A review of research on smart manufacturing in support of environmental sustainability

Industry 4.0, the fourth industrial revolution, offers a chance to enhance the sustainability of manufacturing. It leverages interconnectivity, data sharing, and smart machines/production systems/supply chains. Internet-connected smart machines improve productivity, and energy efficiency, and reduce environmental impact. Machine sensors provide data for analysing energy consumption, optimising efficiency, and quantifying environmental impact. Data assists in scheduling, improving throughput and reducing energy use in production systems. Sharing data across the supply chain decreases energy wasted in unnecessary movements. This paper reviews the literature on Industry 4.0, focusing on smart and sustainable manufacturing at the machine/process, production system, and supply chain levels. It explores applications, benefits, challenges, opportunities, and suggests future research.

In industrial environments, over several decades, Automated Guided Vehicles (AGVs) and Autonomous Mobile Robots (AMRs) have served to improve efficiencies of intralogistics and material handling tasks. However, for system integrators, the choice and effective deployment of improved, suitable and reliable communication and control technologies for these unmanned vehicles remains a very challenging task. Specifics of communication for AGVs and AMRs imposes stringent performance requirements on latency and reliability of communication links which many existing wireless technologies struggle to satisfy. In this paper, a review of latest AGVs and AMRs research results in the past decade is presented. The review encompasses results from different past and present research domains of AGVs. In addition, performance requirements of communication networks in terms of their latencies and reliabilities when they are deployed for AGVs and AMRs coordination, control and fleet management in smart manufacturing environments are discussed. Integration challenges and limitations of present state-of-the-art AGV and AMR technologies when those technologies are used for facilitating AGV-based smart manufacturing and factory of the future applications are also thoroughly discussed. The paper also present a thorough discussion of areas in need of further research regarding the application of 5G networks for AGVs and AMRs fleet management in smart manufacturing environments. In addition, novel integration ideas by which tactile Internet, 5G network slicing and virtual reality applications can be used to facilitate AGV and AMR based factory of the future (FoF) and smart manufacturing applications were motivated.

Industry 4.0 presents an opportunity to gain a competitive advantage through productivity, flexibility, and speed. It also empowers the manufacturing sector to drive the sustainability revolution to achieve net zero carbon by reducing emissions in operations. In this paper, the aim is to demonstrate a practical implementation of a smart manufacturing application using a systematic approach based on conceptual six-gear smart factory roadmap with connectivity, integration and analytics stages to build a smart production management ecosystem using off-the-shelf technologies applied in precision manufacturing. Business benefits from the smart manufacturing application implementation are realized in terms of operational performance, economic benefits, and environmental sustainability over a period of three years (before and after smart manufacturing). The productivity improves as a result of the 47% improvement made to the machines’ utilization and the 53% reduction in the total downtime waste. Economic benefits are realized in terms of a cost saving of GBP 420 K that could cost the business and the returns of the financial investment made, which is recovered within a year. An environmental sustainability impact is realized by a reduction in the total greenhouse gas (GHG) emissions by 43%, mostly due to the reduction in the Scope 2 emissions in operations by 50%, which is significantly impacted by the reduction of energy consumption and better power consumption management. The significance of this work is the bridging of the gap between theory and practice by rapidly applying the six-gear smart factory roadmap to start, scale, and sustain the implementation of smart manufacturing applications in the manufacturing industry. This roadmap can serve as a strategic framework tool for smart manufacturing implementations. The technical architecture can serve as a guide for the practical implementation of smart manufacturing applications to reduce the complexity of development. This work also bridges the gap in academia and in industry by showcasing a real-world actual business benefits realized from smart manufacturing, as well as showcasing the practical implementations, limitations, and opportunities of smart manufacturing applications in the precision manufacturing industry, all of which reduce the internal barriers and challenges facing smart manufacturing and industry 4.0 adoption. The value realized in gaining a competitive advantage and driving environmental sustainability from smart manufacturing in this study can serve as a case study for academics and for industry business leaders, digital champions, and digital lighthouses to support value creation and to drive and accelerate smart manufacturing applications, digital transformation initiatives, and industry 4.0 adoption across the value chain.

Making smart and sustainable manufacturing operations is a top priority for industries in the era of digitization. Numerous studies have demonstrated the feasibility of attaining sustainability goals by incorporating Industry 4.0 technologies. There is still a scarcity of research in the existing literature on deploying smart and sustainable systems within a smart manufacturing context. This study aims to develop an implementation framework for smart sustainable systems and analyze its impact on business practices. It presents a multiple case study analysis of manufacturing organizations based on secondary data collection. The outcomes of these studies assist in developing a framework for a smart sustainable system structured into five layers. These include identification of the area, establishing a correlation, system integration, development of sustainability 4.0, and analyzing the performance based on the Triple Bottom Line (TBL) approach. The study’s results indicate that implementation of smart sustainable systems leads to enhanced organizational performance, which is particularly seen in the areas of sustainable purchasing, sustainable manufacturing, sustainable logistics, and sustainable marketing. Implementation of smart sustainable operations contributes to achieving economic sustainability 4.0, social sustainability 4.0, and environmental sustainability 4.0. The findings of this research will offer guidance to the academic and business communities in their pursuit of sustainability 4.0.

The rise of smart manufacturing under Industry 4.0 demands a harmonious integration between advanced technology and human cognitive abilities. This paper explores the role of cognitive ergonomics in ensuring that complex systems, automation, and artificial intelligence are designed to support, rather than overwhelm, the human operator. By focusing on cognitive load, decision-making processes, mental models, and user interfaces, this study emphasizes the importance of human-centred design in smart work environments. Through a review of empirical research and case studies, the paper highlights the psychological challenges and opportunities arising from human-technology interaction in manufacturing settings. Practical recommendations are provided for optimizing task design, reducing mental fatigue, and improving performance and well-being in technologically advanced workplaces. The paper argues that aligning technological innovation with psychological capabilities is critical to achieving sustainable productivity and employee satisfaction in smart manufacturing systems.

To enable highly automated manufacturing and net-zero carbon emissions, manufacturers have invested heavily in smart manufacturing. Sustainable and smart manufacturing involves improving the efficiency and environmental sustainability of various manufacturing operations such as resource allocation, data collecting and monitoring, and process control. Recently, a lot of artificial intelligence and optimization applications based on smart grid systems have improved the energy usage efficiency in various manufacturing operations. Therefore, this survey collects recent works on applications of artificial intelligence and optimization for smart grids in smart manufacturing and analyzes their features, requirements, and challenges. In addition, potential trends and further challenges for the integration of smart grids with renewable energies for smart manufacturing, applications of 5G and B5G (beyond 5G) technologies in the SG system, and next-generation smart manufacturing systems are discussed to provide references for further research.

The advances in AI/ML, cloud infrastructure, sensor technologies, edge devices, digital twins, sustainability, product lifecycle engineering and management, circular manufacturing, and related enabling and emerging technologies, have made significant impacts on smart and sustainable manufacturing R&D, technologies, and innovation. We are taking this great opportunity to update and modify the scope of ASTM’s journal Smart and Sustainable Manufacturing Systems (SSMS), while not significantly disrupting the nature of the journal, to help the audience (researchers, students, industry practitioners, and possibly policy experts) and, more broadly, the community of researchers and stakeholders. We will ensure a balance between fundamental research and practical applications within the journal’s scope through peer-reviewed research papers, technical notes, review articles, case studies, and discussions. We will also continue to publish special issues that focus on specific topics of interest. We have scoped the journal under the following focused and interconnected domains and topics: Information, Knowledge, AI/ML Data Analytics, and Semantics Modeling Smart and Sustainable Manufacturing and Infrastructure Enabling Technologies, Computing, and Digital Transformation (Digital Thread and Digital Twins) Product Lifecycle Engineering and Management Our aim is to address the needs of different communities that make a significant impact on problems: Theories, experiments, products, processes, and systems, sustainable engineering, lifecycle engineering, manufacturing, and supply chains Computer science, physical science, synthesis/processing science, sustainability science, and engineering R&D, technology, commercial hardware and software solutions, system integration through standards Our hope is to create innovation through a cross-discipline domain approach. We propose to achieve “translational manufacturing” research aimed at translating (converting) results in basic research across disciplines into results that directly benefit humans. Like “bench to bedside” in medical research and “field to fork” in food processing, “translational manufacturing” translates breakthroughs in advanced research into commercial technologies, products, and applications, “lab to market”—linking science and engineering research to commercial outcomes. Thank you for exploring the Smart and Sustainable Manufacturing Systems journal. Please contact the editorial office if you have any questions about submitting a paper to the journal.

Purpose Using smart manufacturing technologies presents potentials for economic, environmental, and social sustainability objectives. Regarding their contribution to sustainability, these potentials are not mutually exclusive but interrelated. This study investigates these interrelationships of utilizing advanced digital technologies for sustainable smart manufacturing. By identifying the importance of the social dimension and its human factor, this study contributes to the recent research on human-centricity in smart manufacturing. Design/methodology/approach We apply a two-step mixed-method approach. First, through 44 expert interviews supported by a literature review, we identify nine key sustainability potentials that influence sustainable smart manufacturing. Second, we analyze the interrelationships and expand our analysis using data from 68 participants. Findings We identify the impact of each factor and the cause-and-effect interrelationships. Our findings show that all environmental potentials can be categorized into effect dimensions. Within the economic and social dimensions, only one factor each is classified as an effect factor, whereas two factors in each domain are recognized as cause factors. Interestingly, employee qualification acts as the strongest lever influencing all other key sustainability dimensions. Originality/value This study elucidates the interplay between smart manufacturing technologies and sustainability in smart manufacturing, offering valuable insights to navigate the interrelatedness of sustainable potentials, in particular regarding human interoperability.

Environmental sustainability remains a focal point of current research in the aviation industry. The purpose of the current study was to create four statistical models which could predict the types of factors that would indicate a consumer's support for the use of sustainable products in the aviation industry. Four models were proposed: overall support for sustainability in aviation, support for the use of aviation biofuels, support of sustainable construction materials in aviation, and support of sustainable aviation manufacturing. The study used an overall sample of 514 participants who were randomly divided into two separate datasets. The first dataset was used to produce the regression equations and the second dataset was used to assess model fit. The results produced four valid models with the amount of variance explained ranging from 29.0% to 41.1%. Perceived value of sustainability was found to be a significant predictor in all four models.

The role of the Internet of Things (IoT) in Smart Manufacturing, aimed to illuminate its transformative impact on operational efficiency, responsiveness, and environmental sustainability. The aim of the investigation was to explore IoT's pivotal significance in reshaping manufacturing processes towards heightened efficiency, responsiveness, and environmental consciousness. The study presents results from performance metric assessments, visualizations, and data simulations. It contains information about way IoT data is shown in manufacturing environments, the clear relationship between temperature and pressure, the distribution of security risks and related safety measures, and the dynamic behaviour of important IoT components. The importance of IoT in real-time environmental management, process optimization, and security enhancement within paradigms of smart manufacturing are the key points of observation. The comparative analysis of Conventional Analytical Models and Composite Models highlights the choice between stability and adaptability, providing crucial insights for modeling approaches tailored to distinct manufacturing requirements. IoT's transformative potential within Smart Manufacturing, emphasizing data integrity, security, sensor dynamics, analytics, and sustainability.

This paper aims to explore the extent to which the Iraqi industrial environment is keeping pace with technological progress. It also attempts to show the role of smart production in environmental sustainability. Using the case study methodology, this paper examines the possibility of applying smart production techniques at an Iraqi factory, Ezz Factory / General Company for electric and Electronics Industries. In addition, this work checks the technical, economic, social, and environmental effects by measuring the gap between the standard conditions as determined by the checklist developed for this purpose and the actual real application of all the smart production dimensions in the factory. Previous works used interviews with many managers and technicians through their field visits to gather information and to check many documents. The assessments showed a gap of 50%. Based on the result, the researchers reached several conclusions, the most important of which is that the factory is interested in developing and extending the environmentally friendly production of solar panels. Another important conclusion is the acceptable level of sustainable production performances without a smart production line.

This paper aims to explore the extent to which the Iraqi industrial environment is keeping pace with technological progress. It also attempts to show the role of smart production in environmental sustainability. Using the case study methodology, this paper examines the possibility of applying smart production techniques at an Iraqi factory, Ezz Factory / General Company for electric and Electronics Industries. In addition, this work checks the technical, economic, social, and environmental effects by measuring the gap between the standard conditions as determined by the checklist developed for this purpose and the actual real application of all the smart production dimensions in the factory. Previous works used interviews with many managers and technicians through their field visits to gather information and to check many documents. The assessments showed a gap of 50%. Based on the result, the researchers reached several conclusions, the most important of which is that the factory is interested in developing and extending the environmentally friendly production of solar panels. Another important conclusion is the acceptable level of sustainable production performances without a smart production line.

As the textile industry shifts towards smart manufacturing, the detection of fabric defects has emerged as a critical element in ensuring product quality, enhancing productivity, and reducing costs. Computer vision and machine learning techniques, particularly deep learning, have emerged as essential tools in modern textile defect detection. This article provides a review of previous research in this field, examining the technological development and future directions for fabric surface defect detection. The article begins by discussing the various stages of fabric defect detection, which are categorized into four phases: image acquisition, image preprocessing, feature extraction and selection, and classification and detection. It also explores the application of image processing technologies at each stage, offering a detailed analysis of the advantages and limitations of each method through a comprehensive comparison. In addition, this article organizes existing fabric defect datasets and discusses the importance and challenges of datasets in model training. Finally, the article discusses the key challenges in fabric defect detection and outlines potential research directions for the future. The goal of this article is to guide the textile industry in selecting and applying machine vision technology, and to support the development of intelligent fabric inspection systems.

Physical parameter monitors are necessary for the conversion of conventional factories to smart factories. The creation of new sensors for monitoring physical parameters in difficult-to-access areas is necessary for this move. In this line, a large number of optical sensors based on integrated optical wave guides or optical fibers have been designed and manufactured over the previous ten years. As a potent tool for real-time monitoring of physical parameters like temperature, pressure, strain, and humidity. Fiber Bragg Grating (FBG)-based sensors have attracted a lot of attention. The main reasons for using FBG sensors in smart factories are immunity to electromagnetic interference and radio frequency; compact in size and offer multiple sensing to different physical parameters; permit remote sensing and are not prone to corrosion. In this paper, a review of FBG-based sensors for strain parameter monitoring and their application in the smart factories is presented. An overview of the historical background is followed by an explanation of the fundamentals of FBG sensing and a discussion of the electromagnetic theory of waveguide modes in optical fibers. Then give a review of current research in FBG strain sensor development. A review of the challenges and applications of FBG strain sensors specifically in smart manufacturing follows.

The integration of Artificial Intelligence (AI), cloud computing, and edge computing has transformed the Internet of Things (IoT) ecosystem by addressing critical challenges such as latency, scalability, resource management, and fault tolerance. IoT applications generate massive amounts of data, requiring real-time decision-making and efficient resource allocation, which traditional cloud-centric architectures often fail to deliver due to inherent latency and bandwidth limitations. Edge computing, as a decentralized extension of the cloud, brings computation closer to the data source, reducing latency and enabling real-time analytics. However, the dynamic and heterogeneous nature of cloud-edge systems presents significant orchestration challenges. This paper explores how AI-driven optimization enhances cloud-edge orchestration by improving task scheduling, predictive analytics, and data processing. AI models, such as reinforcement learning, neural networks, and bio-inspired algorithms, enable dynamic workload distribution, proactive resource allocation, and energy-efficient operations, thereby improving system reliability and scalability. Furthermore, the study highlights innovative integration models, including hierarchical, collaborative, and federated approaches, which cater to diverse IoT requirements by balancing the computational power of the cloud with the agility of edge nodes. Through an extensive review of recent research, this study identifies key challenges in data privacy, scalability, real-time orchestration, and fault tolerance, while also exploring novel opportunities, such as privacy-aware federated learning frameworks, lightweight AI models for edge devices, blockchain for fault resilience, and bio-inspired energy optimization techniques. Real-world use cases in domains such as smart manufacturing, autonomous vehicles, and healthcare demonstrate the practical benefits of AI-powered orchestration, showcasing reductions in latency and energy consumption alongside improvements in system scalability and responsiveness.