Industry 4.0 technologies for manufacturing sustainability: A systematic review and future research directions

Recent developments in manufacturing processes and automation have led to the new industrial revolution termed “Industry 4.0”. Industry 4.0 can be considered as a broad domain which includes: data management, manufacturing competitiveness, production processes and efficiency. The term Industry 4.0 includes a variety of key enabling technologies i.e., cyber physical systems, Internet of Things, artificial intelligence, big data analytics and digital twins which can be considered as the major contributors to automated and digital manufacturing environments. Sustainability can be considered as the core of business strategy which is highlighted in the United Nations (UN) Sustainability 2030 agenda and includes smart manufacturing, energy efficient buildings and low-impact industrialization. Industry 4.0 technologies help to achieve sustainability in business practices. However, very limited studies reported about the extensive reviews on these two research areas. This study uses a systematic literature review approach to find out the current research progress and future research potential of Industry 4.0 technologies to achieve manufacturing sustainability. The role and impact of different Industry 4.0 technologies for manufacturing sustainability is discussed in detail. The findings of this study provide new research scopes and future research directions in different research areas of Industry 4.0 which will be valuable for industry and academia in order to achieve manufacturing sustainability with Industry 4.0 technologies

Developing A sustainability framework for Industry 4.0

With the development in industrialization, sustainability has emerged as a major issue in the global market. Ignorance of sustainability issues in any organization leads to huge financial losses and market reputation. With the development of new technologies developed economies have achieved sustainability in their industry sectors due to strong infrastructure. However, the adoption levels of sustainability practices in emerging economies are still limited. The current manufacturing trend in Industry 4.0 offers new key technologies e.g. cyber-physical systems, IoT (Internet of Things), additive manufacturing and big data analytics which are under the umbrella of Industry 4.0 known as key technologies for 4th industrial revolution. These new technologies are contributing to sustainability in a direct or indirect way. Identification of different enablers is necessary as it facilitates the adoption of sustainability practices in Industry 4.0. The present study aimed at the development of sustainability practices framework for Industry 4.0 for MSMEs (Micro Small Medium enterprises) sector. Initially, enablers to sustainability in Industry 4.0 were identified from the available literature on Sustainability and Industry 4.0. Further, a case study is done in one of MSMEs of India which is working on the adoption of Industry 4.0 practices. A hybrid MCDM (multi-criteria decision making) approach based on F-AHP (Fuzzy-Analytical hierarchy process) and DEMATEL (Decision making trial and evaluation laboratory) is utilized for the framework development. The results revealed that supply chain and environment related enablers are the main cause of barriers to sustainability in Industry 4.0. It is expected that the findings of this study will be beneficial for the researchers, policymakers and managers to improve the sustainability with the use of Industry 4.0 technologies within the emerging economies in MSMEs

Machine Learning Applications for Sustainable Manufacturing: A Bibliometric-based Review for Future Research

The role of data analytics is significantly important in manufacturing industries as it holds the key to address sustainability challenges and handle the large amount of data generated from different types of manufacturing operations. The present study, therefore, aims to conduct a systematic and bibliometric-based review in the applications of machine learning (ML) techniques for sustainable manufacturing (SM). In the present study, we use a bibliometric review approach that is focused on the statistical analysis of published scientific documents with an unbiased objective of the current status and future research potential of ML applications in sustainable manufacturing. The present study highlights how manufacturing industries can benefit from ML techniques when applied to address SM issues. Based on the findings, a ML-SM framework is proposed. The framework will be helpful to researchers, policymakers and practitioners to provide guidelines on the successful management of SM practices. A comprehensive and bibliometric review of opportunities for ML techniques in SM with a framework is still limited in the available literature. This study addresses the bibliometric analysis of ML applications in SM, which further adds to the originalityN/

Review on multi-criteria decision analysis in sustainable manufacturing decision making

At present sustainable development, assessment of sustainable manufacturing practices, and prioritisation of barriers, drivers, and indicators have become complex due to the involvement of existing benchmarks like social, economical, technical, and environmental. Literature review available on sustainable manufacturing practices assessment which considers all three dimensions is relatively limited. Recently, in sustainable manufacturing decision making, approaches to evaluate sustainable manufacturing practices have used both quantitative and qualitative data. This study aims to present a systematic review of multi-criteria decision making (MCDM) applications in sustainable manufacturing. In the present study papers available in the Scopus database were reviewed on the applications of different MCDM techniques in the sustainable manufacturing area. The study highlights how the manufacturing industries can benefit from MCDM techniques in decision making. This review article develops insights into various multi-criteria decision-making techniques progress made by considering the sustainable manufacturing applications over MCDM methods. An extensive review in the sphere of sustainable manufacturing has been performed by considering the Scopus database and utilising MCDM techniques. It is found that most of the studies available in the sustainable manufacturing (SM) area are based on fuzzy-based single model approaches

Industry 4.0: An Indian Perspective

Part 3: Intelligent Systems for Manufacturing Planning and Control in the Industry 4.0International audienceIndustry 4.0 technologies have changed the manufacturing trends in global industries. Industries are adopting Industry 4.0 business models to complete mass customized demands and to compete with global industries. Industry 4.0 can be considered as the current trend of data exchange in manufacturing processes and automation. In India, Industry 4.0 is in its initial stages where the terms digitalization and Industry 4.0 are more widely accepted than fourth industrial revolution. The research work on Industry 4.0 is still limited in India. However, the Government of India has launched some policies and initiatives related to Industry 4.0 and its technologies. The main aim of this paper (1) to provide the more depth insight about the Industry 4.0 and similar terms. (2) to suggest the policies related to India for the transition to Industry 4.0. Indian industries should consider the Industry 4.0 practices seriously as they are shifting their business models from traditional to Industry 4.0 business models. Some issues related to Industry 4.0 implementation like cyber security, machine-to-machine interaction, reliability and stability of CPS should be considered in a better way. In this paper we have discussed about the different initiative by Government of India related for Industry 4.0 technologies. There is need to work on (1) Initiatives related to high investments and technological developments in SMEs and MSMEs industry sectors (2) Identification of infrastructure facilities required for Industry 4.0 and current readiness score of industries. (3) Initiatives related to awareness about Industry 4.0 benefits for industries as well as society

Two decades of research trends and transformations in manufacturing sustainability: a systematic literature review and future research agenda

Sustainable manufacturing (SM) has become an emerging research topic in sustainability due to strict government policies, social awareness, and rapid information and technology changes. Industries are now adopting Industry 4.0 technologies to compete in the global market competition. Very few studies discussed the evolution of SM and the related research themes that evolved in recent years. This study aims to identify the research themes and research transformation evolved in SM in the last twenty-one years. The impact of most productive authors, journals and countries is evaluated by citation analysis, while the co-occurrence of keywords is done to identify the thematic evolution and trending topics in SM. The results of this study provide a clear and detailed picture of various research themes that evolved in SM from 1999 to 2020 i.e., sustainable planning and scheduling, sustainable supply chain, lean and environmental management, sustainable machining, decision making, Industry 4.0 and Lean production systems. The number of publications related to Sustainability and Industry 4.0 has increased in the last 3 years. Cluster analysis shows the evolution of many new trending topics in SM i.e., SM in Industry 4.0. Challenges and enabling factors for the sustainable manufacturing practices have identified and role of decision making in SM implementation is discussed. The content analysis on major research themes in SM is discussed with the help of cluster analysis. Based on the literature analysis a comprehensive research framework for future research work is proposed in Industry 4.0 context. The results obtained from this study will be valuable for academia and practitioners enabling them to be updated about the latest developments in SM research

Machine learning applications for sustainable manufacturing: a bibliometric-based review for future research

Purpose: The role of data analytics is significantly important in manufacturing industries as it holds the key to address sustainability challenges and handle the large amount of data generated from different types of manufacturing operations. The present study, therefore, aims to conduct a systematic and bibliometric-based review in the applications of machine learning (ML) techniques for sustainable manufacturing (SM). Design/Methodology/Approach: In the present study, we use a bibliometric review approach that is focused on the statistical analysis of published scientific documents with an unbiased objective of the current status and future research potential of ML applications in sustainable manufacturing. Findings: The present study highlights how manufacturing industries can benefit from ML techniques when applied to address SM issues. Based on the findings, a ML-SM framework is proposed. The framework will be helpful to researchers, policymakers and practitioners to provide guidelines on the successful management of SM practices. Originality: A comprehensive and bibliometric review of opportunities for ML techniques in SM with a framework is still limited in the available literature. This study addresses the bibliometric analysis of ML applications in SM, which further adds to the originality