group will be defined for preparing a comparative matrix.

Stage 2: Prioritise the most influential green interventions among the listed interventions using Interpretive structural modelling (ISM) technique.

This is to be done by applying ISM method. ISM method is used to prioritise the green interventions based on the expert opinion.

Application of the proposed study: finding the most influential green interventions

The proposed model is applied to prioritise the green interventions to find the most influential interventions among the listed interventions.

Stage 1: Identification of the green interventions for effective green supply chain management and defining a decision making group

Step 1-This is done through a literature search whereby we identify a list of green interventions practiced in manufacturing organisations for green supply chain management.

The literature search was aimed to find the most widely implemented green interventions in supply networks and then summarising them. The Literature review was carried out to explore the contemporary present knowledge and spot the knowledge gap and figure the research question. The research papers staring from year 1994 till 2020 were analysed. Online search engines such as google scholar were used to search keywords like sustainability, green supply chain management, green interventions, multi criteria decision making etc. A list of green interventions were identified. The green interventions thus identified are summarised in the Table 1. The details of these identified green interventions have been discussed under section 3. The criteria (green interventions) are assigned a number for further evaluation. A panel of decision makers consisting of sustainability experts is constituted. To handle the biasness in a qualitative study the number of experts must be at least 5 or more (Gardas et al. 2019; Murry and Hammons 1995). A total of 6 industry experts are involved for decision making. Each expert has the experience of more than 5 yr. in the management of sustainability in supply networks. The decision making team consists of 1 senior manager, 3 supply chain professionals, and 2 environmental representatives. Data were collected either by personal visits or online medium.

Stage 2: Prioritise the most influential green interventions among the listed interventions using interpretive structural modelling (ISM) technique.

Step 2- A structural self-interaction matrix (SSIM) is prepared based on the methodology discussed in appendix 1. SSIM thus obtained is shown in the Table 2 based on the feedback of the decision making group.

Table 2. Structural self-interaction matrix

Interventions	12	11	10	9	8	7	6	5	4	3	2
1	X	A	O	O	X	V	X	V	O	V	V
2	O	A	A	O	X	O	X	X	A	A
3	O	A	V	O	X	V	V	V	A
4	X	X	V	V	V	V	X	V
5	O	A	A	O	X	X	X
6	A	A	A	O	O	X
7	A	A	X	O	X
8	A	A	A	O
9	O	A	O
10	A	A
11	X

Step 3- The SSIM is converted into an initial reachability matrix. The initial reachability matrix thus obtained is given in the Table 3.

Table 3. Initial reachability matrix

	1	2	3	4	5	6	7	8	9	10	11	12
1	1	1	1	0	1	1	1	1	0	0	0	1
2	0	1	0	0	1	1	0	1	0	0	0	0
3	0	1	1	0	1	1	1	1	0	1	0	0
4	0	1	1	1	1	1	1	1	1	1	1	1
5	0	1	0	0	1	1	1	1	0	0	0	0
6	1	1	0	1	1	1	1	0	0	0	0	0
7	0	0	0	0	1	1	1	1	0	1	0	0
8	1	1	1	0	1	0	1	1	0	0	0	0
9	0	0	0	0	0	0	0	0	1	0	0	0
10	0	1	0	0	1	1	1	1	0	1	0	0
11	1	1	1	1	1	1	1	1	1	1	1	1
12	1	0	0	1	0	1	1	1	0	1	1	1

Step 4- The step 4 involves incorporating of transitiveness

Step 5- The final reachability matrix shown in Table 4 is utilised to obtain the reachability set, interventions having value 1 in the row for a particular intervention come in the reachability set for that intervention. The antecedent set lists all the interventions that have values one in the column for that particular intervention. The intersection set is made from the common values among the reachability set and antecedent set. The levels are assigned to the interventions. Tables 5, 6, and 7 show the iterations and the levels assigned to the green interventions.

Table 4. Final reachability matrix

Interventions	1.	2.	3.	4.	5.	6.	7.	8.	9.	10.	11.	12.	Driving power
1.	1	1	1	1*	1	1	1	1	0	1*	1*	1	11
2.	1*	1	1*	1*	1	1	1*	1	0	0	0	0	8
3.	1*	1	1	1*	1	1	1	1	0	1	0	0	9
4.	1*	1	1	1	1	1	1	1	1	1	1	1	12
5.	1*	1	1*	0	1	1	1	1	0	1*	0	0	8
6.	1	1	1*	1	1	1	1	1*	1*	1*	1*	1*	12
7.	1*	1*	1*	1*	1	1	1	1	0	1	0	0	9
8.	1	1	1	0	1	1*	1	1	0	1*	0	0	8
9.	0	0	0	0	0	0	0	0	1	0	0	0	1
10.	1*	1	1*	1*	1	1	1	1	0	1	0	0	9
11.	1	1	1	1	1	1	1	1	1	1	1	1	12
12.	1	1*	1*	1	1*	1	1	1	1*	1	1	1	12
Dependence power	11	11	11	9	11	11	11	11	5	10	5	5

1* entries are made to include transitivity. It states that if a variable A is related to variable B, and the variable B is related to C, then A is definitely related to C.

Table 5. Level partition for sustainability interventions (iteration 1)

Intervention	Reachability	Antecedent	Intersection	Level
1	1 2 3 4 5 6 7 8 10 11 12	1 2 3 4 5 6 7 8 10 11 12	1 2 3 4 5 6 7 8 10 11 12	I
2	1 2 3 4 5 6 7 8	1 2 3 4 5 6 7 8 10 11 12	1 2 3 4 5 6 7 8	I
3	1 2 3 4 5 6 7 8 10	1 2 3 4 5 6 7 8 10 11 12	1 2 3 4 5 6 7 8 10	I
4	1 2 3 4 5 6 7 8 9 10 11 12	1 2 3 4 6 7 10 11 12	1 2 3 4 6 7 10 11 12
5	1 2 3 5 6 7 8 10	1 2 3 4 5 6 7 8 10 11 12	1 2 3 5 6 7 8 10	I
6	1 2 3 4 5 6 7 8 9 10 11 12	1 2 3 4 5 6 7 8 10 11 12	1 2 3 4 5 6 7 8 10 11 12
7	1 2 3 4 5 6 7 8 10	1 2 3 4 5 6 7 8 10 11 12	1 2 3 4 5 6 7 8 10	I
8	1 2 3 5 6 7 8 10	1 2 3 4 5 6 7 8 10 11 12	1 2 3 5 6 7 8 10	I
9	9	4 6 9 11 12	9	I
10	1 2 3 4 5 6 7 8 10	1 3 4 5 6 7 8 10 11 12	1 3 4 5 6 7 8 10
11	1 2 3 4 5 6 7 8 9 10 11 12	1 4 6 11 12	1 4 6 11 12
12	1 2 3 4 5 6 7 8 9 10 11 12	1 4 6 11 12	1 4 6 11 12

Table 6. Level partition for sustainability interventions (iteration 2)

Intervention	Reachability	Antecedent	Intersection	Level
4	4 6 10 11 12	1 2 3 4 6 7 10 11 12	4 6 10 11 12	II
6	4 6 10 11 12	1 2 3 4 5 6 7 8 10 11 12	4 6 10 11 12	II
10	4 6 10	1 3 4 5 6 7 8 10 11 12	4 6 10	II
11	4 6 10 11 12	1 4 6 11 12	4 6 11 12
12	4 6 10 11 12	1 4 6 11 12	4 6 11 12

Table 7. Level partition for sustainability interventions (iteration 3)

Intervention	Reachability	Antecedent	Intersection	Level
11	11 12	1 4 6 11 12	11 12	III
12	11 12	1 4 6 11 12	11 12	III

Step 6- A conical matrix is developed based on the levels assigned to the interventions in the previous step. The conical matrix thus developed is shown in Table 8. This is followed by drawing of an ISM model presented in Figure 2.

Figure 2. ISM based model for sustainability interventions in supply chain

Table 8. Conical matrix obtained by nesting the interventions of the same level thought the rows and columns of the final reachability matrix

Interventions	1.	2.	3.	4.	5.	6.	7.	8.	9.	10.	11.	12.
1.	1	1	1	1*	1	1	1	1	0	1*	1*	1
2.	1*	1	1*	1*	1	1	1*	1	0	0	0	0
3.	1*	1	1	1*	1	1	1	1	0	1	0	0
5.	1*	1	1*	0	1	1	1	1	0	1*	0	0
7.	1*	1*	1*	1*	1	1	1	1	0	1	0	0
8.	1	1	1	0	1	1*	1	1	0	1*	0	0
9.	0	0	0	0	0	0	0	0	1	0	0	0
4.	1*	1	1	1	1	1	1	1	1	1	1	1
6.	1	1	1*	1	1	1	1	1*	1*	1*	1*	1*
10.	1*	1	1*	1*	1	1	1	1	0	1	0	0
11.	1	1	1	1	1	1	1	1	1	1	1	1
12.	1	1*	1*	1	1*	1	1	1	1*	1	1	1

Step 7: MICMAC Analysis

It can be observed in Figure 3 that the interventions that fall in quadrant I include ‘adoption of cutting edge technology’ (1), ‘green supplier selection’ (2), ‘supply chain mapping’ (3), ‘joining industry level initiatives’ (4), ‘reverse logistics’ (5), ‘cleaner production’ (6), ‘green distribution’ (7), ‘effective transportation management’ (8), and ‘eco design’ (10). These green interventions have strong dependence and strong driving power, which means that they are unstable because any impact on these interventions can have impact on other green intervention implementation and on themselves. The next quadrant II containing independent variables lists ‘developing environmental strategies, policies and procedures’ (11), and ‘performance review and long-term action plan’ (12).These interventions have strong driving power and low dependence, which means that they are highly important and influential, because any impact on these interventions will strongly influence other green intervention implementation. The green intervention ‘green buildings’ code-(9) appears in the quadrant III implying that it has weak dependence and driving power and has relatively weak links with the system that might be strong. No intervention falls in the quadrant IV..

Figure 3. Driving power and dependence power diagram

Step 8: The final step is the review of the results by the decision making panel.

5. Discussion

Driver dependence power diagram derived through MIMAC analysis provides an understanding into the comparative importance and interdependences between the interventions adopted by the companies. The dependence and driving power of the interventions is represented in Figure 3. The current research using interpretive structure modelling manifests the given elucidations. Four quadrants are given in the driver and dependence power diagram. The green intervention appearing in the third quadrant is the ‘green buildings’ intervention because it has a driving power of 1 and dependence power of 5. It implies that the green buildings intervention has less driving and dependence power. The Quadrant IV is also referred to as dependent quadrant. It has a low driving and dependence power. No interventions appear in this quadrant. The next is the quadrant I which has high dependence and driving power. The interventions (along with the assigned code) appearing in this quadrant are adoption of cutting edge technology (1), cleaner production (6), joining industry level sustainability initiatives (4), green distribution (7), green supplier selection (2), reverse logistics (5), effective transportation management (8), eco design (10), supply chain mapping (3). Developing environmental strategies, policies and procedures (11), performance review and long term action plan for sustainability implementation (12), come in the quadrant II. The green interventions appearing in this quadrant have high dependence and low driving power, which means these green interventions are the most influential green interventions. Of all the interventions none has an independent character meaning that all the interventions are dependent on each other

6. Managerial insights

The results are discussed with sustainability experts. The decision makers find the application of this study in finding the most influential green interventions. The framework presented here provides a simple yet effective approach to prioritise the green interventions for the sustainability managers. The study contributes tangibly in theory and practice to area of green intervention implementation for effective sustainable supply chain management. The study also presents twelve green interventions based on literature search. The presented twelve green interventions will aid the sustainability managers in their endeavours. The sustainability managers can focus on the identified influential green interventions for better and efficient environmental management. The sustainability managers can use ISM methodology based framework to structure details related to green interventions implementation. Specific implications for managers based on the initiatives identified include:

• Managers must develop environmental strategies, procedures and guidelines at their organisations. These policies will help in streamlined and effective implementation of sustainability in their organisations.

• The managers should develop a performance review mechanism that will periodically measure the performance of the organisation on sustainability front. This will also include developing a long term action plan supported by a performance review mechanism.

7. Conclusion, limitations, and future scope

The results of the study will be beneficial for the companies especially small organisations for effective green supply chain management in a cost effective way. The results show that ‘developing environmental strategies, policies and procedures’, and ‘performance review and long term action plan for sustainability implementation’ have high driving power and low dependence power, which means that these are the most influential green interventions among the set of green interventions. The green interventions, ‘adoption of cutting edge technology’, ‘cleaner production’, ‘joining industry level sustainability initiatives’, ‘green distribution’, ‘green supplier selection’, ‘reverse logistics’, ‘effective transportation management’, ‘eco design’, and ‘supply chain mapping’ are the interventions with high dependence and high driving power.

Once the environmental impact hotspots in a supply network have been identified through techniques like life cycle assessment, the next step is to implement the green interventions to remove these environmental impact hotspots. But before implementing a certain set of green interventions we need to find which one is the most influential or effective. This study fills this gap by providing a framework to rank the green interventions. The manuscript is also different from the previous studies in that it analyses a new set of sustainability interventions.

The study has certain limitations as well. The ISM technique gives interrelationships between the variables, which are dependent upon the expert judgment, and experience, which means that the judgment of the expert may vary the results. The study is based on an identified list of green interventions, though the literature may feature more number of green interventions. The future scope of study may include expanding the study to more number of green interventions and application of different methods like decision-making trial and evaluation laboratory (DEMATEL) to evaluate the green interventions.

Disclosure Statement

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

Additional information

Funding

This work was supported by the DST-EPSRC [Engineering driven sustainable supply network design project].

Notes on contributors

Rachit Kumar Sharma

Rachit Kumar Sharma is a PhD. research scholar in the Department of Mechanical Engineering at Indian Institute of Technology Ropar. His research interests include Sustainability, Sustainable Supply Networks and Life cycle assessment.

Prashant Kumar Singh

Prashant Kumar Singh is a PhD. research scholar in the Department of Mechanical Engineering at Indian Institute of Technology Ropar. His research interests include Sustainability, Eco design and Sustainable design.

Prabir Sarkar

Prabir Sarkar is currently Associate professor in the Department of Mechanical Engineering at Indian Institute of Technology Ropar. His research interests include Product design, Sustainability, Creativity and Design research.

Harpreet Singh

Harpreet Singh is currently Professor in the Department of Mechanical Engineering at Indian Institute of Technology Ropar. His research interests include Surface Engineering, Biomedical Coatings and Sustainable Manufacturing Technology.

Appendix 1.

Interpretive structural modelling (ISM) technique

Interpretive structural modelling (ISM) comes out as an interactive learning process (Mandal and Deshmukh 1994). The method is interpretive in the sense that the judgment of a group is responsible for the how and whether the items are interrelated (Dewangan, Agrawal, and Sharma 2015); it is structural in the sense that the method leads to an extraction of an overall structure of the items, and modelling in the sense that interrelationships and the overall structure are presented in a digraph model (Dewangan, Agrawal, and Sharma 2015; Mandal and Deshmukh 1994). In ISM a set of disparate directly or indirectly connected elements are structured into a thorough systematic model (Dewangan, Agrawal, and Sharma 2015; Kannan, Pokharel, and Sasi Kumar 2009; Mandal and Deshmukh 1994). ISM technique assists in bringing direction and order to the intricacies of relationships among a system’s elements. The various steps involved in carrying out interpretive structural modelling are explained in the following lines.

Step 1: The beginning step involves listing of the variables (green interventions) under consideration.

Step 2: The second step is the development of a structural self-interaction matrix (SSIM) for the variables under study. The SSIM indicates a pairwise relationship among the variables that are being studied. Herein the relationship and its associated direction between two variables is questioned. There are certain symbols that are used to denote the direction of relationship between two variables (Diabat et al., 2014). These symbols are presented below:

V: Variable, i influences variable j

A: Variable j influences variable i

X: Both the variables influence each other

O: Both the variables have no relationship with each other.

Step 3: The third step involves the preparation of an initial reachability matrix from the SSIM developed in the previous step using binary digits. The rules as presented by (Kannan, Govindan, and Rajendran 2014) are given in the following paragraph:

If entry in a particular cell (i,j) in the structural self-interaction matrix is V, then cell (i,j) entry is marked 1 and the corresponding cell (j,i) entry is marked O.

For a cell (i,j) having entry X in SSIM, both the cell entries i.e (i,j) and (i,j) are marked 1 in the initial reachability matrix.

And for a cell (i,j) having an entry O, both the cell entries i.e (i,j) and (i,j) are marked 0 in the initial reachability matrix.

Step 4: The fourth step involves the checking for transitivity in the initial reachability matrix. The transitivity of contextual relation states that if a variable A is related to variable B, and the variable B is related to C, then A is definitely related to C. 1* entries are made to include transitivity to fill the gap in the feedbacks obtained while developing the structural self-interaction matrix.

Step 5: The final reachability matrix obtained in the fourth step is partitioned into different levels. Reachability and antecedent sets are obtained for each variable. The reachability set is comprised of the variable and the other variable, which it might have impact on, while the antecedent set is comprised of the variable and the other variable that might influence it. This is followed by finding the intersection between the sets to derive the intersection set. The variables for which the intersection set and the reachability set are the same have the highest level. The next level of variables are found by the removing the variables that have already attained the highest levels. The iterations are done till the level for each variable is found (Attri, Dev, and Sharma 2013).

Step 6: The relationships obtained in the previous step are used to obtain a conical matrix and to develop an ISM model.

Step 7: The next step is the MICMAC analysis. MIMAC analysis stands for Matriced impacts ‘croises-multiplication applique’ and class-ment analysis (Kannan, Pokharel, and Sasi Kumar 2009; Mandal and Deshmukh 1994). It is technique used to analyse the driver and the dependence power of the alternatives (Attri, Dev, and Sharma 2013; Mandal and Deshmukh 1994). The method classifies the variables into four criteria, autonomous, dependent, linkage, and independent criteria (Kannan, Pokharel, and Sasi Kumar 2009). The variables under autonomous criteria have weak driver power and dependence. The variables under dependent criteria have a weak driver power and strong dependence. The variables under the linkage criteria have a strong driving power as well as strong dependence. The independent criteria variables have a weak dependence but a strong driving power.

Step 8: The final step involves the review of the results of the study.