Digitalisation and Green Strategies: A systematic review of the Textile, Apparel and Fashion Industries

Digitalisation and Green Strategies: A systematic review of the Textile, Apparel and Fashion Industries | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Systematic Review Digitalisation and Green Strategies: A systematic review of the Textile, Apparel and Fashion Industries Emmanuel Ayo Orisadare, Okechukwu Emmanuel Achukwu, Abiodun Afolayan Ogunyemi, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4804089/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The Textile, Apparel, and Fashion (TAF) industries significantly contribute to national gross domestic products but also account for 20% of global pollution. The Industry 4.0 (I 4.0) framework, incorporating technologies like the Internet of Things (IoT), Artificial Intelligence, and robotics, enables smart and efficient manufacturing production, leading to more significant economic outputs. However, it also brings about issues like automation-related tensions, energy efficiency, and waste management and other sustainable practice demands. The Industry 5.0 (I 5.0) framework addresses the issues created by Industry 4.0 in many areas, especially promoting human-centric sustainable practices, social interaction, and a proper synergy between man and machine. This article examined the issues closely based on a systematic review of 42 peer-reviewed studies from 2013 to 2023 exploring the dynamics between technological advancements and sustainable practices in the TAF industries. The review identified technological implementations, circular economy support, and challenges associated with implementing the I 4.0 and 5.0 frameworks. The article analyses significant research using a descriptive literature review to understand the strategies, impact, and challenges of digitalisation and green transition in TAF industries' production and sustainability. The findings reveal a big dichotomy between the Global North and Global South TAF firms, indicating a more contextualised approach is required to integrate I 4.0 and 5.0 approaches and promote sustainable production practices. This study offers a synthesised overview of the current landscape, providing insights for stakeholders, policymakers, and researchers engaged in navigating the TAF industries towards a sustainable, digitally advanced, circular economy future. Digital transformation Textile Apparel and Fashion industries Circular Economy Industry 4.0 and 5.0 Sustainability Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction The Textile, Apparel and Fashion industries are some of the major industries contributing to national economies worldwide (see, for example, [ 1 ]). However, the industry can be said to be operating at a different efficiency level in all the countries globally [ 2 ]. The Global South and the North might differ [ 3 ]. While the benefits of digital transformation are obvious, the challenges are also evident. At the same time, the contribution of TAF Industries to global pollution (20%) is a concern [ 4 , 5 ]. Due to industrial revolution trends, sustainable digital transformation of the manufacturing sectors, especially the TAF Industries, has become necessary. In some developing countries, obsolete or outdated technologies have limited the operations and outputs of the TAF industries [ 6 , 7 ], and in many cases, the Gross Domestic Product (GDP) contributions from the industries have significantly reduced in some countries [ 2 ]. In some countries like Nigeria, the industry is as good as dead, with most of the known textile firms, such as Kaduna Textile Limited mill, closed [ 8 , 9 ]. On the one hand, there is a need to determine whether the digital transformation is easy for TAF Industries to achieve. On the other hand, the industries’ contribution to global pollution is worrisome [ 4 ]. The I 4.0 and 5.0 frameworks seek to promote smart manufacturing and sustainable production and could be perceived as providing guidelines for companies seeking digital transformation and environmental sustainability. The introduction of efficient manufacturing has led to fewer workers being employed for production, thus creating a loss of jobs [ 10 , 11 , 5 ]. Furthermore, there has yet to be a symbiotic relationship between humans and machines because of human animosity toward machines due to their perception of their tendencies to take over human jobs [ 10 ]. Due to heavy mechanisation and the drive for mass production, the negative impact of digital transformation by the TAF industries contributes to environmental issues, including pollution of water bodies, the atmosphere, and land areas [ 4 ]. Specific regulations could be in place for full compliance by the manufacturing industries, especially the TAF Industries, to control their operations through regulations toward recycling and re-use of waste to recover a specific volume of effluents discharged into the environment. The I 5.0 framework - a value-oriented framework that mitigates the shortcomings of its predecessor – industry 4.0, and strives to provide a balance between humans and machines in a way that both can operate in a symbiotic manner. Also, to consider the environment by promoting societal values [ 12 ]. Smart Manufacturing and Environmental Sustainability Under Industries 4.0 and 5.0 Frameworks Industry 4.0, an initiative pioneered in Germany, has ushered in a new era of technological transformation with its theme of "Smart Manufacturing for the Future" [ 10 , 13 ]. This paradigm shift represents the integration of the cyber and physical worlds by introducing advanced technologies in industrial sectors [ 15 ]. With the convergence of operational technology (OT) and information technology (IT), Industry 4.0 is transforming production processes and redefining the way industries operate [ 14 ]. At its core, pursuing increased productivity and mass production through innovative technology drives Industry 4.0 [ 10 ]. Historically, textile manufacturing was vital in driving regional economies and international relationships. From the first industrial revolution, powered by water and steam, to the present era of Industry 4.0, textile manufacturing has witnessed remarkable transformations, underscoring the influence of technology on industries [ 16 ]. The Timeline of Industry 1.0 to 5.0, adapted from [ 10 ], is given in Fig. 1 . Whether textile manufacturers embrace digitalisation and automation, leveraging Industry 4.0 technologies to improve production processes, quality control, and supply chain management is uncertain. The evolution from Industry 4.0 to Industry 5.0 signifies a significant shift in focus and objectives within the manufacturing sector. Initially, Industry 4.0 aimed to improve productivity and efficiency by applying advanced technologies. However, over time, its focus shifted away from sustainability concerns and towards technological advancements. This shift led to the emergence of Industry 5.0, which seeks to transform manufacturing by prioritising social objectives and personalised customer demands. Industry 5.0 aims to make manufacturing a provider of prosperity while respecting environmental limits and prioritising worker welfare [ 17 ]. Industry 5.0 complements Industry 4.0 by placing humans at the centre of development and enhancing resilience while minimising environmental and social impacts [ 17 ]. It encompasses elements such as intelligent devices, systems, automation, and materials [ 18 ]. A key focus of the I 5.0 approach is sustainability, which encompasses economic, environmental, and social dimensions [ 17 ]. Industry 5.0 seeks to balance economic growth, environmental preservation, and societal well-being by considering these dimensions. However, implementing the I 5.0 framework comes with its own set of challenges. Related Studies Some related systematic literature review studies targeting Industry 4.0, digital transformation, environmental sustainability, etc., have been reported. Happonen and Ghoreishi [ 19 ] conducted a mapping study that investigated the possibility of accelerating the circular (recycling) economy in the textile and apparel industries through the use of Industry 4.0 technologies (movement to the digital world) in industrial transition. This move aimed to achieve sustainability goals and reduce the negative environmental effects of growing fast fashion. They accomplished this by reviewing 27 studies that targeted the implementations of digital technologies in various textile recycling processes, such as the extraction of raw materials, manufacturing of fibre, dyeing, washing, and the incineration of wastes from fibres and clothes. The findings showed increasing research attention to topics that border sustainability, circularity, and digitalisation, especially the digital technology roles in the supply chain management of the textile industry, but with a massive lack of implementation despite its profitability. A comprehensive and systematic review of the influences and opportunities of digital transformation of the fashion industry on sustainability-oriented innovations, supply chains, and business models was undertaken by [ 20 ]. The study reviewed emerging companies that are actively using 3-dimensional digital and virtual (3DDV) technologies such as digital twinning (DT), 2- and 3-dimensional scanning, augmented and virtual reality (AR and VR), and 3D modelling. The analysis revealed that digital technology adoption provided openings that would help dematerialise the traditional fashion supply chain model of producing and distributing products and services. In another related study, Akram et al. [ 21 ] reported integrating digital technologies - Internet of Things (IoT), Artificial Intelligence (AI), blockchain, Augmented Reality (AR), and Virtual reality (VR)) to establish a resilient and innovative infrastructure that can guarantee the development of fashionable products that will promote sustainable production and consumption. The study explored the implementation of these technologies in the TAF Industries for virtual and augmented-based shopping experiences, health prediction, fashion trend forecasting, circular economy, supply chain, smart cloth, etc., as well as the limitations of the previous studies that implemented the technologies earlier in the TAF Industries. Abdelmeguid et al. [ 22 ], in their systematic review, reported synthetically the challenges to be faced if a Circular Economy (CE) is implemented in the fashion industry. They discovered business management's soft (consumer-related issues and green intellectual capital) and hard (financial pressures, stakeholders' pressures, regulatory pressures, and business model innovation) aspects. The study finally proposed the management and overcoming of soft aspect challenges first, to have the ability to face the hard aspect challenges to successfully achieve the Sustainable Development Goals (SDG) 12, as it relates to sustainable production, consumption, and efficient management of goods and natural resources. Islam et al. [ 23 ] carried out a comprehensive systematic review and content analysis of 91 peer-reviewed journal articles published over a 10-year period to map and develop a framework for the various manufacturing process practices in the Textile, Apparel, and Fashion industries concerning environmental concerns. The findings highlighted the diverse and complex environmental practices in the TAF Industries, in addition to suggesting the best practices at the various manufacturing stages of garment washing, dyeing, and packaging to include the adoption of a circular economy, energy efficiency strategies, resource savings, product design for longevity, and materials selection. In a closely related study, [ 24 ] highlighted the global environmental pollution linked to the TAF Industries and the push to reduce environmental damage. The study systematically reviewed TAF-related sustainability events in the last 20 years and disclosed three critical areas of research focus: sustainability challenges in the supply chain, circular economy initiatives, and sustainable clothing, as well as their implementation barriers and drivers. Considering the introduction of I 4.0 and I 5.0 and focusing on the introduction of technologies, the drive for smart production, and the demands for sustainable societies, it is imperative to conduct a systematic state-of-the-art study to determine the strategies used in TAF companies, their impacts, challenges, and trajectories for the future. This systematic review investigates these issues. Review objectives i. Identify and classify issues dominating research on digital transformation and sustainability in the TAF industries. ii. Describe the implementation of Industry 4.0 and 5.0 technologies in the TAF industries for supporting circular economy, impacts, and associated challenges. iii. Identify and outline opportunities to drive further research regarding industry 4.0 and 5.0 technologies to enhance the circular economy for the TAF Industries. Research questions What digital transformation and sustainability issues dominate the TAF industries literature, and how are they classified? How have Industry 4.0 and 5.0 technologies been implemented in the TAF industries to support circular economy initiatives, and what are their measurable impacts on resource efficiency and waste reduction and associated challenges? What research opportunities exist for integrating Industry 4.0 and 5.0 technologies in the TAF industries to enhance the Circular Economy? Methods The study employed a descriptive literature review as explained in [ 25 ] and [ 26 ] and inspired by [ 27 ]. Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were used. The PRISMA guideline provides a comprehensive checklist of items researchers should include in the review of publications to enhance transparency and ensure the reproducibility of the review process. We formulated the research questions to investigate the implementation of I 4.0 and 5.0 technologies and strategies for digitisation and green transition in the TAF industries, as well as the documented impacts and challenges confronting the industries concerning digital transformation and environmental sustainability. The literature search was conducted on January 26, 2023, using two digital libraries, Web of Science (WoS) and Scopus—the justification for WoS and Scopus to include comprehensive coverage of high-quality databases. The search strategy employed a combination of keywords related to digital transformation, environmental sustainability, and the TAF industries. Search criteria were designed using combinations of keywords containing 'digital transformation' OR 'digitalisation' OR 'digital technologies' OR 'electronic textiles' OR 'textile consumption' OR 'environmental sustainability' OR 'eco-friendly manufacturing' OR 'green manufacturing' OR 'smart production' OR 'textile smart marketing' AND 'Industry 4.0' OR 'industry 5.0' AND 'textile firms' OR 'textile company*' OR 'textile mills' OR 'Apparel' OR 'fashion' OR 'garment' OR 'fabric' OR 'innovative manufacturing textile industr*' . The selection of studies followed a two-stage screening process. In the first stage, titles and abstracts were screened, retaining articles with relevant keywords in their titles or abstracts. Table 1 shows the detailed results of the search process. The process is also illustrated in Fig. 2 . Inclusion and exclusion criteria were applied to the remaining studies in the second stage. In this stage, results were divided into four parts, and two researchers (one doctoral/master’s student and one senior researcher) were assigned to one part of each of the results. The researchers (both the doctoral/master’s student and the senior researcher) applied the inclusion criteria. The details of the inclusion and exclusion criteria are in 2.1. A senior researcher who coordinated the review process reviewed the decisions of the doctoral/master’s students and their assigned senior researchers. The coordinating senior researcher decides if a paper should be accepted for inclusion or rejected in situations where the doctoral student and the assigned senior researcher differ in their studies assessments. The idea was to give fairness to the process and remove errors or personal biases. There were seven instances where the assessments of the doctoral students and the assigned senior researcher differed, and the coordinating senior researcher reviewed the decisions and made the final decision. Data were extracted from the selected articles using a predefined data extraction spreadsheet following the guidelines of [ 28 ]. The extracted information included the paper type, title, year of publication, country of study, overview of the problem addressed, study design, methodology of the study, data collection method, sample population/company size, sample size, approach for data collection, key findings, conditions for achieving digital transformation, condition for achieving environmental sustainability, benefits of combining human capital development and industrialisation, challenges of using industry 4.0 or 5.0 frameworks to achieve digital transformation, challenges of using industry 4.0 or 5.0 frameworks to achieve environmental sustainability, digital transformation problems, environmental sustainability issues, and other important notes. Quality assessment was conducted to evaluate the methodological rigour of the included studies. Studies were assessed based on adherence to the inclusion criteria, clarity of sample statistics and raw data reporting, and accessibility for download. Duplicates found were excluded from the analysis. Findings from the included studies were synthesised, analysed, and summarised using a narrative approach. Themes—drivers and challenges of digital transformation and environmental sustainability in the TAF industries were identified. Fortunately, downloading all the included studies was possible without contacting the corresponding authors for personal copies. We used ChatGPT 3.5 for texts compression and Grammarly 1 for editing. All the texts were from the authors’ original understanding, reflections and insights gained from the reviewed studies and knowledge of the fields covered. Inclusion and Exclusion Criteria Inclusion Publication must be in English. Publication must be peer-reviewed. Must be a journal article or conference paper. The articles and papers must not be less than 4 pages The study design must be detailed, showing the methodological process. Must be published between 2013 and 2023. The effectiveness of the I 4.0 and 5.0 frameworks must be explicitly investigated, focusing on the digital transformation of textile and allied firms and/or environmental sustainability. Exclusion Articles and papers not written in English. Grey literature and unpublished studies, non-peer-reviewed studies, conference proceedings books, textbooks, work-in-progress, opinion papers, literature reviews, interviews, theses, and blog posts. Studies that failed to report the methodological process. Studies that are not accessible online or impossible to download due to subscription issues. Studies that are inaccessible for download and the corresponding author is not responsive to requests for sharing the author’s copy of the study. Search Results Table 1 Search process and results Sources Initial results Screening by titles and abstracts Screening by applying the inclusion and exclusion criteria Excluded duplicates Screening by quality assessment Final inclusion Web of Science 128 93 40 1 40 Scopus 34 18 3 1 2 Total 162 111 43 1 42 Results and Discussion RQ 1 What digital transformation and sustainability issues dominate the TAF Industries literature, and how are they classified? First, we break down the digital transformation and sustainability issues based on our understanding of the studies (Table 2 ). Following insights from [ 29 ] and [ 30 ], we then classified them into circular economy and sustainability dimensions (Fig. 3 ). Table 2 How the reported issues are classified Classifications of DT and Sustainability Issues Countries of Study Classification of Countries References Optimal path for energy efficiency Taiwan, India, Italy, Germany, Spain GS, GS, GN, GN, GN SRP 1, SRP 10, SRP 16, SRP 26, SRP 40 Industry 4.0 technologies and global value chain Taiwan, China, Italy, Germany, Indonesia, Indonesia GS, GS, GN, GN, GS, GS SRP 4, SRP 6, SRP 21, SRP 25, SRP 29, SRP 32 Capturing operation cycle times Sri Lanka GS SRP 5 Organisational leadership in Industry 4.0 implementation Pakistan GS SRP 7 Digitising operational processes of Industry 4.0 utilisation Germany, Tanzania GN, GS SRP 8, SRP 42 Sustainable practices compliance Turkey, Bangladesh, Iran, Colombia, Portugal, Pakistan & Bangladesh, Bangladesh, South Africa, Taiwan & Vietnam GS, GS, GS, GS, GN, GS, GS, GS, GS SRP 9, SRP 14, SRP 19, SRP 23, SRP 30, SRP 31, SRP 34, SRP 37, SRP 41 Industry 4.0 technologies readiness and usage Taiwan, Portugal, Turkey, Portugal GS, GN, GS, GN SRP 3, SRP 11, SRP 17, SRP 18 Digital fashion technologies Sweden, China GN, GS SRP 13, SRP 39 Manufacturing Execution System South Korea, United Kingdom, India GN, GN, GS SRP 22, SRP 27, SRP 36 Industry 4.0 technologies implementation Brazil, Germany, China, Pakistan GS, GN, GS, GS SRP 12, SRP 20, SRP 24, SRP 28 Digital transformation implementation barriers Germany, Portugal, South Korea GN, GN, GN SRP 2, SRP 33, SRP 35 Digital transformation implementation and human factors impact Germany, South Korea GN, GN SRP 15, SRP 38 *GS 2 - Global South, GN - Global North We also explored the results from the country classifications to gain insights into the peculiarity or otherwise of the issues. We present and discuss the results in this section. The optimal path for energy efficiency - a green strategy issue, appears to be evenly concentrated in the Global North and Global South. Still, the results in Table 2 suggest the problem is more pressing in the Global North. According to the Circular Economy dimension, resource efficiency and waste reduction are still problematic for Global South countries like Taiwan and India (SRP 1, SRP 10). The Global North countries referenced in Table 2 have peculiar resource efficiency and waste reduction issues. In particular, traditional companies in Italy face challenges in keeping pace with technological advancements (SRP 16). Germany's textile producers struggle to maintain competitiveness and flexibility in a demanding market environment (SRP 26). Meanwhile, consulting firms in Spain must find a workaround for automatically detecting anomalies in sensor-controlled buildings, reflecting a push towards more innovative and efficient building management (SRP 40). Table 2 also suggests that Industry 4.0 technologies and global value chain issues are more pressing for Global South Countries than the Global North. In particular, the Global South faces unique challenges such as the integration of cultural elements into the global value chain (Taiwan - SRP 4), the need for better guidance on social and environmental sustainability ( China - SRP 6 ) , and understanding consumer behaviour (SRP 29) along with leveraging CAD technology in the Indonesian garment industry (SRP 32). These issues focus on cultural integration, sustainability practices, and technological adoption to meet market demands. In contrast, the Global North grapples with more advanced market dynamics, such as improving supply chain management for specialised industries in Italy (SRP 21) and adapting to rapidly changing consumer demands driven by digitalisation and individualisation in Germany (SRP 25 ) . These issues emphasise the need for innovation, efficient management, and technological adaptation to remain competitive in highly developed and consumer-driven markets. Capturing operation cycle times and organisational leadership in Industry 4.0 implementation might likely be related more to the Global South, as exemplified in this study. In the Global South , the focus is on adopting and integrating advanced technologies to modernize manufacturing processes and ensure sustainability. Sri Lanka's move towards intelligent assembly lines using cloud technologies underscores the need for infrastructure and skills development to implement these systems effectively (SRP 5). Pakistan's struggle with sustainability amidst rapid digitalization and intelligent manufacturing highlights the broader challenge of managing technological progress while maintaining sustainable practices (SRP 7). The rest of the issues presented in Table 2 also highlight a clear divide between the challenges faced by Global South and Global North countries, emphasising distinct economic, technological, and environmental concerns pertinent to each classification. The issues in the Global North often revolve around upgrading existing advanced industrial systems, integrating cutting-edge technologies, and responding to sophisticated consumer demands. Germany TAF companies face issues related to legacy production machines that lack modularity, scalability, and flexibility (SRP 8). There is also a need for cost-benefit trade-offs and implementing assisted digital working environments in industrial firms. These challenges stem from Germany's advanced industrial base and the necessity to upgrade existing infrastructure to maintain competitiveness (SRP 2). Portuguese companies are grappling with creating and integrating intelligent, sustainable, and resilient future-oriented factories (SRP 30). Another critical issue is the active role of knowledgeable and interventive consumers in relationships with brands and products, necessitating a shift towards more interactive and responsive business models (SRP 33). Swedish TAF companies highlight the application of digital fashion technologies in contemporary fashion design, reflecting its focus on innovative and cutting-edge approaches in the fashion industry (SRP 13). The UK TAF companies are working on seamlessly integrating wearable sensors into mass-fabricated clothing using precise methods like computerised embroidery (SRP 27). This points to an emphasis on technological precision and integration in textile manufacturing. South Korean TAF companies emphasise the need for a manufacturing execution system (MES) for smart factories, aligning with emerging technologies and increased firm cooperation (SRP 22). Additionally, the country experiences some challenges with gas sensors in wearable electronic devices, which have low stretchability and poor stability, reflecting the high-tech focus of its industrial sector (SRP 38). Table 3 provides an overview of the various digitisation and green strategies proposed or used in TAF companies, as reported in the 42 studies reviewed. Table 3 Digitisation, green strategies, and innovations in TAF industries Study Code DT Strategies used or proposed Green Strategies used or proposed Innovation to address the problem SRP 1 N/A Recycling, reuse of wastewater, reuse of waste-heat, reuse of waste cinder from coal combustion, Eco-Brick Mathematical model for profit maximisation and green production planning and control SRP 2 End-to-end value chain creation N/A Setting up of Textile Learning Factory 4.0 SRP 3 Smart manufacturing N/A Decision support system for dyeing machine scheduling SRP 4 Cross-disciplinary value creation N/A Cross-disciplinary value creation framework SRP 5 Garment assembly operations cycle times and balancing workloads N/A Smart production line prototype SRP 6 N/A A measures sustainability framework based on the United Nations Sustainable Development Goals A hybrid multi-situation decision technique SRP 7 N/A Transformational leadership and Innovative performance N/A SRP 8 Exploitation of digitalisation advantages N/A Digitisation process prototype SRP 9 N/A Sustainable global brands Energy and water efficient environmentally friendly production SRP 10 N/A Energy consumption modelling Penalty Based Reinforcement Learning Algorithms SRP 11 Bayesian process for the production system N/A A mathematical model providing a feedback learning/controlling loop for the pre-production, production and post-production processes SRP 12 Maturity assessment N/A A diagnostic tool for assessing the maturity of Industry 4.0 technologies SRP 13 Digitalisation N/A Digital fashion as an emerging subfield SRP 14 N/A Assessing the influence of Institutional Pressure on cleaner production practices and sustainable firm performances A conceptual framework for assessing the influence of Institutional Pressure on cleaner production practices and sustainable firm performances SRP 15 N/A Sticking to standardised implementation practices. Creating Augmented reality-based assistance systems SRP 16 N/A Sustainable production scheduling Discrete-event simulation model SRP 17 Awareness and readiness assessment of industry 4.0 technologies N/A Providing extensive knowledge of Industry 4.0 technologies. SRP 18 Digitisation N/A An in-house ICT solution, the “FLUXOCONF” production monitoring software for evaluating the firm’s production system technologies application SRP 19 N/A Sustainable supplier selection The fuzzy best-worst method (FBWM) and two-stage fuzzy inference system (FIS) model for assessing supplier's selection SRP 20 Smart textile production N/A Self-optimising textile machinery SRP 21 Simulation and data analysis modelling for decision-making support in supply chain optimisation N/A Supply Chain Management Simulator SRP 22 Modelling and developing new MES Systems N/A A cloud-based collaborative MES System to support a value chain process SRP 23 N/A Big data application modelling A structured and automated decision-making system for SMEs in the garments sector SRP 24 Business model adaptation for platform-based servitisation to foster product-service innovation (PSI) N/A A conceptual framework for platform-enabled servitisation pathways SRP 25 Adapting individualisation and digitalisation for in-store fashion production N/A A unique in-store production line that creates custom-designed and shaped woollen sweaters for customers SRP 26 Multi-objective self- optimisation of the weaving process. N/A A software-based programmable logic controller for optimal parameter setting SRP 27 Computerised embroidery for mass manufacturing of textile-based electrical wearable sensors. N/A A low-cost poly(3,4-ethylene-dioxythiophene) polystyrene sulfonate (PEDOT: PSS)-modified cotton conductive thread (PECOTEX) with computerised embroidery SRP 28 A decision-making framework for evaluating and selecting sustainable suppliers through industry 4.0 initiatives in circular economy implementation N/A A multi-criteria decision-making support tool SRP 29 Focusing marketing strategies on message delivery to enhance image-related concerns and boost purchase intention utilising big data from the Internet of Things. N/A A conceptual framework on consumer conformity SRP 30 N/A Collecting and analysing seemingly unrelated activities co-occurring in different parts of smart factories for cyber situational awareness creation SMS Digital Twin (SMS-DT) platform for simulating and monitoring industrial conditions in smart factories SRP 31 N/A Intellectual capital's (IC) dual role in improving a firm's sustainable production in blockchain-driven supply chain management (BCSCM) A conceptual framework of intellectual capital (IC) for improving firms' sustainable production SRP 32 Using Computer-Aided Design (CAD) for fashion design N/A A conceptual framework on competence and industrial work practice SRP 33 Collaborative Design and Mass Customisation N/A Analysis of perceptions towards co-design mass customisation approaches in the Portuguese footwear industry SRP 34 N/A Automatic identification and classification of textile visual pollutants using computer vision techniques Classifying visual pollutants using deep learning networks SRP 35 Analysing a CAD file with customer requirements and implementing a one-person, one-item mass production system to accomplish a tailored service N/A Automated classification of customer demands dress attributes (colour, pattern, size), CAD file generation for each element, and simulation of the entire manufacturing process. SRP 36 Lean manufacturing approaches implementation in MSMEs N/A Cluster analysis of Lean Manufacturing Competitive Scheme (LMCS) in MSMEs SRP 37 N/A Using an enzymatic derivative of bacterial mandarin peelings fermentation to create a sustainable and environmentally friendly textile bio-scouring process Optimising response surface methodology (RSM), using the common low-cost agro-industrial waste (MP) to produce bacterial exudate (HRJ16 laccase) SRP 38 Developing a highly stretchable, sensitive and stable NO 2 gas sensor from reduced graphene oxide sheets and elastic commercial yarns N/A Fabricating the RGO sensors using a pre-strain strategy (strain-release assembly) to accomplish high stretchability and good stability SRP 39 Man-algorithm cooperation intelligent design in multiple links of clothing design N/A Integrating intelligent algorithms (parameterised number state algorithm, Generative Adversarial Networks, and style transfer) into different clothing product design and development aspects and creating a novel approach to designing clothes by combining smart algorithms with distinct functional roles of people. SRP 40 N/A Using a chart that utilises functional data to identify abnormalities and estimate the standard output of industrial processes and services, including those related to energy efficiency. Developing a control process (Phase I and II control charts) based on calculating functional data depth, identifying outliers by smooth bootstrap resampling, and customising nonparametric rank control charts SRP 41 N/A Hybrid method for generating SSCM indicators Fuzzy Delphi method for validating SSCM indicators SRP 42 Digitalisation level assessment N/A Maturity assessment of firm's digitisation, digitalisation and digital transformation In contrast, the Global South faces foundational challenges such as technological adoption, environmental sustainability, and data-driven process optimisation. In Tanzania, TAF companies face low performance and customer satisfaction due to reluctance to adopt digital manufacturing processes (SRP 42). This indicates a technological lag and resistance to change, impacting productivity and market competitiveness. Turkish garment industry struggles with rapid fashion changes, which pose significant challenges (SRP 9). Turkish manufacturing companies are also investigating broad approaches to Industry 4.0 concepts and essential technologies, indicating a transition phase in embracing modern manufacturing paradigms (SRP 17). Bangladesh's clothing industry contributes significantly to pollution, with cleaner production mediating between institutional pressure and firms' environmental performance (SRP 14). Visual pollution in urban environments is also a concern, necessitating comprehensive investigation and assessment (SRP 34). Iranian TAF companies are focusing on identifying essential criteria for selecting sustainable textile suppliers, particularly in the context of Industry 4.0, which stresses a need to align supplier selection with sustainability and technological advancements (SRP 19). Colombia emphasises the behaviour of operational processes and the generation of information and big data to optimise fabric dyeing operations, reflecting a focus on data-driven improvements in textile manufacturing (SRP 23). Pakistan's TAF companies' issues revolve around the significant contribution of product production and consumption to climate change and environmental challenges, which impact future generations and human lives (SRP 31). Limited research also exists on integrating Industry 4.0 and the circular economy in sustainable supplier selection, indicating a gap in knowledge and application (SRP 28). South African TAF companies highlight the need for environmentally friendly methods for textile bio-scouring. These methods can be achieved using enzymatic derivatives of bacterial mandarin peelings, reflecting an emphasis on sustainable and eco-friendly production processes (SRP 37). Taiwan and Vietnam stress determining data-driven indicators for sustainable supply chain management, especially amid industrial disruption and the need for ambidexterity (SRP 41). Taiwan specifically addresses the need for efficient solutions to support mass customisation and dynamic customer demands. China's focus includes the effective use of platforms for servitisation in an Industry 4.0 environment and innovative clothing design methods incorporating intelligent algorithms due to technological advancements (SRP 24, SRP 39). Brazilian TAF companies need internal development to integrate Industry 4.0 technologies and support the transition process, reflecting an ongoing shift towards modernisation and technological adoption (SRP 12). In summary, while the Global North focuses on refining and enhancing advanced systems, the Global South strives to overcome foundational barriers to technology adoption, sustainability, and efficient production processes. Integrating Industry 4.0 and 5.0 technologies in the TAF industries has shown substantial benefits in sustainability, efficiency, and competitiveness. However, the transition is often challenged by high costs, resource limitations, complex decision-making processes, and the need for significant infrastructure and expertise. The digitisation and green strategies outlined in Table 2 highlight the importance of a strategic approach in adopting new technologies, ensuring environmental sustainability, and leveraging digital transformation to enhance circular economy strategies. RQ 2 How have Industry 4.0 and 5.0 technologies been implemented in the TAF industries to support circular economy initiatives, and what are their measurable impacts on resource efficiency and waste reduction? We took records of the I 4.0 and 5.0 technologies mentioned in each of the 42 included studies and counted the number of instances of total mention. We also recorded the impacts of the technologies in TAF literature from the 42 included studies and counted the number of instances of each impact mentioned in total. We only entered one record for each study's technologies and impacts. We compiled the records in a table in ordinal form. Please note that there is no direct mapping of the technologies with the impact. Since we treated the technologies as a unit, we needed to treat the impact as a unit, although there are few negative impacts. We marked them with a minus (-) sign. See Table 4 in Appendix B for details. We then proceeded to find the correlation between the I 4.0 and 5.0 technologies and their impacts using an online calculator 3 . We chose the Pearson Coefficient Correlation since we are more familiar with the technique, and the results obtained using different correlation coefficient techniques are the same. The full results are presented in Table 5 . Table 5 Pearson Correlation Coefficient Results X - M x Y - M y (X - M x ) 2 (Y - M y ) 2 (X - M x )(Y - M y ) 21.909 21.879 480.008 478.681 479.344 11.909 21.879 141.826 478.681 260.556 10.909 13.879 119.008 192.621 151.405 9.909 10.879 98.190 118.348 107.799 8.909 8.879 79.372 78.833 79.102 8.909 3.879 79.372 15.045 34.556 3.909 2.879 15.281 8.287 11.253 0.909 2.879 0.826 8.287 2.617 -0.091 1.879 0.008 3.530 -0.171 -0.091 1.879 0.008 3.530 -0.171 -0.091 0.879 0.008 0.772 -0.080 -0.091 -1.121 0.008 1.257 0.102 -0.091 -1.121 0.008 1.257 0.102 -1.091 -1.121 1.190 1.257 1.223 -1.091 -2.121 1.190 4.500 2.314 -1.091 -2.121 1.190 4.500 2.314 -2.091 -3.121 4.372 9.742 6.526 -3.091 -3.121 9.554 9.742 9.647 -3.091 -3.121 9.554 9.742 9.647 -4.091 -3.121 16.736 9.742 12.769 -4.091 -3.121 16.736 9.742 12.769 -4.091 -3.121 16.736 9.742 12.769 -4.091 -3.121 16.736 9.742 12.769 -4.091 -4.121 16.736 16.984 16.860 -4.091 -4.121 16.736 16.984 16.860 -5.091 -5.121 25.917 26.227 26.072 -5.091 -5.121 25.917 26.227 26.072 -5.091 -5.121 25.917 26.227 26.072 -5.091 -7.121 25.917 50.712 36.253 -5.091 -7.121 25.917 50.712 36.253 -5.091 -7.121 25.917 50.712 36.253 -5.091 -8.121 25.917 65.954 41.344 -5.091 -9.121 25.917 83.197 46.435 Mx: 6.091 My: 6.121 Sum: 1348.727 Sum: 1881.515 Sum: 1517.636 Key X : I 4.0 & 5.0 technologies Values Y : Impacts Values M x : Mean of X Values M y : Mean of Y Values X - M x & Y - M y : Deviation scores (X - M x ) 2 & (Y - M y ) 2 : Deviation Squared (X - M x )(Y - M y ): Product of Deviation Scores Result Details & Calculation X Values ∑ = 201 Mean = 6.091 ∑(X - M x ) 2 = SS x = 1348.727 Y Values ∑ = 202 Mean = 6.121 ∑(Y - M y ) 2 = SS y = 1881.515 X and Y Combined N = 33, r (degress of freedom); degree of freedom = N-2 ∑(X - M x )(Y - M y ) = 1517.636 R Calculation r = ∑((X - M y )(Y - M x )) / √((SS x )(SS y )) r = 1517.636 / √((1348.727)(1881.515)) = 0.9527 r = 0.95, P-value is < .00001 at p < .05. Online Pearson Correlation Coefficient The correlation coefficient value r (31) = .95, p < .01., a significant result at p < .05. The result is a strong positive correlation, which indicates that the more I 4.0 and 5.0 technologies TAF companies deploy, the more impacts are achieved. However, these technologies have a few negative impacts, especially regarding society's sustainability, information security, data complexity, electronic wastes, and job losses/reduced labour. Figure 5 is the scatterplot showing the correlations. We present details of the impacts in the rest of this section. We present the impacts under three themes: production, supply value chain, and workforce/consumers. Production Impacts Enhanced Machine Cognition and Efficiency Advanced data analytics enable the collection and analysis of extensive data sets from different stages of textile production. This real-time data allows machines to adapt to changing conditions, and optimising processes continuously. As a result, overall production efficiency significantly improves, operational costs are reduced, and waste is minimised. For example, predictive maintenance systems can anticipate equipment failures and schedule timely repairs, preventing unexpected downtime and extending the lifespan of machinery. For instance, SRP 36, in a multi-case study of Indian Micro, Small, Medium, and Medium Enterprises (MSMEs), highlighted the imperativeness of Lean manufacturing techniques. The research indicated a 27% progress in productivity through organised workspaces and visual aids, a 26% rise in timely product delivery, and a 73% decrease in defects. Customisation and Flexibility Industry 4.0 technologies facilitate highly flexible production models capable of mass customisation. Computer-aided design (CAD) and 3D modelling allow for the creation of personalised products without the need for extensive physical prototypes. This reduces material waste and accelerates the time to market for new designs. Using CPS in smart factories ensures seamless communication and coordination across different manufacturing stages, enhancing efficiency and customisation capabilities. For instance, SRP 26 reported that implementing a multi-objective self-optimisation cyber-physical system for the weaving process reduced the cost of production and the weaving machine set-up time by 75%. Value Chain Impacts Improved Supply Chain Performance Digital platforms and IoT devices enhance transparency and traceability within the supply chain. Real-time data collection and analysis enable better inventory levels, production processes, and logistics leading to more efficient resource management and waste minimisation. For instance, IoT sensors can track environmental conditions during transportation and storage, ensuring optimal conditions for textile materials and finished products. New Business Models Digitalising the textile industry has led to new business models, such as "as-a-service" and data-driven models. These models open new revenue streams and enhance flexibility in operations. Companies can now offer products on a subscription basis or provide customisation services on-demand, aligning with consumer preferences for personalised and sustainable products. These new models decrease waste and boosts resource use by implementing shared and circular business practices. For example, these advancements could provide a significant opportunity to increase the competitiveness of Germany’s manufacturing industry by over 20% in productivity and 10–40% in maintenance cost savings (SRP 2). Sustainability and Environmental Benefits Industry 4.0 and 5.0 technologies contribute to more sustainable manufacturing processes by optimising resource use and minimising waste. AI-driven optimisation techniques and real-time monitoring systems reduce energy consumption and emissions. For example, integrating IoT-enabled sensors in production facilities helps monitor and control energy usage, leading to significant reductions in the environmental footprint of textile manufacturing. For instance, SRP 38 stated that gas sensors are excellent materials for creating IoT's next-generation environmental applications. Workforce and Consumer Impacts Workforce Skill Enhancement The shift towards digital working environments necessitates enhanced IT skills among the workforce. Employees must manage automated systems and interpret data analytics to optimise production processes. While this transition may displace some traditional jobs, it also creates new opportunities in technology management and data analysis. Continuous training and upskilling programs are essential to equip the workforce with the necessary competencies. Consumer Interaction and Customisation Industry 4.0 technologies cater to the evolving consumer demand for interaction and customisation. Digital platforms enable consumers to participate in the design process, selecting and customising products according to their preferences. This will not only enhance consumer satisfaction but also reduce waste by producing items that are specifically tailored to individual needs. The co-creation of value through digital interfaces strengthens the relationship between consumers and producers, fostering a more sustainable consumption pattern. As seen in Table 4 , Industry 4.0 and 5.0 technologies encompass a range of digital innovations, including IoT, big data analytics, AI, cloud computing, and Cyber-Physical Systems (CPS). Their integration has transformed various aspects of the TAF industries, from production processes to value chain management, ultimately contributing to more sustainable practices. Industry 4.0 and 5.0 technologies have been increasingly implemented in the TAF industries to bolster circular economy initiatives. The goal is to enhance sustainability, resource efficiency, and waste reduction. These industries are adopting circular economy models, emphasising the recycling and reuse of materials to minimise waste and lower environmental impact. Cutting-edge manufacturing techniques like 3D printing and digital knitting are transforming production processes. They enable on-demand production, significantly reducing excess inventory and waste. However, the real game-changer is the integration of AI and big data analytics. These technologies allow for precise demand forecasting and inventory management, mitigating waste and optimising resource use. Customisation and smart manufacturing are crucial in reducing waste by producing only what is needed, tailored to specific consumer preferences. However, implementing digital performance management systems is vital to enhancing resource efficiency. These systems play a significant role in waste reduction, amplifying these practices' benefits. Thanks to the optimised use of materials and energy, they lead to a significant reduction in environmental impact and improved resource efficiency. Customisation and personalisation reduce overproduction and increase consumer satisfaction by providing tailored products. Furthermore, integrating advanced technologies fosters innovation and maintains competitiveness in a fast-evolving market. Efforts to recycle and reuse materials extend the lifecycle of products, with closed-loop systems ensuring continuous recycling within the production process. The human-centric approach of Industry 5.0 is a testament to the value of human creativity in the manufacturing process, combining creativity with machine efficiency to promote personalised and sustainable production processes, thereby making everyone in the manufacturing process feel integrated and valued. Existing studies such as [ 31 ], [ 32 ], [ 33 ], [ 34 ], and [ 35 ] have informed about the animosity between humans and machines due to too much economic benefit focus exacerbated by nonstrategic heavy automation and fear of job loss or replacement, and health hazards. Under Industry 5.0, Robots (cobots) collaborate with human employees to boost productivity and reduce physical strain, contributing to more efficient and sustainable manufacturing practices. This emphasis on reducing physical strain demonstrates a care for the workforce's well-being, making them feel considered and valued in the manufacturing process. Adopting sustainable materials is another critical aspect, with advancements in material science and biotechnology leading to developing eco-friendly fabrics and dyes. Utilising organic cotton, recycled polyester, and biodegradable fabrics helps to reduce the environmental footprint. In a recent study exploring natural fibres as sustainable textile production, the authors highlighted: "Natural (fibres) possess desirable properties, but they often lag behind synthetic (fibres) in terms of both quality and quantity. Genomic-assisted breeding has the potential to improve (fibre) quality traits in cotton, hemp, ramie, and flax. Utilizing available QTLs, marker-trait associations, and candidate genes can contribute to the development of superior (fibre) crops, underscoring the significance of advanced breeding tools" [ 36 , p.1]. Additionally, energy efficiency is achieved using renewable energy sources and energy-efficient technologies, optimising energy consumption during manufacturing processes. Digital transformation, including digital twins and cloud-based manufacturing execution systems (MES), enhances sustainability by providing real-time data for improved decision-making and resource optimisation. These results are consistent with the review of [ 21 ], indicating that integrating digital technologies such as blockchain, AI, and IoT can help TAF companies fulfil relevant United Nations' sustainable development goals. In particular, blockchain technology improves visibility and trackability within the supply chain, ensuring that sustainable methods are upheld throughout every manufacturing phase. [ 37 ] revealed that digital advances and Industry 4.0 technologies have benefitted the CE shift. Using data analysis to examine product lifecycles clearly demonstrates this advantage. Challenges Implementing Industry 4.0 and 5.0 technologies in the TAF industries ensures continuing sustainable or environmentally friendly production. The initial investment required to implement these technologies is substantial, posing a barrier to small and medium-scale companies. Additionally, the challenge includes achieving full transparency and traceability across the entire supply chain, which requires coordination among all stakeholders, security concerns, knowledge and training, initial investment costs, and operational complexities. Cybersecurity Concerns The increased digitisation and interconnectedness of systems has increased connectivity and data exchange inherent in IoT and cloud computing (SRP 7, SRP 14, SRP 28, SRP 30) and raised significant cybersecurity and privacy concerns. Our findings are consistent with [ 38 ], who revealed that privacy concerns and limited data handling experience are substantial cyber security challenges for small companies in the TAF industries. Other studies, such as [ 39 ], have indicated that keeping industrial systems safe from cyber threats and guaranteeing data integrity is extremely important. TAF corporations must, therefore, implement strong security measures and procedures to safeguard sensitive data and information and ensure operational resilience. Knowledge and Training Implementing Industry 4.0 and 5.0 technologies requires substantial expertise and strategic managerial approaches. Organisations should allocate resources to ongoing training initiatives to cultivate technical and analytical competencies essential for maximising the benefits of new technologies. Integrating current systems with new technologies can be complicated, requiring significant process changes and workforce training. It is also crucial to address resistance to change and cultivate an environment of innovation within the workforce to realise the full impact of digital transformation. These findings are consistent with a recent study by [ 31 ], which revealed that making highly automated supply chain systems human-centred in the TAF industries is crucial to smoothly transitioning from industry 4.0 to 5.0. To this effect, TAF organisations must integrate working training and lifelong learning into their practices to narrow skill gaps necessary for fostering the adoption of new technologies. [ 31 ] also revealed that change resistance and ineffectual change management hinder TAF companies from transitioning to industry 5.0. [ 39 ] echoed similar thoughts by indicating that apparel firms are receptive to adopting business intelligence solutions in transitioning to the I 4.0 era. In contrast, our study shows that traditional companies, especially those in the Global South, are slow or reluctant to adopt Industry 4.0 and do not discuss transitioning to Industry 5. The authors of SRP 3 argue for a mid-ground framework more tailored to developing countries. Initial Investment Costs The transition to Industry 4.0 and 5.0 involves significant initial investments in advanced technologies and infrastructure. Although these investments can result in long-term savings and increased efficiency, the cost may be too high for small and medium-sized businesses. Our findings are consistent with the study by [ 32 ], which shows that top management involvement and funding are the topmost challenges for post-COVID Bangladeshi business organisations transitioning to Industry 5.0 for sustainable supply chain practices. We offer additional insights by indicating that introducing changes gradually and using adaptable solutions can help reduce these expenses and potential downsides. These findings contradict [ 22 ], arguing for first prioritising management's soft (consumer-related issues and green intellectual capital) demands. The point of departure is that while the approach might work for developed countries, developing countries might have different results. For example, [ 22 ] identified financial and regulatory pressures as complex aspects of the challenges of applying circular economy principles in the fashion industry. While it is true that those are hard demands, the priority should instead start with this aspect for developing countries that need enabling regulatory policies and financial support [ 32 ] for this transition. Operational Complexity Adopting new methods and technologies increases production complexity. As [ 21 ] argued, integrating digital technologies like IoT, AI, AR, VR, and blockchain can offer a resilient and innovative infrastructure to guarantee fashionable product development that promotes sustainable production. However, the challenges lie with these technologies' complexity and maintenance. For example, IoT in Industry 4.0 involves networked sensors integrated with complex physical machinery and devices, software RFID, barcodes, and tags to collect production data and predict, control, and plan for better business and societal outcomes (SRP 6, SRP 11). This presents potential challenges such as cybersecurity risks, data privacy concerns, and the need for standardised protocols. We argue that TAF companies must manage the integration of various digital systems and ensure seamless communication between different components. This requires robust planning, coordination, and technical expertise. TAF companies' role in this process is crucial to avoid disruptions and achieve the desired outcome. RQ 3 What research opportunities exist for integrating Industry 4.0 and 5.0 technologies in the TAF industries to enhance the Circular Economy? The findings in this review study offer a comprehensive overview of how Industry 4.0 and 5.0 technologies impact the TAF industries. However, several areas require further exploration. We discuss the significant issues below: Adaptable Region-specific Hybrid Frameworks for Industry 4.0 and 5.0 Integration There is a lack of comprehensive frameworks that guide the seamless integration of Industry 4.0 and 5.0 technologies in the TAF industries. Existing studies often focus on isolated applications or specific technologies. There is a need for holistic frameworks that combine multiple technologies, considering the entire lifecycle of textile products from production to recycling. This call is a growing topic among scholars. Our systematic review reveals a big dichotomy between the Global North and the Global South, with each region having its challenges, needs, and expectations. We need context-based hybrid frameworks that allow each region to grow at its own pace. Our sentiments have been echoed by scholars such as [ 40 ] [SRP 3], who argued that traditional companies in developing countries could not catch up the pace with their counterparts in developed countries. The authors successfully developed the Industry 3.5 framework and implemented it in a Taiwanese textile firm. A related study already anticipates an ambitious transition to Industry 6.0, claiming that "there will be no interface with any machine, person, or drafting table/setup" ([ 33 ], p. 4830). We need hybrid frameworks tailored to each region, focusing on their peculiarities regarding needs, challenges, and expectations. Developing integrative models encompassing various Industry 4.0 and 5.0 technologies and addressing interoperability, scalability, and adaptability to different contexts within the TAF industries might be a potential opportunity. Quantitative Assessment of Environmental Impact We observed an insufficient quantitative assessment of the environmental impact of Industry 4.0 and 5.0 technologies in the TAF industries. While qualitative benefits are frequently highlighted, there is limited empirical data quantifying the environmental benefits, such as reduction in carbon footprint, energy consumption, and waste. Conducting empirical studies developing metrics, and conducting longitudinal studies to quantify the environmental impact of digital transformation in the TAF industries, aiding in the validation of sustainability claims, might be a potential opportunity. Quantitative data will help validate the effectiveness of these technologies and guide future investments. A holistic environmental impact analysis will ensure that technological advancements align with sustainability goals. Cost-benefit Analysis and Economic Viability We also observed Limited cost-benefit analysis and assessment of economic viability for small and medium-sized enterprises (SMEs) in the extant TAF industries literature on industry 4.0 and 5.0 framework implementations. Many SMEs face financial constraints that hinder the adoption of advanced technologies. Detailed economic analyses specific to SMEs are scarce. Investigating ways to support SMEs in adopting Industry 4.0 technologies is crucial. This could involve research into successful case studies, exploring innovative funding mechanisms, and analyzing the role of government policies and incentives. Potential opportunities might be to develop tailored economic models and financial frameworks that evaluate the cost-effectiveness of Industry 4.0 and 5.0 technologies for SMEs in the TAF industries, including funding mechanisms and investment strategies. Supporting SMEs will democratize access to advanced technologies and promote broader industry adoption. Real-Time Data and Decision Support Systems There is a Lack of robust real-time data analytics and decision support systems tailored to the TAF industries. While real-time data and decision-making are critical for the success of Industry 4.0 initiatives, there is a gap in tailored solutions that address the unique needs of the textile industry. Designing and implementing real-time data analytics platforms and decision support systems that cater specifically to the production dynamics, supply chain complexities, and sustainability goals of the TAF industries might be potential opportunities. Cybersecurity and Data Privacy Insufficient focus on cybersecurity and data privacy in the context of smart textile manufacturing. As Industry 4.0 and 5.0 technologies rely heavily on interconnected systems and data exchange, ensuring cybersecurity and data privacy is paramount. However, this aspect is often underexplored. Detailed research into cybersecurity threats specific to the textile and apparel industry and effective countermeasures is necessary. This includes studying past incidents, understanding vulnerabilities in interconnected systems, and developing industry-specific security protocols. Researching advanced cybersecurity measures and data privacy frameworks tailored to smart textile manufacturing environments and addressing specific threats and vulnerabilities might be a potential opportunity. Enhancing cybersecurity will protect sensitive data and ensure the stability of digital operations. Workforce Skills and Training Lack of comprehensive strategies for workforce skills development and training. The adoption of advanced technologies requires a skilled workforce, but there is a significant gap in strategies for training and upskilling employees in the TAF sector. Developing training programs and educational curricula that equip the workforce with the necessary skills for operating and managing Industry 4.0 and 5.0 technologies, including digital literacy, technical proficiency, and problem-solving skills, might be a potential opportunity. Ensuring a balanced approach that supports both technological advancement and workforce stability is essential for sustainable development. Cultural and Organizational Change Management Limited research exists on cultural and organizational change management required for technology adoption. Successfully integrating new technologies involves significant cultural and organizational changes, which are often overlooked in technical studies. Investigating best practices for managing cultural and organizational change in the TAF industries, including stakeholder engagement, leadership strategies, and change management frameworks, might provide potential opportunities. Circular Economy Business Models We also observed inadequate development of business models that leverage Industry 4.0 and 5.0 technologies for circular economy principles. There is a need for innovative business models that incorporate advanced technologies and align with circular economy principles such as resource efficiency, product life extension, and recycling. For example, creating and testing new business models that integrate Industry 4.0 and 5.0 technologies with circular economy practices and evaluating their feasibility, scalability, and impact on sustainability might provide potential opportunities. Consumer Behaviour and Market Dynamics Another important area is understanding how Industry 4.0 and 5.0 technologies influence consumer behaviour and market dynamics. Research into how digital customisation, transparency, and sustainability features impact consumer preferences and purchasing decisions can provide valuable insights for developing effective marketing strategies and driving sustainable consumption patterns. Limitations While the systematic literature review has unveiled valuable insights, it is imperative to acknowledge certain limitations inherent in the scope and methodology of the study. One limitation pertains to the limited size of the sample pool drawn from existing literature. However, every effort was made to include a variety of studies. Furthermore, only a few studies were found to align with the inclusion and exclusion criteria of research. Another limitation is that while the study attempted to encompass a wide range of countries and regions, the number of regions included was limited - the contextual diversity of the textile and fashion industry cuts across various countries. Hence, the industry's practices and challenges differ across global regions, so the review may not fully capture localized details and regional specifics. Furthermore, the review's inclusion criteria favoured studies available in English, potentially leading to the omission of valuable insights from non-English sources. Finally, the field of digital transformation and environmental sustainability is rapidly evolving. As a result, the review's focus on existing literature at a specific time might not encompass the latest advancements or trends. Moreover, accumulating new research after the review's completion could contribute to the evolution of knowledge in ways not reflected in the synthesis. Conclusion We have reviewed the extant literature on the textile, apparel, and fashion (TAF) industries and highlighted several digital transformation and sustainability issues that dominate discussions. From circular economy and sustainability dimensions, we classified into three main categories: resource efficiency and waste reduction, supply chain management, technological advancement and optimisation, operational efficiency and lean manufacturing, environmental sustainability and cleaner production, societal and cultural aspects, Innovation in Design and production, work environment and safety, and cost efficiency and performance. We further classified these issues along Global North and Global South countries. The results show that while the Global North focuses on refining and enhancing advanced systems, the Global South strives to overcome foundational barriers to technology adoption, sustainability, and efficient production processes. There is a significant positive correlation between I 4.0 and 5.0 technologies and their impacts on TAF companies, although a few areas show negative impacts, especially in societal sustainability. In production processes, the focus is on integrating advanced manufacturing technologies like IoT, AI, and robotics to enhance efficiency and reduce waste. Supply chain management literature emphasises transparency and traceability, leveraging blockchain and big data analytics to monitor and optimise resource use. Consumer engagement focuses on personalisation and customisation, using digital tools to create tailored products that meet specific customer needs, thereby reducing overproduction and waste. Industry 4.0 technologies, such as IoT, AI, and blockchain, have been widely implemented in the TAF industries to support circular economy initiatives. These technologies enable real-time monitoring, predictive maintenance, and precise inventory management, significantly enhancing resource efficiency. For instance, IoT sensors collect data on machinery performance and environmental conditions, which AI algorithms analyse to predict and prevent potential breakdowns, thus minimising downtime and material waste. Blockchain technology ensures transparency and traceability across the supply chain, verifying the origins of materials and ensuring they meet sustainability standards. These technologies collectively contribute to a reduction in waste and improved resource utilisation. Industry 5.0 builds upon these advancements by emphasising human-centric innovations. Technologies like collaborative robots (cobots) work alongside human workers to enhance productivity and creativity. Customisation and on-demand manufacturing enabled by AI and robotics reduce waste by producing what is needed. Using 3D printing and digital knitting technologies makes it possible to fabricate intricate designs using minimal materials, thus contributing to the principles of a circular economy. Sustainable materials, including biodegradable fabrics and environmentally friendly dyes, are rising due to material science and biotechnology advancements. Despite these advancements, several challenges persist, such as the huge capital requirement for infrastructure and training and the seamless integration of different technologies, which involves the establishment of interoperability standards. The increased use of digital technologies raises data privacy and security concerns, necessitating solid measures to safeguard sensitive data and information. There are abundant research opportunities in integrating Industry 4.0 and 5.0 technologies in the TAF industries for circular economy enhancement. The major areas are: developing more advanced and affordable IoT devices and AI algorithms explicitly tailored for the TAF industries, exploring blockchain technology's potential for end-to-end supply chain transparency and traceability. investigating the use of sustainable materials and their compatibility with advanced manufacturing technologies for further innovation and creating business models that support circular economy principles, such as product-as-a-service models and digital platforms for second-hand fashion. Integrating Industry 4.0 and 5.0 technologies in the TAF industries is driving significant advancements in circular economy initiatives. To further drive these advancements, interdisciplinary collaborations among engineers, data scientists, environmentalists, and business managers are essential. Supportive policies and active cooperation between stakeholders, including manufacturers, retailers, consumers, and policymakers, are crucial for creating a sustainable and resilient fashion industry. Continued innovation and research in this field will be vital to driving positive change and ensuring a responsible and eco-friendly industry for the future. References Tasnim, N. H., Afrin, S., Biswas, B., Anye, A. A., & Khan, R. (2023). Automatic classification of textile visual pollutants using deep learning networks. Alexandria Engineering Journal , 62 , 391–402. https://doi.org/10.1016/j.aej.2022.07.039 Keane, J., & te Velde, D. W. (2008). The role of textile and clothing industries in growth and development strategies. In ODI Working Paper (Issue May). http://www.odi.org/sites/odi.org.uk/files/odi-assets/publications-opinion-files/3361.pdf Ogunyemi, A. A., Diyaolu, I. J., Awoyelu, I. O., Bakare, K. O., & Oluwatope, A. O. (2023). Digital Transformation of the Textile and Fashion Design Industry in the Global South: A Scoping Review. In R. A. Saeed, A. D. Bakari, & Y. H. Sheikh (Eds.), Towards new e-Infrastructure and e-Services for Developing Countries (pp. 391–413). Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-34896-9_24 Öndoğan, E. N., Öndoğan, Z., & Topuzoğlu, B. (2022). A Study on the Investigation of Sustainability Practices of Global Brands in the Fashion Market. Ege Akademik Bakis (Ege Academic Review) , 393–412. https://doi.org/10.21121/eab.1104962 Tsai, W. H. (2018). Green production planning and control for the textile industry by using mathematical programming and industry 4.0 techniques. Energies , 11 (8), 1–24. https://doi.org/10.3390/en11082072 Nasir, A., Zakaria, N., & Zien Yusoff, R. (2022). The influence of transformational leadership on organizational sustainability in the context of industry 4.0: Mediating role of innovative performance. Cogent Business and Management , 9 (1), 1–31. https://doi.org/10.1080/23311975.2022.2105575 Tseng, M. L., Bui, T. D., Lim, M. K., Fujii, M., & Mishra, U. (2022). Assessing data-driven sustainable supply chain management indicators for the textile industry under industrial disruption and ambidexterity. International Journal of Production Economics , 245 , 1–20. https://doi.org/10.1016/j.ijpe.2021.108401 Diyaolu, I. J. (2022). Total Quality Management Practices in Nigerian Textile Industry. In: Msahli, S., Debbabi, F. (Eds) Advances in Applied Research on Textile and Materials - IX. CIRATM 2020. Springer Proceedings in Materials, 17 , 234-242. Springer, Cham. https://doi.org/10.1007/978-3-031-08842-1_39 Renne, E. P. (2020). Death and the Textile Industry in Nigeria. In Death and the Textile Industry in Nigeria . Routledge. https://doi.org/10.4324/9781003058137 Demir, K. A., Döven, G., & Sezen, B. (2019). Industry 5.0 and Human-Robot Co-working. Procedia Computer Roblek, V., Thorpe, O., Bach, M. P., Jerman, A., & Meško, M. (2020). The fourth industrial revolution and the sustainability practices: A comparative automated content analysis approach of theory and practice. Sustainability (Switzerland) , 12 (20), 1–28. https://doi.org/10.3390/su12208497 Dixson-Declève, S., Balland, P.-A., Bria, F., Charveriat, C., Dunlop, K., Giovannini, E., Tataj, D., Huang, A., Hidalgo, C., Isaksson, D., Martins, F., Mir roca, M., Morlet, A., Renda, A., & Schwaag Serger, S. (2022). Industry 5.0: A Transformative Vision for Europe - Governing Systemic Transformations towards a Sustainable Industry (Issue 3). https://doi.org/10.2777/17322 Schwab, K. M. (2016). The fourth industry revolution . World Economic Forum. Gajdzik, B., Grabowska, S., & Saniuk, S. (2021). A Theoretical Framework for Industry 4.0 and Its Implementation with Selected Practical Schedules. Energies , 14 (4), 1–24. https://doi.org/10.3390/en14040940 Bongomin, O., Yemane, A., Kembabazi, B., Malanda, C., Chikonkolo Mwape, M., Sheron Mpofu, N., & Tigalana, D. (2020). Industry 4.0 Disruption and Its Neologisms in Major Industrial Sectors: A State of the Art. Journal of Engineering , 1–45. https://doi.org/10.1155/2020/8090521 Konstantinidis, F. K., Kansizoglou, I., Tsintotas, K. A., Mouroutsos, S. G., & Gasteratos, A. (2021). The role of machine vision in industry 4.0: A textile manufacturing perspective. IST 2021 - IEEE International Conference on Imaging Systems and Techniques, Proceedings , 1–6. https://doi.org/10.1109/IST50367.2021.9651459 Flores-Siguenza, P., Vásquez-Salinas, B., Siguenza-Guzman, L., Arcentales-Carrion, R., & Sucozhañay, D. (2022). Indicators to Evaluate Elements of Industry 5.0 in the Textile Production of MSMEs. Communications in Computer and Information Science , 85–100. https://doi.org/10.1007/978-3-031-18272-3_7 Javaid, M., & Haleem, A. (2020). Critical components of industry 5.0 towards a successful adoption in the field of manufacturing. Journal of Industrial Integration and Management , 5 (3), 327–348. https://doi.org/10.1142/S2424862220500141 Happonen, A., & Ghoreishi, M. (2021). A Mapping Study of the Current Literature on Digitalization and Industry 4.0 Technologies Utilization for Sustainability and Circular Economy in Textile Industries. In Lecture Notes in Networks and Systems (Vol. 217, pp. 697–711). https://doi.org/10.1007/978-981-16-2102-4_63 Casciani, D., Chkanikova, O., & Pal, R. (2022). Exploring the nature of digital transformation in the fashion industry: opportunities for supply chains, business models, and sustainability-oriented innovations. Sustainability: Science, Practice, and Policy , 18 (1), 773–795. https://doi.org/10.1080/15487733.2022.2125640 Akram, S. V., Malik, P. K., Singh, R., Gehlot, A., Juyal, A., Ghafoor, K. Z., & Shrestha, S. (2022). Implementation of Digitalized Technologies for Fashion Industry 4.0: Opportunities and Challenges. Scientific Programming , 1–17. https://doi.org/10.1155/2022/7523246 Abdelmeguid, A., Afy-Shararah, M., & Salonitis, K. (2022). Investigating the challenges of applying the principles of the circular economy in the fashion industry: A systematic review. Sustainable Production and Consumption , 32 , 505–518. https://doi.org/10.1016/j.spc.2022.05.009 Islam, M. M., Perry, P., & Gill, S. (2021). Mapping environmentally sustainable practices in textiles, apparel and fashion industries: a systematic literature review. Journal of Fashion Marketing and Management , 25 (2), 331–353. https://doi.org/10.1108/JFMM-07-2020-0130 Abbate, S., Centobelli, P., Cerchione, R., Nadeem, S. P., & Riccio, E. (2023). Sustainability trends and gaps in the textile, apparel and fashion industries. Environment, Development and Sustainability , 1–28. https://doi.org/10.1007/s10668-022-02887-2 King, W. R., & He, J. (2005). Understanding the Role and Methods of Meta-Analysis in IS Research. Communications of the Association for Information Systems , 16 (32), 665–686. https://doi.org/10.17705/1cais.01632 Paré, G., Trudel, M.-C., Jaana, M., & Kitsiou, S. (2015). Synthesizing information systems knowledge: A typology of literature reviews. Information & Management , 52 (2), 183–199. https://doi.org/10.1016/j.im.2014.08.008 Palvia, P., Leary, D., Mao, E., Midha, V., Pinjani, P., & Salam, A. F. (2004). Research Methodologies in MIS: An Update. Communications of the Association for Information Systems , 14 (24), 526–542. https://doi.org/10.17705/1cais.01424 Xiao, Y., & Watson, M. (2019). Guidance on Conducting a Systematic Literature Review. Journal of Planning Education and Research , 39 (1), 93–112. https://doi.org/10.1177/0739456X17723971 Arruda, E. H., Melatto, R. A. P. B., Levy, W., & Conti, D. de M. (2021). Circular economy: A brief literature review (2015–2020). Sustainable Operations and Computers , 2 (May), 79–86. https://doi.org/10.1016/j.susoc.2021.05.001 Kristensen, H. S., & Mosgaard, M. A. (2020). A review of micro level indicators for a circular economy – moving away from the three dimensions of sustainability? Journal of Cleaner Production , 243 , 118531. https://doi.org/10.1016/j.jclepro.2019.118531 Kazancoglu, Y., Mangla, S. K., Berberoglu, Y., Lafci, C., & Madaan, J. (2023). Towards Industry 5.0 Challenges for The Textile and Apparel Supply Chain for The Smart, Sustainable, and Collaborative Industry in Emerging Economies. Information Systems Frontiers , 1–16. https://doi.org/10.1007/s10796-023-10430-5 Karmaker, C. L., Bari, A. B. M. M., Anam, M. Z., Ahmed, T., Ali, S. M., de Jesus Pacheco, D. A., & Moktadir, M. A. (2023). Industry 5.0 challenges for post-pandemic supply chain sustainability in an emerging economy. International Journal of Production Economics , 258 , 1–12. https://doi.org/10.1016/j.ijpe.2023.108806 Bedi, T., Rana, V., & Gautam, N. (2021). Changing Face of The Apparel Industry by Incorporating Industry 4.0 and Paving Way for Industry 5.0. Turkish Online Journal of Qualitative Inquiry (TOJQI), 12(3), 4814–4832. Rathore, D. B. (2022). Textile Industry 4.0 Transformation for Sustainable Development: Prediction in Manufacturing and Proposed Hybrid Sustainable Practices. Eduzone , 11 (1), 223–241. https://doi.org/10.56614/eiprmj.v11i1.229 Rathore, D. B. (2023). Textile Industry 4.0: A Review of Sustainability in Manufacturing. International Journal of New Media Studies , 10 (1), 38–43. https://doi.org/10.58972/eiprmj.v10i1y23.41 Gudi, S., M, P., Alagappan, P., Raigar, O. P., Halladakeri, P., Gowda, R. S. R., Kumar, P., Singh, G., Tamta, M., Susmitha, P., Amandeep, & Saini, D. K. (2024). Fashion meets science: how advanced breeding approaches could revolutionize the textile industry. Critical Reviews in Biotechnology , 0 (0), 1–27. https://doi.org/10.1080/07388551.2024.2314309 Ghoreishi, M., & Happonen, A. (2022). The Case of Fabric and Textile Industry: The Emerging Role of Digitalization, Internet-of-Things and Industry 4.0 for Circularity. Proceedings of Sixth International Congress on Information and Communication Technology. Lecture Notes in Networks and Systems , 216 , 189–200. https://doi.org/10.1007/978-981-16-1781-2_18 Nouinou, H., Asadollahi-Yazdi, E., Baret, I., Nguyen, N. Q., Terzi, M., Ouazene, Y., Yalaoui, F., & Kelly, R. (2023). Decision-making in the context of Industry 4.0: Evidence from the textile and clothing industry. Journal of Cleaner Production , 391 (January), 136184. https://doi.org/10.1016/j.jclepro.2023.136184 Ahmad, S., Miskon, S., Alabdan, R., & Tlili, I. (2020). Towards Sustainable Textile and Apparel Industry: Exploring the Role of Business Intelligence Systems in the Era of Industry 4.0. Sustainability , 12 (2632), 1–23. https://doi.org/10.3390/su12072632 Ku, C.-C., Chien, C.-F., & Ma, K.-T. (2020). Digital transformation to empower smart production for Industry 3.5 and an empirical study for textile dyeing. Computers & Industrial Engineering , 142 (January), 1–11. https://doi.org/10.1016/j.cie.2020.106297 Footnotes www.grammarly.com https://www.norrag.org/wp-content/uploads/2023/02/List-of-Global-South-and-Global-North-Countries.pdf https://www.socscistatistics.com/tests/pearson/default2.aspx Additional Declarations The authors declare no competing interests. Supplementary Files APPENDICES.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4804089","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Systematic Review","associatedPublications":[],"authors":[{"id":332006474,"identity":"9b2b4cc3-03a2-4399-aa2d-fdc64bdef531","order_by":0,"name":"Emmanuel Ayo Orisadare","email":"","orcid":"","institution":"Department of Computer Science and Engineering, Obafemi Awolowo University, Ile-Ife, Osun State, Nigeria","correspondingAuthor":false,"prefix":"","firstName":"Emmanuel","middleName":"Ayo","lastName":"Orisadare","suffix":""},{"id":332006592,"identity":"0519bf0b-d99f-496d-a35d-56b330ab6b99","order_by":1,"name":"Okechukwu Emmanuel 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20:41:15","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-4804089/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4804089/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":61282323,"identity":"4352ad4e-7a61-4deb-af97-0efc54200a12","added_by":"auto","created_at":"2024-07-29 05:24:08","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":333330,"visible":true,"origin":"","legend":"\u003cp\u003eTimeline of Industry 1.0 to 5.0 adapted from [10]\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-4804089/v1/ed94254e7aa75117f3bf12db.png"},{"id":61282325,"identity":"8b3636cd-6e3c-4fe1-bb16-252b3578887a","added_by":"auto","created_at":"2024-07-29 05:24:08","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":35476,"visible":true,"origin":"","legend":"\u003cp\u003eStudy search and selection process\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-4804089/v1/1f30f4b6ec2ccecfb365333a.png"},{"id":61283510,"identity":"f5652d79-d6ef-41f3-bd05-7ddcf48c49d3","added_by":"auto","created_at":"2024-07-29 05:48:08","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":812805,"visible":true,"origin":"","legend":"\u003cp\u003eClassifications of digital transformation and sustainability issues according to the circular economy and sustainability dimensions.\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-4804089/v1/99a93c88be14faf77a08057d.png"},{"id":61282326,"identity":"14bc548f-9817-411b-b3e4-2ea3cdfee052","added_by":"auto","created_at":"2024-07-29 05:24:08","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":67354,"visible":true,"origin":"","legend":"\u003cp\u003eCountries of studies\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-4804089/v1/1c59ac76f04d67ff77e5c6c4.png"},{"id":61282327,"identity":"3acd0b4a-80ba-4a71-9d3e-ac987327b584","added_by":"auto","created_at":"2024-07-29 05:24:08","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":14603,"visible":true,"origin":"","legend":"\u003cp\u003eScatterplot of I 4.0 \u0026amp; 5.0 technologies and impacts\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-4804089/v1/0354b98e0ee6dfc79c5291d2.png"},{"id":61283976,"identity":"e2461641-6eb7-495f-9393-ae5fedce71b1","added_by":"auto","created_at":"2024-07-29 05:56:10","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2709232,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4804089/v1/16318b5e-b470-4487-a62a-8557d3b26689.pdf"},{"id":61283509,"identity":"a7c28ddd-2e7e-4867-bb34-338f10f1c2d4","added_by":"auto","created_at":"2024-07-29 05:48:08","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":28838,"visible":true,"origin":"","legend":"","description":"","filename":"APPENDICES.docx","url":"https://assets-eu.researchsquare.com/files/rs-4804089/v1/94f0d3a0d3ecc7befcdc9ddb.docx"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eDigitalisation and Green Strategies: A systematic review of the Textile, Apparel and Fashion Industries\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe Textile, Apparel and Fashion industries are some of the major industries contributing to national economies worldwide (see, for example, [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]). However, the industry can be said to be operating at a different efficiency level in all the countries globally [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. The Global South and the North might differ [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. While the benefits of digital transformation are obvious, the challenges are also evident. At the same time, the contribution of TAF Industries to global pollution (20%) is a concern [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDue to industrial revolution trends, sustainable digital transformation of the manufacturing sectors, especially the TAF Industries, has become necessary. In some developing countries, obsolete or outdated technologies have limited the operations and outputs of the TAF industries [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], and in many cases, the Gross Domestic Product (GDP) contributions from the industries have significantly reduced in some countries [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. In some countries like Nigeria, the industry is as good as dead, with most of the known textile firms, such as Kaduna Textile Limited mill, closed [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. On the one hand, there is a need to determine whether the digital transformation is easy for TAF Industries to achieve. On the other hand, the industries\u0026rsquo; contribution to global pollution is worrisome [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. The I 4.0 and 5.0 frameworks seek to promote smart manufacturing and sustainable production and could be perceived as providing guidelines for companies seeking digital transformation and environmental sustainability.\u003c/p\u003e \u003cp\u003eThe introduction of efficient manufacturing has led to fewer workers being employed for production, thus creating a loss of jobs [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Furthermore, there has yet to be a symbiotic relationship between humans and machines because of human animosity toward machines due to their perception of their tendencies to take over human jobs [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Due to heavy mechanisation and the drive for mass production, the negative impact of digital transformation by the TAF industries contributes to environmental issues, including pollution of water bodies, the atmosphere, and land areas [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Specific regulations could be in place for full compliance by the manufacturing industries, especially the TAF Industries, to control their operations through regulations toward recycling and re-use of waste to recover a specific volume of effluents discharged into the environment.\u003c/p\u003e \u003cp\u003eThe I 5.0 framework - a value-oriented framework that mitigates the shortcomings of its predecessor \u0026ndash; industry 4.0, and strives to provide a balance between humans and machines in a way that both can operate in a symbiotic manner. Also, to consider the environment by promoting societal values [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e\n\u003ch3\u003eSmart Manufacturing and Environmental Sustainability Under Industries 4.0 and 5.0 Frameworks\u003c/h3\u003e\n\u003cp\u003eIndustry 4.0, an initiative pioneered in Germany, has ushered in a new era of technological transformation with its theme of \"Smart Manufacturing for the Future\" [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. This paradigm shift represents the integration of the cyber and physical worlds by introducing advanced technologies in industrial sectors [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. With the convergence of operational technology (OT) and information technology (IT), Industry 4.0 is transforming production processes and redefining the way industries operate [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. At its core, pursuing increased productivity and mass production through innovative technology drives Industry 4.0 [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eHistorically, textile manufacturing was vital in driving regional economies and international relationships. From the first industrial revolution, powered by water and steam, to the present era of Industry 4.0, textile manufacturing has witnessed remarkable transformations, underscoring the influence of technology on industries [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. The Timeline of Industry 1.0 to 5.0, adapted from [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], is given in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Whether textile manufacturers embrace digitalisation and automation, leveraging Industry 4.0 technologies to improve production processes, quality control, and supply chain management is uncertain.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe evolution from Industry 4.0 to Industry 5.0 signifies a significant shift in focus and objectives within the manufacturing sector. Initially, Industry 4.0 aimed to improve productivity and efficiency by applying advanced technologies. However, over time, its focus shifted away from sustainability concerns and towards technological advancements. This shift led to the emergence of Industry 5.0, which seeks to transform manufacturing by prioritising social objectives and personalised customer demands. Industry 5.0 aims to make manufacturing a provider of prosperity while respecting environmental limits and prioritising worker welfare [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIndustry 5.0 complements Industry 4.0 by placing humans at the centre of development and enhancing resilience while minimising environmental and social impacts [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. It encompasses elements such as intelligent devices, systems, automation, and materials [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. A key focus of the I 5.0 approach is sustainability, which encompasses economic, environmental, and social dimensions [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Industry 5.0 seeks to balance economic growth, environmental preservation, and societal well-being by considering these dimensions. However, implementing the I 5.0 framework comes with its own set of challenges.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eRelated Studies\u003c/h2\u003e \u003cp\u003eSome related systematic literature review studies targeting Industry 4.0, digital transformation, environmental sustainability, etc., have been reported. Happonen and Ghoreishi [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] conducted a mapping study that investigated the possibility of accelerating the circular (recycling) economy in the textile and apparel industries through the use of Industry 4.0 technologies (movement to the digital world) in industrial transition. This move aimed to achieve sustainability goals and reduce the negative environmental effects of growing fast fashion. They accomplished this by reviewing 27 studies that targeted the implementations of digital technologies in various textile recycling processes, such as the extraction of raw materials, manufacturing of fibre, dyeing, washing, and the incineration of wastes from fibres and clothes. The findings showed increasing research attention to topics that border sustainability, circularity, and digitalisation, especially the digital technology roles in the supply chain management of the textile industry, but with a massive lack of implementation despite its profitability.\u003c/p\u003e \u003cp\u003eA comprehensive and systematic review of the influences and opportunities of digital transformation of the fashion industry on sustainability-oriented innovations, supply chains, and business models was undertaken by [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. The study reviewed emerging companies that are actively using 3-dimensional digital and virtual (3DDV) technologies such as digital twinning (DT), 2- and 3-dimensional scanning, augmented and virtual reality (AR and VR), and 3D modelling. The analysis revealed that digital technology adoption provided openings that would help dematerialise the traditional fashion supply chain model of producing and distributing products and services.\u003c/p\u003e \u003cp\u003eIn another related study, Akram et al. [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] reported integrating digital technologies - Internet of Things (IoT), Artificial Intelligence (AI), blockchain, Augmented Reality (AR), and Virtual reality (VR)) to establish a resilient and innovative infrastructure that can guarantee the development of fashionable products that will promote sustainable production and consumption. The study explored the implementation of these technologies in the TAF Industries for virtual and augmented-based shopping experiences, health prediction, fashion trend forecasting, circular economy, supply chain, smart cloth, etc., as well as the limitations of the previous studies that implemented the technologies earlier in the TAF Industries.\u003c/p\u003e \u003cp\u003eAbdelmeguid et al. [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], in their systematic review, reported synthetically the challenges to be faced if a Circular Economy (CE) is implemented in the fashion industry. They discovered business management's soft (consumer-related issues and green intellectual capital) and hard (financial pressures, stakeholders' pressures, regulatory pressures, and business model innovation) aspects. The study finally proposed the management and overcoming of soft aspect challenges first, to have the ability to face the hard aspect challenges to successfully achieve the Sustainable Development Goals (SDG) 12, as it relates to sustainable production, consumption, and efficient management of goods and natural resources.\u003c/p\u003e \u003cp\u003eIslam et al. [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] carried out a comprehensive systematic review and content analysis of 91 peer-reviewed journal articles published over a 10-year period to map and develop a framework for the various manufacturing process practices in the Textile, Apparel, and Fashion industries concerning environmental concerns. The findings highlighted the diverse and complex environmental practices in the TAF Industries, in addition to suggesting the best practices at the various manufacturing stages of garment washing, dyeing, and packaging to include the adoption of a circular economy, energy efficiency strategies, resource savings, product design for longevity, and materials selection.\u003c/p\u003e \u003cp\u003eIn a closely related study, [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e] highlighted the global environmental pollution linked to the TAF Industries and the push to reduce environmental damage. The study systematically reviewed TAF-related sustainability events in the last 20 years and disclosed three critical areas of research focus: sustainability challenges in the supply chain, circular economy initiatives, and sustainable clothing, as well as their implementation barriers and drivers. Considering the introduction of I 4.0 and I 5.0 and focusing on the introduction of technologies, the drive for smart production, and the demands for sustainable societies, it is imperative to conduct a systematic state-of-the-art study to determine the strategies used in TAF companies, their impacts, challenges, and trajectories for the future. This systematic review investigates these issues.\u003c/p\u003e \u003cp\u003eReview objectives\u003c/p\u003e\u003cp\u003ei. Identify and classify issues dominating research on digital transformation and sustainability in the TAF industries.\u003c/p\u003e \u003cp\u003eii. Describe the implementation of Industry 4.0 and 5.0 technologies in the TAF industries for supporting circular economy, impacts, and associated challenges.\u003c/p\u003e\u003cp\u003eiii. Identify and outline opportunities to drive further research regarding industry 4.0 and 5.0 technologies to enhance the circular economy for the TAF Industries.\u003c/p\u003e \u003cp\u003eResearch questions\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eWhat digital transformation and sustainability issues dominate the TAF industries literature, and how are they classified?\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eHow have Industry 4.0 and 5.0 technologies been implemented in the TAF industries to support circular economy initiatives, and what are their measurable impacts on resource efficiency and waste reduction and associated challenges?\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eWhat research opportunities exist for integrating Industry 4.0 and 5.0 technologies in the TAF industries to enhance the Circular Economy?\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Methods","content":"\u003cp\u003eThe study employed a descriptive literature review as explained in [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e] and [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e] and inspired by [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were used. The PRISMA guideline provides a comprehensive checklist of items researchers should include in the review of publications to enhance transparency and ensure the reproducibility of the review process. We formulated the research questions to investigate the implementation of I 4.0 and 5.0 technologies and strategies for digitisation and green transition in the TAF industries, as well as the documented impacts and challenges confronting the industries concerning digital transformation and environmental sustainability. The literature search was conducted on January 26, 2023, using two digital libraries, Web of Science (WoS) and Scopus\u0026mdash;the justification for WoS and Scopus to include comprehensive coverage of high-quality databases. The search strategy employed a combination of keywords related to digital transformation, environmental sustainability, and the TAF industries.\u003c/p\u003e \u003cp\u003eSearch criteria were designed using combinations of keywords containing \u003cem\u003e'digital transformation' OR 'digitalisation' OR 'digital technologies' OR 'electronic textiles' OR 'textile consumption' OR 'environmental sustainability' OR 'eco-friendly manufacturing' OR 'green manufacturing' OR 'smart production' OR 'textile smart marketing' AND 'Industry 4.0' OR 'industry 5.0' AND 'textile firms' OR 'textile company*' OR 'textile mills' OR 'Apparel' OR 'fashion' OR 'garment' OR 'fabric' OR 'innovative manufacturing textile industr*'\u003c/em\u003e.\u003c/p\u003e \u003cp\u003eThe selection of studies followed a two-stage screening process. In the first stage, titles and abstracts were screened, retaining articles with relevant keywords in their titles or abstracts. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the detailed results of the search process. The process is also illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Inclusion and exclusion criteria were applied to the remaining studies in the second stage. In this stage, results were divided into four parts, and two researchers (one doctoral/master\u0026rsquo;s student and one senior researcher) were assigned to one part of each of the results. The researchers (both the doctoral/master\u0026rsquo;s student and the senior researcher) applied the inclusion criteria. The details of the inclusion and exclusion criteria are in 2.1. A senior researcher who coordinated the review process reviewed the decisions of the doctoral/master\u0026rsquo;s students and their assigned senior researchers. The coordinating senior researcher decides if a paper should be accepted for inclusion or rejected in situations where the doctoral student and the assigned senior researcher differ in their studies assessments. The idea was to give fairness to the process and remove errors or personal biases. There were seven instances where the assessments of the doctoral students and the assigned senior researcher differed, and the coordinating senior researcher reviewed the decisions and made the final decision.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eData were extracted from the selected articles using a predefined data extraction spreadsheet following the guidelines of [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. The extracted information included the paper type, title, year of publication, country of study, overview of the problem addressed, study design, methodology of the study, data collection method, sample population/company size, sample size, approach for data collection, key findings, conditions for achieving digital transformation, condition for achieving environmental sustainability, benefits of combining human capital development and industrialisation, challenges of using industry 4.0 or 5.0 frameworks to achieve digital transformation, challenges of using industry 4.0 or 5.0 frameworks to achieve environmental sustainability, digital transformation problems, environmental sustainability issues, and other important notes. Quality assessment was conducted to evaluate the methodological rigour of the included studies. Studies were assessed based on adherence to the inclusion criteria, clarity of sample statistics and raw data reporting, and accessibility for download. Duplicates found were excluded from the analysis.\u003c/p\u003e \u003cp\u003eFindings from the included studies were synthesised, analysed, and summarised using a narrative approach. Themes\u0026mdash;drivers and challenges of digital transformation and environmental sustainability in the TAF industries were identified.\u003c/p\u003e \u003cp\u003eFortunately, downloading all the included studies was possible without contacting the corresponding authors for personal copies. We used ChatGPT 3.5 for texts compression and Grammarly\u003csup\u003e1\u003c/sup\u003e for editing. All the texts were from the authors\u0026rsquo; original understanding, reflections and insights gained from the reviewed studies and knowledge of the fields covered.\u003c/p\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eInclusion and Exclusion Criteria\u003c/h2\u003e \u003cp\u003e \u003cb\u003eInclusion\u003c/b\u003e \u003c/p\u003e \u003col style=\"list-style-type: lower-roman;\"\u003e\n \u003cli\u003ePublication must be in English.\u003c/li\u003e\n \u003cli\u003ePublication must be peer-reviewed.\u003c/li\u003e\n \u003cli\u003eMust be a journal article or conference paper.\u003c/li\u003e\n \u003cli\u003eThe articles and papers must not be less than 4 pages\u003c/li\u003e\n \u003cli\u003eThe study design must be detailed, showing the methodological process.\u003c/li\u003e\n \u003cli\u003eMust be published between 2013 and 2023.\u003c/li\u003e\n \u003cli\u003eThe effectiveness of the I 4.0 and 5.0 frameworks must be explicitly investigated, focusing on the digital transformation of textile and allied firms and/or environmental sustainability.\u003c/li\u003e\n\u003c/ol\u003e\u003cp\u003e \u003cb\u003eExclusion\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eArticles and papers not written in English.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eGrey literature and unpublished studies, non-peer-reviewed studies, conference proceedings books, textbooks, work-in-progress, opinion papers, literature reviews, interviews, theses, and blog posts.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eStudies that failed to report the methodological process.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eStudies that are not accessible online or impossible to download due to subscription issues.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eStudies that are inaccessible for download and the corresponding author is not responsive to requests for sharing the author\u0026rsquo;s copy of the study.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eSearch Results\u003c/h3\u003e\n\u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSearch process and results\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSources\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInitial results\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eScreening by titles and abstracts\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eScreening by applying the inclusion and exclusion criteria\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eExcluded duplicates\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eScreening by quality assessment\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eFinal inclusion\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeb of Science\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e128\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eScopus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e162\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e111\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e42\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"Results and Discussion","content":"\u003cp\u003e \u003cb\u003eRQ 1 What digital transformation and sustainability issues dominate the TAF Industries literature, and how are they classified?\u003c/b\u003e \u003c/p\u003e \u003cp\u003eFirst, we break down the digital transformation and sustainability issues based on our understanding of the studies (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Following insights from [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e] and [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e], we then classified them into circular economy and sustainability dimensions (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eHow the reported issues are classified\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClassifications of DT and Sustainability Issues\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCountries of Study\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eClassification of Countries\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eReferences\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOptimal path for energy efficiency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTaiwan, India, Italy, Germany, Spain\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGS, GS, GN, GN, GN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSRP 1, SRP 10, SRP 16, SRP 26, SRP 40\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndustry 4.0 technologies and global value chain\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTaiwan, China, Italy, Germany, Indonesia, Indonesia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGS, GS, GN, GN, GS, GS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSRP 4, SRP 6, SRP 21, SRP 25, SRP 29, SRP 32\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCapturing operation cycle times\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSri Lanka\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSRP 5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOrganisational leadership in Industry 4.0 implementation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePakistan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSRP 7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDigitising operational processes of Industry 4.0 utilisation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGermany, Tanzania\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGN, GS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSRP 8, SRP 42\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSustainable practices compliance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTurkey, Bangladesh, Iran, Colombia, Portugal, Pakistan \u0026amp; Bangladesh, Bangladesh, South Africa, Taiwan \u0026amp; Vietnam\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGS, GS, GS, GS, GN, GS, GS, GS, GS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSRP 9, SRP 14, SRP 19, SRP 23, SRP 30, SRP 31, SRP 34, SRP 37, SRP 41\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndustry 4.0 technologies readiness and usage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTaiwan, Portugal, Turkey, Portugal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGS, GN, GS, GN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSRP 3, SRP 11, SRP 17, SRP 18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDigital fashion technologies\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSweden, China\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGN, GS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSRP 13, SRP 39\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eManufacturing Execution System\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSouth Korea, United Kingdom, India\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGN, GN, GS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSRP 22, SRP 27, SRP 36\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndustry 4.0 technologies implementation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBrazil, Germany, China, Pakistan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGS, GN, GS, GS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSRP 12, SRP 20, SRP 24, SRP 28\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDigital transformation implementation barriers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGermany, Portugal, South Korea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGN, GN, GN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSRP 2, SRP 33, SRP 35\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDigital transformation implementation and human factors impact\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGermany, South Korea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGN, GN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSRP 15, SRP 38\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e*GS\u003csup\u003e2\u003c/sup\u003e - Global South, GN - Global North\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eWe also explored the results from the country classifications to gain insights into the peculiarity or otherwise of the issues. We present and discuss the results in this section.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe optimal path for energy efficiency - a green strategy issue, appears to be evenly concentrated in the Global North and Global South. Still, the results in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e suggest the problem is more pressing in the Global North. According to the Circular Economy dimension, resource efficiency and waste reduction are still problematic for Global South countries like Taiwan and India (SRP 1, SRP 10). The Global North countries referenced in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e have peculiar resource efficiency and waste reduction issues. In particular, traditional companies in Italy face challenges in keeping pace with technological advancements (SRP 16). Germany's textile producers struggle to maintain competitiveness and flexibility in a demanding market environment (SRP 26). Meanwhile, consulting firms in Spain must find a workaround for automatically detecting anomalies in sensor-controlled buildings, reflecting a push towards more innovative and efficient building management (SRP 40).\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e also suggests that Industry 4.0 technologies and global value chain issues are more pressing for Global South Countries than the Global North. In particular, the \u003cb\u003eGlobal South\u003c/b\u003e faces unique challenges such as the integration of cultural elements into the global value chain (Taiwan - SRP 4), the need for better guidance on social and environmental sustainability \u003cb\u003e(\u003c/b\u003eChina - SRP 6\u003cb\u003e)\u003c/b\u003e, and understanding consumer behaviour (SRP 29) along with leveraging CAD technology in the Indonesian garment industry (SRP 32). These issues focus on cultural integration, sustainability practices, and technological adoption to meet market demands. In contrast, the \u003cb\u003eGlobal North\u003c/b\u003e grapples with more advanced market dynamics, such as improving supply chain management for specialised industries in Italy (SRP 21) and adapting to rapidly changing consumer demands driven by digitalisation and individualisation in Germany (SRP 25\u003cb\u003e)\u003c/b\u003e. These issues emphasise the need for innovation, efficient management, and technological adaptation to remain competitive in highly developed and consumer-driven markets.\u003c/p\u003e \u003cp\u003eCapturing operation cycle times and organisational leadership in Industry 4.0 implementation might likely be related more to the Global South, as exemplified in this study. In the \u003cb\u003eGlobal South\u003c/b\u003e, the focus is on adopting and integrating advanced technologies to modernize manufacturing processes and ensure sustainability. Sri Lanka's move towards intelligent assembly lines using cloud technologies underscores the need for infrastructure and skills development to implement these systems effectively (SRP 5). Pakistan's struggle with sustainability amidst rapid digitalization and intelligent manufacturing highlights the broader challenge of managing technological progress while maintaining sustainable practices (SRP 7).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe rest of the issues presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e also highlight a clear divide between the challenges faced by Global South and Global North countries, emphasising distinct economic, technological, and environmental concerns pertinent to each classification. The issues in the Global North often revolve around upgrading existing advanced industrial systems, integrating cutting-edge technologies, and responding to sophisticated consumer demands. Germany TAF companies face issues related to legacy production machines that lack modularity, scalability, and flexibility (SRP 8). There is also a need for cost-benefit trade-offs and implementing assisted digital working environments in industrial firms. These challenges stem from Germany's advanced industrial base and the necessity to upgrade existing infrastructure to maintain competitiveness (SRP 2).\u003c/p\u003e \u003cp\u003ePortuguese companies are grappling with creating and integrating intelligent, sustainable, and resilient future-oriented factories (SRP 30). Another critical issue is the active role of knowledgeable and interventive consumers in relationships with brands and products, necessitating a shift towards more interactive and responsive business models (SRP 33). Swedish TAF companies highlight the application of digital fashion technologies in contemporary fashion design, reflecting its focus on innovative and cutting-edge approaches in the fashion industry (SRP 13).\u003c/p\u003e \u003cp\u003eThe UK TAF companies are working on seamlessly integrating wearable sensors into mass-fabricated clothing using precise methods like computerised embroidery (SRP 27). This points to an emphasis on technological precision and integration in textile manufacturing. South Korean TAF companies emphasise the need for a manufacturing execution system (MES) for smart factories, aligning with emerging technologies and increased firm cooperation (SRP 22). Additionally, the country experiences some challenges with gas sensors in wearable electronic devices, which have low stretchability and poor stability, reflecting the high-tech focus of its industrial sector (SRP 38).\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e provides an overview of the various digitisation and green strategies proposed or used in TAF companies, as reported in the 42 studies reviewed.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDigitisation, green strategies, and innovations in TAF industries\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStudy Code\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDT Strategies used or proposed\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGreen Strategies used or proposed\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eInnovation to address the problem\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSRP 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRecycling, reuse of wastewater, reuse of waste-heat, reuse of waste cinder from coal combustion, Eco-Brick\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMathematical model for profit maximisation and green production planning and control\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSRP 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEnd-to-end value chain creation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSetting up of Textile Learning Factory 4.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSRP 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSmart manufacturing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDecision support system for dyeing machine scheduling\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSRP 4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCross-disciplinary value creation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCross-disciplinary value creation framework\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSRP 5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGarment assembly operations cycle times and balancing workloads\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSmart production line prototype\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSRP 6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eA measures sustainability framework based on the United Nations Sustainable Development Goals\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eA hybrid multi-situation decision technique\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSRP 7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTransformational leadership and Innovative performance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSRP 8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExploitation of digitalisation advantages\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDigitisation process prototype\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSRP 9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSustainable global brands\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEnergy and water efficient environmentally friendly production\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSRP 10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEnergy\u003c/p\u003e \u003cp\u003econsumption modelling\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePenalty Based Reinforcement\u003c/p\u003e \u003cp\u003eLearning Algorithms\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSRP 11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBayesian process for the production system\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eA mathematical model providing a feedback learning/controlling loop for the pre-production, production and post-production processes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSRP 12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMaturity assessment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eA diagnostic tool for assessing the maturity of Industry 4.0 technologies\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSRP 13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDigitalisation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDigital fashion as an emerging subfield\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSRP 14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAssessing the influence of Institutional Pressure on cleaner production practices and sustainable firm performances\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eA conceptual framework for assessing the influence of Institutional Pressure on cleaner production practices and sustainable firm performances\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSRP 15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSticking to standardised implementation\u003c/p\u003e \u003cp\u003epractices.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCreating Augmented reality-based assistance systems\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSRP 16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSustainable production scheduling\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDiscrete-event simulation model\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSRP 17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAwareness and readiness assessment of industry 4.0 technologies\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eProviding extensive knowledge of Industry 4.0\u003c/p\u003e \u003cp\u003etechnologies.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSRP 18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDigitisation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAn in-house ICT solution, the \u0026ldquo;FLUXOCONF\u0026rdquo; production monitoring software for evaluating the firm\u0026rsquo;s production system technologies application\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSRP 19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSustainable supplier selection\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eThe fuzzy best-worst method (FBWM) and two-stage fuzzy inference system (FIS) model for assessing supplier's selection\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSRP 20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSmart textile production\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSelf-optimising textile machinery\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSRP 21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSimulation and data analysis modelling for decision-making support in supply chain optimisation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSupply Chain Management Simulator\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSRP 22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModelling and developing new MES Systems\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eA cloud-based collaborative MES System to support a value chain process\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSRP 23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBig data application modelling\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eA structured and automated decision-making system for SMEs in the garments sector\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSRP 24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBusiness model adaptation for platform-based servitisation to foster product-service innovation (PSI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eA conceptual framework for platform-enabled servitisation pathways\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSRP 25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAdapting individualisation and digitalisation for in-store fashion production\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eA unique in-store production line that creates custom-designed and shaped woollen sweaters for customers\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSRP 26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMulti-objective self-\u003c/p\u003e \u003cp\u003eoptimisation of the weaving process.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eA software-based programmable logic controller for optimal parameter setting\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSRP 27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eComputerised embroidery for mass manufacturing of textile-based electrical wearable sensors.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eA low-cost poly(3,4-ethylene-dioxythiophene) polystyrene sulfonate (PEDOT: PSS)-modified cotton conductive thread (PECOTEX) with computerised embroidery\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSRP 28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eA decision-making framework for evaluating and selecting sustainable suppliers through industry 4.0 initiatives in circular economy implementation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eA multi-criteria decision-making support tool\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSRP 29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFocusing marketing strategies on message delivery to enhance image-related concerns and boost purchase intention utilising big data from the Internet of Things.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eA conceptual framework on consumer conformity\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSRP 30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCollecting and analysing seemingly unrelated activities co-occurring in different parts of smart factories for cyber situational awareness creation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSMS Digital Twin (SMS-DT) platform for simulating and monitoring industrial conditions in smart factories\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSRP 31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIntellectual capital's (IC) dual role in improving a firm's sustainable production in blockchain-driven supply chain management (BCSCM)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eA conceptual framework of intellectual\u003c/p\u003e \u003cp\u003ecapital (IC) for improving firms' sustainable production\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSRP 32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUsing Computer-Aided Design (CAD) for fashion design\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eA conceptual framework on competence and industrial work practice\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSRP 33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCollaborative\u003c/p\u003e \u003cp\u003eDesign and Mass Customisation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAnalysis of perceptions towards co-design mass customisation approaches in the Portuguese footwear industry\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSRP 34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAutomatic identification and classification of textile visual pollutants using computer vision techniques\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eClassifying visual pollutants using deep learning networks\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSRP 35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAnalysing a CAD file with customer requirements and implementing a one-person, one-item mass production system to accomplish a tailored service\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAutomated classification of customer demands dress attributes (colour, pattern, size), CAD file generation for each element, and simulation of the entire manufacturing process.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSRP 36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLean manufacturing approaches implementation in MSMEs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCluster analysis of Lean Manufacturing Competitive Scheme (LMCS) in MSMEs\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSRP 37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUsing an enzymatic derivative of bacterial mandarin peelings fermentation to create a sustainable and environmentally friendly textile bio-scouring process\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOptimising response surface methodology (RSM), using the common low-cost agro-industrial waste (MP) to produce bacterial exudate (HRJ16 laccase)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSRP 38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDeveloping a highly stretchable, sensitive and stable NO\u003csub\u003e2\u003c/sub\u003e gas sensor from reduced graphene oxide sheets and elastic commercial yarns\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFabricating the RGO sensors using a pre-strain strategy (strain-release assembly) to accomplish high stretchability and good stability\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSRP 39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMan-algorithm cooperation intelligent design in multiple links of clothing design\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIntegrating intelligent algorithms (parameterised number state algorithm, Generative Adversarial Networks, and style transfer) into different clothing product design and development aspects and creating a novel approach to designing clothes by combining smart algorithms with distinct functional roles of people.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSRP 40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUsing a chart that utilises functional data to identify abnormalities and estimate the standard output of industrial processes and services, including those related to energy efficiency.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDeveloping a control process (Phase I and II control charts) based on calculating functional data depth, identifying outliers by smooth bootstrap resampling, and customising nonparametric rank control charts\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSRP 41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHybrid method for generating SSCM indicators\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFuzzy Delphi method for validating SSCM indicators\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSRP 42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDigitalisation level assessment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMaturity assessment of firm's digitisation, digitalisation and digital transformation\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eIn contrast, the Global South faces foundational challenges such as technological adoption, environmental sustainability, and data-driven process optimisation. In Tanzania, TAF companies face low performance and customer satisfaction due to reluctance to adopt digital manufacturing processes (SRP 42). This indicates a technological lag and resistance to change, impacting productivity and market competitiveness. Turkish garment industry struggles with rapid fashion changes, which pose significant challenges (SRP 9). Turkish manufacturing companies are also investigating broad approaches to Industry 4.0 concepts and essential technologies, indicating a transition phase in embracing modern manufacturing paradigms (SRP 17). Bangladesh's clothing industry contributes significantly to pollution, with cleaner production mediating between institutional pressure and firms' environmental performance (SRP 14). Visual pollution in urban environments is also a concern, necessitating comprehensive investigation and assessment (SRP 34).\u003c/p\u003e \u003cp\u003eIranian TAF companies are focusing on identifying essential criteria for selecting sustainable textile suppliers, particularly in the context of Industry 4.0, which stresses a need to align supplier selection with sustainability and technological advancements (SRP 19). Colombia emphasises the behaviour of operational processes and the generation of information and big data to optimise fabric dyeing operations, reflecting a focus on data-driven improvements in textile manufacturing (SRP 23). Pakistan's TAF companies' issues revolve around the significant contribution of product production and consumption to climate change and environmental challenges, which impact future generations and human lives (SRP 31). Limited research also exists on integrating Industry 4.0 and the circular economy in sustainable supplier selection, indicating a gap in knowledge and application (SRP 28). South African TAF companies highlight the need for environmentally friendly methods for textile bio-scouring. These methods can be achieved using enzymatic derivatives of bacterial mandarin peelings, reflecting an emphasis on sustainable and eco-friendly production processes (SRP 37). Taiwan and Vietnam stress determining data-driven indicators for sustainable supply chain management, especially amid industrial disruption and the need for ambidexterity (SRP 41). Taiwan specifically addresses the need for efficient solutions to support mass customisation and dynamic customer demands. China's focus includes the effective use of platforms for servitisation in an Industry 4.0 environment and innovative clothing design methods incorporating intelligent algorithms due to technological advancements (SRP 24, SRP 39). Brazilian TAF companies need internal development to integrate Industry 4.0 technologies and support the transition process, reflecting an ongoing shift towards modernisation and technological adoption (SRP 12).\u003c/p\u003e \u003cp\u003eIn summary, while the Global North focuses on refining and enhancing advanced systems, the Global South strives to overcome foundational barriers to technology adoption, sustainability, and efficient production processes. Integrating Industry 4.0 and 5.0 technologies in the TAF industries has shown substantial benefits in sustainability, efficiency, and competitiveness. However, the transition is often challenged by high costs, resource limitations, complex decision-making processes, and the need for significant infrastructure and expertise. The digitisation and green strategies outlined in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e highlight the importance of a strategic approach in adopting new technologies, ensuring environmental sustainability, and leveraging digital transformation to enhance circular economy strategies.\u003c/p\u003e \u003cp\u003e \u003cb\u003eRQ 2 How have Industry 4.0 and 5.0 technologies been implemented in the TAF industries to support circular economy initiatives, and what are their measurable impacts on resource efficiency and waste reduction?\u003c/b\u003e \u003c/p\u003e \u003cp\u003eWe took records of the I 4.0 and 5.0 technologies mentioned in each of the 42 included studies and counted the number of instances of total mention. We also recorded the impacts of the technologies in TAF literature from the 42 included studies and counted the number of instances of each impact mentioned in total. We only entered one record for each study's technologies and impacts. We compiled the records in a table in ordinal form. Please note that there is no direct mapping of the technologies with the impact. Since we treated the technologies as a unit, we needed to treat the impact as a unit, although there are few negative impacts. We marked them with a minus (-) sign. See Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e4\u003c/span\u003e in \u003cspan refid=\"Sec24\" class=\"InternalRef\"\u003eAppendix B\u003c/span\u003e for details. We then proceeded to find the correlation between the I 4.0 and 5.0 technologies and their impacts using an online calculator\u003csup\u003e3\u003c/sup\u003e. We chose the Pearson Coefficient Correlation since we are more familiar with the technique, and the results obtained using different correlation coefficient techniques are the same. The full results are presented in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e5\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePearson Correlation Coefficient Results\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eX - M\u003csub\u003ex\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eY - M\u003csub\u003ey\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(X - M\u003csub\u003ex\u003c/sub\u003e)\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(Y - M\u003csub\u003ey\u003c/sub\u003e)\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(X - M\u003csub\u003ex\u003c/sub\u003e)(Y - M\u003csub\u003ey\u003c/sub\u003e)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e21.909\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21.879\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e480.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e478.681\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e479.344\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e11.909\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21.879\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e141.826\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e478.681\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e260.556\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10.909\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13.879\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e119.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e192.621\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e151.405\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9.909\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10.879\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e98.190\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e118.348\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e107.799\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8.909\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.879\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e79.372\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e78.833\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e79.102\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8.909\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.879\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e79.372\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15.045\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e34.556\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3.909\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.879\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.281\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.287\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11.253\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0.909\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.879\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.826\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.287\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.617\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e-0.091\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.879\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.530\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.171\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e-0.091\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.879\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.530\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.171\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e-0.091\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.879\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.772\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.080\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e-0.091\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1.121\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.257\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.102\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e-0.091\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1.121\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.257\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.102\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e-1.091\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1.121\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.190\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.257\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.223\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e-1.091\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-2.121\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.190\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.314\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e-1.091\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-2.121\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.190\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.314\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e-2.091\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-3.121\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.372\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.742\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.526\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e-3.091\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-3.121\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.554\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.742\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.647\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e-3.091\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-3.121\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.554\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.742\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.647\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e-4.091\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-3.121\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16.736\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.742\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12.769\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e-4.091\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-3.121\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16.736\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.742\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12.769\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e-4.091\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-3.121\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16.736\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.742\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12.769\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e-4.091\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-3.121\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16.736\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.742\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12.769\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e-4.091\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-4.121\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16.736\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16.984\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e16.860\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e-4.091\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-4.121\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16.736\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16.984\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e16.860\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e-5.091\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-5.121\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25.917\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26.227\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e26.072\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e-5.091\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-5.121\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25.917\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26.227\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e26.072\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e-5.091\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-5.121\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25.917\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26.227\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e26.072\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e-5.091\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-7.121\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25.917\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e50.712\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e36.253\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e-5.091\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-7.121\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25.917\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e50.712\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e36.253\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e-5.091\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-7.121\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25.917\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e50.712\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e36.253\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e-5.091\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-8.121\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25.917\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e65.954\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e41.344\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e-5.091\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-9.121\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25.917\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e83.197\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e46.435\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMx: 6.091\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMy: 6.121\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSum: 1348.727\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSum: 1881.515\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSum: 1517.636\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eKey\u003c/b\u003e \u003cem\u003eX\u003c/em\u003e: I 4.0 \u0026amp; 5.0 technologies Values\u003cem\u003eY\u003c/em\u003e: Impacts Values\u003cem\u003eM\u003c/em\u003e\u003csub\u003e\u003cem\u003ex\u003c/em\u003e\u003c/sub\u003e: Mean of X Values\u003cem\u003eM\u003c/em\u003e\u003csub\u003e\u003cem\u003ey\u003c/em\u003e\u003c/sub\u003e: Mean of Y Values\u003cem\u003eX - M\u003c/em\u003e\u003csub\u003ex\u003c/sub\u003e \u0026amp; \u003cem\u003eY - M\u003c/em\u003e\u003csub\u003e\u003cem\u003ey\u003c/em\u003e\u003c/sub\u003e: Deviation scores\u003cem\u003e(X - M\u003c/em\u003e\u003csub\u003e\u003cem\u003ex\u003c/em\u003e\u003c/sub\u003e)\u003csup\u003e2\u003c/sup\u003e \u0026amp; \u003cem\u003e(Y - M\u003c/em\u003e\u003csub\u003e\u003cem\u003ey\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e)\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e: Deviation Squared\u003cem\u003e(X - M\u003c/em\u003e\u003csub\u003e\u003cem\u003ex\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e)(Y - M\u003c/em\u003e\u003csub\u003e\u003cem\u003ey\u003c/em\u003e\u003c/sub\u003e): Product of Deviation Scores\u003c/p\u003e \u003cp\u003e \u003cb\u003eResult Details \u0026amp; Calculation\u003c/b\u003e \u003cem\u003eX Values\u003c/em\u003e\u0026sum; = 201 Mean\u0026thinsp;=\u0026thinsp;6.091 \u0026sum;(X - M\u003csub\u003ex\u003c/sub\u003e)\u003csup\u003e2\u003c/sup\u003e = SS\u003csub\u003ex\u003c/sub\u003e = 1348.727\u003cem\u003eY Values\u003c/em\u003e\u0026sum; = 202 Mean\u0026thinsp;=\u0026thinsp;6.121 \u0026sum;(Y - M\u003csub\u003ey\u003c/sub\u003e)\u003csup\u003e2\u003c/sup\u003e = SS\u003csub\u003ey\u003c/sub\u003e = 1881.515\u003cem\u003eX and Y Combined\u003c/em\u003e\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;33, \u003cem\u003er\u003c/em\u003e(degress of freedom); degree of freedom\u0026thinsp;=\u0026thinsp;N-2 \u0026sum;(X - M\u003csub\u003ex\u003c/sub\u003e)(Y - M\u003csub\u003ey\u003c/sub\u003e)\u0026thinsp;=\u0026thinsp;1517.636\u003cem\u003eR Calculation\u003c/em\u003er = \u0026sum;((X - M\u003csub\u003ey\u003c/sub\u003e)(Y - M\u003csub\u003ex\u003c/sub\u003e)) / \u0026radic;((SS\u003csub\u003ex\u003c/sub\u003e)(SS\u003csub\u003ey\u003c/sub\u003e)) r\u0026thinsp;=\u0026thinsp;1517.636 / \u0026radic;((1348.727)(1881.515))\u0026thinsp;=\u0026thinsp;0.9527 r\u0026thinsp;=\u0026thinsp;0.95, P-value is \u0026lt;\u0026thinsp;.00001 at p\u0026thinsp;\u0026lt;\u0026thinsp;.05.\u003c/p\u003e \u003cp\u003eOnline Pearson Correlation Coefficient\u003c/p\u003e \u003cp\u003eThe correlation coefficient value \u003cem\u003er\u003c/em\u003e(31)\u0026thinsp;=\u0026thinsp;.95, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.01., a significant result at p\u0026thinsp;\u0026lt;\u0026thinsp;.05. The result is a strong positive correlation, which indicates that the more I 4.0 and 5.0 technologies TAF companies deploy, the more impacts are achieved. However, these technologies have a few negative impacts, especially regarding society's sustainability, information security, data complexity, electronic wastes, and job losses/reduced labour. Figure\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e is the scatterplot showing the correlations. We present details of the impacts in the rest of this section. We present the impacts under three themes: production, supply value chain, and workforce/consumers.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eProduction Impacts\u003c/h2\u003e \u003cp\u003e \u003cstrong\u003eEnhanced Machine Cognition and Efficiency\u003c/strong\u003e \u003cp\u003eAdvanced data analytics enable the collection and analysis of extensive data sets from different stages of textile production. This real-time data allows machines to adapt to changing conditions, and optimising processes continuously. As a result, overall production efficiency significantly improves, operational costs are reduced, and waste is minimised. For example, predictive maintenance systems can anticipate equipment failures and schedule timely repairs, preventing unexpected downtime and extending the lifespan of machinery. For instance, SRP 36, in a multi-case study of Indian Micro, Small, Medium, and Medium Enterprises (MSMEs), highlighted the imperativeness of Lean manufacturing techniques. The research indicated a 27% progress in productivity through organised workspaces and visual aids, a 26% rise in timely product delivery, and a 73% decrease in defects.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eCustomisation and Flexibility\u003c/strong\u003e \u003cp\u003eIndustry 4.0 technologies facilitate highly flexible production models capable of mass customisation. Computer-aided design (CAD) and 3D modelling allow for the creation of personalised products without the need for extensive physical prototypes. This reduces material waste and accelerates the time to market for new designs. Using CPS in smart factories ensures seamless communication and coordination across different manufacturing stages, enhancing efficiency and customisation capabilities. For instance, SRP 26 reported that implementing a multi-objective self-optimisation cyber-physical system for the weaving process reduced the cost of production and the weaving machine set-up time by 75%.\u003c/p\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eValue Chain Impacts\u003c/h2\u003e \u003cp\u003e \u003cstrong\u003eImproved Supply Chain Performance\u003c/strong\u003e \u003cp\u003eDigital platforms and IoT devices enhance transparency and traceability within the supply chain. Real-time data collection and analysis enable better inventory levels, production processes, and logistics leading to more efficient resource management and waste minimisation. For instance, IoT sensors can track environmental conditions during transportation and storage, ensuring optimal conditions for textile materials and finished products.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eNew Business Models\u003c/strong\u003e \u003cp\u003eDigitalising the textile industry has led to new business models, such as \"as-a-service\" and data-driven models. These models open new revenue streams and enhance flexibility in operations. Companies can now offer products on a subscription basis or provide customisation services on-demand, aligning with consumer preferences for personalised and sustainable products. These new models decrease waste and boosts resource use by implementing shared and circular business practices. For example, these advancements could provide a significant opportunity to increase the competitiveness of Germany\u0026rsquo;s manufacturing industry by over 20% in productivity and 10\u0026ndash;40% in maintenance cost savings (SRP 2).\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eSustainability and Environmental Benefits\u003c/strong\u003e \u003cp\u003eIndustry 4.0 and 5.0 technologies contribute to more sustainable manufacturing processes by optimising resource use and minimising waste. AI-driven optimisation techniques and real-time monitoring systems reduce energy consumption and emissions. For example, integrating IoT-enabled sensors in production facilities helps monitor and control energy usage, leading to significant reductions in the environmental footprint of textile manufacturing. For instance, SRP 38 stated that gas sensors are excellent materials for creating IoT's next-generation environmental applications.\u003c/p\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eWorkforce and Consumer Impacts\u003c/h2\u003e \u003cp\u003e \u003cstrong\u003eWorkforce Skill Enhancement\u003c/strong\u003e \u003cp\u003eThe shift towards digital working environments necessitates enhanced IT skills among the workforce. Employees must manage automated systems and interpret data analytics to optimise production processes. While this transition may displace some traditional jobs, it also creates new opportunities in technology management and data analysis. Continuous training and upskilling programs are essential to equip the workforce with the necessary competencies.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsumer Interaction and Customisation\u003c/strong\u003e \u003cp\u003eIndustry 4.0 technologies cater to the evolving consumer demand for interaction and customisation. Digital platforms enable consumers to participate in the design process, selecting and customising products according to their preferences. This will not only enhance consumer satisfaction but also reduce waste by producing items that are specifically tailored to individual needs. The co-creation of value through digital interfaces strengthens the relationship between consumers and producers, fostering a more sustainable consumption pattern.\u003c/p\u003e \u003c/p\u003e \u003cp\u003eAs seen in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e4\u003c/span\u003e, Industry 4.0 and 5.0 technologies encompass a range of digital innovations, including IoT, big data analytics, AI, cloud computing, and Cyber-Physical Systems (CPS). Their integration has transformed various aspects of the TAF industries, from production processes to value chain management, ultimately contributing to more sustainable practices. Industry 4.0 and 5.0 technologies have been increasingly implemented in the TAF industries to bolster circular economy initiatives. The goal is to enhance sustainability, resource efficiency, and waste reduction. These industries are adopting circular economy models, emphasising the recycling and reuse of materials to minimise waste and lower environmental impact.\u003c/p\u003e \u003cp\u003eCutting-edge manufacturing techniques like 3D printing and digital knitting are transforming production processes. They enable on-demand production, significantly reducing excess inventory and waste. However, the real game-changer is the integration of AI and big data analytics. These technologies allow for precise demand forecasting and inventory management, mitigating waste and optimising resource use.\u003c/p\u003e \u003cp\u003eCustomisation and smart manufacturing are crucial in reducing waste by producing only what is needed, tailored to specific consumer preferences. However, implementing digital performance management systems is vital to enhancing resource efficiency. These systems play a significant role in waste reduction, amplifying these practices' benefits.\u003c/p\u003e \u003cp\u003eThanks to the optimised use of materials and energy, they lead to a significant reduction in environmental impact and improved resource efficiency. Customisation and personalisation reduce overproduction and increase consumer satisfaction by providing tailored products. Furthermore, integrating advanced technologies fosters innovation and maintains competitiveness in a fast-evolving market.\u003c/p\u003e \u003cp\u003eEfforts to recycle and reuse materials extend the lifecycle of products, with closed-loop systems ensuring continuous recycling within the production process.\u003c/p\u003e \u003cp\u003eThe human-centric approach of Industry 5.0 is a testament to the value of human creativity in the manufacturing process, combining creativity with machine efficiency to promote personalised and sustainable production processes, thereby making everyone in the manufacturing process feel integrated and valued. Existing studies such as [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e], [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e], [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e], [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e], and [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e] have informed about the animosity between humans and machines due to too much economic benefit focus exacerbated by nonstrategic heavy automation and fear of job loss or replacement, and health hazards. Under Industry 5.0, Robots (cobots) collaborate with human employees to boost productivity and reduce physical strain, contributing to more efficient and sustainable manufacturing practices. This emphasis on reducing physical strain demonstrates a care for the workforce's well-being, making them feel considered and valued in the manufacturing process.\u003c/p\u003e \u003cp\u003eAdopting sustainable materials is another critical aspect, with advancements in material science and biotechnology leading to developing eco-friendly fabrics and dyes. Utilising organic cotton, recycled polyester, and biodegradable fabrics helps to reduce the environmental footprint. In a recent study exploring natural fibres as sustainable textile production, the authors highlighted: \"Natural (fibres) possess desirable properties, but they often lag behind synthetic (fibres) in terms of both quality and quantity. Genomic-assisted breeding has the potential to improve (fibre) quality traits in cotton, hemp, ramie, and flax. Utilizing available QTLs, marker-trait associations, and candidate genes can contribute to the development of superior (fibre) crops, underscoring the significance of advanced breeding tools\" [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, p.1].\u003c/p\u003e \u003cp\u003eAdditionally, energy efficiency is achieved using renewable energy sources and energy-efficient technologies, optimising energy consumption during manufacturing processes. Digital transformation, including digital twins and cloud-based manufacturing execution systems (MES), enhances sustainability by providing real-time data for improved decision-making and resource optimisation. These results are consistent with the review of [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], indicating that integrating digital technologies such as blockchain, AI, and IoT can help TAF companies fulfil relevant United Nations' sustainable development goals. In particular, blockchain technology improves visibility and trackability within the supply chain, ensuring that sustainable methods are upheld throughout every manufacturing phase. [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e] revealed that digital advances and Industry 4.0 technologies have benefitted the CE shift. Using data analysis to examine product lifecycles clearly demonstrates this advantage.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eChallenges\u003c/h2\u003e \u003cp\u003eImplementing Industry 4.0 and 5.0 technologies in the TAF industries ensures continuing sustainable or environmentally friendly production. The initial investment required to implement these technologies is substantial, posing a barrier to small and medium-scale companies. Additionally, the challenge includes achieving full transparency and traceability across the entire supply chain, which requires coordination among all stakeholders, security concerns, knowledge and training, initial investment costs, and operational complexities.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eCybersecurity Concerns\u003c/strong\u003e \u003cp\u003eThe increased digitisation and interconnectedness of systems has increased connectivity and data exchange inherent in IoT and cloud computing (SRP 7, SRP 14, SRP 28, SRP 30) and raised significant cybersecurity and privacy concerns. Our findings are consistent with [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e], who revealed that privacy concerns and limited data handling experience are substantial cyber security challenges for small companies in the TAF industries. Other studies, such as [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e], have indicated that keeping industrial systems safe from cyber threats and guaranteeing data integrity is extremely important. TAF corporations must, therefore, implement strong security measures and procedures to safeguard sensitive data and information and ensure operational resilience.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eKnowledge and Training\u003c/strong\u003e \u003cp\u003eImplementing Industry 4.0 and 5.0 technologies requires substantial expertise and strategic managerial approaches. Organisations should allocate resources to ongoing training initiatives to cultivate technical and analytical competencies essential for maximising the benefits of new technologies. Integrating current systems with new technologies can be complicated, requiring significant process changes and workforce training. It is also crucial to address resistance to change and cultivate an environment of innovation within the workforce to realise the full impact of digital transformation. These findings are consistent with a recent study by [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e], which revealed that making highly automated supply chain systems human-centred in the TAF industries is crucial to smoothly transitioning from industry 4.0 to 5.0. To this effect, TAF organisations must integrate working training and lifelong learning into their practices to narrow skill gaps necessary for fostering the adoption of new technologies. [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e] also revealed that change resistance and ineffectual change management hinder TAF companies from transitioning to industry 5.0. [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e] echoed similar thoughts by indicating that apparel firms are receptive to adopting business intelligence solutions in transitioning to the I 4.0 era. In contrast, our study shows that traditional companies, especially those in the Global South, are slow or reluctant to adopt Industry 4.0 and do not discuss transitioning to Industry 5. The authors of SRP 3 argue for a mid-ground framework more tailored to developing countries.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eInitial Investment Costs\u003c/strong\u003e \u003cp\u003eThe transition to Industry 4.0 and 5.0 involves significant initial investments in advanced technologies and infrastructure. Although these investments can result in long-term savings and increased efficiency, the cost may be too high for small and medium-sized businesses. Our findings are consistent with the study by [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e], which shows that top management involvement and funding are the topmost challenges for post-COVID Bangladeshi business organisations transitioning to Industry 5.0 for sustainable supply chain practices. We offer additional insights by indicating that introducing changes gradually and using adaptable solutions can help reduce these expenses and potential downsides. These findings contradict [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], arguing for first prioritising management's soft (consumer-related issues and green intellectual capital) demands. The point of departure is that while the approach might work for developed countries, developing countries might have different results. For example, [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] identified financial and regulatory pressures as complex aspects of the challenges of applying circular economy principles in the fashion industry. While it is true that those are hard demands, the priority should instead start with this aspect for developing countries that need enabling regulatory policies and financial support [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e] for this transition.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eOperational Complexity\u003c/strong\u003e \u003cp\u003eAdopting new methods and technologies increases production complexity. As [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] argued, integrating digital technologies like IoT, AI, AR, VR, and blockchain can offer a resilient and innovative infrastructure to guarantee fashionable product development that promotes sustainable production. However, the challenges lie with these technologies' complexity and maintenance. For example, IoT in Industry 4.0 involves networked sensors integrated with complex physical machinery and devices, software RFID, barcodes, and tags to collect production data and predict, control, and plan for better business and societal outcomes (SRP 6, SRP 11). This presents potential challenges such as cybersecurity risks, data privacy concerns, and the need for standardised protocols. We argue that TAF companies must manage the integration of various digital systems and ensure seamless communication between different components. This requires robust planning, coordination, and technical expertise. TAF companies' role in this process is crucial to avoid disruptions and achieve the desired outcome.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eRQ 3 What research opportunities exist for integrating Industry 4.0 and 5.0 technologies in the TAF industries to enhance the Circular Economy?\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe findings in this review study offer a comprehensive overview of how Industry 4.0 and 5.0 technologies impact the TAF industries. However, several areas require further exploration. We discuss the significant issues below:\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eAdaptable Region-specific Hybrid Frameworks for Industry 4.0 and 5.0 Integration\u003c/h2\u003e \u003cp\u003eThere is a lack of comprehensive frameworks that guide the seamless integration of Industry 4.0 and 5.0 technologies in the TAF industries. Existing studies often focus on isolated applications or specific technologies. There is a need for holistic frameworks that combine multiple technologies, considering the entire lifecycle of textile products from production to recycling. This call is a growing topic among scholars. Our systematic review reveals a big dichotomy between the Global North and the Global South, with each region having its challenges, needs, and expectations. We need context-based hybrid frameworks that allow each region to grow at its own pace. Our sentiments have been echoed by scholars such as [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e] [SRP 3], who argued that traditional companies in developing countries could not catch up the pace with their counterparts in developed countries. The authors successfully developed the Industry 3.5 framework and implemented it in a Taiwanese textile firm. A related study already anticipates an ambitious transition to Industry 6.0, claiming that \"there will be no interface with any machine, person, or drafting table/setup\" ([\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e], p. 4830). We need hybrid frameworks tailored to each region, focusing on their peculiarities regarding needs, challenges, and expectations. Developing integrative models encompassing various Industry 4.0 and 5.0 technologies and addressing interoperability, scalability, and adaptability to different contexts within the TAF industries might be a potential opportunity.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eQuantitative Assessment of Environmental Impact\u003c/h2\u003e \u003cp\u003eWe observed an insufficient quantitative assessment of the environmental impact of Industry 4.0 and 5.0 technologies in the TAF industries. While qualitative benefits are frequently highlighted, there is limited empirical data quantifying the environmental benefits, such as reduction in carbon footprint, energy consumption, and waste. Conducting empirical studies developing metrics, and conducting longitudinal studies to quantify the environmental impact of digital transformation in the TAF industries, aiding in the validation of sustainability claims, might be a potential opportunity. Quantitative data will help validate the effectiveness of these technologies and guide future investments. A holistic environmental impact analysis will ensure that technological advancements align with sustainability goals.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eCost-benefit Analysis and Economic Viability\u003c/h2\u003e \u003cp\u003eWe also observed Limited cost-benefit analysis and assessment of economic viability for small and medium-sized enterprises (SMEs) in the extant TAF industries literature on industry 4.0 and 5.0 framework implementations. Many SMEs face financial constraints that hinder the adoption of advanced technologies. Detailed economic analyses specific to SMEs are scarce. Investigating ways to support SMEs in adopting Industry 4.0 technologies is crucial. This could involve research into successful case studies, exploring innovative funding mechanisms, and analyzing the role of government policies and incentives. Potential opportunities might be to develop tailored economic models and financial frameworks that evaluate the cost-effectiveness of Industry 4.0 and 5.0 technologies for SMEs in the TAF industries, including funding mechanisms and investment strategies. Supporting SMEs will democratize access to advanced technologies and promote broader industry adoption.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eReal-Time Data and Decision Support Systems\u003c/h2\u003e \u003cp\u003eThere is a Lack of robust real-time data analytics and decision support systems tailored to the TAF industries. While real-time data and decision-making are critical for the success of Industry 4.0 initiatives, there is a gap in tailored solutions that address the unique needs of the textile industry. Designing and implementing real-time data analytics platforms and decision support systems that cater specifically to the production dynamics, supply chain complexities, and sustainability goals of the TAF industries might be potential opportunities.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eCybersecurity and Data Privacy\u003c/h2\u003e \u003cp\u003eInsufficient focus on cybersecurity and data privacy in the context of smart textile manufacturing. As Industry 4.0 and 5.0 technologies rely heavily on interconnected systems and data exchange, ensuring cybersecurity and data privacy is paramount. However, this aspect is often underexplored. Detailed research into cybersecurity threats specific to the textile and apparel industry and effective countermeasures is necessary. This includes studying past incidents, understanding vulnerabilities in interconnected systems, and developing industry-specific security protocols. Researching advanced cybersecurity measures and data privacy frameworks tailored to smart textile manufacturing environments and addressing specific threats and vulnerabilities might be a potential opportunity. Enhancing cybersecurity will protect sensitive data and ensure the stability of digital operations.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eWorkforce Skills and Training\u003c/h2\u003e \u003cp\u003eLack of comprehensive strategies for workforce skills development and training. The adoption of advanced technologies requires a skilled workforce, but there is a significant gap in strategies for training and upskilling employees in the TAF sector. Developing training programs and educational curricula that equip the workforce with the necessary skills for operating and managing Industry 4.0 and 5.0 technologies, including digital literacy, technical proficiency, and problem-solving skills, might be a potential opportunity. Ensuring a balanced approach that supports both technological advancement and workforce stability is essential for sustainable development.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eCultural and Organizational Change Management\u003c/h2\u003e \u003cp\u003eLimited research exists on cultural and organizational change management required for technology adoption. Successfully integrating new technologies involves significant cultural and organizational changes, which are often overlooked in technical studies. Investigating best practices for managing cultural and organizational change in the TAF industries, including stakeholder engagement, leadership strategies, and change management frameworks, might provide potential opportunities.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eCircular Economy Business Models\u003c/h2\u003e \u003cp\u003eWe also observed inadequate development of business models that leverage Industry 4.0 and 5.0 technologies for circular economy principles. There is a need for innovative business models that incorporate advanced technologies and align with circular economy principles such as resource efficiency, product life extension, and recycling. For example, creating and testing new business models that integrate Industry 4.0 and 5.0 technologies with circular economy practices and evaluating their feasibility, scalability, and impact on sustainability might provide potential opportunities.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eConsumer Behaviour and Market Dynamics\u003c/h2\u003e \u003cp\u003eAnother important area is understanding how Industry 4.0 and 5.0 technologies influence consumer behaviour and market dynamics. Research into how digital customisation, transparency, and sustainability features impact consumer preferences and purchasing decisions can provide valuable insights for developing effective marketing strategies and driving sustainable consumption patterns.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eLimitations\u003c/h2\u003e \u003cp\u003eWhile the systematic literature review has unveiled valuable insights, it is imperative to acknowledge certain limitations inherent in the scope and methodology of the study. One limitation pertains to the limited size of the sample pool drawn from existing literature. However, every effort was made to include a variety of studies. Furthermore, only a few studies were found to align with the inclusion and exclusion criteria of research. Another limitation is that while the study attempted to encompass a wide range of countries and regions, the number of regions included was limited - the contextual diversity of the textile and fashion industry cuts across various countries. Hence, the industry's practices and challenges differ across global regions, so the review may not fully capture localized details and regional specifics. Furthermore, the review's inclusion criteria favoured studies available in English, potentially leading to the omission of valuable insights from non-English sources.\u003c/p\u003e \u003cp\u003eFinally, the field of digital transformation and environmental sustainability is rapidly evolving. As a result, the review's focus on existing literature at a specific time might not encompass the latest advancements or trends. Moreover, accumulating new research after the review's completion could contribute to the evolution of knowledge in ways not reflected in the synthesis.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eWe have reviewed the extant literature on the textile, apparel, and fashion (TAF) industries and highlighted several digital transformation and sustainability issues that dominate discussions. From circular economy and sustainability dimensions, we classified into three main categories: resource efficiency and waste reduction, supply chain management, technological advancement and optimisation, operational efficiency and lean manufacturing, environmental sustainability and cleaner production, societal and cultural aspects, Innovation in Design and production, work environment and safety, and cost efficiency and performance. We further classified these issues along Global North and Global South countries. The results show that while the Global North focuses on refining and enhancing advanced systems, the Global South strives to overcome foundational barriers to technology adoption, sustainability, and efficient production processes.\u003c/p\u003e \u003cp\u003eThere is a significant positive correlation between I 4.0 and 5.0 technologies and their impacts on TAF companies, although a few areas show negative impacts, especially in societal sustainability. In production processes, the focus is on integrating advanced manufacturing technologies like IoT, AI, and robotics to enhance efficiency and reduce waste. Supply chain management literature emphasises transparency and traceability, leveraging blockchain and big data analytics to monitor and optimise resource use. Consumer engagement focuses on personalisation and customisation, using digital tools to create tailored products that meet specific customer needs, thereby reducing overproduction and waste.\u003c/p\u003e \u003cp\u003eIndustry 4.0 technologies, such as IoT, AI, and blockchain, have been widely implemented in the TAF industries to support circular economy initiatives. These technologies enable real-time monitoring, predictive maintenance, and precise inventory management, significantly enhancing resource efficiency. For instance, IoT sensors collect data on machinery performance and environmental conditions, which AI algorithms analyse to predict and prevent potential breakdowns, thus minimising downtime and material waste. Blockchain technology ensures transparency and traceability across the supply chain, verifying the origins of materials and ensuring they meet sustainability standards. These technologies collectively contribute to a reduction in waste and improved resource utilisation.\u003c/p\u003e \u003cp\u003eIndustry 5.0 builds upon these advancements by emphasising human-centric innovations. Technologies like collaborative robots (cobots) work alongside human workers to enhance productivity and creativity. Customisation and on-demand manufacturing enabled by AI and robotics reduce waste by producing what is needed. Using 3D printing and digital knitting technologies makes it possible to fabricate intricate designs using minimal materials, thus contributing to the principles of a circular economy. Sustainable materials, including biodegradable fabrics and environmentally friendly dyes, are rising due to material science and biotechnology advancements.\u003c/p\u003e \u003cp\u003eDespite these advancements, several challenges persist, such as the huge capital requirement for infrastructure and training and the seamless integration of different technologies, which involves the establishment of interoperability standards. The increased use of digital technologies raises data privacy and security concerns, necessitating solid measures to safeguard sensitive data and information.\u003c/p\u003e \u003cp\u003eThere are abundant research opportunities in integrating Industry 4.0 and 5.0 technologies in the TAF industries for circular economy enhancement. The major areas are:\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003edeveloping more advanced and affordable IoT devices and AI algorithms explicitly tailored for the TAF industries,\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eexploring blockchain technology's potential for end-to-end supply chain transparency and traceability.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003einvestigating the use of sustainable materials and their compatibility with advanced manufacturing technologies for further innovation and\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003ecreating business models that support circular economy principles, such as product-as-a-service models and digital platforms for second-hand fashion.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cp\u003eIntegrating Industry 4.0 and 5.0 technologies in the TAF industries is driving significant advancements in circular economy initiatives. To further drive these advancements, interdisciplinary collaborations among engineers, data scientists, environmentalists, and business managers are essential. Supportive policies and active cooperation between stakeholders, including manufacturers, retailers, consumers, and policymakers, are crucial for creating a sustainable and resilient fashion industry. Continued innovation and research in this field will be vital to driving positive change and ensuring a responsible and eco-friendly industry for the future.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eTasnim, N. H., Afrin, S., Biswas, B., Anye, A. A., \u0026amp; Khan, R. (2023). Automatic classification of textile visual pollutants using deep learning networks. \u003cem\u003eAlexandria Engineering Journal\u003c/em\u003e, \u003cem\u003e62\u003c/em\u003e, 391\u0026ndash;402. https://doi.org/10.1016/j.aej.2022.07.039\u003c/li\u003e\n\u003cli\u003eKeane, J., \u0026amp; te Velde, D. W. (2008). The role of textile and clothing industries in growth and development strategies. In \u003cem\u003eODI Working Paper\u003c/em\u003e (Issue May). http://www.odi.org/sites/odi.org.uk/files/odi-assets/publications-opinion-files/3361.pdf\u003c/li\u003e\n\u003cli\u003eOgunyemi, A. A., Diyaolu, I. J., Awoyelu, I. O., Bakare, K. O., \u0026amp; Oluwatope, A. O. (2023). Digital Transformation of the Textile and Fashion Design Industry in the Global South: A Scoping Review. In R. A. Saeed, A. D. Bakari, \u0026amp; Y. H. Sheikh (Eds.), \u003cem\u003eTowards new e-Infrastructure and e-Services for Developing Countries\u003c/em\u003e (pp. 391\u0026ndash;413). Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-34896-9_24\u003c/li\u003e\n\u003cli\u003e\u0026Ouml;ndoğan, E. N., \u0026Ouml;ndoğan, Z., \u0026amp; Topuzoğlu, B. (2022). A Study on the Investigation of Sustainability Practices of Global Brands in the Fashion Market. \u003cem\u003eEge Akademik Bakis (Ege Academic Review)\u003c/em\u003e, 393\u0026ndash;412. https://doi.org/10.21121/eab.1104962\u003c/li\u003e\n\u003cli\u003eTsai, W. H. (2018). Green production planning and control for the textile industry by using mathematical programming and industry 4.0 techniques. \u003cem\u003eEnergies\u003c/em\u003e, \u003cem\u003e11\u003c/em\u003e(8), 1\u0026ndash;24. https://doi.org/10.3390/en11082072\u003c/li\u003e\n\u003cli\u003eNasir, A., Zakaria, N., \u0026amp; Zien Yusoff, R. (2022). The influence of transformational leadership on organizational sustainability in the context of industry 4.0: Mediating role of innovative performance. \u003cem\u003eCogent Business and Management\u003c/em\u003e, \u003cem\u003e9\u003c/em\u003e(1), 1\u0026ndash;31. https://doi.org/10.1080/23311975.2022.2105575\u003c/li\u003e\n\u003cli\u003eTseng, M. L., Bui, T. D., Lim, M. K., Fujii, M., \u0026amp; Mishra, U. (2022). Assessing data-driven sustainable supply chain management indicators for the textile industry under industrial disruption and ambidexterity. \u003cem\u003eInternational Journal of Production Economics\u003c/em\u003e, \u003cem\u003e245\u003c/em\u003e, 1\u0026ndash;20. https://doi.org/10.1016/j.ijpe.2021.108401\u003c/li\u003e\n\u003cli\u003eDiyaolu, I. J. (2022). Total Quality Management Practices in Nigerian Textile Industry. In: Msahli, S., Debbabi, F. (Eds) \u003cem\u003eAdvances in Applied Research on Textile and Materials - IX. CIRATM 2020. Springer Proceedings in \u003c/em\u003e\u003cem\u003e \u003c/em\u003e\u003cem\u003eMaterials,\u003c/em\u003e\u003cem\u003e \u003c/em\u003e\u003cem\u003e17\u003c/em\u003e\u003cem\u003e, \u003c/em\u003e234-242. Springer, Cham. https://doi.org/10.1007/978-3-031-08842-1_39\u003c/li\u003e\n\u003cli\u003eRenne, E. P. (2020). Death and the Textile Industry in Nigeria. In \u003cem\u003eDeath and the Textile Industry in Nigeria\u003c/em\u003e. Routledge. https://doi.org/10.4324/9781003058137\u003c/li\u003e\n\u003cli\u003eDemir, K. A., D\u0026ouml;ven, G., \u0026amp; Sezen, B. (2019). Industry 5.0 and Human-Robot Co-working. \u003cem\u003eProcedia Computer \u003c/em\u003e\u003c/li\u003e\n\u003cli\u003eRoblek, V., Thorpe, O., Bach, M. P., Jerman, A., \u0026amp; Me\u0026scaron;ko, M. (2020). The fourth industrial revolution and the sustainability practices: A comparative automated content analysis approach of theory and practice. \u003cem\u003eSustainability (Switzerland)\u003c/em\u003e, \u003cem\u003e12\u003c/em\u003e(20), 1\u0026ndash;28. https://doi.org/10.3390/su12208497\u003c/li\u003e\n\u003cli\u003eDixson-Decl\u0026egrave;ve, S., Balland, P.-A., Bria, F., Charveriat, C., Dunlop, K., Giovannini, E., Tataj, D., Huang, A., Hidalgo, C., Isaksson, D., Martins, F., Mir roca, M., Morlet, A., Renda, A., \u0026amp; Schwaag Serger, S. (2022). \u003cem\u003eIndustry 5.0: A Transformative Vision for Europe - Governing Systemic Transformations towards a Sustainable Industry\u003c/em\u003e (Issue 3). https://doi.org/10.2777/17322\u003c/li\u003e\n\u003cli\u003eSchwab, K. M. (2016). \u003cem\u003eThe fourth industry revolution\u003c/em\u003e. World Economic Forum.\u003c/li\u003e\n\u003cli\u003eGajdzik, B., Grabowska, S., \u0026amp; Saniuk, S. (2021). A Theoretical Framework for Industry 4.0 and Its Implementation with Selected Practical Schedules. \u003cem\u003eEnergies\u003c/em\u003e, \u003cem\u003e14\u003c/em\u003e(4), 1\u0026ndash;24. https://doi.org/10.3390/en14040940\u003c/li\u003e\n\u003cli\u003eBongomin, O., Yemane, A., Kembabazi, B., Malanda, C., Chikonkolo Mwape, M., Sheron Mpofu, N., \u0026amp; Tigalana, D. (2020). Industry 4.0 Disruption and Its Neologisms in Major Industrial Sectors: A State of the Art. \u003cem\u003eJournal of Engineering\u003c/em\u003e, 1\u0026ndash;45. https://doi.org/10.1155/2020/8090521\u003c/li\u003e\n\u003cli\u003eKonstantinidis, F. K., Kansizoglou, I., Tsintotas, K. A., Mouroutsos, S. G., \u0026amp; Gasteratos, A. (2021). The role of machine vision in industry 4.0: A textile manufacturing perspective. \u003cem\u003eIST 2021 - IEEE International Conference on Imaging Systems and Techniques, Proceedings\u003c/em\u003e, 1\u0026ndash;6. https://doi.org/10.1109/IST50367.2021.9651459\u003c/li\u003e\n\u003cli\u003eFlores-Siguenza, P., V\u0026aacute;squez-Salinas, B., Siguenza-Guzman, L., Arcentales-Carrion, R., \u0026amp; Sucozha\u0026ntilde;ay, D. (2022). Indicators to Evaluate Elements of Industry 5.0 in the Textile Production of MSMEs. \u003cem\u003eCommunications in Computer and Information Science\u003c/em\u003e, 85\u0026ndash;100. https://doi.org/10.1007/978-3-031-18272-3_7\u003c/li\u003e\n\u003cli\u003eJavaid, M., \u0026amp; Haleem, A. (2020). Critical components of industry 5.0 towards a successful adoption in the field of manufacturing. \u003cem\u003eJournal of Industrial Integration and Management\u003c/em\u003e, \u003cem\u003e5\u003c/em\u003e(3), 327\u0026ndash;348. https://doi.org/10.1142/S2424862220500141\u003c/li\u003e\n\u003cli\u003eHapponen, A., \u0026amp; Ghoreishi, M. (2021). A Mapping Study of the Current Literature on Digitalization and Industry 4.0 Technologies Utilization for Sustainability and Circular Economy in Textile Industries. In \u003cem\u003eLecture Notes in Networks and Systems\u003c/em\u003e (Vol. 217, pp. 697\u0026ndash;711). https://doi.org/10.1007/978-981-16-2102-4_63\u003c/li\u003e\n\u003cli\u003eCasciani, D., Chkanikova, O., \u0026amp; Pal, R. (2022). Exploring the nature of digital transformation in the fashion industry: opportunities for supply chains, business models, and sustainability-oriented innovations. \u003cem\u003eSustainability: Science, Practice, and Policy\u003c/em\u003e, \u003cem\u003e18\u003c/em\u003e(1), 773\u0026ndash;795. https://doi.org/10.1080/15487733.2022.2125640\u003c/li\u003e\n\u003cli\u003eAkram, S. V., Malik, P. K., Singh, R., Gehlot, A., Juyal, A., Ghafoor, K. Z., \u0026amp; Shrestha, S. (2022). Implementation of Digitalized Technologies for Fashion Industry 4.0: Opportunities and Challenges. \u003cem\u003eScientific Programming\u003c/em\u003e, 1\u0026ndash;17. https://doi.org/10.1155/2022/7523246\u003c/li\u003e\n\u003cli\u003eAbdelmeguid, A., Afy-Shararah, M., \u0026amp; Salonitis, K. (2022). Investigating the challenges of applying the principles of the circular economy in the fashion industry: A systematic review. \u003cem\u003eSustainable Production and Consumption\u003c/em\u003e, \u003cem\u003e32\u003c/em\u003e, 505\u0026ndash;518. https://doi.org/10.1016/j.spc.2022.05.009\u003c/li\u003e\n\u003cli\u003eIslam, M. M., Perry, P., \u0026amp; Gill, S. (2021). Mapping environmentally sustainable practices in textiles, apparel and fashion industries: a systematic literature review. \u003cem\u003eJournal of Fashion Marketing and Management\u003c/em\u003e, \u003cem\u003e25\u003c/em\u003e(2), 331\u0026ndash;353. https://doi.org/10.1108/JFMM-07-2020-0130\u003c/li\u003e\n\u003cli\u003eAbbate, S., Centobelli, P., Cerchione, R., Nadeem, S. P., \u0026amp; Riccio, E. (2023). Sustainability trends and gaps in the textile, apparel and fashion industries. \u003cem\u003eEnvironment, Development and Sustainability\u003c/em\u003e, 1\u0026ndash;28. https://doi.org/10.1007/s10668-022-02887-2\u003c/li\u003e\n\u003cli\u003eKing, W. R., \u0026amp; He, J. (2005). Understanding the Role and Methods of Meta-Analysis in IS Research. \u003cem\u003eCommunications of the Association for Information Systems\u003c/em\u003e, \u003cem\u003e16\u003c/em\u003e(32), 665\u0026ndash;686. https://doi.org/10.17705/1cais.01632\u003c/li\u003e\n\u003cli\u003ePar\u0026eacute;, G., Trudel, M.-C., Jaana, M., \u0026amp; Kitsiou, S. (2015). Synthesizing information systems knowledge: A typology of literature reviews. \u003cem\u003eInformation \u0026amp; Management\u003c/em\u003e, \u003cem\u003e52\u003c/em\u003e(2), 183\u0026ndash;199. https://doi.org/10.1016/j.im.2014.08.008\u003c/li\u003e\n\u003cli\u003ePalvia, P., Leary, D., Mao, E., Midha, V., Pinjani, P., \u0026amp; Salam, A. F. (2004). Research Methodologies in MIS: An Update. \u003cem\u003eCommunications of the Association for Information Systems\u003c/em\u003e, \u003cem\u003e14\u003c/em\u003e(24), 526\u0026ndash;542. https://doi.org/10.17705/1cais.01424\u003c/li\u003e\n\u003cli\u003eXiao, Y., \u0026amp; Watson, M. (2019). Guidance on Conducting a Systematic Literature Review. \u003cem\u003eJournal of Planning Education and Research\u003c/em\u003e, \u003cem\u003e39\u003c/em\u003e(1), 93\u0026ndash;112. https://doi.org/10.1177/0739456X17723971\u003c/li\u003e\n\u003cli\u003eArruda, E. H., Melatto, R. A. P. B., Levy, W., \u0026amp; Conti, D. de M. (2021). Circular economy: A brief literature review (2015\u0026ndash;2020). \u003cem\u003eSustainable Operations and Computers\u003c/em\u003e, \u003cem\u003e2\u003c/em\u003e(May), 79\u0026ndash;86. https://doi.org/10.1016/j.susoc.2021.05.001\u003c/li\u003e\n\u003cli\u003eKristensen, H. S., \u0026amp; Mosgaard, M. A. (2020). A review of micro level indicators for a circular economy \u0026ndash; moving away from the three dimensions of sustainability? \u003cem\u003eJournal of Cleaner Production\u003c/em\u003e, \u003cem\u003e243\u003c/em\u003e, 118531. https://doi.org/10.1016/j.jclepro.2019.118531\u003c/li\u003e\n\u003cli\u003eKazancoglu, Y., Mangla, S. K., Berberoglu, Y., Lafci, C., \u0026amp; Madaan, J. (2023). Towards Industry 5.0 Challenges for The Textile and Apparel Supply Chain for The Smart, Sustainable, and Collaborative Industry in Emerging Economies. \u003cem\u003eInformation Systems Frontiers\u003c/em\u003e, 1\u0026ndash;16. https://doi.org/10.1007/s10796-023-10430-5\u003c/li\u003e\n\u003cli\u003eKarmaker, C. L., Bari, A. B. M. M., Anam, M. Z., Ahmed, T., Ali, S. M., de Jesus Pacheco, D. A., \u0026amp; Moktadir, M. A. (2023). Industry 5.0 challenges for post-pandemic supply chain sustainability in an emerging economy. \u003cem\u003eInternational Journal of Production Economics\u003c/em\u003e, \u003cem\u003e258\u003c/em\u003e, 1\u0026ndash;12. https://doi.org/10.1016/j.ijpe.2023.108806\u003c/li\u003e\n\u003cli\u003eBedi, T., Rana, V., \u0026amp; Gautam, N. (2021). Changing Face of The Apparel Industry by Incorporating Industry 4.0 and Paving Way for Industry 5.0. Turkish Online Journal of Qualitative Inquiry (TOJQI), 12(3), 4814\u0026ndash;4832.\u003c/li\u003e\n\u003cli\u003eRathore, D. B. (2022). Textile Industry 4.0 Transformation for Sustainable Development: Prediction in Manufacturing and Proposed Hybrid Sustainable Practices. \u003cem\u003eEduzone\u003c/em\u003e, \u003cem\u003e11\u003c/em\u003e(1), 223\u0026ndash;241. https://doi.org/10.56614/eiprmj.v11i1.229\u003c/li\u003e\n\u003cli\u003eRathore, D. B. (2023). Textile Industry 4.0: A Review of Sustainability in Manufacturing. \u003cem\u003eInternational Journal of New Media Studies\u003c/em\u003e, \u003cem\u003e10\u003c/em\u003e(1), 38\u0026ndash;43. https://doi.org/10.58972/eiprmj.v10i1y23.41\u003c/li\u003e\n\u003cli\u003eGudi, S., M, P., Alagappan, P., Raigar, O. P., Halladakeri, P., Gowda, R. S. R., Kumar, P., Singh, G., Tamta, M., Susmitha, P., Amandeep, \u0026amp; Saini, D. K. (2024). Fashion meets science: how advanced breeding approaches could revolutionize the textile industry. \u003cem\u003eCritical Reviews in Biotechnology\u003c/em\u003e, \u003cem\u003e0\u003c/em\u003e(0), 1\u0026ndash;27. https://doi.org/10.1080/07388551.2024.2314309\u003c/li\u003e\n\u003cli\u003eGhoreishi, M., \u0026amp; Happonen, A. (2022). The Case of Fabric and Textile Industry: The Emerging Role of Digitalization, Internet-of-Things and Industry 4.0 for Circularity. \u003cem\u003eProceedings of Sixth International Congress on Information and Communication Technology. Lecture Notes in Networks and Systems\u003c/em\u003e, \u003cem\u003e216\u003c/em\u003e, 189\u0026ndash;200. https://doi.org/10.1007/978-981-16-1781-2_18\u003c/li\u003e\n\u003cli\u003eNouinou, H., Asadollahi-Yazdi, E., Baret, I., Nguyen, N. Q., Terzi, M., Ouazene, Y., Yalaoui, F., \u0026amp; Kelly, R. (2023). Decision-making in the context of Industry 4.0: Evidence from the textile and clothing industry. \u003cem\u003eJournal of Cleaner Production\u003c/em\u003e, \u003cem\u003e391\u003c/em\u003e(January), 136184. https://doi.org/10.1016/j.jclepro.2023.136184\u003c/li\u003e\n\u003cli\u003eAhmad, S., Miskon, S., Alabdan, R., \u0026amp; Tlili, I. (2020). Towards Sustainable Textile and Apparel Industry: Exploring the Role of Business Intelligence Systems in the Era of Industry 4.0. \u003cem\u003eSustainability\u003c/em\u003e, \u003cem\u003e12\u003c/em\u003e(2632), 1\u0026ndash;23. https://doi.org/10.3390/su12072632\u003c/li\u003e\n\u003cli\u003eKu, C.-C., Chien, C.-F., \u0026amp; Ma, K.-T. (2020). Digital transformation to empower smart production for Industry 3.5 and an empirical study for textile dyeing. \u003cem\u003eComputers \u0026amp; Industrial Engineering\u003c/em\u003e, \u003cem\u003e142\u003c/em\u003e(January), 1\u0026ndash;11. https://doi.org/10.1016/j.cie.2020.106297\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Footnotes","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003e \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e\u003cspan\u003ewww.grammarly.com\u003c/span\u003e\u003c/span\u003e\u003cspan address=\"http://www.grammarly.com\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.norrag.org/wp-content/uploads/2023/02/List-of-Global-South-and-Global-North-Countries.pdf\u003c/span\u003e\u003cspan address=\"https://www.norrag.org/wp-content/uploads/2023/02/List-of-Global-South-and-Global-North-Countries.pdf\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.socscistatistics.com/tests/pearson/default2.aspx\u003c/span\u003e\u003cspan address=\"https://www.socscistatistics.com/tests/pearson/default2.aspx\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[{"identity":"767b6110-029d-4715-8e41-1437e65aa962","identifier":"10.13039/501100008895","name":"Tertiary Education Trust Fund","awardNumber":"Nigerian Tertiary Education Trust Fund (TETFUND) 2020 Grant Agreement No NRF/CC/EWC00049","order_by":0}],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Obafemi Awolowo University","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Digital transformation, Textile, Apparel and Fashion industries, Circular Economy, Industry 4.0 and 5.0, Sustainability","lastPublishedDoi":"10.21203/rs.3.rs-4804089/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4804089/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe Textile, Apparel, and Fashion (TAF) industries significantly contribute to national gross domestic products but also account for 20% of global pollution. The Industry 4.0 (I 4.0) framework, incorporating technologies like the Internet of Things (IoT), Artificial Intelligence, and robotics, enables smart and efficient manufacturing production, leading to more significant economic outputs. However, it also brings about issues like automation-related tensions, energy efficiency, and waste management and other sustainable practice demands. The Industry 5.0 (I 5.0) framework addresses the issues created by Industry 4.0 in many areas, especially promoting human-centric sustainable practices, social interaction, and a proper synergy between man and machine. This article examined the issues closely based on a systematic review of 42 peer-reviewed studies from 2013 to 2023 exploring the dynamics between technological advancements and sustainable practices in the TAF industries. The review identified technological implementations, circular economy support, and challenges associated with implementing the I 4.0 and 5.0 frameworks. The article analyses significant research using a descriptive literature review to understand the strategies, impact, and challenges of digitalisation and green transition in TAF industries' production and sustainability. The findings reveal a big dichotomy between the Global North and Global South TAF firms, indicating a more contextualised approach is required to integrate I 4.0 and 5.0 approaches and promote sustainable production practices. This study offers a synthesised overview of the current landscape, providing insights for stakeholders, policymakers, and researchers engaged in navigating the TAF industries towards a sustainable, digitally advanced, circular economy future.\u003c/p\u003e","manuscriptTitle":"Digitalisation and Green Strategies: A systematic review of the Textile, Apparel and Fashion Industries","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-07-29 05:24:04","doi":"10.21203/rs.3.rs-4804089/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"6797240e-7844-4184-884d-35184abba768","owner":[],"postedDate":"July 29th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-07-29T05:24:04+00:00","versionOfRecord":[],"versionCreatedAt":"2024-07-29 05:24:04","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4804089","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4804089","identity":"rs-4804089","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}