Modelling the enablers of industry 4.0 in the Indian manufacturing industry

Jain, Vineet; Ajmera, Puneeta

doi:10.1108/ijppm-07-2019-0317

Cited by 60 publications

(43 citation statements)

References 61 publications

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“…Here we provide a thorough discussion of the identified actions and sub-actions, in light of the existing literature on Industry 4.0. Some identified actions confirm the messages already provided by some scholars in the literature, such as the importance to innovate by adopting the new technologies and reviewing both strategy and organizational structure (Veile et al, 2019;Cimini et al, 2020); the promotion of an innovation culture (Jain and Ajmera, 2021); the development of training activities through knowledge management programs (Kahle et al, 2020;Veile et al, 2019); and the need to develop proper (ICT) infrastructures (Moktadir et al, 2018).…”

Section: Discussionsupporting

confidence: 61%

The role of innovation ecosystems in Industry 4.0 adoption

Matt

Molinaro

Orzes

et al. 2021

JMTM

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PurposeThe purpose of this paper is to identify actions and guidelines for enabling and fostering the Industry 4.0 adoption, as well as to understand the role of three ecosystem actors in these actions (i.e. companies, educational organizations and regional policy makers).Design/methodology/approach52 semi-structured expert interviews in the Tyrol-Veneto cross-border macro-region were carried out and interpreted using the innovation ecosystem concept. In particular, drawing from this latter, six ecosystem building blocks were identified and used to analyze the interviews' content.FindingsThe findings allow not only to build a comprehensive framework for action to support Industry 4.0 adoption, but also to confirm the importance of exploring Industry 4.0 through the lens of the ecosystem concept. Indeed, the authors show that R&D activities should be complemented with interorganizational actions, such as training and networking, and that all ecosystem actors should be involved in the Industry 4.0 adoption.Originality/valueThis is among the few studies that adopt the innovation ecosystem perspective to explore best practices for Industry 4.0 adoption, thus overcoming the weakness of existing papers based on a firm-level perspective. It also complements previous ecosystem-based research on Industry 4.0 by exploring the technology adoption side, rather than the technology provision one, and by considering the adoption of a wide set of technologies.

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Section: Discussionsupporting

confidence: 61%

The role of innovation ecosystems in Industry 4.0 adoption

Matt

Molinaro

Orzes

et al. 2021

JMTM

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“…, 2018), which were conducted in the manufacturing industry of western countries such as Denmark, Norway and Germany, respectively. Similarly, in the eastern context, Jain and Ajmera (2020) and Subramanian et al. (2021) did not report these factors.…”

Section: Discussionmentioning

confidence: 86%

“…(2020) suggested that improvements in government regulation and technological infrastructure could facilitate the adoption of Industry 4.0 technologies. Similarly, Jain and Ajmera (2020) showed that Internet facility, financial support and skill training were the major enablers of I4.0. Hence, facilitating factors as such may play a significant role in the decision to start the digital transformation, as they may expedite the acquisition of required resources.…”

Section: Literature Reviewmentioning

confidence: 98%

Factors influencing Industry 4.0 adoption

Khin

Kee

2022

JMTM

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PurposeThe digital transformation towards Industry 4.0 (I4.0) has become imperative for manufacturers, as it makes them more flexible, agile and responsive to customers. This study aims to identify the factors influencing the manufacturing firms’ decision to adopt I4.0 and develop a triadic conceptual model that explains this phenomenon.Design/methodology/approachThis study used a qualitative exploratory study design based on multiple case studies (n = 15) from the manufacturing industry in Malaysia by conducting face-to-face interviews. The data were analysed using NVivo. The conceptual model was developed based on grounded theory and deductive thematic analysis.FindingsResults demonstrate that driving, facilitating and impeding factors play influential roles in a firms’ decision-making to adopt I4.0. The major driving factors identified are expected benefits, market opportunities, labour problem, customer requirements, competition and quality image. Furthermore, resources, skills and support are identified as facilitating factors and getting the right people, lack of funding, lack of knowledge, technical challenges, training the operators and changing the mindset of operators to accept new digital technologies are identified as impeding factors.Research limitations/implicationsDue to its qualitative design and limited sample size, the findings of this study need to be supplemented by quantitative studies for enhanced generalizability of the proposed model.Practical implicationsKnowledge of the I4.0 decision factors identified would help manufacturers in their decision to invest in I4.0, as they can be applied to balancing advantages and disadvantages, understanding benefits, identifying required skills and support and which challenges to expect. For policymakers, our findings identify important aspects of the ecosystem in need of improvement and how manufacturers can be motivated to adopt I4.0.Originality/valueThis study lays the theoretical groundwork for an alternative approach for conceptualizing I4.0 adoption beyond UTAUT (Unified Theory of Acceptance and Use of Technology). Integrating positive and negative factors enriches the understanding of decision-making factors for I4.0 adoption.

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“…This analysis comes under the purview of data science. Traditional data science has been experiencing a paradigm shift due to the advent of deep learning technology (Bag & Pretorius, 2020 ; Jain & Ajmera, 2020 ), which has gained popularity for surpassing traditional machine learning (Ghobakhloo, 2018 ; LeCun et al, 2015 ). Deep learning can handle all types of structured, semi-structured, or even unstructured data, including big data.…”

Section: Theoretical Background and Development Of Conceptual Modelmentioning

confidence: 99%

Examining the impact of deep learning technology capability on manufacturing firms: moderating roles of technology turbulence and top management support

et al. 2022

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Data science can create value by extracting structured and unstructured data using an appropriate algorithm. Data science operations have undergone drastic changes because of accelerated deep learning progress. Deep learning is an advanced process of machine learning algorithm. Its simple process of presenting data to the system is sharply different from other machine learning processes. Deep learning uses advanced analytics to solve complex problems for accurate business decisions. Deep leaning is considered a promising area for creating additional value in firms’ productivity and sustainability as they develop their smart manufacturing activities. Deep learning capability can help a manufacturing firm’s predictive maintenance, quality control, and anomaly detection. The impact of deep learning technology capability on manufacturing firms is an underexplored area in the literature. With this background, the purpose of this study is to examine the impact of deep learning technology capability on manufacturing firms with moderating roles of deep learning related technology turbulence and top management support of the manufacturing firms. With the help of literature review and theories, a conceptual model has been prepared, which is then validated with the PLS-SEM technique analyzing 473 responses from employees of manufacturing firms. The study shows the significance of deep learning technology capability on smart manufacturing systems. Also, the study highlights the moderating impacts of top management team (TMT) support as well as the moderating impacts of deep learning related technology turbulence on smart manufacturing systems.

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Modelling the enablers of industry 4.0 in the Indian manufacturing industry

Cited by 60 publications

References 61 publications

The role of innovation ecosystems in Industry 4.0 adoption

The role of innovation ecosystems in Industry 4.0 adoption

Factors influencing Industry 4.0 adoption

Examining the impact of deep learning technology capability on manufacturing firms: moderating roles of technology turbulence and top management support

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