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The purpose is to assess Industry 4.0 (I4.0) readiness index using fuzzy logic and multi-grade fuzzy approaches in an automotive component manufacturing organization.

I4.0 implies fourth industrial revolution that necessitates vital challenges to be dealt with. In this viewpoint, this article presents the evaluation of I4.0 Readiness Index. The evaluation includes two levels with appropriate criteria and factors. Fuzzy logic approach is used for assessment. Furthermore, the results obtained from fuzzy logic have been benchmarked with multi-grade fuzzy approach.

The proposed assessment model has successfully utilized fuzzy logic approach for assessment of I4.0 readiness index of automotive component manufacturing organization. Based on fuzzy logic approach, readiness index of I4.0 has been found to be (4.74, 6.26, 7.80) which is further benchmarked using multi-grade fuzzy approach. Industry 4.0 readiness index obtained from multi-grade fuzzy approach is 6.258 and thus, validated. Furthermore, 20 weaker areas have been identified and improvement suggestions are provided.

The assessment module include two levels (Six Criteria and 50 Factors). The assessment model could be expanded based on advancements in industrial developments. Therefore, future researchers could utilize findings of the readiness model to further develop multi-level assessment module for Industry 4.0 readiness in organization. The developed readiness model helped researchers in understanding the methodology to assess I4.0 readiness of organization.

The model has been tested with reference to automotive component manufacturing organization and hence the inferences derived have practical relevance. Furthermore, the benchmarking strategy adopted in the present study is simple to understand that makes the model unique and could be applied to other organizations. The results obtained from the study reveal that fuzzy logic-based readiness model is efficient to assess I4.0 readiness of industry.

The development of model for I4.0 readiness assessment and further analysis is the original contribution of the authors. The developed fuzzy logic based I4.0 readiness model indicated the readiness level of an organization using I4RI. Also, the model provided weaker areas based on FPII values which is essential to improve the readiness of organization that already began with the adoption of I4.0 concepts. Further modification in the readiness model would help in enhancing I4.0 readiness of organization. Moreover, the benchmarking strategy adopted in the study i.e. MGF would help to validate the computed I4.0 readiness.

The present study aimed to assess performance of Industry 4.0 (I4.0) in case organization by considering potential performance measures and analysis using scoring approach.

50 performance measures grouped into five dimensions namely manufacturing management, manufacturing economics, manufacturing strategy, manufacturing technology and workforce were considered for the analysis. The study had been done with relevance to automotive component manufacturing organization. Further, questionnaire for each performance measure was developed to gather expert inputs regarding different performance aspects of I4.0 in case organization. Reliability of the expert responses towards questionnaire was assessed by computing Cronbach's alpha (a) using Statistical Package for the Social Sciences (SPSS) software.

Findings of the study revealed overall I4.0 performance index (OIPI) of 0.71, i.e. 71% signifying improvement scope of 29% pertaining to I4.0 adoption. Gap analysis was performed across dimensions and performance measures to realize the weaker areas. Gap analysis revealed workforce dimension with highest gap and manufacturing management with lowest gap. The gaps that obstruct performance of I4.0 are being recognized and proposals for improvement were provided to the industrial practitioners. Based on further analysis, dimensions and performance measures found to be weaker.

The study helped industrial practitioners and managers to create the foundation for evaluating performance of I4.0-focused organization. Industry practitioners can employ the study to understand different performance measures with respect to different dimensions and realize the significance of I4.0 adoption.

The identification of performance dimensions and measures for I4.0 performance measurement and assessment using scoring approach is the original contribution of the authors.

To achieve changing customer demands, organizations are striving hard to embrace cutting-edge technologies facilitating a high level of customization. Industry 4.0 (I4.0) implementation aids in handling big data that could help generate customized products. Lean six sigma (LSS) depends on data analysis to execute complex problems. Hence, the present study aims to empirically examine the key operational characteristics of LSS and I4.0 integration such as principles, workforce skills, critical success factors, challenges, LSS tools, I4.0 technologies and performance measures.

To stay competitive in the market and quickly respond to market demands, industries need to go ahead with digital transformation. I4.0 enables building intelligent factories by creating smart manufacturing systems comprising machines, operators and information and communication technologies through the complete value chain. This study utilizes an online survey on Operational Excellence professionals (Lean/Six Sigma), Managers/Consultants, Managing Directors/Executive Directors, Specialists/Analysts/Engineers, CEO/COO/CIO, SVP/VP/AVP, Industry 4.0 professionals and others working in the field of I4.0 and LSS. In total, 83 respondents participated in the study.

Based on the responses received, reliability, exploratory factor analysis and non-response bias analysis were carried out to understand the biasness of the responses. Further, the top five operational characteristics were reported for LSS and I4.0 integration.

One of the limitations of the study is the sample size. Since I4.0 is a new concept and its integration with LSS is not yet explored; it was difficult to achieve a large sample size.

Organizations can utilize the study findings to realize the top principles, workforce skills, critical success factors, challenges, LSS tools, I4.0 tools and performance measures with respect to LSS and I4.0 integration. Moreover, these operational characteristics will help to assess the organization's readiness before and after the implementation of this integration.

The authors' original contribution is the empirical investigation of operational characteristics responsible for I4.0 and LSS integration.

The purpose of this paper is to report a study on analysis of barriers for cyber-physical system (CPS) adoption in small and medium enterprises (SMEs).

In Industry 4.0 scenario, Indian SMEs are struggling to bring their manufacturing processes in line with large manufacturing sector. CPS is considered as the backbone of Industry 4.0, and its implementation in SMEs will make significant changes pertaining to manufacturing automation. However, due to the lack of a proper CPS implementation strategy, SMEs face many challenges in its adoption. Hence, this study identified 18 possible barriers and seven performance measures pertaining to CPS adoption in Indian SMEs. Interpretive ranking process (IRP) is used to develop the contextual relationships among CPS barriers. IRP process include structured step-by-step matrix-based approach in which dominance among various alternatives is determined using performance measures developing a structured ranking model.

The developed IRP model revealed that CPS barriers “Lack of skilled manpower (CPSB2)” and “Lack of robustness with respect to environmental conditions in automotive environments (CPSB7)” are the most significant barriers (top two) hindering CPS adoption in SMEs.

In the present study, barriers for CPS adoption has been analyzed. In future, barriers for adopting other Industry 4.0 technologies could be analyzed.

The present research work is one of the few studies which analyzed CPS barriers in SMEs and provided improvement suggestions to the most significant barriers for its smooth adoption. The managerial and practical implications have been derived.

The analysis of barriers for CPS adoption in SMEs is the original contribution of the authors.

The manufacturing domain presently focusing on Industry 4.0 (I4.0). One such domain is the automotive sector. The purpose of this study is to analyse the I4.0 research studies with a focus on the automotive sector using a systematic literature review (SLR).

This paper presents a SLR of previous studies on I4.0 characteristics from its inception to performance measures focusing on the automotive sector. A total of 90 papers published in reputed journals during 2014–2020 were collected from major publishers, namely, Elsevier, Springer, Taylor and Francis, Emerald, Institute of Electrical and Electronics, MDPI, etc.

The findings of the study provided vital insights on various perspectives of I4.0 in an automotive organization. Moreover, this systematic analysis would help the automotive industry policymakers in implementing I4.0 in an organization. Based on the SLR, a conceptual framework is established to guide industry practitioners towards I4.0 implementation. The review findings could be used to carry out future studies in assessing the readiness of I4.0 in the organization with the help of a survey.

The limitation of the study is in the adoption of the sampling approach. In the present study, conference papers and refereed journals have been considered based on the relevance of I4.0 in the automotive industry. As I4.0 is a growing concept, non-refereed articles, book chapters and white papers may cover practical aspects regarding I4.0 implementation that need to be considered for depth analysis. Moreover, the framework needs to be validated with various automotive industries for ensuring practical validity.

The unique contribution of the study is the SLR of I4.0 in manufacturing with a focus on the automotive sector.

Sustainable development goals (SDGs) are gaining significant importance in the current environment. Many businesses are keen to adopt SDGs to get a competitive edge. There are certain challenges in realigning the present working scenario for sustainable development, which is a primary concern for society. Various firms are adopting sustainable engineering (SE) practices to tackle such issues. Artificial intelligence (AI) is an emerging technology that can help the ineffective adoption of sustainable practices in an uncertain environment. In this regard, there is a need to review the current research practices in the field of SE in AI. The purpose of the present study is to comprehensive review the research trend in the field of SE in AI.

This work presents a review of AI applications in SE for decision-making in an uncertain environment. SCOPUS database was considered for shortlisting the articles. Specific keywords on AI, SE and decision-making were given, and a total of 127 articles were shortlisted after implying inclusion and exclusion criteria.

Bibliometric study and network analyses were performed to analyse the current research trends and to see the research collaboration between researchers and countries. Emerging research themes were identified by using structural topic modelling (STM) and were discussed further.

Research propositions corresponding to each research theme were presented for future research directions. Finally, the implications of the study were discussed.

This work presents a systematic review of articles in the field of AI applications in SE with the help of bibliometric study, network analyses and STM.

Supply chain finance is an emergent research area dealing with the financial performance of a firm throughout its supply chain. It has been drawing significant attention among industrial practitioners and researchers. However, there is need to identify improvements in supply chain finance (SCF) practices to ensure sustainable growth. In recent years, circular economy practices are being adopted worldwide with a motivation to achieve the 17 Sustainable Development Goals (SDGs). Moreover, integration of circular economy practices in the financial aspects of supply chain is still in infant age.

Adoption of circular SCF in firms enhances both restorative and regenerative capacities of the firm. In this regard, this study aims to review articles on circular practices in SCF. The study identified 329 articles related to circular practices and sustainable practices in SCF from the Scopus database. The shortlisted articles were reviewed and discussed.

The findings of the study help to recognize the most influential and productive research in circular SCF in terms of journals and trends. Further research is recommended to explore this area in depth to recognize potential integrating factors that help in smooth acceptance of circular finance in supply chains.

Bibliometric and network analyses were performed to identify research trends and networks in the field of circular SCF. In addition, emerging research themes in the field of circular SCF were identified and discussed, and research propositions are proposed to delineate future research directions.

The purpose of this paper is to develop a model based on the total interpretive structural modeling (TISM) approach for analysis of factors of additive manufacturing (AM) and industry 4.0 (I4.0) integration.

AM integration with I4.0 is attributed due to various reasons such as developing complex shapes with good quality, real-time data analysis, augmented reality and decentralized production. To enable the integration of AM and I4.0, a structural model is to be developed. TISM technique is used as a solution methodology. TISM approach supports establishing a contextual relationship-based structural model to recognize the influential factors. Cross-impact matrix multiplication applied to classification (MICMAC) analysis has been used to validate the TISM model and to explore the driving and dependence power of each factor.

The derived structural model indicated the dominant factors to be focused on. Dominant factors include sensor integration (F9), resolution (F12), small build volumes (F19), internet of things and lead time (F14). MICMAC analysis showed the number of driving, dependent, linkage and autonomous factors as 3, 2, 12 and 3, respectively.

In the present study, 20 factors are considered. In the future, additional factors could be considered based on advancements in I4.0 technologies.

The study has practical relevance as it had been conducted based on inputs from industry practitioners. The industry decision-makers and practitioners may use the developed TISM model to understand the inter-relationship among the factors to take appropriate measures before adoption.

The study on developing a structural model for analysis of factors influencing AM and I4.0 is the original contribution of the authors.

This work aims to review past and present articles about data-driven quality management (DDQM) in supply chains (SCs). The motive behind the review is to identify associated literature gaps and to provide a future research direction in the field of DDQM in SCs.

A systematic literature review was done in the field of DDQM in SCs. SCOPUS database was chosen to collect articles in the selected field and then an SLR methodology has been followed to review the selected articles. The bibliometric and network analysis has also been conducted to analyze the contributions of various authors, countries and institutions in the field of DDQM in SCs. Network analysis was done by using VOS viewer package to analyze collaboration among researchers.

The findings of the study reveal that the adoption of data-driven technologies and quality management tools can help in strategic decision making. The usage of data-driven technologies such as artificial intelligence and machine learning can significantly enhance the performance of SC operations and network.

The paper discusses the importance of data-driven techniques enabling quality in SC management systems. The linkage between the data-driven techniques and quality management for improving the SC performance was also elaborated in the presented study.

The aim of this study is to analyze workforce attributes related to Industry 4.0 using fuzzy decision-making trial and evaluation laboratory (DEMATEL) and fuzzy combinative distance-based assessment (CODAS).

Technological trends stipulate various revolution in industries. Industry 4.0 is a vital challenge for modern manufacturing industries. Workforce adoption to such challenge is gaining vital importance. Therefore, such workforce-related attributes need to be identified for enhancing their performance in Industry 4.0 environment. In this context, this article highlights the analysis of 20 workforce attributes for Industry 4.0. Relevant criteria are prioritized using fuzzy DEMATEL. Workforce attributes are prioritized using fuzzy CODAS.

The key attributes are “Skills/training in decision-making (WA2)”, “Competences in complex system modelling and simulation (WA1)” and “Coding skills (WA20)”.

In the present study, 20 workforce attributes are being considered. In future, additional workforce attributes could be considered.

The study has been conducted based on inputs from industry experts. Hence, the inferences have practical relevance.

The analysis of workforce attributes for Industry 4.0 using MCDM methods is the original contribution of the authors.