Challenges in Adopting Industry 4.0 for Indian Automobile Industries: A Key Experts’ Perspective

The fourth industrial revolution, Industry 4.0, integrates digital technologies with physical systems, decentralising decision-making in manufacturing [1]. Originating from Germany in 2011 [2], Industry 4.0 has gained global traction, especially in developed countries, where it transforms production environments through tools like cyber-physical systems (CPS) and the Internet of Things (IoT) [3]. This digital transformation extends to the automobile sector, where Industry 4.0 tools facilitate designing, product development, and manufacturing processes [4]. It helps manufacture high-quality automobiles with smart features that can be customised per customer requirements [5]. Industry 4.0 can support all the value chain components, from designing and development, manufacturing, supply chain, sales, and service of automobiles [6-8]. Therefore, it is crucial to identify and evaluate the challenges for automobile companies implementing Industry 4.0, especially in emerging and developing economies such as India [9]. Much literature with a comprehensible and clear description presents the challenges and barriers to Industry 4.0 implementation in the manufacturing sector [10, 11]. However, there is a need for an empirical study to establish clarity in understanding the challenges/barriers of Industry 4.0 implementation specific to an industry considering the economic and geographical factors of that location.

The Indian automobile sector faced a significant slump of 40% during the third quarter of 2018-19, starkly contrasting its annual sales growth of 9.3% in 2017 [12]. This downturn underscores the pressing need to address the challenges hindering the adoption of Industry 4.0 within the sector. Factors contributing to this downturn include the global economic crisis, dwindling consumer demand, ineffective government policies, and the industry’s gradual transition towards electric mobility. Additionally, delays in technology upgrades and reluctance to invest in Industry 4.0 tools have further exacerbated the challenges automobile companies face. As a result, this study seeks to shed light on the hurdles impeding the implementation of Industry 4.0 in the Indian automobile industry, thereby providing valuable insights to overcome these obstacles and foster sustainable growth in the sector [13].

This study employs the fuzzy Decision-Making Trial and Evaluation Laboratory (DEMATEL) technique to assess the implementation challenges faced by the Indian automobile industry in adopting Industry 4.0 [14]. The fuzzy-DEMATEL method is chosen for its ability to analyse complex relationships among criteria and handle inherent biases in expert evaluations. Unlike other multi-criteria decision-making approaches, fuzzy-DEMATEL prioritises criteria and illustrates causality, essential for evaluating challenges and their interdependencies in Industry 4.0 implementation. This study identifies critical challenges and provides insights into their managerial and theoretical implications by prioritising challenges and elucidating their causal relationships. The purpose of this study is not only to prioritise the challenges but also to assess the interactions that affect the implementation of Industry 4.0 in the Indian automotive industry. The present research work expands on the concurrent theme of analysing the factors challenging the implementation of Industry 4.0.

The research objectives of this paper are:

(1) Identify the challenges to implementing Industry 4.0 tools in the Indian automobile industries.

(2) Structure the identified challenges to find their interdependency and causal relationship.

(3) Elucidate the managerial and theoretical implications of the result obtained.

The novelty lies in (i) the use of text analysis to identify the themes using the machine learning tool named Latent Dirichlet Allocation (LDA) algorithm, (ii) applying the fuzzy-DEMATEL method through Python programming to prioritise the challenges and (iii) the exclusivity of its relevance to the automobile manufacturing industries in India.

The paper is organised into five parts. The next part contains a review of past research works. The third part presents the research framework, data collection methods, and the calculation of fuzzy-DEMATEL weight used to prioritise the challenges. The results obtained by applying the fuzzy-DEMATEL approach are presented in the fourth part, followed by their implications. In the last part, the conclusion and future research scope is discussed.

Implementing Industry 4.0 tools in the Indian automobile industry has its challenges. Identifying the interdependency between them and the significance level of each challenge is necessary to address these challenges. This study aims to identify the challenges in implementing Industry 4.0 in the Indian automobile industry and prioritise them by analysing the responses obtained from a questionnaire survey of experts from OEMs, supplier industries, and academia using the fuzzy-DEMATEL method.

The research methodology, as shown in Figure 1, involved the following steps:

(1) Identification and selection of challenges through Machine Learning text analysis (LDA algorithm), followed by selecting challenges from the database and refining through Delphi-based consensus building: The initial step of the study was to conduct a literature review of the challenges faced by the Indian automobile industry in implementing Industry 4.0.

(2) Collection of data through Questionnaire-based Survey: Based on the selected challenges after the Delphi method, a questionnaire was developed to survey experts from OEMs, supplier industries, and academia. Experts were asked to rate the influence of one challenge over others in implementing Industry 4.0 in the Indian automobile industry.

(3) Data analysis using Fuzzy-DEMATEL technique: The data obtained from the questionnaire survey were analysed using the fuzzy-DEMATEL method. The fuzzy-DEMATEL approach is a decision-making method that can determine the interdependency between challenges and the cause-effect analysis of the challenges.

The expected outcomes of this study are to identify the challenges faced by the Indian automobile industry in implementing Industry 4.0, prioritise them based on their significance, and determine the interdependency and cause-effect relationship between challenges. This study can help develop a comprehensive strategy to address the challenges. The study will be helpful for policymakers, industry experts, and academics working in the field of Industry 4.0 in the Indian automobile industry.

1.jpg

Figure 1. The research framework of the current study

3.1 Questionnaire-based survey

A questionnaire was developed to survey experts on the influence of challenges in implementing Industry 4.0 in the Indian automobile industry. The questionnaire included challenges identified through LDA analysis and the Delphi method with Industry 4.0 experts. Experts from OEMs, supplier industries, and academia were asked to rate the influence of one challenge over others using a 5-point Likert scale. Triangular fuzzy values were assigned to each code to reduce the vagueness, ambiguity, and distortion in responses, as shown in Table 5.

Table 5. Scoring of responses by participants

The triangular membership functions (graphically shown in Figure 2) corresponding to the 5-point Likert scale are:

$\begin{gathered}\mu_A(x ; 0,0,0.25)=\max \left(\left(\frac{0.25-x}{0.25}\right), 0\right) \\ 0 \leq x \leq 0.25\end{gathered}$                   (1)

$\begin{gathered}  \mu_B(x ; 0,0.25,0.5)= \max \left(\min \left(\frac{x}{0.25}, \frac{0.5-x}{0.25}\right), 0\right) \\  0 \leq x \leq 0.5\end{gathered}$            (2)

$\begin{gathered}  \mu_C(x ; 0.25,0.5,0.75) =\max \left(\min \left(\frac{x-0.25}{0.25}, \frac{0.75-x}{0.25}\right), 0\right) \\  0.25 \leq x \leq 0.75\end{gathered}$                 (3)

$\begin{gathered}\mu_D(x ; 0.5,0.75,1.0) =\max \left(\min \left(\frac{x-0.5}{0.25}, \frac{1.0-x}{0.25}\right), 0\right) \\ 0.5 \leq x \leq 1.0\end{gathered}$                 (4)

$\begin{gathered}\mu_c(x ; 0.75,1.0,1.0)=\max \left(\left(\frac{x-0.75}{0.25}\right), 0\right) \\ 0.75 \leq x \leq 1.0\end{gathered}$             (5)

2.png

Figure 2. Triangular membership functions are used to represent the response of the experts

3.2 Data collection

The selection of experts for this study followed a deliberate and systematic approach to ensure expertise diversity and sector representation within the Indian automobile industry. The snowball sampling method, a widely recognised technique in qualitative research, was used to identify and recruit individuals with significant knowledge and experience relevant to Industry 4.0 implementation in the Indian automobile sector. Initially, key informants possessing expertise in the field were identified based on their publications, professional affiliations, and contributions to the industry. These key informants were then asked to recommend additional experts known to them who could provide valuable insights into the challenges of Industry 4.0 adoption. Through this iterative process, we expanded our network and ultimately recruited a diverse panel of 17 experts comprising representatives from OEMs, supplier industries, and academia. It was ensured that the sample size meets the criteria of fuzzy-DEMATEL analysis [53, 54] and is proportionally representative of focus groups, with three experts from OEMs, eight experts from supplier industries, and six experts from academia, as shown in Table 6. Careful consideration was given to the composition of the expert panel, with efforts made to include individuals representing each sector proportionally. By recruiting experts from diverse backgrounds and sectors, we aimed to capture a comprehensive range of perspectives and insights into the challenges of implementing Industry 4.0 across different industry segments. The expertise and qualifications of each expert were verified through their professional affiliations, publications, and contributions to the field, ensuring that they possessed the requisite knowledge and experience to provide meaningful input for the study. The survey questionnaire was designed based on the challenges identified through the LDA analysis and selected by the Delphi method involving Industry 4.0 experts. The language used in the questionnaire was ensured to be easy to understand for the participants. Participants had enough time to complete the survey, which was carried out using a web-based platform. The responses from the participants, which were personal and identifiable, were kept anonymous and confidential.

Table 6. Demographics of experts

3.3 Fuzzy-DEMATEL technique

The fuzzy-DEMATEL technique is a multi-criteria decision-making (MCDM) framework that evaluates and scores complex relationships between variables. This method’s key advantage is its ability to analyse cause-and-effect linkages. The original Decision-Making Trial and Evaluation Laboratory (DEMATEL) technique was developed by the Science and Human Affairs Programme at the Battelle Memorial Institute of Geneva between 1972 and 1976 [14]. Since then, it has gained popularity among researchers, with various studies utilising it in some variation and combination with other MCDM methods [55, 56]. The DEMATEL technique transforms the relationship between the causes and consequences of criteria into a precise system structural model. The mathematics behind the fuzzy-DEMATEL technique is described in greater detail below.

3.3.1 Average response matrix

The average of the fuzzy set values of all the responses was taken using Eq. (6) for further calculations.

$\begin{aligned} a_{i j}^* & =\frac{\sum a_{i j k}}{n} \\ b_{i j}^* & =\frac{\sum b_{i j k}}{n} \\ c_{i j}^* & =\frac{\sum c_{i j k}}{n}\end{aligned}$                    (6)

where,  (a, b, c) is the fuzzy triangular set corresponding to the response by participant k on the influence of barrier i on barrier j, n is the total number of participants in the survey.

Corresponding Python code:

3.3.2 Normalised average response matrix

The average response matrix was normalised using Eq. (7), as shown below:

$\begin{aligned} a_i^N & =\frac{a_i^*}{\max \left(\sum c_i^*\right)} \\ b_i^N & =\frac{b_i^*}{\max \left(\sum c_i^*\right)} \\ c_i^N & =\frac{c_i^*}{\max \left(\sum c_i^*\right)}\end{aligned}$                  (7)

Corresponding Python code:

3.3.3 Total relation matrix

The Total Relation Matrix is calculated using Eq. (8), where I denote an Identity Matrix.

$\begin{aligned} a_i^T & =a_i^N \times\left(I-a_i^N\right)^{-1} \\ b_i^T & =b_i^N \times\left(I-b_i^N\right)^{-1} \\ c_i^T & =c_i^N \times\left(I-c_i^N\right)^{-1}\end{aligned}$               (8)

Corresponding Python Code:

The variable ‘dataset’ stores the matrix size, which must be the same as the normalised response matrix.

3.3.4 Defuzzified total relation matrix

The fuzzy values from the Total Relation Matrix are de-fuzzified into crisp values using the Eqs. (9)-(12)

$\begin{aligned} & a_i^n=\frac{a_i^T-\min \left(a_i^T\right)}{\partial} \\ & b_i^n=\frac{b_i^T-\min \left(a_i^T\right)}{\partial} \\ & c_i^n=\frac{c_i^T-\min \left(a_i^T\right)}{\partial}\end{aligned}$                        (9)

where,

$\partial=\left(c_i^T\right)-\min \left(a_i^T\right)$                       (10)

The upper and lower limit of the total relation matrix is found using the following:

$\begin{aligned} & u_i=\frac{c_i^n}{1+c_i^n-a_i^n} \\ & l_i=\frac{b_i^n}{1+b_i^n-a_i^n}\end{aligned}$                     (11)

The de-fuzzified value $(d)$ corresponding to the fuzzy value $\left(a^T, b^T, c^T\right)$, is found using Eq. (12).

$d_i=\frac{l_i\left(1-l_i\right)+u_i^2}{1-l_i+u_i}$                 (12)

Corresponding Python Code:

3.3.5 Threshold value

A threshold level t was set to filter out the negligible values from matrix d to explain the interrelation among challenges while keeping the complexity of the problem at a manageable level. Those challenges whose effect in the matrix d is greater than the threshold value are chosen for further analysis [57].

$t=$ mean $\left(d_i\right)$                  (13)

The de-fuzzified matrix is then filtered using the threshold value:

$\begin{aligned} d_i & =d_i \text { if } d_i>t \\ & =0 \text { if } d_i<t\end{aligned}$                   (14)

Corresponding Python Code:

3.3.6 Importance and interdependency

The importance and interdependency of challenges are calculated based on the row-wise (D) and column-wise (R) sum of the de-fuzzified total relation matrix, as shown in Eqs. (15)-(18).

$D=\sum_{i=0}^m d_{i j}$                      (15)

$R=\sum_{j=0}^m d_{i j}$                       (16)       

The importance of the challenges is calculated by adding the row-wise sum and column-wise sum of the de-fuzzified total relation matrix. The relationship value is calculated by subtracting the row-wise sum and column-wise sum of the de-fuzzified total relation matrix.

Importance $=D+R$                  (17)

Relation $=D-R$                           (18)

Corresponding Python Code:

The Importance value is used to calculate the fuzzy-DEMATEL weight of the challenges, ultimately giving the rank of the challenges. At the same time, the Relation value is used to draw the cause-effect chart.

3.3.7 Fuzzy-DEMATEL weight

The fuzzy-DEMATEL weight is calculated using the Eq. (19):

${Weight}_i=\frac{\left( Importance )_i\right.}{\sum( Importance )}$                   (19)

Corresponding Python Code:

The findings of this study can help policymakers and industrialists build strategies to solve the problems of implementing Industry 4.0 in the Indian automobile sector. This investigation of the Indian automobile sector provides several managerial insights and theoretical consequences.

5.1 Managerial implications

The findings of this study have several managerial implications in the Indian automobile sector:

(1) Collaborative Approach: The results highlight the importance of a collaborative approach among policymakers, industry players, and academic institutions. OEMs and supplier industries should actively partner with academia to enhance their understanding of Industry 4.0 tools, standards, and best practices [67]. Collaboration with academia can also facilitate research and development efforts, enabling the industry to stay at the forefront of technological advancements [68]. Successful collaborative projects such as between Siemens and BMW [69] and Volkswagen and Amazon Web Services (AWS) [70] are a few case examples of this approach.

(2) Infrastructure Investment: Managers need to prioritise investments in infrastructure to address challenges such as outdated machinery and poor internet connectivity. Upgrading infrastructure can create a solid foundation for implementing Industry 4.0 technologies and ensure smooth integration with existing operations.

(3) Financial Support: Lack of financial support emerged as a significant challenge across all expert groups. Managers should advocate for government initiatives and policies that provide financial incentives and support for Industry 4.0 implementation. It can help alleviate the high initial costs associated with adopting new technologies and encourage greater participation from the industry.

(4) Upskilling the Workforce: All expert groups identified the shortage of skilled workers as challenging. Managers should focus on upskilling their workforce to meet the demands of Industry 4.0. It can be achieved through training programs, partnerships with educational institutions, and creating a culture of continuous learning within the organisation. The OEMs should take capacity-building and hand-holding initiatives for their concerned supplier industries to help them overcome the challenges.

(5) Standardisation and Strategy: OEM experts and supplier industries identified the lack of strategy and standards as critical challenges. Automobile industries should invest in developing clear implementation strategies and guidelines for Industry 4.0 adoption. Emphasising the importance of standardisation can enable interoperability and seamless integration of different technologies and processes.

(6) Data Security and Organizational Transformation: Concerns related to data security and organisational transformation were highlighted as dependent challenges by supplier industries. Industries should prioritise developing robust data security protocols and strategies to address these concerns. Additionally, they should proactively manage organisational change and provide necessary support and training to employees to ensure a smooth transition to Industry 4.0. Firstly, for data security, industries should adopt a multi-layered approach that includes encryption, access controls, and regular security audits to safeguard sensitive information. Implementing industry standards such as ISO 27001 [71] can provide a robust framework for managing data security risks. Organisations should prioritise employee training and awareness programs to promote a culture of data security throughout the company. Companies can follow Kotter’s 8-Step Change Model [72] or Prosci’s ADKAR model [73] to manage change effectively for organisational transformation. It’s essential to involve employees in the transformation process, communicate openly about the changes, and provide adequate support and resources to facilitate a smooth transition.

(7) Government Advocacy: Industries should actively engage policymakers to advocate for supportive policies and regulations promoting the adoption of Industry 4.0. This includes incentives, funding schemes, and a conducive regulatory environment encouraging innovation and collaboration.

5.2 Theoretical implications

The findings of this study have several theoretical implications for policymakers and academicians in implementing Industry 4.0 in the Indian automobile industry.

(1) Holistic Approach: The results highlight the need for policymakers to adopt a holistic approach that considers multiple dimensions of Industry 4.0 implementation. Policymakers should focus not only on technological aspects but also on social, organisational, and policy-related challenges. This holistic perspective can help formulate comprehensive strategies and policies that address stakeholders’ diverse challenges.

(2) Stakeholder Collaboration: The study emphasises the importance of collaboration among stakeholders, including policymakers, industry players, and academia. Policymakers should actively promote and facilitate collaborations to bridge the gap between industry and academia. This can foster knowledge sharing, research and development, and the co-creation of innovative solutions that cater to the specific needs of the Indian automobile industry.

(3) Financial Support and Incentives: More financial support emerged as a significant challenge in implementing Industry 4.0. Policymakers should explore various mechanisms to provide financial support and incentives to industrialists, particularly small and medium-sized enterprises (SMEs), to overcome the barriers associated with the high initial costs of adopting Industry 4.0 technologies. This can include grants, subsidies, tax incentives, and funding programs tailored explicitly for Industry 4.0 implementation.

(4) Policy Framework: Policymakers are crucial in establishing a supportive policy framework that encourages the adoption of Industry 4.0. They should develop policies and regulations addressing data security, privacy, intellectual property rights, and interoperability concerns. A clear and conducive policy framework can provide a favourable environment for industrialists to embrace Industry 4.0 technologies with confidence.

(5) Skills Development and Education: Policymakers should prioritise initiatives to foster skills development and education to meet the changing demands of Industry 4.0. This includes collaborating with educational institutions to design relevant curricula, promoting lifelong learning programs, and providing training opportunities for the existing workforce. By nurturing a skilled and adaptable workforce, policymakers can facilitate a smooth transition to Industry 4.0 and ensure the industry’s long-term sustainability.

(6) Regional Disparities: Policymakers should recognise regional disparities regarding Industry 4.0 readiness and challenges. They should form policies and support mechanisms to address the specific needs and challenges of different regions within India. This can help promote balanced growth and ensure that the benefits of Industry 4.0 are equitably distributed across the country.