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The purposes of this research article are as follows: to explore the understanding of the Industry 4.0 (I4.0) concept among Indian manufacturing industries, to determine the motivating factors for I4.0 implementation, to identify I4.0 enabling technologies which are used by Indian manufacturing industries and assess their sustainability, to explore the impact of above identified enabling technologies on sustainability pillars, to determine how Indian manufacturing industries interpret the concept of I4.0 and to develop a road map for I4.0 implementation and sustainability.

To perform this research work, a dual research methodology was adopted. Questionnaires were sent to 16 Indian manufacturing industries, and expert interviews were conducted with seven experts who have been practicing the I4.0 concept since the last three years in their business. Also, a sustainability measurement tool was developed to measure the sustainability of the used I4.0 enabling technologies.

In this research article, it is found that smart sensors and robot arms have high sustainability, whereas cyber physical systems (CPSs) and big data analytics have low sustainability. During an expert interview, it has been found that adoption of the I4.0 concept in Indian manufacturing industries is creating job loss fear in employees. Also, it is found that Indian workers must be trained to adopt and sustain I4.0 enabling technologies.

The sustainability of I4.0 enabling technologies in Indian manufacturing industries was indicated by analyzing responses received through questionnaires and expert interviews. There are other measures of sustainability which are beyond this study. Further studies are expected to fill the gap.

The authors have explored reasons for low sustainability of I4.0 enabling technologies in Indian manufacturing industries, suggested a road map for its implementation and sustainability and identified the relationship between different parameters (such as job loss, job creation, workers’ qualification and business profit) and I4.0 sustainability, therefore helping Indian organizations to develop sustainable manufacturing systems based on the I4.0 concept.

This research article gives an idea about sustainability of I4.0 enabling technologies in Indian manufacturing industries.

To develop a structural model based on Interpretive Structural Modelling (ISM) approach to analyse the barriers to integration of Lean with Industry 4.0.

Integrated lean and I4.0 is essential for optimising customer value, rapid product design and redesign to comply with customer demand on time. Also, manufacturing processes to be made more flexible, intelligent and agile. In this context, integrated lean and Industry 4.0 barriers were identified and an ISM approach is employed to identify hierarchical structure of barriers and analysed.

The result obtained reflects that barriers “increasing competitive pressure”, “lack of long-term vision”, “lack of management support”, “lack of capital fund” are found to be the important barriers that affect the integration of lean and I4.0.

In the present study, 16 barriers are analysed. In future, additional barriers could be included.

The driving and dependence power of barriers were analysed from ISM model and it provided guidance for practitioners to concentrate on barriers for integrating lean and I4.0.

The idea of developing structural model for analysis of barriers to integration of lean with I4.0 is the original contribution.

The concept of sustainable manufacturing has been adopted by manufacturing organizations to develop eco-friendlier products and processes. In recent times, industries are progressing toward Industry 4.0 (I4.0). Guided with smart intelligent devices, I4.0 can possibly decrease excess production, material movement and consumption of energy. If so, it is hypothesized that there is a good synergy between I4.0 and sustainability, which warrants an integrated approach for implementation. This amalgamation is termed as “Sustainable industry 4.0.” Hence, this paper aims to systematically identify and analyze the drivers for this integration.

This paper presents the analysis of 20 drivers identified from literature review for simultaneous deployment of I4.0 and sustainable manufacturing. Interpretive structural modeling (ISM) is used to derive the structural model for analyzing the causal association between drivers. Cross-Impact Matrix Multiplication Applied to Classification (MICMAC) analysis is being performed to group the drivers.

The results showed that the dominant drivers derived are societal pressure and public awareness (D18), government policies on support I4.0 (D12), top management involvement and support (D15) and government promotions and regulations (D16). Also, the MICMAC analysis revealed many driving, dependent, linkage and autonomous drivers.

The opinion from experts with combined expertise on I4.0 and sustainability was obtained. The respondent size could be increased in future studies.

The study has been done based on inputs from industry practitioners. Managerial and practical implications are presented. ISM shows that the drivers for deploying sustainable I4.0 are highly inter-related. It also reveals the pre-requisites for each level of the drivers.

The idea of analyzing the drivers for sustainable I4.0 is the original contribution of the authors.

The study aims to analyze the barriers in the adoption of Industry 4.0 (I4.0) practices in terms of prioritization, cluster formation and clustering of empirical responses, and then narrowing them with identification of the most influential barriers for further managerial implications in the adoption of I4.0 practices by developing an enhanced understanding of I4.0.

For the survey-based empirical research, barriers to I.40 are synthesized from the review of relevant literature and further discussions with academician and industry persons. Three widely acclaimed statistical techniques, viz. principal component analysis (PCA), fuzzy analytical hierarchical process (fuzzy AHP) and K-means clustering are applied.

The novel integrated approach shows that lack of transparent cost-benefit analysis with clear comprehension about benefits is the major barrier for the adoption of I4.0, followed by “IT infrastructure,” “Missing standards,” “Lack of properly skilled manpower,” “Fitness of present machines/equipment in the new regime” and “Concern to data security” which are other prominent barriers in adoption of I4.0 practices. The availability of funds, transparent cost-benefit analysis and clear comprehension about benefits will motivate the business owners to adopt it, overcoming the other barriers.

The present study brings out the new fundamental insights from the barriers to I4.0. The new insights developed here will be helpful for managers and policymakers to understand the concept and barriers hindering its smooth implementation. The factors identified are the major thrust areas for a manager to focus on for the smooth implementation of I4.0 practices. The removal of these barriers will act as a booster in the way of implementing I4.0. Real-world testing of findings is not available yet, and this will be the new direction for further research.

The new production paradigm is highly complex and evolving. The study will act as a handy tool for the implementing manager for what to push first and what to push later while implementing the I4.0 practices. It will also empower a manager to assess the implementation capabilities of the industry in advance.

PCA, fuzzy AHP and K means are deployed for identifying the significant barriers to I4.0 first time. The paper is the result of the original conceptual work of integrating the three techniques in the domain of prioritizing and narrowing the barriers from 16 to 6.

This research aims to outline the key factors responsible for industry 4.0 (I4.0) application in industries and establish a factor stratification model.

This article identifies the factor pool responsible for I4.0 from the extant literature. It aims to identify the set of key factors for the I4.0 application in the manufacturing industry and validate, classify factor pool using appropriate statistical tools, for example, factor analysis, principal component analysis and item analysis.

This study would shed light on critical factors and subfactors for implementing I4.0 in manufacturing industries from the factor pool. This study would shed light on critical factors and subfactors for implementing I4.0 in manufacturing industries. Strategy, leadership and culture are found key elements of transformation in the journey of I4.0. Additionally, design and development in the digital twin, virtual testing and simulations were also important factors to consider by manufacturing firms.

The proposed I4.0 factor stratification model will act as a starting point while designing strategy, adopting readiness index for I4.0 and creating a roadmap for I4.0 application in manufacturing. The I4.0 factors identified and validated in this paper will act as a guide for policymakers, researchers, academicians and practitioners working on the implementation of Industry 4.0. This work establishes a solid groundwork for developing an I4.0 maturity model for manufacturing industries.

The existing I4.0 literature is critically examined for creating a factor pool that further presented to experts to ensure sufficient rigor and comprehensiveness, particularly checking the relevance of subfactors for the manufacturing sector. This work is an attempt to identify and validate major I4.0 factors that can impact its mass adoption that is further empirically tested for factor stratification.

With the increasing pressures towards global sustainability and the transition to Industry 4.0 (I4.0), the collaboration between firms and other key stakeholders is essential. Value is no longer created by firms acting autonomously, but rather by firms acting together with external parties. Therefore, the aim of this study is to explore the potential contribution of capability providers to a Sustainable I4.0 Environment as an additional perspective regarding the management decisions of a smart and sustainable business model (SSBM) transformation of big corporations.

An in-depth qualitative case study of Futuryng INC., which is a company based in Silicon Valley, New York and Italy, is presented and analysed through interviews, secondary sources and using a triangulation approach. The company is a Connected Technologies Ecosystem, which acts as a provider of technology building blocks (capability) able to design and release end-to-end Information Technologies–Internet of Things–Operational Technologies (IT-IoT-OT) Solutions.

From the case study, the authors determine that the success of big corporations' SSBM transformation requires a Sustainable I4.0 Environment approach where capability providers play a relevant role and act as enablers. Then, the authors develop a framework by adopting an actor perspective, called the Sustainable I4.0 Environment, highlighting the contribution of the capability provider in the sustainable I4.0 business model transformation of a big corporation.

The authors’ analysis clarifies that the successful execution of a sustainable I4.0 business model transformation requires integrated thinking for management decisions and a co-creation approach with capability providers, along with an open innovation process.

In the analysis of I4.0 and sustainability issues, previous studies only focus on implementing firms and view the environment merely as a background in which act forces of sustainability and I4.0. A comprehensive overview of the Sustainable I4.0 Environment, which considers actors and their contribution, is lacking. By integrating the literature review with the case study, the authors’ research proposes a comprehensive framework to guide the decision process of transformation from a traditional business model (TBM) to an SSBM and considers one of the key actors involved, the capability providers.

The main purpose of this paper is to assess the technological view of apparel new product development (NPD) process through a framework for improving the efficiency of NPD process in Sri Lankan apparel industry.

The study uses literature review, a survey and in-depth interviews of industry experts for data collection. Descriptive analysis and correlation analysis are used to identify the relationship between NPD process performance and Industry 4.0 (I4) technology components.

All technology components are enablers of NPD process, yet the relative importance of technology components varies within the NPD life cycle. Technoware is identified as the most significant, while Humanware component is the least significant for the advancement of I4 technologies in the NPD process.

Despite the novelty of research investigation into NPD process using I4 technologies in apparel industry, it is limited to one geographical location and a small segment of the industry.

This paper assists apparel industry practitioners to better understand and prioritize I4 technology components in the NPD life cycle for their successful adoption and for reaping the benefits.

This research generates new knowledge on the adoption of I4 technologies using industry insights into technology components mapped/aligned with key stages of NPD life cycle.

Industry 4.0 is dramatically affecting businesses behaviours and strategies, transforming products design, manufacture, operations and services. An outcome of this transformation is digital servitization. This paper aims to contribute to the extant literature about digital servitization in B2B contexts by analysing how I4.0-based servitization affects the quality of supplier–customer relationships.

The authors adopted a qualitative methodology based on an exploratory multiple case study. In particular, the study included 22 Italian B2B manufacturing firms whose I4.0-based digital servitization approaches are described and, then, analysed in relation to the quality of supplier–customer relationships.

The access to customers and data is critical to enable advanced digital services and for improving relationship quality; the levels of relational intimacy and informational openness lead to two subsequent levels of data-driven efficiency and data-driven effectiveness, impacting significantly on relationship quality and enabling relational innovation.

The research explores the link, so far underestimated, between digital servitization and relationship quality in industrial contexts.

The digital revolution has brought many challenges and opportunities for the manufacturing firms. The impact of Industry 4.0 technology adoption on sustainable manufacturing and circular economy has been under-researched. This paper aims to review the latest articles in the area of Industry 4.0, sustainable manufacturing and circular economy and further developed a research framework showing key paths.

Qualitative research is performed in two stages. In the first stage, a review of the extant literature is performed to identify the barriers, drivers, challenges and opportunities. In the second stage, a research framework is proposed to integrate Industry 4.0 technology (big data analytics powered artificial intelligence) adoption, sustainable manufacturing and circular economy capabilities.

This research extends the knowledge base by providing a detailed review of Industry 4.0, sustainable manufacturing, and circular economy and proposes a research framework by integrating these three contemporary concepts in the context of supply chain management. Through an exploration of this integrative research framework, the authors propose a future research agenda and seven research propositions.

It is important to understand the interplay between institutional pressures, tangible resources and human skills for Industry 4.0 technology (big data analytics powered artificial intelligence) adoption. Industry 4.0 technology (big data analytics powered artificial intelligence) adoption can positively influence sustainable manufacturing and circular economy capabilities. Managers must also put more attention to sustainable manufacturing to develop circular economic capabilities.

Factory workers and the local communities generally suffer from various adverse effects resulting from the traditional manufacturing process. The quality of the environment is deteriorating to such an extent that people even staying miles away from the factory are also affected due to environmental pollution that is generated from factory operations. Hence, sustainable manufacturing is the only choice left to manufacturers that can help in the transition to a circular economy. The research framework can help firms to enhance circular economy capabilities.

This review paper contains the most updated work on Industry 4.0, sustainable manufacturing and circular economy. It also proposes a research framework to integrate these three concepts.

This paper aims to explore the nature of Industry 4.0 (I4.0) cluster, to establish and empirically verify in the pilot study the role of clusters in developing the fourth industrial revolution. It aims to find out if the cluster can provide a conducive knowledge environment fostering the advancement of I4.0; simplify the implementation of I4.0 by making it faster, easier, and cheaper and finally be applied as policy tool organising the development of I4.0.

This paper is based on the narrative literature review mapping the cluster’s nature with I4.0 features. It adopts the case study approach and uses simple statistical analysis to explore the basic characteristics of I4.0 clusters combined with a survey technique – short questionnaire organised in three major blocks.

Drawing on German pilot study it can be confirmed that clusters offer conducive environment facilitating the emergence, testing and development of I4.0 specific solutions. They provide favourable knowledge environment, simplify and increase the efficiency of the business processes and organise the policymaking in this area.

The results of presented pilot study rooted in Germany – a country seen as the front-runner in implementing the solutions of the fourth industrial revolution – can add a certain value to the emerging research on unearthed linkages between clusters and I4.0. This paper might be seen as a contribution to the emerging literature on the spatial dimension of I4.0. It expands previous research on cluster channels’ likely impact on I4.0.