Blockchain Papers

Marc Hübschke, Marius Gros, Benedikt Latos, Elmar Holschbach · 5 authors

Purpose Blockchain technology is widely discussed as an enabler of transparency, efficiency and trust in supply chain management (SCM). However, empirical evidence on which blockchain-related success dimensions translate into value perceptions remains limited. This study aims to examine how the perceived relevance of blockchain success dimensions relates to realized benefits and whether these benefits contribute to overall perceived blockchain value. Design/methodology/approach A quantitative survey of 41 companies with blockchain experience in SCM is conducted. Success dimensions are prioritized using best–worst scaling (MaxDiff). Relationships between perceived relevance, dimension-specific benefits and overall perceived value are analyzed using partial least squares structural equation modeling (PLS-SEM). Exploratory analyses assess company characteristics. Findings Contrary to dominant expectations in academic and practitioner narratives, even highly prioritized blockchain success dimensions fail to translate into measurable firm-level value perceptions. While transparency and traceability are associated with significant dimension-specific benefits, these improvements do not produce statistically significant direct or indirect effects on overall perceived blockchain value. This suggests that localized operational gains alone may be insufficient to generate overall perceived value and indicates that blockchain benefits may depend on broader organizational and technological complements. Originality/value The study moves beyond identifying potential blockchain benefits by empirically differentiating which success dimensions matter and which do not. By combining MaxDiff with PLS-SEM, it offers a structured, mechanism-oriented framework for evaluating blockchain success and highlights boundary conditions for value realization in SCM.

Zhengguo Wang, Jingru Liu, Wenjuan Wang, Wenyu Zhao · 5 authors

With the standardization of the logistics market and advancements in innovation, trust issues arising from information asymmetry among supply chain participants have become increasingly prominent. This paper examines a blockchain-enabled collaborative regulatory system for logistics service supply chains involving the government, logistics enterprises, and the logistics market. Using evolutionary game theory, a three-party evolutionary game model is constructed and validated through system dynamics simulations to explore the impacts of various factors on the collaborative regulatory system. The results indicate that, during the system’s evolution, logistics enterprises stabilize first, followed by the government, with the logistics market converging the slowest. The added value of logistics services is identified as the core factor driving logistics enterprises to adopt blockchain, exhibiting a significantly stronger impact compared to regulatory benefits or improvements in quality and safety. While robust incentive policies can rapidly increase enterprises’ willingness to adopt blockchain technology, they concurrently weaken the government’s enthusiasm for regulation.

Zui Xu, Mei Sha, Dayong Yu, Shuang Li · 6 authors

Introduction Shippers may undeclare maritime dangerous goods as general cargo to avoid the preparation time, documentation burden, and freight premium associated with dangerous goods transportation, creating serious risks for vessel safety, port operations, and the marine environment. This paper examines whether blockchain-enabled documentation can mitigate such undeclaration by improving provenance information and shortening preparation and verification time in the dangerous goods channel. Methods We develop a game-theoretic model with one carrier and a continuum of heterogeneous shippers who differ in their marginal willingness to pay for service level, and in which the carrier endogenously sets the dangerous goods transportation price. We characterize the equilibrium declaration behavior under a benchmark scenario and under blockchain adoption, and extend the model to settings with carrier competition and enhanced detection. Results Undeclaration motives vary with the service environment: when dangerous goods service is relatively low, service-sensitive shippers undeclare to access the faster generalcargo channel; when service is sufficiently high, price-sensitive shippers undeclare to avoid the dangerous goods tariff. The carrier’s profit-maximizing price therefore does not generally coincide with the regulatory objective of zero undeclaration. Blockchain adoption introduces two opposing forces: a service-enhancement effect that encourages truthful declaration and a cost-escalation effect that raises the dangerous goods price. As a result, blockchain reduces undeclaration only when documentation-time savings dominate the price premium induced by adoption costs; when this condition fails, adoption may increase undeclaration and reduce compliant shipper surplus. Discussion Blockchain-enabled documentation is not a universal safety remedy. Its effect on undeclaration depends on the interaction between documentation-time savings and the adoption-driven price increase, and the extensions with carrier competition and enhanced detection show that blockchain deployment should be evaluated jointly with pricing, detection, competition, and regulatory policies.

Martin Končár, Adel Aazami, Sebastian Kummer, Navid Mohammadi

Digital technologies are widely expected to reshape supply chains, yet the conditions under which they deliver operational and sustainability-related value in practice remain insufficiently understood. This study examines how digital technologies influence supply chain operations and performance, where performance is operationalized through demand forecasting accuracy, cost, lead time, visibility, and inter-organizational collaboration, and asks how these technologies can support more resilient, resource-efficient, and sustainable supply chain operations. The study adopts an exploratory qualitative design based on semi-structured interviews with eight supply chain professionals at manager level or above, drawn from the aluminum, elevator, railway, food, and consumer goods industries in Austria, Slovakia, and the Czech Republic, and conducted between March and June 2025. A structured literature review complements the interview evidence. Within this exploratory sample, Artificial Intelligence and Data Analytics were the most widely adopted technologies (five of eight participants each), followed by the Internet of Things (four of eight) and Automation (five of eight), while no participant reported active Blockchain deployment. Reported benefits concentrated on forecasting accuracy, operational efficiency, visibility, and collaboration, whereas high implementation costs, legacy system integration, skill shortages, and regulatory uncertainty formed the principal barriers. The central finding is a conditional relationship between adoption and competitiveness: internal operational gains did not automatically translate into competitive advantage among the professionals interviewed, but appeared to require strategic alignment, cross-functional integration, and performance measurement. Because digitally enabled forecasting, inventory positioning, and resource optimization also reduce waste and improve resource utilization, the findings link digital supply chain transformation to sustainable development objectives. This exploratory study of eight participants therefore provides practitioner-level propositions and a research agenda for digital and sustainable supply chain transformation rather than statistically generalizable findings.

Gulden Zhalbirova, Zhalgaskali Arystanov, Kateryna Zupanets, Zaru Kapassova · 6 authors

Background Scientific output on digital transformation in healthcare and pharmaceutical supply chains increased substantially after 2020, indicating growing research attention to resilient and digitally integrated logistics systems. However, the literature remains fragmented across technologies such as blockchain, artificial intelligence, Internet of Things, predictive analytics, cold chain monitoring and healthcare logistics optimization. Methods This study conducted a bibliometric analysis of scientific publications related to digital transformation and emerging technologies in healthcare and pharmaceutical supply chains. Data were retrieved from Scopus, PubMed and Web of Science databases following PRISMA 2020 screening principles. After duplicate removal and eligibility assessment, 83 peer-reviewed English-language journal articles published between 2015 and 2026 were included in the final analysis. Bibliometric mapping and thematic analysis were performed using VOSviewer and Bibliometrix/Biblioshiny. Results Within the analyzed corpus, the results showed a substantial increase in scientific publications after 2020, consistent with growing research attention to resilient and digitally integrated healthcare supply chains. Blockchain showed the highest visibility in keyword and citation-based analyses, particularly in relation to traceability, transparency and anti-counterfeit systems. Additional major research areas included artificial intelligence, predictive analytics, IoT-based cold chain monitoring and healthcare logistics optimization. Thematic analysis identified strong literature-based associations between digital technologies, supply chain resilience and pharmaceutical traceability systems. Conclusions Digital technologies are increasingly represented in research on healthcare and pharmaceutical supply chain transformation. The findings suggest that blockchain, AI and IoT technologies may support transparency, traceability and operational resilience. However, implementation barriers related to interoperability, infrastructure costs, data privacy and regulatory complexity remain significant challenges. These findings should be interpreted as bibliometric and thematic patterns within the analyzed English-language journal literature rather than direct evidence of technology implementation effectiveness.

Alifta Dicasani, Galih Mahardika Munandar, Muhammad Nur Wahyu Hidayah, Faradhina Azzahra

Post-disaster aid logistics is frequently affected by fragmented data, mismatches between needs and availability, distribution delays, and weak accountability. This study integrates a systematic literature review (SLR) and a proof-of-concept (PoC) to examine how blockchain can support data governance and aid traceability. Articles published in 2020-2025 were retrieved from ScienceDirect and Scopus, selected using PRISMA principles, and synthesized thematically from 31 eligible studies. The synthesis identified coordination and information alignment, along with traceability of goods, funds, beneficiaries, and delivery status, as the dominant issues. Frequently reported mechanisms included distributed ledgers, smart contracts, audit trails, cryptographic identities, and role verification. These findings were translated into a Solidity-based smart-contract PoC on a local Ganache network covering requests, stock, allocation, shipment, receipt validation, and event logs. Three synthetic scenarios and an access-control test showed that valid transactions were recorded, over-allocation was rejected, receipt discrepancies were flagged, and unauthorized operations were reverted. Blockchain is therefore better positioned as an infrastructure for data governance and transaction validation than as a standalone solution to all disaster-logistics problems.

Ugochukwu Damian Umeakubuike

Blockchain and distributed ledger technology (DLT) have been proposed for humanitarian operations because their shared-ledger characteristics may support transparency, traceability, accountability and coordination across organisations. This systematic evidence review synthesises research on operational benefits, adoption barriers, implementation conditions and evidence gaps in humanitarian supply chains. The evidence includes systematic reviews, empirical pilot research, expert-based barrier analysis, case-based design research and implementation-framework studies. Across the literature, the most consistently reported potential benefits are visibility, traceability, transparency, auditability, trust and inter-organisational information sharing. The empirical base is smaller than the conceptual literature, and barriers include regulatory uncertainty, skills and training, sustainability costs, privacy, infrastructure, scalability, interoperability and governance. The review proposes an eight-stage implementation pathway centred on problem diagnosis, technology justification, governance, privacy-aware architecture, piloting, capacity building, evaluation and controlled scaling. The framework is a synthesis proposed by the author from the reviewed evidence, rather than a tested causal model. The review concludes that blockchain should be selected conditionally, where multiple independent actors need a shared auditable record and where the expected coordination value justifies the additional technological and governance complexity.

Yassin Selouani, Ikram El Hachimi, Driss Ouslimane

Le management de la chaîne logistique connaît aujourd'hui de profondes mutations, dans lesquelles la transformation digitale s'impose comme un levier essentiel pour améliorer la traçabilité, renforcer la résilience et accroître la compétitivité des organisations. Si cette évolution est largement documentée dans les économies développées, les connaissances restent encore limitées en Afrique, où les contextes infrastructurels, institutionnels et socio-économiques présentent des spécificités importantes. Cette étude propose une revue systématique de la littérature afin de dresser un état des lieux des recherches consacrées à la transformation digitale des chaînes logistiques sur le continent. En suivant le protocole PRISMA, une recherche menée dans sept sources de recherche documentaire a permis de retenir 56 études parmi 742 références initialement recensées. Les résultats montrent un champ de recherche encore émergent, principalement centré sur l'Afrique du Sud, le Nigeria, le Ghana et le Kenya, ainsi que sur les secteurs agricole et pharmaceutique. La blockchain et l'Internet des objets dominent les travaux, tandis que l'intelligence artificielle et le big data demeurent encore peu mobilisés. Malgré une accélération de l'adoption sous l'effet de la pression concurrentielle et de la COVID-19, des obstacles persistants, telles que l’insuffisance des infrastructures, les coûts élevés, le déficit de compétences et la faiblesse des cadres réglementaires, limitent les bénéfices attendus. Cette étude fournit ainsi une synthèse structurée en langue française d’un corpus principalement anglophone et met en évidence plusieurs pistes pour la recherche et les politiques de digitalisation des chaînes logistiques africaines.

Sharma V A, Shinde S M, Harle S M

The convergence of AI, Blockchain, IoT, Digital Twins, quantum computing, and FKF spectral methods constitutes a decisive research direction for next-generation logistics, especially when examined through the lens of Smart Mobility and Intelligent Transportation Systems [9]. Future work must prioritize real-time fusion of connected-vehicle and smart-infrastructure data streams, construction of scalable multi-resolution Digital Twin environments that span both supply chains and urban mobility networks, development of trustworthy AI models for predictive and prescriptive control, realization of practical hybrid quantum–classical optimizers for the combinatorial problems that dominate intelligent transportation and logistics, and computationally efficient multi-scale spectral analysis of complex temporal dynamics. Empirical validation across transportation, inventory, energy, and disruption scenarios incorporating advances in battery technologies [5], [10], solar-assisted and hybrid vehicle architectures [12], [28], and Industry 5.0 human-centric automation [27]will be indispensable. Realizing these advances will transform intelligent logistics from a conceptual integration into operational, sustainable, and resilient supply-chain systems that fully exploit the emerging capabilities of smart mobility ecosystems [1]–[28].

Sharma V A, Shinde S M, Harle S M

A convergent supply-chain architecture is proposed by integrating Digital Twins with Artificial Intelligence (AI), Blockchain, Internet of Things (IoT), quantum computing, and FKF spectral analysis to address the growing complexity, uncertainty, and disruption risks in modern logistics. Digital Twins enable real-time virtual representation, simulation, monitoring, and disruption-response analysis, while AI extracts predictive and prescriptive intelligence from continuous IoT-generated data. Blockchain strengthens data integrity, transparency, security, and end-to-end traceability across supply-chain transactions. Quantum annealing supports computationally intensive logistics optimization problems, including routing, resource allocation, scheduling, and ULD configuration. FKF-based spectral features provide a mathematical representation of shifts, modulation effects, lead-time behavior, transient variations, and multiscale supply-chain dynamics. The integration of these complementary technologies enables information to flow from real-time sensing and trusted data management to spectral analysis, predictive intelligence, simulation, and advanced optimization. Together, the proposed architecture provides an adaptive, intelligent, sustainable, and resilient framework for monitoring supply-chain conditions, anticipating disruptions, evaluating alternative decisions, and improving overall logistics performance.

Shinde SM, Harle SM

This study introduces an FKF-enabled intelligent supply-chain framework that integrates Artificial Intelligence (AI), Blockchain, Internet of Things (IoT), Digital Twins, and quantum optimization into a unified architecture. The FKF transform provides a mathematical spectral representation of supply-chain signals, enabling the identification of temporal shifts, modulation effects, multiscale patterns, demand fluctuations, and lead-time dynamics. These spectral features can be supplied to AI and machine-learning models to improve forecasting, anomaly detection, disruption prediction, and resilience assessment. IoT devices continuously provide real-time operational data from transportation, inventory, production, and logistics processes, while Blockchain supports secure data sharing, traceability, and transaction transparency across supply-chain participants. Digital Twins complement these technologies by creating dynamic virtual representations of physical supply-chain systems, allowing alternative scenarios, disruptions, and recovery strategies to be simulated before implementation. Quantum annealing is incorporated to address selected computationally intensive combinatorial decisions, such as routing, scheduling, resource allocation, and logistics configuration. By connecting FKF-based mathematical spectral intelligence with AI-driven analytics, trusted digital infrastructure, simulation capabilities, and emerging quantum optimization, the proposed framework provides an integrated pathway toward more predictive, adaptive, transparent, sustainable, and resilient supply-chain management. The content should be logically organized in a single paragraph, maintaining coherence and clarity throughout. Ensure that the abstract captures the research context, problem statement, approach, key results, and final conclusions in a balanced manner. Keywords— Quantum Computing; Quantum Annealing; Logistics Optimization; Unit Load Device Configuration; Supply Chain Management; Artificial Intelligence; Digital Twins; Blockchain; Supply Chain Resilience; FKF Transform.

Kanade U V, Shinde S M, Harle S M

This study proposes a convergent framework integrating AI, Blockchain, IoT, Digital Twins, quantum computing, and FKF spectral analysis for sustainable and resilient supply chains. AI supports prediction and optimization, Blockchain improves transparency and traceability, IoT enables real-time sensing, and Digital Twins facilitate simulation and adaptive disruption management. Quantum annealing addresses selected combinatorial logistics problems, including ULD configuration, while FKF methods characterize temporal shifts, modulation, multiscale dynamics, and lead-time variations. The integration establishes a closed-loop architecture linking sensing, spectral analysis, intelligence, simulation, trust, and optimization for adaptive logistics decision-making.