Barriers in sustainable industry 4.0: a case study of the footwear industry

ABSTRACT

Industry 4.0 (I4.0) has a great perspective in the reshaping the industry as it creates a working environment of integrated productivity between workers and machines. However, the adoption of I4.0 in developing countries like India needs deliberation in the perception of sustainability. This paper aims to identify sustainable I4.0 barriers and evaluates interrelationship amongst the barriers. Twenty barriers to sustainable I4.0 are determined from the literature review. Expert’s opinions are taken to finalise the identified barriers. Data are collected from Indian footwear industries and have been further analysed using the Fuzzy-DEMATEL approach. The results show that ‘Lack of new organizational policy’, ‘Lack of customer feedback and cooperation towards I4.0 and sustainable practices,’ and ‘Lack of infrastructure’ are the most significant barriers. The study guides the industries of developing economies to formulate an effective strategy for the adoption of I4.0. The future scope of the survey includes an extension to other emerging economies to improve the reliability and validation.

KEYWORDS-:

• Industry-4.0
• adoption
• sustainability
• footwear
• fuzzy-DEMATEL
• decision support system

1. Introduction

‘Industrie 4.0’ (I4.0) was brought to life in Germany in 2011 from a ‘High-tech Strategy 2020’ project (Lu 2017). Ang et al. (2017) aptly stated that I4.0 took a paradigm shift from centralised to decentralised manufacturing systems. I4.0 is formed by a cluster of different technologies like ‘Big Data Analytics, Internet of Things, Cyber Security,’ which belong to the ‘cyber-physical system (CPS)’ domain, to which other manufacturing technologies are added, as additive manufacturing, 3D printing, advanced manufacturing systems etc. (Rüßmann et al. 2015). I4.0 is the integration of several techniques that embraces both processes and products (Porter and Heppelmann 2014). Horizontal and vertical system integration offers a holistic view of technologies, skills, tools, and knowledge for system autonomy. Vertical integration is through human, organisation, equipment, process, and product can be helpful in the service sector, whereas horizontal integration through procurement, planning, and service management (Pérez-Lara et al. 2018). This integration will be useful for improving the performance of manufacturing organisations.

Many researchers have emphasised the opportunity and need to find a new path for financial development (Sousa-Zomer and Miguel 2018; Parajuly and Wenzel 2017). To deal with waste generation, a new circular economy (CE) model has been proposed (Centobelli et al. 2020; Lieder and Rashid 2016). However, it is seen that CE models to achieve sustainability are highly uncertain (Wyrwicka and Mrugalska 2017), which makes them challenging to optimise and adapt the CE methods appropriate for the given application domain (Baccarelli et al. 2017). Researchers suggest that there is a disconnect between CE and I4.0 (Baccarelli et al. 2017). Sustainable I4.0 has a positive impact on recycling, workforce optimisation, and productivity (Dey et al. 2020). I4.0 will advance operational competence, enhance data control operations, energy optimisation, and wastes reduction from processes and machines (Ivanov et al. 2016; Thoben, Wiesner, and Wuest 2017).

According to the HDFC Bank Investment Advisory Group (2018) report on the Footwear Sector, the Indian footwear industry is said to hold an essential place in the economy. India overtook the US as the second-largest consumer of footwear (Gupta and Kaur 2017). It accounted for 9% of the worldwide annual production of twenty-two billion pairs (Bata India 2019). However, the leather industry is facing issues of wastewater processing and tanning (Sawalha et al. 2019), water reuse (de Aquim, Hansen, and Gutterres 2019), and carbon footprint (Roibás, Loiseau, and Hospido 2017). The footwear organisations of developing countries like India are in quest of adopting I4.0 in recent years (Majeed and Rupasinghe 2017). However, the organisations are continually struggling with constant changes in a popular category, sports category, health category, apparel, and accessories products. Thus barriers of sustainable I4.0 must be identified, and the purpose of this study is to address the following RQs (research questions).

RQ1- What are critical barriers to sustainable I4.0 adoption for developing countries?

RQ2- What is the relationship (cause-effect) between the critical barriers?

RQ3- What necessary steps must be taken to overcome the significant barriers?

To address the above RQs, the literature on ‘Industry-4.0’, ‘smart manufacturing,’ ‘sustainability’ was reviewed. The barriers to sustainable I4.0 were identified through a literature search, followed by approval from the experts. Later, the Fuzzy-DEMATEL methodology was used to determine the causal relationship between shortlisted barriers. The fuzzy logic approach captures human biases and data uncertainty (Zimmermann 2011). DEMATEL method recognises the interrelationship between the barriers. DEMATEL categorises barriers into two groups, namely ‘cause’ and ‘effect.’ It also ranks barriers to identify the most significant barriers. The ROs (research objectives) of the paper is as follows:

RO1- To determine the critical barriers to sustainable I4.0 adoption in the background of Indian footwear manufacturers

RO2- To identify the causal relationship between the barriers to sustainable I4.0 adoption

RO3- To rank the barriers and propose strategies to overcome the barriers for sustainable I4.0 adoption

The structure of the paper is the following: Sections 2 provides a literature review on the adoption of I4.0. Section 3 presents the proposed research methodology. Data analysis and results have been elaborated in Section 4. Section 5 details the discussion and managerial implication, followed by the conclusion of the study in Section 6.

2. Literature review

The high-tech strategy project in Germany addressed to the amalgamation of physical entities along with smart machines, assembly lines, which were meant for real-time sharing and integration of information (Hozdić 2015). It was implied that this would affect the entire supply chains (SCs), humans, and organisations. However, the complexity level associated is on high. The current research on the Internet of Things, smart manufacturing, smart factory, and the cyber-physical system shows potential use in manufacturing (Liao et al. 2017).

A variety of digital production technologies like advanced robotics, sensors or network production, etc., along with the inclusion of IT-enabled management processes like capacity planning, enterprise resource planning, management production control using data analytics, etc., are shaping this industrial revolution (Dachs, Kinkel, and Jäger 2017). Gerlitz (2016) stated that I4.0 is usually diluted down to ‘smartness.’ According to Kühnle and Bitsch (2015) smartness can be implemented by applying six principles: virtualisation, interoperability, decentralisation, service orientation, real-time capability, and modularity. This deals with achieving the goal of boosting productivity and add value to industrial operations/processes and stimulating economic growth, for instance, through smart objects (Atzori, Iera, and Morabito 2014), smart manufacturing and industry (Davis et al. 2012), smart products and services (Porter and Heppelmann 2014), intelligent machines and factories (Kagermann et al. 2013), smart cities (Letaifa 2015), and smart spaces (Leminen et al. 2012).

Organisations must use sustainable practices for being competitive in this global market (Shao 2019). Sustainability is a comprehensive concept that can be outlined through intertwining social, environmental, and economic performance layers (García‐Sánchez and Martínez‐Ferrero 2019). The characteristics of sustainability are most significant as a response to the increasing awareness of the environment, globalisation, demographic trends, and current economic challenges, etc. Ever since the Brundtland Commission report, awareness of environmental and societal impacts has been increased in the industrial domain. (Elkington 1994). The need of the hour is to build a reliable infrastructure, introduce skill development programs, and enhance policy to support this ecosystem (Albrizio, Kozluk, and Zipperer 2017). As an emerging economy, the Indian manufacturing sector needs to deal with several challenges to the adoption of I4.0 initiatives in developing ecological, social, economic sustainability of the supply chain (Luthra and Mangla 2018). In any case, Indian companies still have a lot to do for the timely and successful implementation I4.0. As discussed before, the unpredictability and uncertainty for I4.0 adoption is the investment required, and the indistinct cost benefits for the I4.0 application areas (Koch et al. 2014).

Furthermore, the employees lack enough skills necessary for coping up with the forthcoming automation, which leads to a lack of clarity in I4.0 implementation. This change in the digital decade will be filled with plenty of obstacles and hurdles before being successfully welcomed by industry (Müller, Kiel, and Voigt 2018). Table 1 shows a summary of the literature papers on the adoption of I4.0.

Table 1. Literature papers on adoption of I4.0

Sr. No.	Publication	Purpose of the study	Barrier Identification	Sustainability Considerations	Country	Type of Industry	Tools/Techniques used
							Main tool	Uncertainty consideration
	Oesterreich and Teuteberg (2016)	Elements of I4.0 and triangulation approach for the construction business	√	X	Germany	Construction	Qualitative research method	X
	Ang et al. (2017)	Energy efficiency in an I4.0 Environment	√	√	Singapore	Manufacturing	Conceptual Analysis	X
	Dachs, Kinkel, and Jäger (2017)	Adoption of I4.0 in manufacturing	X	X	Germany	Manufacturing	Statistical analysis, t-test	X
	Kiel et al. (2017)	Challenges and benefits of sustainable I4.0	√	√	Germany	–	Exploratory multiple case study approach	X
	Lin, Shyu, and Ding (2017)	Policy for I4.0 and Sustainability	X	√	Taiwan	–	Conceptual Analysis	X
	Preuveneers, Joosen, and Ilie-Zudor (2017)	Trust aspect of I4.0 for Customer-orientated Plants	X	X	Belgium	Manufacturing	Conceptual Analysis	X
	Bertola and Teunissen (2018)	Digital transformation in the fashion industry (Fashion 4.0)	X	X	Italy	Fashion	Conceptual Analysis	X
	Dalenogare et al. (2018)	I4.0 technologies for manufacturing performance	√	X	Brazil	Manufacturing	Regression Analysis	X
	Kamble, Gunasekaran, and Sharma (2018)	Barriers in the adoption of I 4.0 in manufacturing	√	X	India	Manufacturing	ISM, fuzzy MICMAC analysis	√
	Kazancoglu and Ozkan-Ozen (2018)	Human aspect from the perspective of operations management (Workforce 4.0)	X	X	Turkey	High-Technology Firm	Fuzzy DEMATEL	√
	Lin et al. (2018)	A strategic response to I4.0 for the manufacturing industry	X	X	China	Automotive Industry	ISM	X
	Lu and Weng (2018)	Maturity analysis of smart manufacturing	X	X	Taiwan	Manufacturing	Survey and Delphi Method	X
	Luthra and Mangla (2018)	Sustainable supply chain −4.0	√	X	India	Manufacturing	AHP, EFA	X
	Moktadir et al. (2018)	Challenges to I4.0- environmental and process safety aspect	√	√	Bangladesh	Leather Industry	BWM	X
	Nascimento et al. (2019)	I4.0 technologies for the circular economy	√	√	Brazil	Manufacturing	Qualitative research method	X
	Szalavetz (2019)	Subsidiaries requirements for I4.0 in manufacturing	√	X	Hungary	Manufacturing	Exploratory study approach	X
	Tortorella and Fettermann (2018)	Integration of lean manufacturing and I4.0	X	X	Brazil	Manufacturing	Multivariate analysis	X
	Agostini and Filippini (2019)	To understand managerial and organisational challenges in I4.0	√	X	Italy	Manufacturing	Cluster Analysis, t-test	X
	Frank, Dalenogare, and Ayala (2019)	Implementation patterns of I4.0 in a manufacturing organisation	X	X	Brazil	Manufacturing	Hierarchical Cluster Analysis, t-test	X
	Rajput and Singh (2019a)	Connecting I4.0 and circular economy	√	√	India	Manufacturing	DEMATEL	X
	Rajput and Singh (2019b)	I4.0 challenges for circular economy implementation	√	√	India	Manufacturing	ISM-MICMAC	X
	Our work	Barriers of sustainable I4.0 adoption	√	√	India	Footwear	Fuzzy DEMATEL	√

Technological developments have always disrupted the Industrial processes and brought in a way to revolutionise processes. Rapid industrialisation leads to comprise of the safety and health of the workforce. It is essential to identify the barriers of I4.0 in the manufacturing sector of India. In this paper, the barriers for I4.0 are recognised based on systematic literature papers and experts’ opinions from academia and industry. This is a 2-step procedure to find and evaluate the barriers. In the first step, critical barriers were recognised based on the literature. The search keyword used included terms like Industry 4.0, adoption, smart manufacturing, manufacturing, challenges, etc. In the second step, twenty barriers were confirmed by an industrial expert for Industry 4.0 in the context of the Indian manufacturing sector. The list of barriers is presented in Table 2 given below.

Table 2. Barriers to I4.0 sustainable manufacturing

S.N.	Barrier	Brief Description	References
FW1	Lack of awareness about I4.0	There is a lack of awareness among practitioners and researchers on I4.0 and its effects on sustainability. Literature shows a need for an effective strategic plan for the adoption of I4.0 in engineering firms. Even though working and industrial managers understand the importance of I4.0, they are not sure about its implications on achieving sustainability objectives.	Mittal et al. (2018), Müller, Kiel, and Voigt (2018)
FW2	Lack of supplier involvement towards I4.0 and sustainable practices	Coordination with the supplier is essential in improving sustainable practices. The collaboration needs communication mechanisms, and transparency between members is critical. However, it can be achieved through standardised interfaces, data synchronisation, and compatibility of hardware and software.	Agostini and Filippini (2019)
FW3	Lack of customer feedback and cooperation towards I4.0 and sustainable practices	Manufacturing companies are struggling with constantly changing customers’ preferences. Firms need to be responsive to fulfill customer demands. The involvement of the customer is a must for ensuring sustainability. However, the mechanism for customer feedback needs due consideration.	Oesterreich and Teuteberg (2016), Müller, Kiel, and Voigt (2018), Tortorella and Fettermann (2017)
FW4	Lack of trust between upstream and downstream members	Many industries, particularly in an urban and rural part of developing economies, are still not familiar with I4.0. Due to the interdisciplinary nature of the technologies, upstream and downstream members lack confidence in the benefits of I4.0.	Basl (2017), Luthra and Mangla (2018),
FW5	Lack of support from top management	Top management dedication and support is crucial for the development of effective I4.0. Management needs to focus on cultivating organisational capability through training and development of employees. Initial investment and return of investments (ROI) are significant concerns in the implementation of I4.0.	de Sousa Jabbour et al. (2018a), Luthra and Mangla (2018)
FW6	Lack of implementation knowledge about I4.0	Implementation of I4.0 needs IT support along with cloud computing, IoT, CPS, and big data. However, lack of expertise and experience in these technologies is a concern. Lighthouse projects and testbeds implementation can enhance implementation knowledge.	Oesterreich and Teuteberg (2016), Kamble, Gunasekaran, and Sharma (2018)
FW7	Lack of information sharing	I4.0 demands organisations to share their business insights with various service providers like cloud computing, data analyst, etc. Risks associated with information sharing are dependency, leaking data to competitors, and loss of information. However, proper information and knowledge sharing can help manufacturing firms to improve dynamic capability and agility.	Agostini and Filippini (2019), de Sousa Jabbour et al. (2018b)
FW8	Lack of financial support	I4.0 in manufacturing needs substantial initial investments. The economic discussion of I4.0 is still in its early stages. Defined ROI is one of the significant challenges to I4.0 to accomplish sustainability in manufacturing. The government of developing countries must provide subsidies to encourage the adoption of I4.0.	Moktadir et al. (2018)
FW9	Lack of trained and skilled manpower	There is a lack of skilled manpower to execute the technologies in I4.0. Highly qualified employees are needed to use advanced machines, equipment, and facilities. Execution of I4.0 demands technical and management skills. HR team of companies must focus on developing skills of current employees.	Kamble, Gunasekaran, and Sharma (2018), Müller, Kiel, and Voigt (2018), Agostini and Filippini (2019)
FW10	Concerns of QoS and QoE	There is uncertainty in the market as the benefits from the implementation of I4.0 are not proven and is also requiring high capital investment. Such a case is not favourable in the Indian context, which is a developing nation. Implementation of I4.0 must achieve expected benefits on products, services, and others. Further, this ambiguity is fuelling concerns of QoS (Quality of Service) with reliability and QoE (Quality of Experience) for all stakeholders of an organisation.	Dalenogare et al. (2018), Gerlitz (2016)
FW11	Lack of security and privacy	Being an internet-based technology, I4.0 is vulnerable to attacks. Privileged data can get stolen, which can affect the organisational process and its suppliers. Thus security and privacy concerns are significant challenges to I4.0. The manufacturing system needs reliable and secure frameworks for operators and machines.	Oesterreich and Teuteberg (2016), Kiel et al. (2017)
FW12	Lack of infrastructure	The adoption of I4.0 needs IT-based technologies and high-tech facilities. Footwear manufacturing firms in India lack high infrastructure. Also, rural and urban part lacks internet connectivity, which is a significant barrier to I4.0. Manufacturing firms must enhance technological infrastructure by using IoT and cloud computing technologies.	Luthra and Mangla (2018), Müller, Kiel, and Voigt (2018)
FW13	Lack of Government support and legal policy	Directives and policies of central and state governments are essential for the implementation of sustainable I4.0. However, most of the developing countries lack specific guidelines on I4.0. Governments are not sure about the effects of these advancements and thus not able to develop the roadmap and legal policies.	Oesterreich and Teuteberg (2016), Lin et al. (2018), Lin, Shyu, and Ding (2017)
FW14	High issues of data sharing and interoperability,	Lack of data sharing protocols is a significant concern in the adoption of I4.0 in manufacturing. With the interlinking of various sensors and machines, issues of data interoperability must be addressed. Data privacy issues must be considered while sharing data with suppliers and service providers.	Kamble, Gunasekaran, and Sharma (2018), Szalavetz (2019), Oesterreich and Teuteberg (2016)
FW15	Lack of coordination between vertical and horizontal supply chain members	Horizontal integration is intra and inter-organisational, while vertical integration is cross-link between factories, marketing, and sales. Intelligent digitisation, virtual reality, cyber-physical systems (CPS), and big data analytics (BDA) are prime requirements for the I4.0 model of integration. However, designing a flexible interface for the integration of different components is the main hurdle in horizontal and vertical integration.	Müller, Kiel, and Voigt (2018), Frank, Dalenogare, and Ayala (2019), Luthra and Mangla (2018)
FW16	Lack of incentives and wages	Implementation of I4.0 needs the support of middle managers and technicians. However, as top management is not sure about return on investment, I4.0 lack incentive support. Organisations must support prototype implementation of technologies of I4.0.	Luthra and Mangla (2018), Müller, Kiel, and Voigt (2018)
FW17	Concerns of job loss	Some job opportunities can be lost due to the replacement of current manpower to highly automatic systems. This is a concern as countries like India have the highest population of young age people. Existing employees must enhance their skill set so that they can serve the company mission towards I4.0.	Moktadir et al. (2018)
FW18	Lack of new organisational policy	To withstand global competition, manufacturing systems must be flexible and need to adapt to new business policies. I4.0 can bring unique insight to the organisation. However, firms must have a strategic plan for its adoption. Applying a new business model needs insights, and big data analytics can help predict and plan new projects.	Agostini and Filippini (2019)
FW19	Resistance to change the organisational culture	Manufacturing firms in developing economies are yet to be familiar with I4.0. Because of this ignorance, most of the industries are unenthusiastic for the adoption of I4.0 based technologies. Interdisciplinary natures of I4.0 and product digitisation constitute a significant reason for resistance to I4.0.	Kamble, Gunasekaran, and Sharma (2018)
FW20	Lack of collaboration between sustainable practices and I4.0	The cooperation and coordination between members are a must for effective communication. However, compatibility of software and hardware, data synchronisation, and customised interfaces are significant concerns. I4.0 driven sustainable manufacturing is possible with an integrated platform.	Dalenogare et al. (2018)

3. Research Methodology

Figure 1 shows the adopted research methodology.

Figure 1. Research methodology

In this work, barriers for sustainable adoption of I4.0 in Indian footwear industries are modelled using the fuzzy-DEMATEL approach. As discussed in the previous section, the barriers are identified through two steps, i.e., firstly, by exhaustive literature search and secondly validated them through expert opinions. Further, the fuzzy-DEMATEL tool is used to study the causal-relationship between identified barriers. The detailed description of fuzzy-DEMATEL is explained in the next sub-section. The results obtained from fuzzy-DEMATEL were sent for approval by the same domain experts and is finalised only after their adoption. Moreover, after approval by domain experts, various concluding remarks and implications about the study are made.

3.1 Fuzzy-DEMATEL Method

‘Decision Making and Trial Evaluation Laboratory (DEMATEL)’ method was proposed by Bastille National Laboratory for visualising causal relationships (Gabus and Fontela 1973). The contextual relationship is depicted between directed graphs or digraphs in comparison with directionless figures. Digraph portrays the causal connection amongst the factors (Raut et al. 2019).

The standard set can deal with binary terms True/False (1 or 0). However, it fails to deal with uncertainties and human vagueness. Fuzzy logic was proposed by Zadeh to deal with the shortcomings of the standard set (Zadeh 1965). The fuzzy set allows values between 1 and 0, depending on the degree of membership for intermediate values (Zadeh 1968). It mimics human reasoning by considering linguistic variables and membership functions. Membership functions commonly used are triangular, sigmoid, trapezoidal, etc. Researchers popularly use the triangular membership function i.e. TRIFUN (b1, b2, b3), and µ (T) as:

(1) $\mu \left(\mathrm{T}\right)=\left\{\begin{array}{c}0,t\le t1\\ \frac{t-t1}{2-t1},t1\le t\le t2\\ \frac{t3-t}{t3-t2},t2\le t\le t3\\ 0,t\ge t3,\end{array}\right.$(1)

With the constraint of $t1\le t2\le t3.$Figure 2 shows a ‘triangular membership function,’ corresponding to equation 1(1) $\mu \left(\mathrm{T}\right)=\left\{\begin{array}{c}0,t\le t1\\ \frac{t-t1}{2-t1},t1\le t\le t2\\ \frac{t3-t}{t3-t2},t2\le t\le t3\\ 0,t\ge t3,\end{array}\right.$(1) .

Figure 2. Triangular membership function

Table 3 shows the relationship between fuzzy triangular numbers of linguistic variables used for the study. Figure 3 shows the range of linguistic variables.

Figure 3. Range of linguistic variables

Table 3. Relationship between fuzzy triangular numbers and linguistic variables

Linguistic Variables	Abbreviation	Range
No influence	N	(0, 0, 2.5)
Less influence	L	(0, 2.5, 5.0)
Moderate influence	M	(2.5, 5.0, 7.5)
High influence	H	(5.0, 7.5, 10.0)
Very high influence	VH	(7.5, 10, 10)

In this paper, the Fuzzy DEMATEL method is used to assess causal relations of barriers of I4.0 adoption. This hybrid approach provides an intelligent solution as DEMATEL identifies and analyse the potential adoption parameters of barriers of sustainable I4.0 concerning causal effect relation diagram, fuzzy logic deal with the vagueness in decision-making, and human judgements. The procedure of Fuzzy DEMATEL adopted by Luthra et al. (2016) is given below:

Step 1: Constitution of experts panel/group and identification of assessment criteria

In this work, the assessment criteria (barriers in our case) are identified through a literature search and were validated by the opinion of domain experts.

Step 2: Construct fuzzy Initial Direct Relation Matrix (IDRM) and average IDRM

The second step is to establish a pairwise comparison of influence on assessment criteria. For this, experts are asked to give their opinion on a linguistic scale, as shown in Table 3. Here in our case, the triangular fuzzy number is used. A triangular fuzzy number (e, f, g) represents upper limit ‘g’, lower limit ‘e’, and at ‘f’ degree of membership is 1 (100 %). Note that e ≤ f ≤ g.

Further, as the mathematical computation is challenging with fuzzy numbers, a process of defuzzification is applied to get the crisp value. This study used a signed distance method, as illustrated by Yao and Wu (2000) in equation 2(2) $Crispvalue=\frac{1}{4}\left(e+2f+g\right)$(2) . This forms the basis of IDRM, which is h x h matrix where h is a number of assessment criteria.

(2) $Crispvalue=\frac{1}{4}\left(e+2f+g\right)$(2)

Moreover, the average IDRM (h x h) is computed by the arithmetic average of k IDRM, where k is the number of total exports. Let average IDRM is denoted by $z$.

Step 3: Obtain normalised IDRM

The normalised IDRM is obtained using equation 3(3) $A=\frac{Z}{s}$(3) .

(3) $A=\frac{Z}{s}$(3)

Where

$s=max\left(\underset{1\le i\le h}{max}\sum _{j=1}^h{a}_{ij},\underset{1\le i\le h}{\mathrm{m}\mathrm{a}\mathrm{x}}\sum _{i=1}^h{a}_{ij}\right)$

Every element in matrix $A$ obeys the rule $0\le a_{ij}\le 1,0\le \sum _{j=1}^h{a}_{ij}\le 1$and we have at least one i such that$\sum _{j=1}^h{z}_{ij}\le s$.

Step 4: Find the ‘total influence matrix’:

The ‘total influence matrix’ $R={\left[r_{ij}\right]}_{h\ast h}$is calculated by equation 4(4) $R=A+A^2+A^3+\dots A^c=A{\left(I-A\right)}^{-1}$(4) :

(4) $R=A+A^2+A^3+\dots A^c=A{\left(I-A\right)}^{-1}$(4)

Where $c\to \mathrm{\infty },$ and $I$ is denoted as identity matrix.

Step 5: Plotting initial relation diagram

The sum of columns and rows were ‘Q’ and ‘P’ respectively and can be computed using equations (5)(5) $Q=\sum _{i=1}^h{r}_{ij}\left(i=1,2,\dots ,h\right)$(5) and (6(6) $P=\sum _{j=1}^h{r}_{ij}\left(j=1,2,\dots ,h\right)$(6) ), respectively.

(5) $Q=\sum _{i=1}^h{r}_{ij}\left(i=1,2,\dots ,h\right)$(5)

(6) $P=\sum _{j=1}^h{r}_{ij}\left(j=1,2,\dots ,h\right)$(6)

The above two parameters were used to plot the influence relation diagram. The horizontal axis (i.e., X) denotes the $\left(P+Q\right)$ value and is known as ‘Prominence’ while vertical axis (i.e., Y) denotes the $\left(P-Q\right)$value and is called as ‘Relation’.

Ranks of the barriers are calculated using equation (7)(7) $v\left(L_x\right)=order{\left(L_x\right)}_{\left(P-Q\right)},x\in \left\{1,2,3,\dots \dots .,h-1,h\right\}h=no.ofbarriers$(7) as follows.

(7) $v\left(L_x\right)=order{\left(L_x\right)}_{\left(P-Q\right)},x\in \left\{1,2,3,\dots \dots .,h-1,h\right\}h=no.ofbarriers$(7)

4. Empirical case study of footwear organisation

4.1 Data collection

Data is collected from 11 footwear manufacturing firms and three academic institutions located across different parts of India. Out of the 11 footwear manufacturing firms, 5 are ‘Multi-National Enterprises (MNEs)’, 3 are ‘Small and Medium Enterprises (SMEs),’ and the other 3 are ‘Micro, Small and Medium Enterprises (MSME).’ The demographic profile of experts considered for this research is given in Table 4:

Table 4. Demographic profile of experts

Particular	Classifications	Number of Experts	%
Experts category	Manager	26	55.31
	Senior Manager	12	25.53
	Executive Director	3	06.38
	Professor (footwear design and development)	2	04.26
	Professor (Computer science)	2	04.26
	Professor (Management studies)	2	04.26
Total		47	100
Year of Experience	<10	10	21.28
	<15	21	44.68
	<20	7	14.89
	>20	9	19.15
Total		47	100
Gender	Male	43	91.49
	Female	4	8.51
Total		47	100
Educational Qualifications	Graduation (B.E./BBA/B.Tech)	6	12.76
	Post-graduation (M Tech/MBA)	29	61.71
	PhD (Technology/Management)	12	25.53
Total		47	100
Organisation size (no. of employees)	Below 100	3	21.43
	100–150	6	42.86
	151–200	5	35.71
Total		14	100

4.2. Application of fuzzy DEMATEL Method

In this study, the ‘fuzzy Decision-Making Trial and Evaluation Laboratory (DEMATEL)’ is used to investigate the causal relationship of the barriers for the adoption of sustainable I4.0. The average direct, normalised, and total influence matrix is shown in Table 5, 6, and 7, respectively. Further, Table 8 illustrates the significant causal relationship between identified barriers. The meaningful relationship is derived from the average value of the total influence matrix (Gardas et al. 2019), which is 0.22 in our case, and any amount above this threshold is considered to have a significant causal relationship. Moreover, Table 9 shows the ranking of barriers based on $\left(P-Q\right)$ value.

Table 5. Generating the average initial direct-relation matrix

	FW1	FW2	FW3	FW4	FW5	FW6	FW7	FW8	FW9	FW10	FW11	FW12	FW13	FW14	FW15	FW16	FW17	FW18	FW19	FW20
FW1	0	8.75	3	7	8.75	8.75	3	8.75	8.75	8.75	8.75	3	3	3	3	3	3	8.75	8.75	8.75
FW2	3	0	3	8.75	8.75	3	8.75	3	3	3	3	8.75	7	8.75	8.75	8.75	8.75	3	8.75	3
FW3	8.75	3	0	7	1	8.75	7	8.75	3	8.75	8.75	7	8.75	7	3	7	7	7	7	8.75
FW4	7	8.75	5	0	7	7	8.75	7	8.75	7	3	8.75	3	8.75	8.75	8.75	8.75	3	7	7
FW5	8.75	8.75	7	7	0	8.75	7	8.75	7	8.75	8.75	3	8.75	7	7	8.75	3	3	7	8.75
FW6	3	3	3	3	3	0	8.75	3	8.75	8.75	7	8.75	3	8.75	8.75	8.75	3	8.75	8.75	3
FW7	8.75	8.75	8.75	8.75	8.75	3	0	3	8.75	8.75	8.75	7	8.75	7	3	7	8.75	7	8.75	3
FW8	7	7	7	7	7	8.75	3	0	7	1	8.75	3	7	8.75	8.75	3	7	3	7	8.75
FW9	8.75	8.75	8.75	8.75	8.75	7	8.75	5	0	7	7	8.75	3	3	7	8.75	8.75	3	3	7
FW10	3	3	8.75	3	3	3	8.75	7	7	0	8.75	7	8.75	8.75	8.75	8.75	3	3	3	7
FW11	8.75	8.75	3	8.75	8.75	8.75	3	3	3	3	0	8.75	7	7	8.75	7	8.75	7	8.75	3
FW12	7	7	8.75	7	7	7	8.75	8.75	8.75	8.75	3	0	8.75	8.75	8.75	8.75	3	3	3	7
FW13	8.75	8.75	7	8.75	8.75	8.75	7	7	7	7	8.75	3	0	7	1	3	8.75	3	7	8.75
FW14	3	3	8.75	3	8.75	7	8.75	8.75	8.75	8.75	8.75	8.75	5	0	7	8.75	7	3	3	7
FW15	8.75	3	3	8.75	3	8.75	3	3	8.75	7	3	3	7	7	0	8.75	8.75	3	7	8.75
FW16	3	3	8.75	7	3	7	8.75	8.75	3	8.75	8.75	8.75	8.75	3	3	0	8.75	7	8.75	3
FW17	3	3	3	3	8.75	3	3	7	8.75	8.75	3	7	3	8.75	8.75	3	0	3	7	8.75
FW18	8.75	8.75	8.75	8.75	8.75	8.75	7	7	7	7	8.75	8.75	8.75	8.75	7	7	7	0	8.75	7
FW19	3	8.75	3	3	7	8.75	8.75	3	8.75	7	7	7	7	8.75	3	7	8.75	3	0	7
FW20	8.75	8.75	7	8.75	8.75	8.75	7	7	7	7	8.75	3	7	7	7	7	8.75	3	4	0

Table 6. Normalising the average direct-relation matrix (A)

	FW1	FW2	FW3	FW4	FW5	FW6	FW7	FW8	FW9	FW10	FW11	FW12	FW13	FW14	FW15	FW16	FW17	FW18	FW19	FW20
FW1	0.00	0.06	0.02	0.05	0.06	0.06	0.02	0.06	0.06	0.06	0.06	0.02	0.02	0.02	0.02	0.02	0.02	0.06	0.06	0.06
FW2	0.02	0.00	0.02	0.06	0.06	0.02	0.06	0.02	0.02	0.02	0.02	0.06	0.05	0.06	0.06	0.06	0.06	0.02	0.06	0.02
FW3	0.06	0.02	0.00	0.05	0.01	0.06	0.05	0.06	0.02	0.06	0.06	0.05	0.06	0.05	0.02	0.05	0.05	0.05	0.05	0.06
FW4	0.05	0.06	0.03	0.00	0.05	0.05	0.06	0.05	0.06	0.05	0.02	0.06	0.02	0.06	0.06	0.06	0.06	0.02	0.05	0.05
FW5	0.06	0.06	0.05	0.05	0.00	0.06	0.05	0.06	0.05	0.06	0.06	0.02	0.06	0.05	0.05	0.06	0.02	0.02	0.05	0.06
FW6	0.02	0.02	0.02	0.02	0.02	0.00	0.06	0.02	0.06	0.06	0.05	0.06	0.02	0.06	0.06	0.06	0.02	0.06	0.06	0.02
FW7	0.06	0.06	0.06	0.06	0.06	0.02	0.00	0.02	0.06	0.06	0.06	0.05	0.06	0.05	0.02	0.05	0.06	0.05	0.06	0.02
FW8	0.05	0.05	0.05	0.05	0.05	0.06	0.02	0.00	0.05	0.01	0.06	0.02	0.05	0.06	0.06	0.02	0.05	0.02	0.05	0.06
FW9	0.06	0.06	0.06	0.06	0.06	0.05	0.06	0.03	0.00	0.05	0.05	0.06	0.02	0.02	0.05	0.06	0.06	0.02	0.02	0.05
FW10	0.02	0.02	0.06	0.02	0.02	0.02	0.06	0.05	0.05	0.00	0.06	0.05	0.06	0.06	0.06	0.06	0.02	0.02	0.02	0.05
FW11	0.06	0.06	0.02	0.06	0.06	0.06	0.02	0.02	0.02	0.02	0.00	0.06	0.05	0.05	0.06	0.05	0.06	0.05	0.06	0.02
FW12	0.05	0.05	0.06	0.05	0.05	0.05	0.06	0.06	0.06	0.06	0.02	0.00	0.06	0.06	0.06	0.06	0.02	0.02	0.02	0.05
FW13	0.06	0.06	0.05	0.06	0.06	0.06	0.05	0.05	0.05	0.05	0.06	0.02	0.00	0.05	0.01	0.02	0.06	0.02	0.05	0.06
FW14	0.02	0.02	0.06	0.02	0.06	0.05	0.06	0.06	0.06	0.06	0.06	0.06	0.03	0.00	0.05	0.06	0.05	0.02	0.02	0.05
FW15	0.06	0.02	0.02	0.06	0.02	0.06	0.02	0.02	0.06	0.05	0.02	0.02	0.05	0.05	0.00	0.06	0.06	0.02	0.05	0.06
FW16	0.02	0.02	0.06	0.05	0.02	0.05	0.06	0.06	0.02	0.06	0.06	0.06	0.06	0.02	0.02	0.00	0.06	0.05	0.06	0.02
FW17	0.02	0.02	0.02	0.02	0.06	0.02	0.02	0.05	0.06	0.06	0.02	0.05	0.02	0.06	0.06	0.02	0.00	0.02	0.05	0.06
FW18	0.06	0.06	0.06	0.06	0.06	0.06	0.05	0.05	0.05	0.05	0.06	0.06	0.06	0.06	0.05	0.05	0.05	0.00	0.06	0.05
FW19	0.02	0.06	0.02	0.02	0.05	0.06	0.06	0.02	0.06	0.05	0.05	0.05	0.05	0.06	0.02	0.05	0.06	0.02	0.00	0.05
FW20	0.06	0.06	0.05	0.06	0.06	0.06	0.05	0.05	0.05	0.05	0.06	0.02	0.05	0.05	0.05	0.05	0.06	0.02	0.03	0.00

Table 7. Total relationship matrix = A(I-A)^−1.

	FW1	FW2	FW3	FW4	FW5	FW6	FW7	FW8	FW9	FW10	FW11	FW12	FW13	FW14	FW15	FW16	FW17	FW18	FW19	FW20	P	P + Q	P-Q
FW1	0.17	0.23	0.18	0.22	0.24	0.24	0.20	0.22	0.24	0.24	0.24	0.20	0.19	0.21	0.19	0.21	0.20	0.18	0.23	0.23	4.29	8.60	−0.02
FW2	0.18	0.16	0.18	0.22	0.23	0.20	0.23	0.18	0.20	0.20	0.20	0.22	0.21	0.24	0.22	0.23	0.23	0.13	0.22	0.19	4.07	8.45	−0.31
FW3	0.24	0.20	0.18	0.23	0.20	0.26	0.24	0.23	0.22	0.26	0.26	0.23	0.24	0.25	0.20	0.24	0.24	0.18	0.23	0.24	4.57	8.73	0.42
FW4	0.23	0.24	0.21	0.20	0.24	0.25	0.26	0.23	0.26	0.26	0.23	0.25	0.21	0.27	0.25	0.26	0.26	0.15	0.24	0.24	4.74	9.27	0.213
FW5	0.25	0.25	0.23	0.25	0.21	0.27	0.25	0.24	0.26	0.27	0.27	0.22	0.25	0.26	0.24	0.27	0.23	0.16	0.25	0.25	4.88	9.49	0.27
FW6	0.18	0.18	0.18	0.19	0.19	0.18	0.23	0.18	0.24	0.24	0.22	0.22	0.19	0.24	0.22	0.24	0.20	0.17	0.22	0.19	4.10	8.89	−0.69
FW7	0.25	0.25	0.24	0.26	0.26	0.24	0.21	0.21	0.27	0.27	0.27	0.25	0.25	0.26	0.22	0.26	0.27	0.18	0.26	0.22	4.90	9.55	0.25
FW8	0.21	0.22	0.21	0.22	0.23	0.25	0.20	0.17	0.23	0.20	0.24	0.19	0.22	0.25	0.23	0.21	0.23	0.14	0.22	0.23	4.29	8.53	0.06
FW9	0.24	0.24	0.23	0.25	0.25	0.25	0.25	0.21	0.20	0.25	0.25	0.25	0.21	0.23	0.23	0.26	0.26	0.15	0.21	0.24	4.68	9.46	−0.10
FW10	0.18	0.18	0.21	0.19	0.19	0.20	0.23	0.20	0.22	0.18	0.24	0.21	0.22	0.24	0.22	0.24	0.20	0.14	0.19	0.21	4.11	8.97	−0.75
FW11	0.23	0.23	0.19	0.24	0.24	0.25	0.21	0.19	0.22	0.22	0.19	0.24	0.22	0.25	0.23	0.24	0.25	0.17	0.24	0.20	4.47	9.21	−0.28
FW12	0.23	0.23	0.24	0.24	0.24	0.25	0.26	0.24	0.26	0.27	0.23	0.19	0.25	0.27	0.25	0.27	0.23	0.16	0.22	0.24	4.77	9.22	0.32
FW13	0.24	0.24	0.22	0.25	0.25	0.26	0.24	0.22	0.25	0.25	0.26	0.21	0.19	0.25	0.19	0.22	0.25	0.15	0.24	0.24	4.62	9.03	0.21
FW14	0.20	0.20	0.23	0.21	0.25	0.24	0.25	0.23	0.25	0.26	0.25	0.24	0.22	0.20	0.23	0.26	0.24	0.15	0.21	0.23	4.56	9.45	−0.33
FW15	0.21	0.18	0.17	0.22	0.19	0.23	0.19	0.18	0.23	0.23	0.20	0.19	0.21	0.22	0.16	0.23	0.23	0.13	0.21	0.22	4.05	8.43	−0.32
FW16	0.19	0.19	0.22	0.22	0.20	0.24	0.24	0.22	0.21	0.25	0.24	0.23	0.23	0.22	0.19	0.19	0.24	0.17	0.24	0.20	4.35	9.15	−0.44
FW17	0.17	0.17	0.16	0.17	0.21	0.19	0.18	0.19	0.22	0.22	0.18	0.20	0.17	0.22	0.21	0.19	0.16	0.12	0.20	0.21	3.75	8.43	−0.94
FW18	0.27	0.27	0.26	0.28	0.29	0.29	0.28	0.26	0.28	0.29	0.29	0.28	0.28	0.30	0.26	0.28	0.28	0.15	0.28	0.27	5.44	8.53	2.35
FW19	0.19	0.23	0.18	0.20	0.23	0.24	0.24	0.19	0.24	0.24	0.23	0.22	0.22	0.25	0.19	0.23	0.24	0.14	0.18	0.22	4.29	8.81	−0.22
FW20	0.24	0.25	0.23	0.25	0.26	0.26	0.25	0.23	0.25	0.26	0.26	0.22	0.24	0.26	0.24	0.25	0.26	0.16	0.23	0.20	4.78	9.27	0.30
Q	4.31	4.38	4.16	4.53	4.61	4.79	4.65	4.23	4.78	4.86	4.74	4.45	4.41	4.89	4.38	4.79	4.69	3.09	4.51	4.48	89.74

Table 8. Significant causal relationship between identified barriers

	FW1	FW2	FW3	FW4	FW5	FW6	FW7	FW8	FW9	FW10	FW11	FW12	FW13	FW14	FW15	FW16	FW17	FW18	FW19	FW20
FW1		0.23		0.22	0.24	0.24		0.22	0.24	0.24	0.24								0.23	0.23
FW2				0.22	0.23		0.23					0.22		0.24	0.22	0.23	0.23		0.22
FW3	0.24			0.23		0.26	0.24	0.23	0.22	0.26	0.26	0.23	0.24	0.25	0.20	0.24	0.24		0.23	0.24
FW4	0.23	0.24			0.24	0.25	0.26	0.23	0.26	0.26	0.23	0.25		0.27	0.25	0.26	0.26		0.24	0.24
FW5	0.25	0.25	0.23	0.25		0.27	0.25	0.24	0.26	0.27	0.27	0.22	0.25	0.26	0.24	0.27	0.23		0.25	0.25
FW6							0.23		0.24	0.24	0.22	0.22		0.24	0.22	0.24			0.22
FW7	0.25	0.25	0.24	0.26	0.26	0.24			0.27	0.27	0.27	0.25	0.25	0.26	0.22	0.26	0.27		0.26	0.22
FW8		0.22		0.22	0.23	0.25			0.23		0.24		0.22	0.25	0.23		0.23		0.22	0.23
FW9	0.24	0.24	0.23	0.25	0.25	0.25	0.25			0.25	0.25	0.25		0.23	0.23	0.26	0.26			0.24
FW10			0.21				0.23		0.22		0.24	0.21	0.22	0.24	0.22	0.24
FW11	0.23	0.23	0.19	0.24	0.24	0.25			0.22	0.22		0.24	0.22	0.25	0.23	0.24	0.25		0.24
FW12	0.23	0.23	0.24	0.24	0.24	0.25	0.26	0.24	0.26	0.27	0.23	0.19	0.25	0.27	0.25	0.27	0.23		0.22	0.24
FW13	0.24	0.24	0.22	0.25	0.25	0.26	0.24	0.22	0.25	0.25	0.26	0.21		0.25		0.22	0.25		0.24	0.24
FW14			0.23		0.25	0.24	0.25	0.23	0.25	0.26	0.25	0.24	0.22		0.23	0.26	0.24			0.23
FW15				0.22		0.23			0.23	0.23				0.22		0.23	0.23			0.22
FW16			0.22	0.22		0.24	0.24	0.22		0.25	0.24	0.23	0.23	0.22			0.24		0.24
FW17									0.22	0.22				0.22
FW18	0.27	0.27	0.26	0.28	0.29	0.29	0.28	0.26	0.28	0.29	0.29	0.28	0.28	0.30	0.26	0.28	0.28		0.28	0.27
FW19		0.23			0.23	0.24	0.24		0.24	0.24	0.23	0.22	0.22	0.25		0.23	0.24			0.22
FW20	0.24	0.25	0.23	0.25	0.26	0.26	0.25	0.23	0.25	0.26	0.26	0.22	0.24	0.26	0.24	0.25	0.26		0.23

Alpha Value = 0.22

Table 9. Cause-Effect group and their ranking (using equations no.7)

Symbol	P-Q	Group	Barrier	Ranking
FW18	2.35	Cause	Lack of new organisational policy	1
FW3	0.42	Cause	Lack of customer feedback and cooperation towards I4.0 and sustainable practices	2
FW12	0.32	Cause	Lack of infrastructure	3
FW20	0.3	Cause	Lack of collaboration between sustainable practices and I4.0	4
FW5	0.27	Cause	Lack of support from top management	5
FW7	0.25	Cause	Lack of information sharing	6
FW4	0.213	Cause	Lack of trust between upstream and downstream members	7
FW13	0.21	Cause	Lack of Government support and legal policy	8
FW8	0.06	Cause	Lack of financial support	9
FW1	−0.02	Effect	Lack of awareness about I4.0	10
FW9	−0.1	Effect	Lack of trained and skilled manpower	11
FW19	−0.22	Effect	Resistance to change the organisational culture	12
FW11	−0.28	Effect	Lack of security and privacy	13
FW2	−0.31	Effect	Lack of supplier involvement towards I4.0 and sustainable practices	14
FW15	−0.32	Effect	Lack of coordination between vertical and horizontal supply chain members	15
FW14	−0.33	Effect	High issues of data sharing and interoperability	16
FW16	−0.44	Effect	Lack of incentives and wages	17
FW6	−0.69	Effect	Lack of implementation knowledge about I4.0	18
FW10	−0.75	Effect	Concerns of QoS and QoE	19
FW17	−0.94	Effect	Concerns of job loss	20

5.0 Results and Discussion

The main aim of this research is three-fold, which was explained as three research objectives in the introduction section of this article. The first objective about the identification of the barriers is addressed by carrying out an exhaustive literature search and later validating them by domain expert’s opinion. The second objective of evaluating causal-relationship between identified barriers is addressed by the application of the fuzzy-DEMATEL approach. Further, the last objective of suggesting a suitable strategy for overcoming these barriers are discussed in the previous subsection of this section in the form of managerial implications.

For application and research, I4.0 offers value addition to manufacturing systems with the integration of internet-based technologies (Zhong et al. 2017). The causal relation map is shown in Figure 4, which shows that mainly nine barriers are causal, while eleven barriers are in effect group. The causal group barriers are FW18, FW3, FW12, FW20, FW5, FW7, FW4, FW13, and FW8. The effect group barriers are FW1, FW9, FW19, FW11, FW2, FW15, FW14, FW16, FW6, FW10, and FW17.

Figure 4. The cause and effect diagram

5.1 Influential or causal group barriers

The causal barriers are the most significant challenges for the adoption of sustainable I4.0. Hence they must be given higher priority for their elimination. The elimination of these barriers would help to reduce the obstacles of effect group as these influence effect category barriers. Analysis of the results shows that ‘Lack of new organizational policy (FW18)’ is the most critical barrier (i.e., highest $\left(P-Q\right)$ value). Thus it could be said that Indian organisation is missing proper strategy to combat I4.0 implementation challenges.

Further, the Government of India has come up with ‘SAMARTH (Smart Advanced Manufacturing and Rapid Transformation Hub (SAMARTH) UDYOG BHARAT 4.0,’ which could be a good move. The second significant barrier is ‘Lack of customer feedback and cooperation towards I4.0 and sustainable practices (FW3)’. This shows that we are still missing the trick of cooperation and collaboration. The third critical barrier is ‘Lack of infrastructure (FW12),’ which is very much evident since India is a developing nation. The updated and modern-day infrastructure is a prerequisite for sustainable I4.0 adoption. A fourth critical barrier is ‘Lack of collaboration between sustainable practices and I4.0 (FW20),’ which again hints towards better collaboration ties for the transfer of technology and resources. A fifth critical barrier is ‘Lack of support from top management (FW5)’ as top management support can bring both financial and moral help for I4.0 adoption. A sixth critical barrier is ‘Lack of information sharing (FW7),’ which is of utmost importance for autonomous maintenance and decision making in the I4.0 environment. A seventh significant barrier is ‘Lack of trust between upstream and downstream members (FW4)’ as concerned members believe that I4.0 initiatives and practices may eliminate their importance. An eighth critical barrier is ‘Lack of Government support and legal policy (FW13),’ which is further hindering the growth of I4.0. A ninth significant barrier is ‘Lack of financial support (FW8),’ as the implementation of I4.0 requires massive investment in technology, resources, people, and process. The fear of return on investment (ROI) is evident in the mind of concerned stakeholders.

5.2 Influenced or effected a group of barriers

Causal factors mainly influence the barriers in effect group. Among them, ‘Concerns of job loss (FW17)’ receive the highest impact. Employees are suspicious about the loss of jobs due to excessive use of automation in I4.0 adoption. Further, management is too ‘Concerns of QoS and QoE (FW10)’ through I4.0. This is due to the ‘Lack of implementation knowledge about I4.0 (FW6)’. The other factors in this priority list follow the sequence ‘Lack of incentives and wages (FW16)’, ‘High issues of data sharing and interoperability (FW14)’, ‘Lack of coordination between vertical and horizontal supply chain members (FW15)’, ‘Lack of supplier involvement towards I4.0 and sustainable practices (FW2)’, ‘Lack of security and privacy (FW11)’, ‘Resistance to change the organizational culture (FW19)’, ‘Lack of trained and skilled manpower (FW9)’ and ‘Lack of awareness about I4.0 (FW1)’. Employees would be required to train, learn, and update themselves as a prerequisite for I4.0 adoption, but incentives for this are still not manifest. Further, as I4.0 requires data sharing, the problem of interoperability and related security issues will have a high impact and are needed to be carefully managed. Moreover, the collaboration between stakeholder and their concerns will be significant hurdles to overcome. Therefore, awareness about I4.0 adoption is required to be performed through workshops, training sessions, hands-on experience, and conferences.

5.3 Discussion in Indian Perspective

With growing awareness about sustainability and I4.0, there is increasing demand and competition to adopt these strategies by all stakeholders of manufacturing firms. In addition, with changing scenario there is need to adopt new organisation policy which are compliant with sustainable I4.0 adoption. However, Indian footwear organisations are not ready and hence this is biggest challenge for adoption of sustainable I4.0. Moreover, the current footwear organisations still focus on cost parameters and don’t have customer feedback mechanism which can force the adoption of sustainable I4.0 practices. Infrastructure is another big hurdle which need to focused for easing the implementation process. Further, as the technology is the heart of sustainable I4.0 adoption. There is need to build research infrastructure to develop present day technological needs at an effective cost. For this, cooperation amongst industrial fraternity and research institutions is needed. Such efforts can be only made possible through top management support of organisation. In addition, there is need to facilitate information sharing which is another big hurdle for sustainable I4.0 adoption. The organisations also need to work on building trust between upstream and downstream members whereas suitable policy along with support is expected from the government side for sustainable I4.0 adoption. However, as the sustainable I4.0 implementation needs large investment, there is a need to raise the financial capabilities of organisations. Such type of support can come from PPP (i.e., private-public partnership) model.

Another aspect where Indian footwear organisations need to focus on is building an efficient and smooth supply chain that has proper coordination between the upstream and downstream supply chain. As the raw material is one of the most important constituents for the footwear organisation, it is mandatory to involve their supplier into sustainable I4.0 practices so that the adoption process is fastened. Further, as the footwear organisation’s workforce is not much technology savvy, thus there is a need to create awareness and arrange various training programs to enrich them with required I4.0 skillsets. Employee may fear to lose their job and resist such changes which need to be dealt with carefully as the labour union in India are extreme. For this, the workers and their unions need to be taken under confidence so that any conflict can be peacefully resolved. Also, the incentive and wages may be improved to boost the employee’s confidence.

5.4 Managerial Implications

This work enables footwear organisation, concerned policymakers and practitioners to recognise and analyse the barriers for the adoption of sustainable I4.0 in Indian footwear organisations. This information may be used by managers to frame suitable strategies to overcome the barriers in sustainable I4.0 adoption. Significant take away from this work is as follows:

Government roles and support: It is expected that the government would extend its support to the manufacturing organisation to increase the adoption of sustainable I4.0. Further, incentives and subsidy may be granted by the government for the same. There is also a lack of regularity frameworks and policies which come under the direct control of the government, and providing a suitable environment would result in an increased rate of sustainable I4.0 adoption. Through such an approach, the goal of sustainability can also be achieved.

Organisation Strategy: Organisation must align their strategy for the adoption of sustainable I4.0. This will require framing the company’s vision for I4.0 adoption, and the top management can provide support from the same. The financial assistance would act as the thrust for I4.0 adoption through acquiring the adequate infrastructure and developing the things prerequisite to sustainable I4.0 adoption.

Technology transfer and collaboration: The adoption of sustainable I4.0 would not be fulfilled through the efforts of single stakeholders. Thus, collaborative efforts are to be made by all stakeholders from suppliers to consumers. This would require the sharing of resources, data, information, and technology. The collective efforts can also be made for shorting out the arising dispute between various stakeholders and any other concerns of stakeholders.

6. Conclusion

Shortly, the adoption of sustainable I4.0 will emerge key aspects in manufacturing industries. Further, only those organisations will survive, which would adopt the changing scenarios and can combat global competition through the adoption of sustainable I4.0. To this effect, the present work identifies and analyse the critical barriers for the adoption of sustainable I4.0 in the Indian footwear organisation. For this, a structural model using fuzzy-DEMATEL is used, and twenty barriers have been identified through existing literature and opinion of domain experts related to footwear organisations. Analysis of results showed that the barriers FW18, FW3, FW12, FW20, FW5, FW7, FW4, FW13, and FW8 are causal barriers and remaining eleven barriers, i.e., FW1, FW9, FW19, FW11, FW2, FW15, FW14, FW16, FW6, FW10, and FW17 belong to effect group. Cause group barriers are significant obstacles in the adoption of sustainable I4.0 and have a high impact on the system. Thus, a considerable focus is required to handle the cause group barriers. It was also observed that ‘Lack of new organizational policy (FW18)’, ‘Lack of customer feedback and cooperation towards I4.0 and sustainable practices (FW3)’, ‘Lack of infrastructure (FW12)’ are the main hurdles for the adoption of sustainable I4.0. From a managerial perspective, the elimination of cause group barriers will reduce the impact of effect group barriers. Further, the experts considered in this work also agreed on the findings of this study. They found that the insight of the study would help them to frame a suitable strategy for the implementation of sustainable I4.0 in their organisations.

This work makes some unique practical and theoretical and contributions as follows.

• Twenty significant barriers are identified and analysed, which are obstacles in the adoption of sustainable I4.0 in Indian footwear organisations.

• The study makes use of the Fuzzy-DEMATEL approach, which not only identifies the causal relationship but also helps to understand the relationship strength under uncertain circumstances.

• The present work can be considered as the benchmark and may guide the practitioners, managers, and policymakers in drawing suitable strategies for the successful adoption of sustainable I4.0.

The present work also has a few limitations, which form the basis for future studies. The work was carried out for the Indian context, which is a developing nation and thus can’t be generalised. This is because existing infrastructure and scenarios may be different for different countries. Further, as the Fuzzy-DEMATEL approach makes use of an expert’s input, the procedure needs to be performed carefully. Moreover, sensitivity examination may be accomplished to check the consistency of results. The findings may also be evaluated with other MCDM approaches like TOPSIS, ANP, ISM, TISM, ELECTRE, and VIKOR, etc.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Notes on contributors

Vaibhav S. Narwane

Dr. Vaibhav S. Narwane is working as an Associate Professor in the Mechanical Engineering Department at K. J. Somaiya College of Engineering, Mumbai. Dr. Vaibhav received his Ph.D. from VJTI, Mumbai, from the Production Engineering Department. He holds his ME in CAD/CAM from Shri Guru Gobind Singhji Institute of Engineering and Technology, Nanded. He has thirteen years of teaching and one year of industrial experience and has few papers in journals and conferences of national and international repute to his credit. His current area of research includes cloud computing, industrial engineering, and AI techniques.

Rakesh D. Raut

Dr. Rakesh D. Raut is an Asst. Professor of Operations and Supply Chain Management at the National Institute of Industrial Engineering, Mumbai. Rakesh D. Raut received his Post-Doctoral Fellow from EPFL, Switzerland, and Fellowship (Ph.D.) from the National Institute of Industrial Engineering (NITIE), Mumbai. He holds his M. Tech (Mechanical) and BE (Production) Degree from the Nagpur University. His research interest includes Industry 4.0, Cloud Computing Adoption, Cloud-IoT Adoption, Big Data Analytics, Green/Sustainable Supply Chain Management, Reverse Logistics, Green/Sustainable Human Resource Management, and Talent Management.

Vinay Surendra Yadav

Mr. Vinay Surendra Yadav is a research scholar in the Department of Mechanical Engineering at the National Institute of Technology (NIT) Raipur, India. He also holds a Master's Degree in Industrial Engineering and Management from NIT Raipur, India. He received a gold medal for excellence performance in his M.Tech degree. He has completed his Bachelor of Engineering Degree in Mechanical Engineering from Pune University, India. His research areas include supply chain management, blockchain, optimization techniques, multi-criteria decision-making, and industrial engineering. He has published more than ten papers in reputed International Journals and Conferences.

A. R. Singh

Dr. A. R. Singh is working as an assistant professor in mechanical engineering at the National Institute of Technology Raipur, India. He also holds a Ph.D. degree in mechanical engineering from Motilal Nehru National Institute of Technology (MNNIT), Allahabad, India. He completed his Master of Technology degree in CAD-CAM from MNNIT, Allahabad, while Bachelor of Engineering degree in mechanical engineering from U.P.T.U, India. His specialization areas are operation research, supply chain management, industry 4.0, optimization techniques, multi-criteria decision making, blockchain, lean six sigma, and education management. He has published more than 50 papers in international journals and conferences.