The KarhunenâLoève (KL) expansion provides an optimal orthogonal series representation of second-order stochastic processes and random fields. This paper presents a complete mathematical formulation of the KL theory using native Office Math Markup Language (OMML) equations, covering the governing Fredholm integral eigenvalue problem, the expansion and its coefficients, truncation error bounds, and the normalized representation. Building upon this theoretical foundation, we systematically explore practical applications of the KL expansion in contemporary supply-chain research themes: quantum-inspired optimization and uncertainty quantification in logistics networks; construction of resilient and adaptive digital twins for global supply chains; stochastic modelling supporting artificial-intelligence and blockchain integration for transparency, traceability and resilience; and uncertainty-aware modelling in sustainable and green logistics. The KL expansion emerges as a rigorous, computationally tractable tool for dimensionality reduction, random-field generation and risk quantification across these domains, thereby bridging classical stochastic process theory with the emerging requirements of Industry 4.0 and quantum-era logistics systems. Keywordsâ KarhunenâLoève expansion; stochastic processes; uncertainty quantification; digital twin; supply chain resilience; quantum logistics; green logistics; AIâblockchain integration.
Open access
Supply Chain Resilience and Risk Management
Risk and Portfolio Optimization
Infrastructure Resilience and Vulnerability Analysis
Purpose To analyze shock transmission, shock absorption and systemic interconnectedness in decentralized cryptocurrency markets by examining how structural differences across cryptocurrency subcategories (green, energy and Bitcoin Gold) influence contagion dynamics and network resilience. Design/methodology/approach This study employs an R-squared decomposed connectedness approach to investigate contemporaneous and lagged spillovers among eight cryptocurrencies that have been classified as green (Cardano, XRP, Polygon and Stellar), energy-centric (Powerledger, Electrify Asia and Sun Contract) and Bitcoin Gold for the period ranging from December 31, 2019 to July 22, 2024. It evaluates directional shock transmission (âTOâ), shock absorption (âFROMâ) and net connectedness to identify the role of individual assets as transmitters and receivers within the network. Additionally, hedge ratios and portfolio weights are calculated to offer insights into diversification potential and hedging effectiveness across cryptocurrency subcategories. Findings The findings indicate a high degree of systemic interconnectedness among closely linked decentralized networks. Contemporaneous connectedness is more pronounced than lagged connectedness, indicating rapid information diffusion within cryptocurrency platforms. Network diffusion analysis identifies Stellar and Cardano as net transmitters (sneezers), and Sun Contract and Electrify Asia as net receivers (catchers), exhibiting systemic risk elevation and diversification capabilities, respectively. Originality/value This study contributes to Information Systems research by integrating digital contagion theory and a socio-technical perspective into the empirical analysis of cryptocurrency platforms. It introduces network-based decomposition of connectedness to differentiate between immediate and persistent contagion and offers one of the initial empirical analyses of heterogeneity across cryptocurrency subcategories. This study connects infrastructure design with contagion dynamics and provides innovative perspectives on governance-by-design, network resilience and systemic vulnerabilities in developing a digital ecosystem.
Abstract Artificial Intelligence (AI) has revolutionized supply chain management by improving decision-making, sustainability, and operational efficiency. Startups in sustainable agriculture are depending more and more on AI-powered technology to boost traceability throughout the agricultural value chain, optimize output, cut waste, and enhance logistics. Businesses have been prompted to include intelligent supply chain systems that reduce environmental impacts while guaranteeing product quality and transparency due to the increased consumer demand for environmentally friendly products. By analyzing recent research, identifying AI applications, talking about implementation issues, and putting forth a conceptual framework for sustainable AI-driven supply chains, this paper investigates the role of AI in supply chain management for eco-friendly products and sustainable agriculture startups. Using a methodical approach to literature research, the study synthesizes information from international organizations, industry publications, and peer-reviewed journals. Demand forecasting, precision agriculture, inventory optimization, cold-chain monitoring, transportation efficiency, blockchain-enabled traceability, and circular economy practices are all greatly improved by AI, according to the results. But obstacles including high implementation costs, inadequate digital infrastructure, cybersecurity issues, and a lack of skilled workers continue to pose serious problems for companies. In order to promote social responsibility, economic viability, and environmental sustainability, the paper suggests an integrated AI-enabled sustainable supply chain framework. Keywords: Artificial Intelligence, Sustainable Agriculture, Supply Chain Management, Eco-Friendly Products, Agriculture Startups, Green Supply Chain, Machine Learning, Blockchain.
The paper provides the Abelian and Tauberian theorems for the generalized Mellin-Whittaker transform. The asymptotic results obtained here are also relevant to emerging interdisciplinary applications that rely on transform methods for the analysis of complex dynamical systems, including smart logistics ecosystems that converge the Internet of Things, artificial intelligence and quantum computing, human-centric automation under the Industry 5.0 paradigm, resilient and adaptive global supply chains enabled by digital-twin technology, and quantum-assisted optimization frameworks for transportation and logistics. Further connections are drawn to battery-management algorithms for electric and hybrid vehicles, blockchain-secured supply-chain transparency, and systematic reviews of supply-chain resilience in the era of digital transformation. In the quantum-computing literature the short-time embedding of continuous dynamics into discrete Ising or QUBO Hamiltonians is a recognized bottleneck. The initial-value theorem guarantees that the leading order asymptotic of the physical signal is correctly represented by the lowest-order terms of the quantum Hamiltonian, thereby improving the quality of the solutions returned by quantum annealers and variational quantum algorithms alike. Key-words: edge computing, IoT-enabled logistics, multi-agent logistics, multi-commodity flows, edge computing, IoT-enabled networks, real-time decision support, digital-twin consistency, supply-chain resilience, Industry 5.0 automation, blockchain audit trails, smart mobility, battery residual capacity, green logistics.
Food supply chains continue to be susceptible to fraud, contamination incidents, and unclear provenance records, which erode consumer confidence and significantly harm the world economy each year. Because blockchain technology provides immutable, shareable, cryptographically verified ledgers among people who distrust each other, it is frequently suggested as a solution. The oracle problem, however, is inherited by the majority of deployed systems: a ledger ensures that recorded data is not altered, but it does not ensure that the data was accurate when it was entered. In addition to reviewing the opportunities it presents for food safety and sustainability reporting, this study examines the technological, financial, and regulatory obstacles of blockchain-based food traceability and proposes a new architecture called the Dynamic Trust-Weighted Oracle Consensus (DTW-OC) framework. We present the architecture, the scoring algorithm, a comparison against Proof-of-Work, Proof-of-Stake, and PBFT, an example dairy cold-chain scenario, and a research agenda for standardisation and interoperability. DTW-OC introduces a reputation-weighted, cross-validated oracle layer that scores every IoT sensor and human data source in real time and feeds that score into block-validator selection, so a source's influence on the ledger is proportionate to its demonstrated reliability.
Supply chain finance (SCF) plays a pivotal role in maintaining liquidity and operational continuity across global value networks. However, systemic supply chain disruptions, macroeconomic volatility, and information asymmetry frequently expose SCF programs to severe friction and default risks. While digital transformation is widely touted as a catalyst for supply chain resilience, empirical evidence regarding the explicit mechanisms through which distinct digital transformation capabilities enhance Supply Chain Finance Resilience (SCFR) remains fragmented. Grounded in the Resource-Based View (RBV), Dynamic Capabilities Theory (DCT), and Information Processing Theory (IPT), this study develops and tests an integrated framework evaluating the direct and indirect impacts of Artificial Intelligence Capability (AIC), Blockchain Capability (BC), and Data Analytics Capability (DAC) on SCFR, mediated by Digital Trust in SCF Platforms (DT).Using a computational research simulation methodology, a respondent-level dataset (N=500) representing supply chain, finance, operations, and IT decision-makers across international enterprises was algorithmically generated under a defensible latent-variable covariance structure. Partial Least Squares Structural Equation Modeling (PLS-SEM) with 5,000 bootstrap resamples was executed to evaluate the measurement and structural models. The structural analysis reveals that AIC (β=0.241,p<.001), BC (β=0.312,p<.001), and DAC (β=0.284,p<.001) significantly and positively drive Digital Trust in SCF Platforms, explaining 54.2% of its variance (R^2=0.542). Digital Trust, in turn, exerts a substantial direct effect on SCFR (β=0.385,p<.001). Furthermore, direct effects on SCFR were confirmed for DAC (β=0.218,p<.001) and AIC (β=0.152,p=.002), whereas the direct link from BC to SCFR was non-significant (β=0.071,p=.158). Formal mediation testing using percentile bootstrapping confirmed that Digital Trust fully mediates the relationship between Blockchain Capability and SCFR, while partially mediating the relationships for AIC and DAC. The overall structural model accounts for 58.6% of the variance in Supply Chain Finance Resilience (R^2=0.586,Q_"predict" ^2=0.412).This methodological prototype advances theoretical understanding by unpacking the granular capability configurations necessary to foster digital trust and financial resilience in supply networks. For practitioners and policymakers, the findings highlight that investing in blockchain technology yields minimal resilience benefits unless coupled with platform-wide digital trust mechanisms, whereas AI and analytics offer dual-pathway benefits across operational and relational domains.
Circular economy (CE) has become prevalent worldwide due to its economic and environmental benefits. More and more practitioners have deployed CE in supply chains to improve operational efficiency and sustainability. Thus, the integration of CE and supply chain has developed a new concept: circular supply chain (CSC). In particular, the CE model could remarkably reduce food waste within food supply chains (FSCs) and diminish the environmental impact from FSCs. Such a FSC that adopts CE principle is known as circular food supply chain (CFSC). However, lack of technical support for effective communication is a significant difficulty in developing CFSCs. Blockchain technology (BCT) could be the potential solution to this problem because of its decentralisation, public information sharing, high traceability and transparency, high security etc. Although many researchers have studied CFSCs in recent years, few studies investigate BCT as an impetus for CFSC development. Therefore, this research will fill this gap, focusing on the feasibility of BCT-based CFSCs, how BCT can establish CFSCs, and possible barriers. In addition, a few recommendations on developing BCT-based CFSCs will be provided.
Blockchain technology offers a distributed, immutable ledger that can improve transparency, traceability and trust among multiple parties in logistics and supply-chain networks. This expanded review synthesises systematic literature from 2018â2025, presents key real-world case studies, quantifies reported benefits, catalogues persistent challenges, and examines the convergence of blockchain with IoT sensors, artificial intelligence and digital twins. Emphasis is placed on food traceability, maritime shipping, pharmaceuticals, sustainability reporting and the socio-technical conditions required for successful adoption. The review also draws on related recent work on supply-chain resilience, digital twins, AIâblockchain integration and emerging quantum approaches to logistics optimization. Illustrative figures and a market-growth curve accompany the analysis. Keywords: Blockchain; Logistics; Supply Chain Management; Traceability; Smart Contracts; Trade Lens; IBM Food Trust; Permissioned Ledger; Interoperability; Digital Twin; IoT Integration; Literature Review; Supply Chain Resilience; Quantum Logistics.
This paper substantiates the theoretical and applied foundations of investment management for forming and developing resilient distribution networks in agribusiness. Under global food market transformations, systemic macroeconomic instability, and geopolitical shocks, conventional linear investment models prove ineffective for long-term planning. To bridge this gap, this research adapts advanced economic frameworks directly to agricultural supply chains, shifting the focus from discrete physical asset valuation to ecosystem-wide synergy. This is achieved by combining classic capital planning with portfolio diversification, real options valuation (ROV), behavioral finance, stakeholder-driven ESG metrics, and decentralized financial tools (DeFi). The study proposes a hierarchical digitization model of the investment process powered by artificial intelligence (AI) and Big Data. This system operates at three spatial levels: national (for comprehensive stress-testing against geopolitical shocks), regional (deploying predictive digital twins of logistics clusters to optimize infrastructure placement), and local (facilitating agile capital allocation and behavioral consumer analysis). This structure ensures capital flows efficiently into highperforming channels while minimizing bottlenecks. To address the trade-off between environmental requirements and financial risks, the study introduces the "Two-Factor Balanced Development Matrix." This model links financial credit scoring with multidimensional ESG profiling. Counterparties are categorized into operational quadrants (e.g., Green Leaders, Traditional Pragmatists, Eco-Startups) to determine customized trade credit lines and commercial terms. Finally, the research outlines integrated risk mitigation instruments, including green trade finance (IFC, EBRD), eco-premium forward contracts, and parametric climate insurance. These measures reduce non-performing loans, lower the cost of capital, and improve the Scope 3 emission rating for distributors.
The digital transformation of agricultural supply chains requires efficient coordination among heterogeneous stakeholders and reliable information exchange across distributed logistics networks. As a key component linking agricultural production and downstream distribution, collaboration between agricultural product distribution and textile packaging enterprises has become increasingly dependent on intelligent communication and data-sharing infrastructures. This study systematically investigates the strategic management mechanisms and implementation pathways for collaborative development by integrating transaction cost economics, complex adaptive systems theory, and network effects theory. A four-dimensional management framework encompassing technological support, organizational coordination, benefit distribution, and risk prevention is established, in which entropy weightâTOPSIS is employed for strategic objective alignment, blockchain-based architectures enable trusted information sharing, Shapley value optimization supports dynamic benefit allocation, and Value-at-Risk (VaR) models facilitate quantitative risk control. The proposed framework further incorporates smart contracts and permission-controlled data interaction to improve collaboration efficiency while preserving data security. The resulting management architecture provides a quantitative and scalable solution for digital supply chain coordination and demonstrates practical value for intelligent logistics systems. Moreover, its distributed information-sharing mechanisms and network-oriented optimization strategies offer methodological references for communication-enabled industrial ecosystems, wireless sensing infrastructures, and electromagnetic information transmission environments requiring reliable multi-node coordination and secure data exchange.
With the rapid advancement of industrial Internet technologies and intelligent wireless sensing infrastructures, efficient data acquisition and information transmission have become fundamental to modern textile supply chain management. The integration of electromagnetic-enabled Internet of Things (IoT) devices, RFID technologies, and intelligent communication networks provides essential support for real-time financial monitoring and digital taxation services. Against this background, this paper investigates the application of intelligent finance and taxation in textile industry supply chains by proposing an integrated framework based on artificial intelligence, blockchain, cloud computing, and IoT technologies. The framework enables transparent financial management, automated tax compliance, dynamic supply chain finance, and end-to-end traceability through seamless integration of operational, financial, and logistics data. Key applications, including blockchain-based material provenance verification, AI-driven credit assessment, automated customs and tax processing, and intelligent risk management, are systematically analyzed. The proposed architecture improves supply chain transparency, operational efficiency, sustainability, and resilience while facilitating data-driven decision-making across textile production and distribution processes. Furthermore, the study demonstrates that intelligent finance and taxation can establish a unified digital ecosystem for financial governance and supply chain collaboration, providing valuable technical references for wireless industrial information acquisition, smart sensing, and communication-assisted digital management in future intelligent manufacturing environments.
A transformação digital tem ampliado a adoção de tecnologias capazes de fortalecer a eficiĂŞncia, a transparĂŞncia e a confiabilidade dos processos organizacionais. Nesse contexto, este estudo propĂľe um modelo conceitual de integração entre a tecnologia blockchain e o Ăndice de ResiliĂŞncia Organizacional (IRO), com o objetivo de fortalecer a produtividade, a eficiĂŞncia empresarial, a governança corporativa e a confiabilidade dos indicadores utilizados na gestĂŁo organizacional. A pesquisa possui abordagem qualitativa, exploratĂłria e descritiva, fundamentada em revisĂŁo bibliogrĂĄfica e anĂĄlise documental sobre blockchain, transformação digital, produtividade, eficiĂŞncia organizacional, governança corporativa e resiliĂŞncia organizacional. O modelo proposto incorpora o blockchain como uma camada tecnolĂłgica de confiança aplicada ao IRO, possibilitando maior integridade, autenticidade, rastreabilidade, transparĂŞncia e auditabilidade dos dados utilizados na avaliação da resiliĂŞncia organizacional. A integração tambĂŠm considera o potencial dos contratos inteligentes para automatizar procedimentos, validar evidĂŞncias, atualizar indicadores e fortalecer mecanismos de controle interno. A anĂĄlise demonstra que informaçþes organizacionais mais seguras e verificĂĄveis podem contribuir para decisĂľes baseadas em evidĂŞncias, redução de vulnerabilidades, melhoria dos processos, fortalecimento da governança e maior capacidade adaptativa das organizaçþes. Como contribuição teĂłrica, o estudo aproxima dois campos ainda pouco integrados na literatura: blockchain e avaliação da resiliĂŞncia organizacional. Conclui-se que a integração entre blockchain e IRO constitui uma proposta inovadora para ampliar as aplicaçþes da tecnologia blockchain na Administração e apoiar o desenvolvimento de organizaçþes mais produtivas, eficientes, transparentes, resilientes e sustentĂĄveis.
In blockchain-enabled supply chain finance, traditional credit risk assessment models suffer from conflicts between data sharing and privacy protection, reliance on static evaluation methods, and limited data credibility. To overcome these challenges, this paper proposes a blockchain-based dynamic credit risk assessment model that integrates privacy computing and intelligent risk monitoring. First, blockchainâs immutability and traceability ensure the authenticity and transparency of supply chain transaction data, effectively mitigating information asymmetry and data tampering. Second, privacy-preserving technologies, including homomorphic encryption based on the Paillier algorithm and zk-SNARKs, enable secure data sharing and validity verification without exposing sensitive enterprise information, thereby improving assessment reliability. Third, a dynamic risk monitoring framework is constructed by combining smart contracts, long short-term memory (LSTM) networks, and an improved dynamic graph neural network (DGNN). LSTM models temporal risk evolution in transaction data, while DGNN captures risk propagation among upstream and downstream enterprises. Smart contracts synchronize transaction states in real time, allowing continuous updates of credit risk levels. The proposed secure information processing and dynamic graph modeling strategy also provides a valuable reference for trustworthy data interaction and intelligent decision-making in distributed electromagnetic sensing and communication networks, where reliable information propagation and adaptive resource management are essential. Experimental results based on a textile supply chain dataset show that the proposed model achieves approximately 94% credit assessment accuracy, outperforming traditional static models by 15%â20%, while maintaining excellent response speed and throughput for dynamic financial decision-making. The proposed framework provides a practical and secure solution for blockchain-based credit risk management and offers methodological insights for data-driven engineering systems requiring secure information fusion and dynamic network analysis.
Background: Despite the growing adoption of hybrid contract models in construction, energy, and agricultural procurement, there remains a significant gap in understanding how lump-sum and unit-price contracts differentially allocate risk across sectors and country contexts. This study addresses this gap by examining risk mitigation strategies through document analysis and thematic synthesis. Objective: The aim of this study was to identify key risk allocation strategies, contractual mechanisms, and the effectiveness of hybrid models in managing uncertainty across developed and developing country contexts. Methods: A qualitative approach based on thematic analysis and cross-case comparison was applied, drawing on 48 peer-reviewed sources published between 2015 and 2025, alongside relevant sector documents and procurement reports. Results: The analysis identified that hybrid contracts reduced cost overrun variability by incorporating performance-based incentives aligned with Expected Utility Theory and Principal-Agent Theory, while developing economies such as Indonesia and Bangladesh exhibited distinct risk profiles requiring adaptive contract mechanisms. However, significant gaps remain, particularly regarding the empirical validation of blockchain-enabled contract enforcement and AI-driven risk prediction, as well as the underrepresentation of developing economy contexts in existing research. Conclusion: The findings carry both scientific and practical implications. Theoretically, this study advances an integrative multi-theory framework combining Expected Utility Theory, Game Theory, and Principal-Agent Theory to analyse contract risk across diverse contexts. Practically, the results provide evidence-based guidance for procurement professionals and policymakers in selecting and designing contract structures that balance cost certainty with adaptive flexibility.
Economic Guarantees of Security (EGS), Internal Resistance Series, Working Paper No. 4 Work in Progress âAugust, 2026 Affiliation: International Institute of Political Philosophy (Kyiv, Ukraine) Author: Prof. Aleksandr Rozenfeld Contact: aleksrozenfeld2021@gmail.com Abstract This paper is part of the research series Internal Economic Resistance within the broader research program Economic Guarantees of Security (EGS). It develops the concept of business economic resistance as an endogenous constraint on military aggression and examines its role within a two-loop model of deterrence. Unlike conventional approaches that regard business primarily as a passive object of wartime mobilization, this study conceptualizes business as a decentralized network of autonomous economic agents possessing independent objectives, assets, contractual obligations, and decision-making authority. The paper argues that the principal source of business resistance lies not merely in expected financial losses but in the anticipated erosion of entrepreneurial freedom, property rights, contractual stability, market access, and institutional predictability. These institutional threats generate rational behavioral responses, including reduced investment, capital flight, production adjustment, contract restructuring, market reallocation, informal economic activity, and business exit. Although these responses rarely take the form of organized political protest, their diffusion through production, financial, contractual, and logistical networks gradually reduces the fiscal, technological, and organizational capacity of the state. The paper introduces the concept of an economic mobilization limit, defined as the point beyond which additional state pressure no longer increases, but instead diminishes, the effective resources available for military mobilization. Particular attention is devoted to the anticipatory nature of business behavior. Economic resistance frequently begins before the outbreak of war, as firms respond to expected sanctions, mobilization measures, regulatory restrictions, and institutional uncertainty. Consequently, well-designed systems of Economic Guarantees of Security can influence expectations at the decision-making stage, activating endogenous economic constraints before military aggression occurs. The proposed framework extends traditional deterrence theory by integrating external economic measures with internally generated behavioral responses of business. It demonstrates how decentralized economic decisions can complement international sanctions and other preventive mechanisms, thereby strengthening both the prevention of aggression and the conditions for its termination. One of the key conclusions of this work is that military aggression can be not only prevented but even stopped not only by external pressure measures but also by the economic behavior of businesses. The work presents and expands on a two-loop deterrence model that links international pressure measures with the internal disobedience of economic agents.
Open access
2 source records
Infrastructure Resilience and Vulnerability Analysis
Purpose This study aims to conduct a comprehensive scientometric review of social sustainability in supply chains, analyzing 970 articles published between 2002 and 2024 from Web of Science (WoS). The research addresses the critical gap in understanding social sustainability aspects compared to environmental dimensions in supply chain literature. Design/methodology/approach The study uses CiteSpace software to create structure-based visualizations and networks, analyzing prominent authors, documents, keywords, journals, countries and institutions in the field. The methodology involves systematic review and bibliometric analysis of the literature to identify key themes and patterns in social sustainability based supply chain research Findings The analysis reveals that research is predominantly concentrated in tier-1 and high-GDP nations. Key industries focusing on social sustainability include agriculture, food and beverage, transportation and logistics, manufacturing, fashion and retail sectors. These sectors primarily address issues such as labor regulations, fair wages and local community involvement and diversity. The study identifies major motor themes through co-citation and cluster analysis. Research limitations/implications The study is limited to articles indexed in WoS, potentially excluding relevant research from other databases. Future research directions should focus on advancing social supply chain management, integrating emerging technologies such as Blockchain, sensors and digital transformation, improving risk management, implementing fuzzy logic decision-making and enhancing transparency. Practical implications The findings provide organizations with insights into implementing social sustainability practices across supply chains. The study offers guidance for industry practitioners on addressing social challenges and integrating sustainable practices into their operations, particularly in areas of labor rights, community engagement and technological integration. Social implications The research highlights the importance of addressing social sustainability in global supply chains and its impact on local communities, labor conditions and societal well-being. It emphasizes the need for greater attention to social aspects of sustainability, particularly in developing nations and lower-tier supply chain partners. Originality/value To the best of the authorsâ knowledge, this study presents the first large-scale scientometric analysis of social sustainability in supply chains, offering a comprehensive overview of the fieldâs evolution from 2002 to 2024. It provides valuable insights for policymakers, firms, society and academia while establishing a roadmap for future research and practical implementation of social sustainability in supply chains.
Ms. Gunavarthani S, Dr. Princy J, Ms. Samyuktha S K
The textile industry has undergone a dramatic change in recent times because organizations are incorporating digital technology solutions for addressing issues related to sustainability and fast-tracking the journey toward a circular economy. These include Digital Product Passports (DPP), blockchain, Radio Frequency Identification (RFID), the Internet of Things (IoT), Artificial Intelligence (AI), and Industry 4.0 technologies, among others. The current research intends to conduct a systematic review of the literature on the topic of digital transformation and sustainability in the textile industry. A Systematic Literature Review (SLR) was conducted following the preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines. In all, 55 peer-reviewed journals from 2020 to 2026 have been reviewed based on a structured selection process and analyzed using the thematic analysis approach. Six themes have been identified in the literature, which are as follows: Digital Product Passport, Digital Traceability Technologies, Industry 4.0 & Artificial Intelligence, Circular Economy Practices and Circular Supply Chains, Sustainability and Environmental, Social & Governance (ESG), and Barriers, Challenges and Future Research Directions. The results show that digital technology greatly improves the traceability of products, efficiency, and resource recycling, facilitating sustainability along the supply chain. Yet, issues such as costly digital technology implementation, inadequate digital infrastructure, the absence of standardization in digital data structures, and organizational readiness hinder digital technologies' broader application. This research fills a gap in the literature in that it identifies a consolidated thematic framework explaining the role of digital technologies in transforming the industry sustainably. The results provide insights useful for academic studies, industry professionals, and policymakers working on sustainable textile ecosystems powered by digital technology.
This study develops and validates an ecological economics framework integrating green logistics practices to enhance supply chain resilience in Southeast Asia's automotive sector. Using mixed-methods analysis of 272 firms across Thailand, Indonesia, and Malaysia, structural equation modelling confirms three hypotheses: green logistics adoption significantly predicts resilience (β=0.38, p 4.0 thresholds flipping green logistics from cost to profit centre. Findings advance dynamic capabilities theory with biophysical limits, resolve triple bottom line tensions, and deliver managerial roadmaps (rail pilotsâFCA trainingâblockchain Scope 3) plus ASEAN policy blueprints (CBAM harmonisation, $500M capacity fund). The framework positions the ASEAN automotive sector for regenerative leadership, converting natural capital from externality to competitive asset amid global decarbonisation pressures. These findings offer actionable insights for managers, investors, and policymakers seeking to align profitability with ecological resilience in emerging economies.
Open access
Supply Chain Resilience and Risk Management
Sustainable Supply Chain Management
Infrastructure Resilience and Vulnerability Analysis
This study investigates the multidimensional impacts of the Industry 5.0 paradigm on logistics and supply chain management, focusing primarily on human-centric digitalization and sustainability dynamics. Within this scope, aspects of operational efficiency, resilience, and financial performance are analysed as complementary dimensions within the thematic synthesis. Emphasising human-centric digitalization, it examines how the integration of advanced technologies with social responsibility principles reshapes supply chain strategies, fosters organisational transformation, and creates competitive advantages in the context of sustainable development. Following the PRISMA protocol, and using the Web of Science Core Collection as the primary database, a systematic literature review of 47 peer-reviewed studies was conducted, mapping thematic linkages among digitalization, resilience, financial outcomes, and sustainability. The synthesis identifies a conceptual framework that positions humanâmachine collaboration as a central enabler for sustainable transformation, enhancing decision-making, adaptability, energy efficiency, carbon footprint reduction, green innovation, and financial outcomes. Findings highlight interconnected pathways through which digitalization generates both operational gains and long-term strategic resilience. This study contributes an original analytical lens that unites human-centric digitalization, sustainability, resilience, and financial performance within a single framework, offering actionable insights for aligning technological innovation with sustainable supply chain strategies. In particular, the study points to practical pathways such as the use of digital twins for resource optimisation, blockchain for supply chain transparency, and AI-driven solutions for emission reduction.
Digital and intelligent fresh-product supply chains increasingly rely on third-party logistics providers (TPLs) to record and disclose transport-process information. However, the TPL bears data-collection and digital-governance costs while capturing only part of the market value created by credible disclosure. This study develops a supplier-led Stackelberg game for a supplierâTPLâretailer supply chain. Contractual terms are negotiated before operation. Conditional on the negotiated contract, the supplier sets the wholesale price, the TPL selects the disclosure level, and the retailer determines the retail price. We derive decentralized equilibria under blockchain and non-blockchain regimes and compare cost-sharing and joint cost-sharing/revenue-sharing contracts. The results show that cost-sharing increases the TPLâs optimal disclosure level, but disclosure upgrades occur through discrete threshold jumps. Blockchain adoption depends jointly on fixed implementation costs and reliability improvements, and cost-sharing alone may not ensure both adoption and high-level disclosure. Introducing revenue-sharing allows the TPL to internalize part of the demand-side value generated by credible disclosure, leading to a Pareto-improving coordination interval for all supply-chain members. The findings provide a mathematical basis for designing incentive-compatible contracts for blockchain-enabled disclosure in digital fresh product supply chains.