Search Results (86)

Fast fashion is a rapidly expanding sector characterized by high production volumes, low costs, and short product lifecycles. While recent efforts have focused on improving sustainability within supply chains, consumer behavior remains a critical yet underexplored driver of environmental impacts. This study presents a web-based calculator tool designed to estimate both the carbon and plastic footprints associated with individual fast fashion consumption, with a particular focus on shopping behaviors, garment disposal, and laundry habits. Adopting a geographical perspective, the analysis explicitly considers the spatial dynamics of consumption and logistics within the urban context of Milan (Italy), a dense metropolitan area representative of high fashion activity and mobility. By incorporating user-reported travel patterns, logistics routes, and localized emission factors, the tool links consumer habits to place-specific environmental impacts. By involving over 360 users, the tool not only quantifies emissions and plastic waste (including microfibers) but also serves an educational function, raising awareness about the hidden consequences of fashion-related choices. Results reveal high variability in environmental impacts depending on user profiles and behaviors, with online shopping, frequent use of private vehicles, and improper garment disposal contributing significantly to emissions and plastic pollution. Our findings highlight the importance of integrating consumer-focused educational tools into broader sustainability strategies. The tool’s dual function as both calculator and awareness-raising platform suggests its potential value for educational and policy initiatives aimed at promoting more sustainable fashion consumption patterns. Full article

Fashion products are typically sold through both online and offline channels during two distinct phases: the launch and markdown period. Pricing strategies present significant challenges for manufacturers, particularly as consumers increasingly adopt strategic purchasing behaviors. Key factors, including product fashion utility, purchase timing, and consumer characteristics, complicate manufacturers’ channel selection, pricing decisions, and service strategy formulation—necessitating deeper investigation. This paper establishes a two-echelon supply chain model featuring a fashion manufacturer and a retailer to determine optimal channel, pricing, and service strategies across both selling periods amid strategic consumer behavior. We examine four channel strategies: (1) the MM strategy: the manufacturer operates both channels (online and offline channels) during both periods (launch and markdown period); (2) the MR strategy: the manufacturer operates both channels during the launch stage, and the retailer sells online during the markdown period; (3) the RR strategy: the manufacturer sells offline, and the retailer operates the online channel during both stages; (4) the RM strategy: the manufacturer sells online during both stages, and the retailer sells through the offline channel. Our analysis yields critical insights: When off-season discounts are limited, the manufacturer should maintain direct control of both channels. However, when the off-season discount is significant, the manufacturer needs to set the channel strategy according to the fashion utility. If the fashion utility is small, direct sales through offline channels during the launch period, while entrusting the retailer to distribute in online channels during both periods, should be adopted. If the fashion utility is large, a dual-channel, two-stage, entirely direct sales strategy should be adopted. This study elucidates the optimal manufacturer channel and pricing strategy options and provides some theoretical contributions and practical implications. Full article

In the shift toward sustainable and resource-efficient manufacturing, Artificial Intelligence (AI) is playing a transformative role in overcoming the limitations of traditional production scheduling methods. This study, based on a Systematic Literature Review (SLR), explores how AI techniques enhance Advanced Planning and Scheduling (APS) systems, particularly under finite-capacity constraints. Traditional scheduling models often overlook real-time resource limitations, leading to inefficiencies in complex and dynamic production environments. AI, with its capabilities in data fusion, pattern recognition, and adaptive learning, enables the development of intelligent, flexible scheduling solutions. The integration of metaheuristic algorithms—especially Ant Colony Optimization (ACO) and hybrid models like GA-ACO—further improves optimization performance by offering high-quality, near-optimal solutions without requiring extensive structural modeling. These AI-powered APS systems enhance scheduling accuracy, reduce lead times, improve resource utilization, and enable the proactive identification of production bottlenecks. Especially relevant in high-variability sectors like fashion, these approaches support Industry 5.0 goals by enabling agile, sustainable, and human-centered manufacturing systems. The findings have been highlighted in a structured framework for AI-based APS systems supported by metaheuristics that compares the Industry 4.0 and Industry 5.0 perspectives. The study offers valuable implications for both academia and industry: academics can gain a synthesized understanding of emerging trends, while practitioners are provided with actionable insights for deploying intelligent planning systems that align with sustainability goals and operational efficiency in modern supply chains. Full article

This paper examines the strategic decisions of fashion brands to develop and sell non-fungible tokens (NFTs) within the metaverse. We construct two operational models based on whether NFTs are adopted: the traditional fashion model without NFT (Scenario T) and the digital fashion model with NFT (Scenario D). By comparing the equilibrium outcomes of fashion brands in Scenarios T and D, we derive valuable insights into the implementation of digital strategies in the fashion industry. Our analysis reveals three key findings. First and foremost, the proportion of fashion customers to conventional customers, as well as the digital value and cost of NFTs, are direct factors influencing the adoption of digital strategies by fashion brands. Secondly, opportunistic pricing by manufacturers is an indirect factor influencing fashion brands’ strategic choices, and a fixed production price contract can effectively avoid this case. Finally, we find that personalized pricing and a free NFT strategy are effective tools to boost fashion brands’ digital revenues. Full article

The fashion industry is facing increasing pressure to adopt sustainable practices given its significant impact on the environment. This research aims to analyze the implications of marketing strategies in the fashion industry’s transition towards a sustainable and responsible business model. The study starts from the premise that marketing can influence consumer behavior and turn sustainability into a competitive advantage. To investigate this aspect, SmartPLS software was used and hypotheses were tested on the relationship between marketing strategies to educate and sensitize consumers on sustainability issues in the fashion industry and the creation of a sustainably engaged community. Moreover, it becomes essential to collaborate with non-governmental organizations and other brands that share their sustainability values. The research was based on a sample of 227 respondents, and the data were analyzed using structural equation modeling. The results indicate that marketing strategies that promote transparency in supply chain and production processes, enhance brand reputation and credibility and, promote innovation in sustainable materials and production processes through marketing strategies contributing to creating an engaged community, as well as through brand commitment to sustainability through concrete actions and access to new markets and growth opportunities. Marketing strategies to educate and sensitize consumers on sustainability issues in the fashion industry contribute to increasing consumer interest in sustainable products. The implications of the study highlight the need for coherent marketing approaches to support the sustainable transformation of the fashion industry. Full article

This study focuses on the shift to a circular economy (CE) in the textile, apparel, and fashion (TAF) sectors, which generate tons of waste annually. Thus, embracing CE practices is essential for contributing to UN Sustainable Development Goals. This study employs a mixed-methods approach, integrating PRISMA for systematic literature selection, BERTopic modeling and AI-driven SDG mapping, and case study analysis to explore emerging CE themes, implemented circular practices, and systemic barriers. Machine-learning-based SDG mapping reveals strong linkages to SDG 9 and SDG 12, emphasizing technological advancements, industrial collaborations, and circular business models. Moderately explored SDGs, namely, SDG 8, SDG 6, and SDG 7, highlight research on labor conditions, water conservation, and clean energy integration. Reviewing 655 peer-reviewed papers, the BERTopic modeling extracted six key themes, including sustainable recycling, circular business models, and consumer engagement, whereas case studies highlighted regulatory frameworks, stakeholder collaboration, and financial incentives as critical enablers. The findings advance institutional theory by demonstrating how certifications, material standards, and regulations drive CE adoption, reinforcing SDG 12 and SDG 16. The natural resource-based view is extended by showing that technological resources alone are insufficiently aligned with SDG 9. Using the Antecedents–Decisions–Outcomes framework, this study presents a structured, AI-driven roadmap for scaling CE in the TAF industry, addressing systemic barriers, and supporting global sustainability goals, highlighting how multistakeholder collaboration, digital traceability, and inclusive governance can improve the impact of CE. The results recommend that CE strategies should be aligned with net-zero targets, carbon credit systems, and block-chain-based supply chains. Full article

The fashion industry is undergoing significant transformation through blockchain technology, which enhances product traceability, authenticity, and transactional transparency. This study explores blockchain’s potential to revolutionize the fashion supply chain by enabling detailed monitoring from design and manufacturing to certification, quality control, storage, transportation, and delivery. To assess customers’ readiness to adopt these authenticated products, an innovative model for fashion product traceability and authenticity based on blockchain was proposed. Since the adoption of blockchain models relies on widespread user involvement, it is crucial to examine the factors that motivate individuals to take part. To this end, an acceptance study was conducted using the modified UTAUT2 (Unified Theory of Acceptance and Use of Technology) framework, with data analyzed using SMART PLS software. The results indicate that the proposed blockchain model can improve transparency, authenticity, and customer trust in fashion products. Furthermore, the findings identify expected effort, perceived efficiency, and social influence as key factors influencing blockchain adoption in the fashion industry. These insights show the importance of targeted education and customer engagement strategies for successful implementations of blockchain technology in the fashion industry. Full article

One of the core objectives of the European Green Deal in pursuing climate neutrality and sustainable development is the decarbonization of high-impact sectors. Among the most polluting is the fast fashion industry, driven by linear business models that must urgently transition to circular economy frameworks and decarbonized supply chains. Fast fashion poses significant environmental and social challenges due to its high greenhouse gas emissions, excessive resource consumption, and substantial waste generation. To foster greater sustainability within the sector, this study examines environmental indicators defined by the European Sustainability Reporting Standards (ESRS), in accordance with the EU’s Corporate Sustainability Reporting Directive (CSRD) 2022/2464. Aligned with the Global Reporting Initiative (GRI), these standards aim to harmonize sustainability disclosures and enable better decision-making across environmental, social, and governance (ESG) dimensions throughout Europe. This research focuses on five key environmental aspects—climate change, pollution, water resource management, biodiversity, and circular economy/resource use—across four leading fast fashion brands: Mango, Zara, H&M, and Shein. Using an exploratory web-based methodology, this study evaluates how these companies disclose and implement ESG strategies in their supply chains. The central aim is to assess the sustainability and resilience of their operations, with particular emphasis on communication strategies that support the transition from linear to circular business models. Ultimately, this study seeks to highlight both the progress and persistent challenges faced by the fast fashion industry in aligning with ESG and ESRS requirements. Full article

Efficient supply chain management (SCM) is essential for enterprises seeking to enhance operational efficiency, reduce costs, and mitigate risks while ensuring product quality and customer satisfaction. Addressing quality concerns within the supply chain proactively helps minimize rework, recalls, and returns, leading to significant cost savings and improved profitability. This study presents a machine learning (ML)-driven predictive analytics framework designed to forecast defect rates and optimize quality control processes. The research leverages a dataset sourced from a real-world fashion and beauty startup, hosted in a public repository. The framework employs advanced ML algorithms, including extreme gradient boosting (XGBoost), support vector machines (SVMs), and random forests (RFs), to accurately predict defect rates and derive actionable insights for supply chain optimization. Results demonstrate the effectiveness of predictive analytics in improving supply chain quality management, enabling enterprises to proactively reduce defect rates, minimize costs, and optimize return on investment (ROI). The proposed framework is designed to be scalable and transferable, ensuring adaptability across various industries, including fashion, e-commerce, and manufacturing. These findings underscore the economic and operational benefits of integrating machine learning into supply chain quality control, offering a data-driven, proactive approach to achieving high-efficiency, high-quality supply chain operations. Full article

The present article analyses the theme of circularity in the fashion industry, with particular attention to the role of technology in favoring the adoption of circular economic models. The article explores the role of technology in supporting the circular economy in the fashion industry, focusing on the implementation of circular business models by three companies: SHEIN, Ralph Lauren, and Cotopaxi. The aim is to investigate the challenges and opportunities associated with the adoption of circular economy practices in an industry known for its environmental impact. The research highlights how technology, particularly software such as Materia MX, can facilitate streamlined supply chains, reduced waste, and optimized resource efficiency, thus, overcoming obstacles such as high investment costs and supply chain complexities. The case studies demonstrate how each company has integrated circular practices, such as recycling, reusing materials, and improving transparency through technologies such as AI, blockchain, and IoT, to promote sustainability. Thus, the study emphasizes the importance of technological innovation in enabling a more sustainable and circular future for the fashion industry while addressing challenges related to consumer awareness, regulatory pressures, and infrastructure. The results suggest that technology is a key factor in the fashion industry’s transition to a circular economy, offering a competitive advantage and facilitating the achievement of environmental objectives. Full article

This paper introduces an integrated inventory model that employs customized neural networks to tackle the challenge of non-stationary demand for newsvendor-type products, such as vegetables and fashion items. In this newsvendor context, demand is intertemporal-dependent and influenced by external factors such as prices, promotions, and holidays. Contrary to traditional machine-learning-based inventory models that assume stationary and independent demand, our method accounts for the temporal dynamics and the impact of external factors on demand. Our customized neural network model integrates demand estimation with inventory optimization, circumventing the potential suboptimality of sequential estimation and optimization methods. We conduct a case study on optimizing the vegetable ordering decisions for a supermarket. The findings indicate the proposed model’s effectiveness in enhancing ordering decisions, thereby reducing inventory costs by up to 21.14%. By customizing an integrated neural network, this paper presents a precise and cost-effective inventory management solution to address real-world complexities of demand, like seasonality and external factor dependency. The proposed approach is particularly beneficial for retailers in industries dealing with perishable items and market volatility, enabling them to mitigate waste (e.g., unsold vegetables) and stockouts (e.g., seasonal fashion items). This directly confronts challenges related to sustainability and profitability. Furthermore, the integrated framework provides a methodological inspiration for adapting neural networks to other time-sensitive supply chain problems. Full article

The textile and fashion industry produce a significant amount of solid waste annually. Most of this waste is either disposed of or burned; very little is reused. This research investigates the potential of reusing and recycling remnant fabric to create sustainable luxury fashion products in a circular economy. Focusing on three factories namely Factory A, Factory B, and Factory C, this study analyzes product diversification, capacity, capital investment, supply chain dynamics, and manufacturing operations. A qualitative approach with semi-structured interviews with industry practitioners from the case factories was employed. Data from purchasing orders, production reports, inventory reports, and monthly invoices were analyzed. The factories manufactured a range of sustainable products, emphasizing floor mats, ladies’ handbags, and bedding items (pillow covers). The analysis revealed that each factory achieved a 30–40% profit margin on their monthly sales. Notably, approximately 95% of the remnant fabric was recycled in the production process, with merely 5% being discarded and resold for further use. These findings indicate local employment opportunities and substantial contributions to socio-economic advancement. This study recommends adopting a circular economy model to generate new business opportunities and income streams from remnant fabric. It encourages new financial investment and technical innovations to promote growth in this sector and benefit wider stakeholders. Full article

The fashion industry has a detrimental environmental impact throughout its supply chain operations that needs immediate attention. Limited work focuses on measuring the environmental sustainability of clothing products made from post-consumer waste in the circular economy. This research aims to evaluate the environmental sustainability of fashion products made from post-consumer waste using the Higg Index tool developed by the Sustainable Apparel Coalition. Three t-shirt manufacturers—namely factory A (LEED certified), factory B (non-LEED certified), and factory C (Sub-contract) were considered as case studies. Data were collected through practice-based qualitative questions to manufacturing practitioners, which were supplemented by triangulations, and scores were obtained using the Higg Index product environmental sustainability tool. The findings highlight significant variations in most subsection analyses for product environment sustainability dimensions, scoring 369.5 (73.9%), 277.6 (55.5%), and 153.5 (30.7%) out of 500 by factories A, B, and C, respectively. Findings reveal significant differences in scores (from low to high) for the three t-shirt manufacturers. Various subsections revealed deficiencies in actual policies including product design, materials selection, manufacturing operations, and priorities regarding subcategories. Products made in a green factory and embracing the circular economy achieved the highest score, while the sub-contractor factory product obtained the lowest score. Findings highlight poor and emerging sustainable practices, identify challenges, and suggest improvement in the above-mentioned categories. The research urges stakeholders to embrace sustainable practices for fashion products to reduce environmental impact through life cycle stages and benefit the industry. Full article

With the rise of the green economy and the increasing awareness of consumer sustainability, green marketing has become a global competition strategy adopted by the textile and apparel industry. This study explores key factors and challenges in green marketing to support the industry’s sustainable development. Using the PRISMA 2020 methodology, 54 studies were systematically reviewed to identify core themes: green marketing and branding, consumer behavior and green consumption, circular economy and sustainability, green supply chain management, and textile and fashion culture. The findings reveal that consumers’ understanding of green knowledge and corporate green branding strategies are key factors in the success of green marketing. This review integrates effective strategies for practitioners and new directions for future research. It also highlights the added value of green marketing in fostering sustainability and proposes a conceptual framework for industry application. By addressing gaps in existing research, this study provides actionable recommendations for bridging theory and practice in green marketing. Future research should investigate region-specific dynamics and further develop strategies to enhance the industry’s sustainability efforts. This work contributes to a deeper understanding of the interplay between green marketing and sustainable development in textiles and apparel. Full article

The fast fashion industry significantly impacts the environment at every stage of a product’s life cycle. The transport of products to wholesalers and stores, as one of the stages of the product life cycle, is often overlooked during the analysis of their carbon footprint. Therefore, research is needed on the impact of fast fashion on the environment through its logistics and transport, especially in relation to air emissions. This article attempts to determine how extensively fast fashion companies disclose the impact of their transport operations on their overall corporate carbon footprint, determine the proportion of total emissions attributed to transport, and evaluate the effectiveness of companies’ efforts to mitigate their transport-related environmental impact. This research used a case study method on selected brands (H&M Group, Inditex, Shein), using secondary data available in non-financial reports for 2023. As a result of the analysis of their non-financial reports on transport emissions, several key findings have been presented. Companies predominantly rely on external frameworks and standards, particularly the Global Reporting Initiative. However, the level of detail of the data presented varies. The priority for the surveyed companies is to reduce emissions from significant sources, such as production processes. The data disclosed in the reports (scope 1, 2, 3) must be clearly formulated and can be used to calculate the level of their CO₂ emission reduction or increase. Companies should improve their environmental reporting by including indicators of their transport-related emissions. This approach provides a comprehensive view of their environmental impact, highlighting absolute values and efficiency metrics. Full article