Logistics doi: 10.3390/logistics10080191

Authors: Vuk Mirčetić Tijana Đukić Aleksandar Ignjatović Pertini Stefan Milojević Aleksandra Vujko

Background: Algorithmic Operational Control increasingly shapes logistics operations through AI-enabled decision systems, yet its relationships with Human Operational Agency and innovation remain insufficiently understood. This study examines relationships among Algorithmic Operational Control, Leadership Mediation Capacity, Human Operational Agency, and perceived Logistics Innovation Quality. Methods: Survey data were collected from 3613 logistics employees across six Western Balkan countries. A split-sample design combined exploratory and confirmatory factor analyses with structural equation modeling, supplemented by organizational clustering diagnostics, multi-group CFA, and a reduced-overlap sensitivity analysis. Results: Algorithmic Operational Control was negatively associated with Human Operational Agency and Logistics Innovation Quality, whereas Leadership Mediation Capacity was positively associated with Human Operational Agency. Human Operational Agency partially mediated the association between Algorithmic Operational Control and Logistics Innovation Quality and fully mediated the association between Leadership Mediation Capacity and Logistics Innovation Quality. Organizational clustering was negligible, the measurement structure was stable across countries, and sensitivity analysis preserved all principal relationships and mediation effects. Conclusions: The findings indicate that preserving human judgment and adaptive decision-making is associated with more exploratory and long-term-oriented innovation and position leadership as an interpretive mechanism reconciling algorithmic recommendations with human judgment.

Logistics doi: 10.3390/logistics10080190

Authors: Amlan Baruah Mohammad Moshref-Javadi

Background: Generative artificial intelligence (GenAI) has attracted significant attention in supply chain management (SCM) due to its potential to improve data-driven decision-making and operational performance. However, existing studies mainly focus on individual GenAI models or specific supply chain applications, lacking a comprehensive understanding of how different GenAI architectures support decision-making across the supply chain. Methods: This study conducts a systematic literature review using the PRISMA framework to examine the applications of Generative Adversarial Networks (GANs), Transformers, Variational Autoencoders (VAEs), and flow-based models within a six-level supply chain decision-making framework. A total of 692 peer-reviewed publications were analyzed using bibliometric methods, including keyword co-occurrence, temporal and density analyses, and Supervised Embedding Visualization. Results: Current research is concentrated on Transformer and GAN applications, particularly in data analytics, optimization, forecasting, manufacturing, transportation, logistics, and quality management. The analyses also reveal major research themes, the evolution of GenAI in SCM, and limited attention to sustainability, cybersecurity, resilience, and reverse logistics. Conclusions: This study provides a comprehensive overview of GenAI applications in SCM, identifies key research gaps, and offers a foundation for future research while helping practitioners evaluate opportunities and limitations of GenAI for supply chain decision-making.

Logistics doi: 10.3390/logistics10080189

Authors: Aisha Mussabekova Vladislav Galyandin Saltanat Massakova Gulnara Ayazbayeva

Background: Managing agrologistics supply chains under infrastructure scarcity requires integrative, spatially explicit decision-support tools. This study develops a macro-level digital twin of the multimodal agricultural supply chain in Kazakhstan’s Almaty region to optimize freight allocation and guide strategic investment planning. Methods: Our methodology integrates Earth observation data (ESA WorldCover 10 m) with a large-scale multimodal road–rail graph network (1.39 million nodes) to identify 135 crop production clusters. Using linear programming in MATLAB, we optimize the regional distribution of 322.2 thousand tons of seasonal maize, wheat, and soybeans while localizing new storage silos using Green Field Analysis. Results: The baseline simulation reveals a critical storage capacity deficit, yielding a Capacity Coverage Ratio of only 23.8%. However, implementing optimal multimodal rail-road routing mathematically reduces the Logistics Cost Index from 8,642,195 to 4,716,175 units, achieving overall cost savings of 45.4%. Conclusions: The proposed digital twin and its performance metrics provide a scientifically grounded, data-driven toolkit for public–private partnerships, ensuring robust infrastructure investment localization and facilitating the transition toward the Agriculture 4.0 paradigm.

Logistics doi: 10.3390/logistics10080188

Authors: Yue Zhao Jingfeng Dong

Background: Manufacturing productivity increasingly depends on reliable interorganizational flows, yet supply chain disruptions can interrupt materials, information, finance, and efficient use of productive inputs. Although supply chain resilience is widely treated as a continuity capability, its relationship with firm-level total factor productivity remains insufficiently established. Methods: This study uses 22,509 firm-year observations for Chinese A-share listed manufacturing firms from 2009 to 2024. An entropy-weighted resilience index is constructed from adaptability, resistance, recovery capacity, human capital, institutional support. Firm-level revenue productivity is estimated using the Olley Pakes method, and the analysis employs fixed effects regressions, robustness tests, a two-step selection correction test, mechanism regressions, heterogeneity analysis, and dimension-specific tests. Results: Supply chain resilience is positively associated with firm-level total factor productivity, and this association remains robust to alternative productivity and resilience measures, sample restrictions, industry-by-year fixed effects, and selection correction. Resilience is also associated with lower financing constraints and investment inefficiency. The association is stronger for firms with higher managerial incentives, high-technology industries, and competitive markets, while recovery capacity is negatively associated with contemporaneous productivity. Conclusions: Supply chain resilience supports efficient resource utilization, but its productivity value depends on capability composition, timing, and efficient resilience investment rather than maximizing resilience resources.

Logistics doi: 10.3390/logistics10080187

Authors: Joash Mageto Makgosi Tlholwe Hugo van den Berg

Background: Population growth and rising healthcare demand increasingly strain public healthcare in emerging economies. Further, poor supply visibility, frequent stock-outs, and medicine expiry weaken healthcare supply chain performance (SCP) and patient outcomes. In response, the Internet of Things (IoT) offers promising ways to improve the monitoring, distribution, and management of medical supplies. However, empirical evidence on how technological, organisational, and environmental factors influence IoT adoption and its effect on public healthcare SCP remains limited. This study examined factors influencing IoT adoption and its effect on supply chain performance in public healthcare facilities. Methods: Data were collected from 102 respondents drawn from 90 public healthcare facilities. Results: Factors associated with technological factors have the most significant influence on the adoption of IoT in public healthcare SCs. The lack of significance of organisational and environmental factors may be attributed to the early stage of IoT adoption in public healthcare SCs. Conclusions: This study contributes to the theoretical understanding of IoT adoption by highlighting the dominant role of technological factors over organisational and environmental considerations in resource-constrained public healthcare settings. From a practical perspective, the findings encourage policymakers and healthcare managers to prioritise investments in relevant ICT infrastructure to accelerate IoT adoption.

Logistics doi: 10.3390/logistics10080186

Authors: Jugendra Singh Amit Kumar Gupta Imlak Shaikh

Background: The downstream oil and gas processing industry faces substantial environmental, regulatory, safety, and operational risks. Advanced technologies can reduce these risks and improve productivity, compliance, sustainability, and worker safety. However, despite adopting AI, analytics, machine learning, and IoT, Indian firms continue to lag behind their global counterparts, and Industry 5.0 adoption in this sector remains underexplored. Methods: This study examines Industry 5.0 adoption barriers using data from an Indian public-sector oil and gas organisation. Guided by the Resource-Based View and supply chain integration perspective, it applies a mixed-methods design combining a literature review, focus group discussion, Delphi analysis, and Fuzzy ISM–MICMAC. Forty-two barriers were identified and reduced to eight critical barriers. Results: Low technological maturity and lack of value-chain integration emerged as the principal driving barriers, influencing implementation failure, organisational technological readiness, and management commitment, which subsequently affect data quality. Geopolitics emerged as an autonomous barrier with both positive and adverse effects. Conclusions: This study develops a sector-specific framework explaining the hierarchical relationships among technological, organisational, sociotechnical, and value-chain barriers. It extends Industry 5.0 research in hazardous, human–technology-dependent operations and offers practical guidance for a human-centric, sustainable, and resilient transformation.

Logistics doi: 10.3390/logistics10080185

Authors: Ruaa BinSaddig Abdulla Subhi Ruzieh Bahaa Subhi Razia Reem Khamis Bahaa Subhi Awwad

Background: This study investigates information-flow and logistics coordination challenges in Saudi Arabian construction projects within the context of ongoing national infrastructure developments, severe climatic conditions, and evolving labor regulations. Methods: Utilizing the Supply Chain Operations Reference (SCOR) framework, semi-structured interviews were conducted with 29 construction professionals spanning site-level engineers, logistics managers, project executives, and material suppliers over an extended 20-month monitoring period (March 2024–October 2025). Data were analyzed using thematic coding and mapped across the SCOR domains. Results: The findings reveal that logistics vulnerabilities stem predominantly from systemic information-sharing deficiencies, fragmented digital workflows, and weak stakeholder integration rather than standalone material constraints. Operational disruptions across Plan, Source, Make, Deliver, and Return are compounded by region-specific barriers including extreme thermal stress, migrant labor turnover, and high administrative fees for reverse logistics. Conclusions: To address these challenges, the study formulates a tiered, context-adjusted deployment framework for digital integration (BIM, IoT, and real-time tracking) tailored for both large infrastructure schemes and small-to-medium contractors. Staged implementation pathways and policy recommendations are provided to enhance supply chain resilience.

Logistics doi: 10.3390/logistics10080184

Authors: Evripidis P. Kechagias Nikolaos A. Panayiotou Sotiris P. Gayialis Georgios A. Papadopoulos

Background: Global food supply chains have become increasingly complex, sourcing ingredients from multiple countries and intermediaries, creating opportunities for fraud, adulteration, and mislabeling that may compromise consumer safety and market confidence. Digital traceability technologies have been suggested as potential countermeasures, but there is little concrete evidence of their impact in practice. This research presents an assessment of the maturity and effectiveness of these technologies, identifies implementation barriers and security/privacy concerns, and maps research gaps/future directions. Methods: A scoping review of 64 studies from 2023 to 2026 with data extracted from the Scopus and IEEE databases was carried out according to the PRISMA-ScR guidelines and a structured pre-specified data extraction framework. Results: The field is empirically immature, with none of the reviewed solutions offering a provably correct, adversarially tested solution to the oracle problem. There is a lack of alignment between on-chain immutability and GDPR right to erasure and an unequal burden of implementation costs imposed on smallholder producers. Conclusions: A gradual implementation of traceability regulations, along with cost-of-ownership models and harmonized certification measures that do not disadvantage smaller producers are proposed. Finally, field trials, adversarial testing and reporting results in a standardized format, capturing detection performance and implementation costs, are essential.

Logistics doi: 10.3390/logistics10080183

Authors: Małgorzata Grzelak Daniela Voicu Ramona-Monica Stoica Radu Vilau

Background: Predictive maintenance (PdM) is increasingly recognized as essential for reducing fleet downtime and maintenance costs, yet the existing literature on transport and logistics fleets relies predominantly on traditional or single-model approaches, with ensemble methods such as random forest and gradient boosting remaining comparatively underexplored. Methods: This study develops and compares three classification algorithms—logistic regression, random forest and XGBoost—for predicting vehicle maintenance needs. Each classifier was first fitted once on a 70:30 train–test split (35,000/15,000 observations) to support interpretation of coefficients, predictor importance, and ROC curves, using precision, recall, and ROC-AUC. Performance and model ranking were then confirmed for robustness using 5-fold stratified cross-validation with formal significance testing. Results: Reported issues and service history emerged as the dominant predictors of maintenance needs. Random forest and XGBoost achieved comparable predictive performance under 5-fold cross-validation (mean AUC-ROC of 0.8546 and 0.8528, respectively), with the difference between them not statistically significant; both clearly outperformed logistic regression (AUC-ROC 0.8215). Conclusions: By integrating operational and technical data within a validated, comparative machine learning framework, the study provides a practical basis for decision-support systems enabling the reduction of unnecessary interventions, minimizing downtime, and improving fleet cost efficiency and reliability.

Logistics doi: 10.3390/logistics10080182

Authors: Mohammad Hori Bernd Noche

Background: The allocation of non-divisible inbound deliveries across multiple warehouses requires the simultaneous consideration of capacity constraints, category-specific restrictions, workload balance, and long-term allocation consistency. Methods: This study proposes a hierarchical two-level allocation framework combining strategic category-rotation policies, normalized marginal scoring, and signed historical feedback. The algorithm is executed once per day to generate warehouse assignments for the following operational day. Historical correction is based on a rolling window covering the preceding 30 daily planning periods. Results: The framework was evaluated using daily simulation instances ranging from 100 to 1500 pallets, with an average of approximately 130 lots per pallet. Across all evaluated instances, the complete allocation procedure was completed in less than 5 s on the specified test system. The results indicate balanced warehouse utilization, progressive reductions in category–location imbalance, stable historical correction, and preservation of hard operational constraints. Conclusions: The framework provides an interpretable and computationally efficient approach for next-day inbound allocation. By combining explicit feasibility filtering, strategic policy signals, and a 30-day historical correction mechanism, it supports both short-term operational decisions and longer-term allocation balance.

Logistics doi: 10.3390/logistics10080181

Authors: Adeeb Alshakhs Dawn Gregg

Background: Blockchain adoption in supply chain management has attracted growing academic and practitioner attention; yet the impact varies significantly by implementation context, and the knowledge of the conditions driving this variation remain limited. Methods: This study conducts a systematic review of 112 papers to systematically synthesize how blockchain integration affects supply chain management practices (SCMPs), including upstream practices (e.g., supplier partnerships), downstream practices (e.g., customer relationships), and practices spanning both sides of the supply chain (e.g., information sharing and information quality). Results: The review finds that the benefit of blockchain adoption depends on a firm’s supply chain position, cost structures, and market conditions. Two theoretical perspectives are used to interpret these findings: the resource-based view, which viewed blockchain as a capability for integrating processes and transactions across organizations, and the practice-based view, which viewed blockchain as an imitable activity requiring context-specific deployment conditions. Fourteen propositions are developed to guide future research. Conclusions: This review provides practitioner guidance for evaluating when and how blockchain is likely to generate values across different SCMPs. Also, it provides researchers with a theory-grounding agenda for testing the proposed propositions empirically.

Logistics doi: 10.3390/logistics10080180

Authors: Lujain Hussein Alamoudi Mohammad Asif Salam

Background: Smart Parcel Lockers (SPLs) have emerged as an important solution for improving last-mile delivery (LMD) efficiency in response to the rapid growth in e-commerce and increasing consumer demand for flexible delivery options. Despite growing research interest, the factors influencing consumer adoption of SPLs remain fragmented across different theoretical and geographical contexts. This scoping review aims to synthesize the existing literature and identify the key factors influencing consumer adoption of SPLs. Methods: A structured literature search using PRISMA Extension for Scoping Reviews (PRISMA-ScR) guidelines was conducted to identify peer-reviewed studies published between 2014 and 2024. Following the screening and selection process, 38 studies were included in the scoping synthesis. Results: The findings show that convenience, security, service reliability, and perceived value are the most frequently reported factors shaping SPL adoption. The review also shows that the TAM, DOI, and UTAUT are the most commonly applied theoretical frameworks. The studies were also methodologically dominated by quantitative survey-based studies and geographically concentrated mainly in Asian markets. Conclusions: Overall, the review identifies the main drivers and barriers of SPL adoption and provides guidance for future research and practical implementation.

Logistics doi: 10.3390/logistics10080179

Authors: Aizhan Mukhametzhanova Marat Baiseitov Aliya Izbairova Dariga Kushtayeva Gabit Bakyt

Background: Kazakhstan’s railway network exhibits substantial spatial heterogeneity, limiting the effectiveness of uniform freight management strategies and necessitating differentiated analytical approaches to regional transport planning. This study aims to develop a spatially constrained clustering model for railway freight management based on the economic, infrastructural, and operational characteristics of the regions served by KTZh—Freight Transportation LLP. Methods: The proposed methodology integrates principal component analysis (PCA) with a minimum spanning tree (MST) algorithm under railway connectivity constraints. A dataset comprising 20 standardized indicators for 17 regions of Kazakhstan was analyzed. Results: PCA reduced the original variable space to five principal components, explaining 77.9% of the cumulative variance. Cluster validity was confirmed using the Elbow, Silhouette, and Calinski–Harabasz indices, resulting in the identification of six spatially connected transport clusters with distinct functional profiles. The clusters revealed significant regional differences in freight generation, logistics infrastructure, transit potential, and investment characteristics. Conclusions: The proposed framework provides an evidence-based analytical tool for railway freight management, infrastructure planning, and the prioritization of regional development strategies while accounting for spatial connectivity constraints.

Logistics doi: 10.3390/logistics10080178

Authors: Peter Kačmáry Martin Straka

Background: Reducing production time while making efficient use of resources is a key challenge in modern manufacturing, particularly in environments with high variability in specific components for the automotive industry. Methods: This paper presents a hybrid approach to production scheduling that combines linear programming (LP) principles with heuristic decision-making. A structured literature review is conducted to compare exact methods, heuristics, and metaheuristics in terms of their applicability and limitations. Based on this analysis, a hybrid scheduling method is proposed, where LP defines the objective function and constraints, while heuristic rules enable efficient assignment of operations to workstations under capacity limitations. The approach is validated through a case study involving over 900 product variants in an automotive part production system characterized by interchangeable workstations. The proposed heuristic algorithm was tested in terms of real company daily scheduling performance and compared with former scheduling performance. Results: The results show that the proposed approach achieves better solution quality with significantly lower computational effort, while also improving time utilization and production efficiency. Conclusions: The hybrid LP-heuristic approach provides a computationally efficient and practical tool for real-time production scheduling in high-variability manufacturing environments, effectively balancing solution quality and sub-minute execution speed under strict capacity constraints.

Logistics doi: 10.3390/logistics10080177

Authors: Samiullah Khairy Mariano Falcitelli

Background: Maritime container shipping carries over 80% of global trade, yet compliance verification creates a confidentiality–verifiability conflict: carriers treat telemetry as commercially sensitive, while regulators, insurers, and port authorities require verifiable proof that cargo remained within specification. The EU Ecodesign for Sustainable Products Regulation (ESPR) mandates Digital Product Passports (DPPs), but no standardised DPP architecture exists for the multi-stakeholder maritime domain. Methods: We present Ocean DPP, a blockchain-anchored platform combining GS1 EPCIS 2.0, oneM2M, IOTA, and Groth16 zero-knowledge proofs (ZKPs), letting stakeholders verify compliance predicates without revealing raw sensor values; Merkle-tree batching reduces anchoring costs. We evaluate it in 16 experiments on a single-host testbed using synthetic workloads and a local IOTA network. Results: The platform achieved 95th-percentile latency of 48 ms without ZKP and 500 ms with proof generation, throughput of 7 events/s per host, 304 ms mean proof generation and 9.8 ms verification, 100% EPCIS 2.0 compliance, and zero permanent message loss across four failure-injection scenarios; horizontal scaling reduced the median latency by 37%. Conclusions: To the best of our knowledge, Ocean DPP is the first implemented, quantitatively evaluated platform integrating EPCIS 2.0, oneM2M, IOTA, and Groth16 ZKPs for privacy-preserving maritime DPPs; broader multi-host and public-network validation remains for future work.

Logistics doi: 10.3390/logistics10080176

Authors: Jonathan P. Resop John S. Cundiff

Background: The cost to operate a truck fleet to haul feedstock from satellite storage locations (SSLs) to a biorefinery is typically more than 30% of the logistics cost. The use of central control can minimize truck wait times and maximize truck productivity (Mg hauled per day). Methods: An agent-based model, developed in Python 3.12.7, simulated truck hauling operations and SSL loading operations at a 1 min time step for each day in a six-day workweek over a 48-week hauling season for a theoretical biorefinery centered in Gretna, VA, USA. Several truck and SSL operational parameters included stochastic components to allow for random variability (e.g., drive speed and loading rate). Results: The theoretical minimum fleet, assuming no unproductive time, was 6 trucks. Assuming realistic delays, a fleet of 13 trucks could supply the biorefinery with one unloading operation, but unproductive time was over 50% of the hauling day. Conclusions: By adding a second unloading operation at the biorefinery, average truck idle time was reduced, and a fleet of 8 trucks could achieve the same average truck productivity with total unproductive time reduced to about 20% of the total truck fleet operating time.

Logistics doi: 10.3390/logistics10080175

Authors: Mohammed Machkour Abdellah El Barkany Bilal Harras

Background: Manufacturing supply chains must increasingly coordinate cost, environmental impact, and service continuity under demand uncertainty and limited capacity. Methods: This study develops a stochastic mixed-integer linear programming framework for carbon-aware production–distribution planning in an automotive supply chain. The model jointly optimizes production quantities, inventory levels, shipments, truck usage, and lost sales over a multi-period horizon. Demand uncertainty is represented through scenarios, while production- and transportation-related emissions are monetized using an internal carbon price. Lost-sales penalties capture service degradation when demand cannot be fulfilled by the focal plant, and a rolling-horizon analysis evaluates planning responsiveness as demand information is updated. The framework is applied to an industrially inspired, capacity-constrained automotive case with multiple products, production lines, destinations, and demand scenarios. Computational experiments assess carbon pricing, lost-sales penalties, demand volatility, deterministic versus stochastic planning, and rolling-horizon replanning. Results: Results show that carbon pricing mainly acts as an economic valuation mechanism under the studied fixed-structure configuration, whereas lost-sales penalties strongly influence service performance. Demand volatility increases unmet demand, and lower emissions may reflect lower fulfilled demand rather than improved efficiency. Conclusions: The study provides a decision-support framework for evaluating cost–carbon–service trade-offs under stochastic demand while acknowledging single-plant and fixed-routing limitations.

Logistics doi: 10.3390/logistics10080174

Authors: Abishae Noel László Buics Eszter Sós

Background: Lean management is widely recognized as an effective approach to process optimization. However, its implementation in Small and Medium Enterprises (SMEs), particularly in logistics and supply chain contexts, remains challenging. Resource constraints and inconsistent organizational commitment often hinder effective implementation. This study examines Lean adoption, leadership commitment, and implementation outcomes in SME logistics and supply chains. Methods: A mixed-methods design was used, combining a systematic literature review guided by PRISMA and PEO frameworks, followed by a structured survey. A total of 780 valid responses from SME professionals were analyzed using descriptive statistics, correlation, regression, and reliability assessment. Results: Top and middle management commitment was identified as a significant predictor of perceived Lean implementation success. A measurable gap was observed between respondents’ knowledge of Lean methods and their practical application, emphasizing the importance of strategic alignment, organizational culture, and employee engagement. Conclusions: The findings provide practical implications for strengthening Lean implementation in SMEs through enhanced managerial commitment and employee involvement. The study is limited by its focus on SMEs from a single country and the literature retrieved from one bibliographic database. Future research should include broader geographical coverage, multiple databases, and objective organizational performance indicators.

Logistics doi: 10.3390/logistics10080173

Authors: Lujain Hussein Alamoudi Mohammad Asif Salam

Background: The growth of e-commerce has intensified last-mile delivery challenges, including failed deliveries, delivery-time uncertainty, and pressure on urban logistics. Smart parcel lockers (SPLs) offer a technology-enabled out-of-home delivery solution, yet limited evidence explains consumer adoption in Saudi Arabia. This study examines which factors motivate Saudi consumers to adopt SPLs, how trust shapes adoption intention, and whether perceived risk affects intention, using an extended UTAUT2 framework. Methods: Data were collected through an online self-administered questionnaire from 415 residents in Saudi Arabia, and the model was analyzed using partial least squares structural equation modelling (PLS-SEM). Results: Performance expectancy was the strongest determinant of behavioral intention. Effort expectancy, social influence, trust, facilitating conditions, and hedonic motivation also had significant positive effects, whereas price value and perceived risk did not directly influence intention. Conclusions: The study contributes to SPL adoption literature by validating an extended UTAUT2 model in an underexamined Saudi context and highlighting the role of trust in technology-enabled LMD acceptance.

Logistics doi: 10.3390/logistics10080172

Authors: Mahmoud Abdulhadi Alabdali Mohammad Asif Salam Mohammed Abu Jahed Ummee Kulsum

Total quality management (TQM) is widely regarded as a critical approach for improving firm performance, yet evidence on the mechanisms linking TQM practices to financial and market outcomes remains fragmented, with most prior studies testing only direct effects. This study examines whether supply chain resilience (SCR) and corporate social performance (CSP) mediate the relationship between TQM practices and firms’ financial and market performance (FMP). Drawing on the resource-based view and stakeholder theory, a conceptual model was tested using partial least squares structural equation modeling (PLS-SEM) complemented by necessary condition analysis (NCA), based on survey data from 330 firms operating in Saudi Arabia. The results show that TQM practices do not have a significant direct effect on FMP but have strong positive effects on SCR and CSP. SCR and CSP, in turn, significantly predict FMP, and both fully mediate the TQM–FMP relationship (indirect effects: 0.381 via SCR; 0.252 via CSP). The NCA further identifies SCR and CSP as necessary conditions for achieving higher FMP. The findings indicate that the financial and market benefits of TQM are realized through building resilient supply chains and stronger social performance, rather than directly. These results offer managers and policymakers a clearer roadmap for translating TQM investments into sustainable performance outcomes.

Logistics doi: 10.3390/logistics10080171

Authors: Pablo Emilio Mora Lozano Jairo R. Montoya-Torres

Background: The Authorized Economic Operator (AEO) status has become the “gold standard” for supply chain security and trade facilitation under the WCO SAFE Framework. Nevertheless, contemporary certification remain largely limited to binary compliance with minimum requirements, restricting deeper strategic evaluation of security management; Methods: This article develops a Maturity Model for Authorized Supply Chains (MMASC), categorizing operators into five evolutionary levels across eight critical dimensions—Leadership, Risk Management, Collaboration and External Partnership, Access Control and Physical Security, Supply Chain Visibility and Protection, Cybersecurity, Personnel Security, and Security Culture. The model was validated through a two-round modified Delphi process and a post-Delphi consensus workshop with ten regional experts (Cronbach’s α = 0.935), operationalized through a diagnostic engine grounded in Mamdani-type fuzzy logic to reduce subjectivity in qualitative assessments; Results: The framework converts qualitative self-assessment into a crisp 0–100 maturity index, enabling customs administrations and private enterprises to move beyond regulatory compliance while aligning with the 2025 SAFE Framework updates—environmental sustainability, MSME inclusion, and ethical conduct codes; Conclusions: The MMASC provides a basis for a proportional allocation of trade facilitation benefits, such as expedited release, according to an operator’s maturity level, promoting a more resilient, transparent, and sustainable global trade ecosystem.

Logistics doi: 10.3390/logistics10080170

Authors: Rebecca Fussone Rachele Fussone Enrico Favazza Azar Mahmoum Gonbadi Paz Perez Gonzalez Jose Manuel Framinan Salvatore Cannella

Background: Circular economy transitions challenge conventional supply chain design and require decision-support tools that remain valid across multiple plausible futures. However, futures thinking in circular supply chain research is still used mainly as a narrative device, with limited guidance on how to translate scenario insights into model-ready assumptions. Methods: This paper proposes a four-step framework that converts qualitative circular-futures narratives into modelling factors and parameterised assumptions for waste electrical and electronic equipment supply chain simulation and optimisation. Four plausible circular futures, defined along economic-orientation and governance axes, are translated into operational and circularity factors and then into numerical parameters or structural modelling choices. The framework is illustrated through three applications: circular Supply Chain Resilience, electric-vehicle battery remanufacturing, and regional collection and treatment optimisation. Results: Circular strategies create value only when reverse flows, capacity and allocation choices are coherently aligned. Higher return rates improve resilience and material recovery but can destabilise upstream inventories when remanufacturing capacity and return shares are unbalanced. Different scenario priorities lead to distinct trade-offs across cost, emissions, resilience and circularity. Conclusions: This paper offers a replicable pathway for embedding plausible futures into quantitative circular supply chain models and supports more transparent decision-making under uncertainty.

Logistics doi: 10.3390/logistics10080169

Authors: Daniela Ambrosino Veronica Asta Khawar Bashir Costanza Chiesa

Background: Rail operations form a critical interface between maritime container terminals and their hinterlands, yet their operational planning remains underexplored; this paper introduces the Train Unloading Planning Problem (TUPP), which jointly determines the container unloading sequence, gantry crane movements, reach stacker routing, and the assignment of containers to compatible export yard bay-locations. Methods: We propose a mixed-integer linear programming (MILP) formulation that captures the tightly coupled interactions among these decisions, strengthened by valid inequalities that reduce CPU time; because exact optimization becomes intractable at operationally realistic sizes, we develop an Iterated Local Search (ILS) heuristic that refines an MILP warm-start using two complementary neighborhood operators, each embedding a greedy reach stacker re-routing procedure, together with a controlled perturbation mechanism. Results: On 16 groups of benchmark instances reflecting Italian terminal operations, the MILP proves optimality only for the smallest instances, with optimality gaps high on medium sized instances. Averaged across all groups, the ILS improves solution quality by 16.62% over the time-limited MILP while requiring about 360 s of total solution time, roughly a ten-fold reduction, with gains reaching 36.56% on the largest instances. Conclusions: The framework provides an effective decision support tool for operational planning in non-automated maritime container terminals.

Logistics doi: 10.3390/logistics10080168

Authors: Salma Tallaki Mourad Abouelala Abderahmane Kebe Sekoun Faycal Mimouni Mario Di Nardo

Background: The confluence of the Internet of Things (IoT) and the Physical Internet (PI) is a major accelerant of Logistics 4.0, which can provide significant upsides to supply chain performance. While prior reviews mainly concentrated on technological advancement and applications, limited consideration has been given to the mechanisms through which the PI-IoT system integration generates benefits, namely operational, economic, and environmental benefits. Methods: To fill this gap, this study undertakes a systematic literature review of 43 peer-reviewed studies guided by PRISMA. This study uses the Context, Intervention, Mechanism, Outcome (CIMO) framework to automatically conduct a mechanism-based synthesis of PI-IoT integration, unlike previous reviews. Results: The results show that enhanced operational and economic efficiency, as well as environmentally sustainable performance, can be achieved by using real-time visibility, predictive decision-making, collaborative resource optimization, intelligent automation, and adaptive system responsiveness. Conclusions: This review also proposes a conceptual framework, linking contextual conditions, technological interventions, mechanisms, and outcomes, while also identifying important research gaps and future research directions. This study offers essential findings that offer practical insights for researchers, logistics managers, and policymakers intending to implement collaborative, data-driven, and sustainable PI-IoT-enabled logistics systems.

Logistics doi: 10.3390/logistics10070167

Authors: Stefan Ugrinov Sanja Stanisavljev Dragan Ćoćkalo Mihalj Bakator Edit Terek Stojanović Mića Đurđev

Background: Technological innovation in logistics (TIL), logistics capability (LC), and lean logistics (LL) are receiving wider attention in logistics and supply chain research. They are often examined in separate models and less often in relation to both business performance (BP) and market performance (MP). Their joint effects on organizational performance remain insufficiently examined in previous research. Methods: This study examined the effects of TIL, LC, and LL on BP and MP through a quantitative survey conducted among enterprises in Serbia, a transition economy with limited empirical evidence on these relationships. Data from 129 valid responses were analyzed through descriptive statistics, correlation analysis, linear regression analysis, and multicollinearity diagnostics. Results: The findings show that LC has a positive and significant effect on both BP and MP. LL has a positive and significant effect on BP, while TIL has a positive and significant effect on MP. The model explains a larger share of variance in BP than in MP. Conclusions: The results support a differentiated view of logistics transformation and indicate that BP and MP should be examined as related but distinct outcome dimensions. The study integrates TIL, LC, and LL within a unified performance framework.

Logistics doi: 10.3390/logistics10070166

Authors: Gelmar García-Vidal Laritza Guzmán-Vilar Alexander Sánchez-Rodríguez Rodobaldo Martínez-Vivar Reyner Pérez-Campdesuñer

Background: Logistics and supply chain resilience research typically assumes access to formal infrastructure, including contracted fleets, warehouses, inventory systems, and institutionalized contingency mechanisms. Yet many micro and small enterprises (MSEs) in emerging economies operate without these resources, leaving their logistics practices underexplored. This study examines how informal MSEs sustain operational continuity and recover from disruptions through tacit relational support. Methods: Fuzzy-set Qualitative Comparative Analysis (fsQCA) was applied to 400 informal MSEs in Ecuador’s manufacturing, services, and commerce sectors. Tacit network structure was measured using four function-specific name generators covering transport, storage, contingency management, and informal financing. Operational resilience was assessed through order fulfillment rate and recovery time after disruption. Results: No relational-support domain was individually necessary for resilience. Six sufficient configurations were identified, with consistency values from 0.817 to 0.978 and solution coverage of 0.433. One configuration combined contingency-management and informal-financing ties despite low membership in transport and storage networks, indicating a compensatory relational pathway. Conclusions: Tacit personal networks are better understood as context-dependent relational logistics support systems than as substitutes for formal infrastructure. The findings offer bounded, exploratory configurational evidence on selected pathways associated with operational resilience in informal MSEs.

Logistics doi: 10.3390/logistics10070165

Authors: Lydie Beassemda Alim Hamadou Henri Thaddée Mba Gilbert Roméo Hubert Ngock Marius Tony Kibong Marcel Rodrigue Ewodo-Amougou Jean Marie Stevy Sama Jean Gaston Tamba

Background: This study analyses the predictive causal relationships between sesame supply chain performance, participatory governance, and domestic credit to the private sector in Chad. In Sahelian economies, agricultural value chains continue to be subject to structural constraints relating to limited access to finance and substandard institutions. Therefore, understanding the interactions between local governance, productive finance and agricultural performance is essential for guiding public policy. Methods: This analysis uses annual data from 2002 to 2021. Sesame production serves as an indicator of supply chain performance, while participatory governance is assessed using a composite index based on citizen participation, government effectiveness, and regulatory quality. The study applies the Granger causality test in the Toda–Yamamoto sense. Results: The findings reveal unidirectional causality from participatory governance to sesame production. The findings also reveal bidirectional causality between domestic credit to the private sector and sesame production. No direct causality was observed between participatory governance and private credit. Impulse response functions confirm that institutional and financial effects on production manifest themselves gradually over time. Conclusions: These results imply that sustainable development of the sesame sector in Chad necessitates an integrated strategy combining stronger local governance, improved access to private finance, and agricultural value chain modernisation.

Logistics doi: 10.3390/logistics10070164

Authors: Eduardo Molina

Background: Last-mile delivery is the most operationally complex and consumer-visible segment of E-commerce fulfillment, and direct-from-store (DFS) operations account for a growing share of online orders; yet packaging has been studied largely as a materials or sustainability problem rather than as a consumer-facing logistics decision. This study characterizes consumer packaging expectations across fulfillment channels and compares them with observed DFS practice. Methods: We integrate two empirical efforts: an online survey of general E-commerce packaging perceptions (n = 1947 U.S. consumers) and a DFS investigation comprising an exploratory international field audit of 51 orders across four countries and an online DFS consumer survey (314 participants). Analyses include professional–consumer comparisons, internal-consistency and exploratory factor analyses, and a mixed-effects model of the repeated scenario ratings. Results: Consumers consistently prioritize functional protection, content concealment, and practical sustainability over experiential and branding attributes; packaging professionals rate experiential attributes more highly than consumers do (medium-to-large effects); and observed DFS practices frequently misalign with consumer-prioritized functions. Conclusions: The findings support treating packaging as a managed, consumer-centric logistics variable and identify packaging choice at checkout as a feasible lever for DFS providers.

Logistics doi: 10.3390/logistics10070163

Authors: Leonardo G. Hernández Landa Carolina Solís Peña Juan M. Hernández Ramos Jania A. Saucedo Martínez

Background: Traditional value-based ABC inventory classification allocates protection according to economic value, overlooking operational risk factors such as demand variability, lead time, and assembly criticality, and it couples safety-stock and replenishment-cycle decisions. Methods: We propose a Hybrid Risk-Value framework that decouples these two decisions: stock-keeping units (SKUs) are segmented by multivariate K-means clustering on operational risk variables to set safety-stock factors (Z), while ABC economic value sets replenishment cycle coverages (d). The framework is validated through stochastic discrete-event simulation on an anonymized dataset of 200 SKUs from an automotive supplier, under base, high-demand-variability, and extended-lead-time scenarios (20 replications each), and is benchmarked against both classic ABC and a coupled ABC-XYZ policy. Results: Across all scenarios, the Hybrid framework reduces average inventory investment and total logistical cost by approximately 26–28% relative to ABC (p<0.001) while maintaining the service level; stockout days remain statistically unchanged except under extended lead times. The coupled ABC-XYZ benchmark performs almost identically to ABC, indicating that the gains arise from decoupling rather than from variability-based segmentation alone. Conclusions: Decoupling safety-stock and replenishment decisions offers a capital-efficient, data-driven alternative to static financial segmentation for resilient industrial inventories.

Logistics doi: 10.3390/logistics10070162

Authors: Wei Yang Jiaxin Liu

Background: Within the fresh food e-commerce supply chain, government effort-contingent subsidies and logistics partnerships critically impact order pricing, preservation, and profitability. This study aims to investigate how different logistics cooperation modes interact with effort-contingent subsidies to optimize supply chain performance under preservation effort and random demand. Methods: We developed three Stackelberg game models: an independent logistics model, a logistics-supplier cooperation model without subsidies and a logistics-supplier cooperative model with government effort-contingent subsidies. Equilibrium strategies were derived theoretically and analyzed through numerical simulations to examine preservation efforts and pricing. Results: The findings show that logistics cooperation combined with effort-contingent subsidies not only optimizes the decision-making behaviors, but also effectively shares preservation costs and mitigates double marginalization. This synergy incentivizes higher preservation efforts and lowers wholesale prices, ultimately reducing retail prices and expanding market demand. Furthermore, the analysis reveals that excessively optimistic demand forecasts (over-ordering) sharply increase systemic costs, causing a reverse drop in actual demand. Conclusions: Government subsidies are an effective macro-policy tool for alleviating cold-chain costs when integrated with logistics cooperation. To build a sustainable supply chain, e-commerce platforms must adopt prudent ordering strategies, while policymakers should structurally design subsidies to incentivize preservation investments rather than purely subsidizing profits.

Logistics doi: 10.3390/logistics10070161

Authors: Saeed Sadeghi Darvazeh Farzaneh Mansoori Mooseloo Andres Acero Yasel Costa Abdoulmohammad Gholamzadeh Chofreh

Background: Supply chains are becoming increasingly complex, interconnected, and stakeholder-intensive, making effective problem formulation as important as identifying optimal solutions. Although problem structuring methods (PSMs) have been widely applied in logistics and supply chain management (LSCM), the literature remains fragmented, providing limited guidance on selecting appropriate methodologies for different problem contexts. Methods: Following the PRISMA guidelines, this review analyzes 187 journal articles published between 1997 and 2026. Bibliometric analysis and qualitative synthesis were combined to examine the evolution of the field, compare the characteristics and applications of different PSMs, and identify emerging methodological trends. Results: PSM research has grown substantially since 2015, with System Dynamics, Cognitive Mapping, and Scenario Planning emerging as the dominant methodologies. The review also identifies a clear shift toward mixed-methodology approaches that integrate PSMs with optimization, simulation, and multi-criteria decision-making techniques. Stakeholder complexity, uncertainty, and system dynamics were identified as the primary factors influencing methodology selection. Conclusions: This study advances theory and practice by developing an integrated decision-support framework and methodological guidance for selecting appropriate PSMs in complex logistics and supply chain environments. The proposed framework provides a practical roadmap for diagnosing ill-structured problems and choosing suitable problem structuring approaches across diverse supply chain contexts.

Logistics doi: 10.3390/logistics10070160

Authors: Kristína Kováčiková Andrej Novák Martina Kováčiková Alena Novák Sedláčková

Background: The air cargo sector is undergoing significant digital transformation driven by increasing demands for operational efficiency, transparency, and real-time information exchange. Despite the growing importance of digitalization, substantial differences remain in the level of technological implementation and system integration among air cargo carriers, while sector-specific evaluation frameworks remain limited. Methods: This study develops an evaluation framework for measuring digitalization in air cargo logistics. The framework is based on five evaluation pillars and a structured set of indicators covering electronic documentation, real-time shipment tracking, automation, digital communication, technological capabilities, and system integration. A scoring-based assessment methodology was applied to three international air cargo carriers to enable comparative evaluation of their digital maturity. Results: The assessment revealed substantial differences in digitalization among the evaluated carriers, indicating that higher levels of system integration, automation, and real-time data utilization revealed substantial differences in digital maturity among the evaluated carriers. Conclusions: The proposed framework provides a structured, sector-specific approach for assessing digitalization in air cargo logistics. It contributes to existing research by enabling systematic comparison of digital maturity among air cargo carriers while providing a practical benchmarking and decision-support tool for digital transformation initiatives.

Logistics doi: 10.3390/logistics10070159

Authors: Rick Acosta-Vega Nathalia Chaparro-Hernandez Enrique Delahoz-Domínguez

Background: Perishable agri-food supply chains require logistics networks that remain economically viable despite fluctuations in demand, transportation costs, and market prices. This study develops and evaluates a two-echelon logistics network for the banana supply chain in Magdalena, Colombia. Methods: A mixed-integer linear programming model was formulated to maximise daily profit by jointly determining collection-centre activation and product flows among 14 producers, five candidate collection centres, and two commercial buyers. The deterministic solution was complemented by sensitivity analysis and 1000 Monte Carlo optimisation scenarios incorporating variability in demand, transportation costs, and selling prices. Results: Under nominal conditions, all five collection centres were activated, the full demand of 42,000 kg/day was served, and the optimal profit was USD 3093/day. Centres C1–C4 operated at full capacity, whereas C5 reached 42.9% utilization. Under uncertainty, the mean profit decreased to USD 1955.70/day, the mean unmet demand was 954.03 kg/day, and shortages occurred in 72.3% of scenarios. C1–C4 remained the network core, while C5 acted as a flexible contingency facility. Conclusions: The proposed framework reveals an efficiency–resilience trade-off overlooked by deterministic optimisation. Demand growth and capacity reductions are the principal operational risks, supporting investment in collection capacity and proactive demand management.

Logistics doi: 10.3390/logistics10070158

Authors: Zahra Abdalla Sajwani Hamdi Bashir Ridvan Aydin

Background: As supply chains undergo rapid transformation, the concept of supply chain viability (SCV) has gained increasing attention among scholars and industry practitioners. Despite this growing interest, the existing literature lacks a comprehensive and integrated understanding of readiness factors across SCV dimensions. Methods: To address this gap, this study conducted a systematic review of the literature. A total of 86 peer-reviewed articles published between 2015 and 2025 were included, synthesizing evidence across four SCV dimensions: agility, resilience, sustainability, and digitalization. Two-mode network representation was used to analyze cross-dimensional connections between readiness factors and SCV dimensions via degree centrality. Results: The analysis identified 36 readiness factors, 9 of which are cross-dimensional and connected to all four SCV dimensions: leadership and decision-making, stakeholder integration, customer relationship and engagement, partner collaboration and readiness, advanced analytics, technological/digital capabilities, real-time data visibility, information and communication technology infrastructure availability, and digital transformation/smart technologies in supply chain management. Conclusions: Readiness factors vary across SCV dimensions, with some spanning multiple dimensions and others remaining specific. SCV strategies should account for these differences rather than treating factors as uniform.

Logistics doi: 10.3390/logistics10070157

Authors: Leon Döhler Alexander Grahle Michael Görges Marius Held Volkmar Lüthen Diego Fadranski Dietmar Göhlich

Background: Road transport accounts for 73% of transport-related greenhouse gas emissions within the EU, 27% of which are attributable to heavy-duty vehicles. In order to reduce emissions in the area of heavy-duty commercial vehicles, electrifying the fleet offers a perspective. As part of a cooperation between TU Berlin, Siemens and BLG Logistics within the Mobility2Grid research campus, an analysis was carried out to determine how an exemplary BLG depot for regional logistics transport with six diesel trucks can be converted to battery–electric trucks. Methods: This analysis was conducted under a fixed depot schedule with defined dwell times and charging opportunities, with the aim of developing practical recommendations for the acquisition of suitable vehicles and infrastructure. To this end, simulations were carried out using the eFlips consumption and depot simulation software developed at TU Berlin. Results and Conclusions The results show that electrification for regional logistics transport can already be fully implemented with the current state of the art technology and that neither very large batteries nor very high charging powers are required for technically feasible and economically balanced operation. Notably, the cost-optimal battery capacities identified (approximately 200–230 kWh) are currently smaller than those of commercially available 40 t electric trucks, revealing a gap between the model-optimal configuration and present market offerings. Based on the identified optimal configuration, a life cycle assessment (LCA) is conducted to evaluate the environmental impact of fleet electrification. Over a 10-year lifetime, the battery–electric fleet reduces cumulative greenhouse gas emissions by approximately 53% compared to the diesel baseline, with operational-phase savings clearly outweighing higher production-related emissions. The combined techno-economic and environmental assessment provides a structured decision basis for depot-centered fleet electrification.

Logistics doi: 10.3390/logistics10070156

Authors: Amina Meskaoui Hakim Nasaoui Rania Rejjaoui Adil El Amri Abdelhak Sahib Eddine

Background: Global logistics generates 16–25% of greenhouse gas emissions, yet small and medium-sized enterprises (SMEs) in developing economies lack the digital infrastructure to measure and improve their sustainability performance. Business intelligence (BI) systems can support data-driven sustainability decisions, but their application in SME logistics remains poorly understood, and no prior review has examined this intersection with a developing-economy focus. Methods: We conducted a systematic literature review following PRISMA 2020 guidelines, searching Scopus, Web of Science, and IEEE Xplore for peer-reviewed articles published between 2015 and April 2026. Two reviewers independently screened 412 records; 67 studies met inclusion criteria. Results: Five thematic clusters emerged: BI tools for logistics, sustainability KPI frameworks (triple bottom line [TBL], environmental–social–governance [ESG], Sustainable Development Goals [SDGs]), ERP integration, SME-specific barriers, and geographic gaps. Of the studies, 92% originate from developed economies; Africa and MENA remain almost entirely absent. No study combines open-source ERP with a validated TBL KPI framework for logistics SMEs. Conclusions: We propose a six-priority research agenda targeting empirical validation in developing economies and open-source BI solutions for SME logistics sustainability.

Logistics doi: 10.3390/logistics10070155

Authors: Giacomo Lupi Andrea L’Afflitto Riccardo Manzini Gabriele Sirri

Background: Four-way shuttle-based storage and retrieval systems (FSS/RSs) have recently emerged as flexible and scalable solutions for high-density warehousing, enabling shuttle movement in four directions and supporting multi-deep dual-access storage configurations. However, these features increase the complexity of vehicle coordination and collision management. This study proposes a multi-agent path-planning methodology for FSS/RSs with multi-deep dual-access lanes hosting homogeneous items. Methods: An A*-based path-planning framework was developed and integrated with a dynamic collision-management strategy comprising collision detection, priority assignment, and collision avoidance. The methodology was evaluated through a multi-scenario analysis considering different fleet sizes, priority-assignment strategies, safety-area extensions, transaction-entry patterns, and collision-management policies. Results: The results show that fleet size is the most influential operational parameter, significantly affecting throughput, waiting times, and collision frequency. Increasing the number of vehicles improves system productivity but also increases traffic interactions and congestion. The analyses further highlight the effects of safety-area size, priority rules, and transaction-entry patterns on operational performance and system robustness. Conclusions: The proposed methodology effectively combines path planning and collision management in four-way shuttle systems, providing a decision-support tool for evaluating operational trade-offs among throughput, congestion control, and system stability in multi-deep dual-access warehouse environments.

Logistics doi: 10.3390/logistics10070154

Authors: Chanikarn Kamonsook Vatcharapol Sukhotu

Background: Digital Supply Chain Adoption (DSCA) has emerged as a critical strategic imperative associated with Operational Performance (OP); however, empirical evidence within Thailand’s food and beverage manufacturing industry remains limited. Methods: A structured survey of 200 supply chain professionals was analysed using Partial Least Squares Structural Equation Modeling (PLS-SEM) via SmartPLS 4. To ensure analytical rigor and mitigate potential method bias, a retrospective comparison design was employed. Furthermore, DSCA was operationalized as a Type II reflective-formative higher-order construct, and Digital Dynamic Capabilities (DDC) as a Type I higher-order construct. Results: DSCA was positively associated with all perceived performance dimensions, with cost reduction exhibiting the strongest direct association (β = 0.510, p < 0.001). DDC significantly moderated the statistical relationships for quality and productivity performance. However, the moderating role of DDC on cost reduction was not statistically significant. Conclusions: Within the investigated sample, DSCA was more closely associated with perceived cost efficiencies, whereas DDC showed a supplementary moderating role specific to quality and productivity outcomes. The findings are also consistent with the integrated Dynamic Capabilities View and Technology, Organization, and Environment (DCV-TOE) perspective, suggesting that statistical associations between digital transformation and operational outcomes may vary across performance dimensions rather than follow a uniform pattern.

Logistics doi: 10.3390/logistics10070153

Authors: José M. Pérez-Canosa M. Natividad López-López Will van’t Hek Lidia Pérez-López

Background: Current international regulations for cargo securing rely on simplified assumptions that may not accurately represent the behavior of oversized cargo, particularly regarding rotational inertia and ship motion effects, which are critical under tipping conditions. Methods: This study presents a methodology for the evaluation, modeling and optimization of lashing systems for large cargo units. A comparative analysis between the CSS Code and DNV guidelines is performed to assess differences in the estimation of ship-induced accelerations and the resulting tipping moments. Simulations are performed considering different stowage positions, environmental conditions, and lashing configurations. Predictive mathematical models are developed using Design of Experiments (DOE) and Response Surface Methodology (RSM). Results: The results reveal that the CSS Code systematically leads to more conservative requirements, significantly increasing the number of required lashing devices compared to DNV guidelines. The influence of lashing angles is quantified, identifying optimal configurations that minimize securing effort while ensuring tipping equilibrium. The developed models accurately estimate the number of lashing devices required for both longitudinal and transverse tipping. Conclusions: The methodology is validated through a real case study involving heavy offshore cargo and provides practical decision-support tools. The proposed approach enables more efficient and reliable securing design in maritime cargo transport.

Logistics doi: 10.3390/logistics10070152

Authors: Vangelis Malamas Georgios Lagoumitzis Panayiotis Kotzanikolaou Theodore G. Voutsinas Thomas K. Dasaklis

Background: Traceability has gained significant importance in supply chain (SC) networks, especially driven by the growing need for transparency, regulatory compliance and consumer assurance. Although blockchain technology addresses several traditional traceability challenges, granularity, interoperability and integration with existing Enterprise Resource Planning (ERP) systems remain open problems. Methods: In this paper, we propose an integrated blockchain-enabled framework aimed at granular traceability in SC networks. The proposed framework supports the tokenization of the Bill of Materials (BoM) and introduces append-only versioned tokens that capture state evolution across SC stages. Security and privacy are managed through a baseline Role-Based Access Control (RBAC) policy, while EPCIS GS1 event modeling supports standardized data exchange between ERP middleware and the blockchain layer. To examine its applicability, we implement a proof-of-concept prototype and apply the framework to an illustrative wheat SC scenario. Results: Finally, we provide a controlled performance and feasibility analysis based on a private Ethereum/Ganache environment. Conclusions: The results should be interpreted as proof-of-concept evidence of technical feasibility rather than as production-grade validation.

Logistics doi: 10.3390/logistics10070151

Authors: Qian Huang Yao Hu Shunichi Ohmori

Background: The Physical Internet (PI) promotes resource sharing among independent firms. This can improve logistics efficiency, but shared route capacity and limited compensation may also create unequal outcomes among firms. Methods: This study develops a framework for coordinated logistics planning under shared route capacity constraints. The framework includes two coordination rules. Model 3.3 is an efficiency-oriented participation-guaranteeing rule with individual rationality constraints. Model 3.4 is a fairness-oriented rule that minimizes the maximum firm-level disadvantage under a limited compensation budget. Numerical experiments are conducted using a stylized Japanese domestic consumer goods distribution network. Results: Coordinated planning reduces total logistics cost compared with decentralized sequential allocation. Model 3.3 achieves the lowest system cost but gives benefits unevenly. Model 3.4 gives more balanced firm-level outcomes and improves the worst-off firm in the tested scenarios. The results also show that substantial fairness improvements can be obtained through small route-allocation changes. Conclusions: The study shows how two coordination rules can be used in PI-oriented logistics coordination. Model 3.3 is useful when firms need a no-loss guarantee, especially in an early stage. Model 3.4 is useful in a mature or repeated coordination stage, where the platform needs to avoid excessive disadvantage.

Logistics doi: 10.3390/logistics10070150

Authors: James Byrne Paul Liston

Background: Humanitarian logistics decisions extend beyond immediate relief delivery to include preparedness, recovery, service continuity and the development of durable local capabilities. Simulation can support these decisions under uncertainty, yet the evidence remains fragmented across logistics domains, modelling approaches and phases of the relief–development continuum. This review synthesises how simulation has been used in humanitarian logistics and identifies where the evidence is concentrated and where important gaps remain. Methods: A systematic scoping review was conducted in accordance with PRISMA-ScR and PRISMA-S, using multi-disciplinary and specialist database searches supplemented by backward and forward citation searching. Included studies were coded by logistics decision problem, continuum phase, decision level, performance outcome, simulation approach and operational grounding. Results: The literature is concentrated in preparedness and response, particularly around coordination, network design, inventory, allocation, transport and capacity. System dynamics, agent-based modelling and discrete-event simulation are well established, whereas hybrid simulation and digital twin applications remain limited. Early recovery, reconstruction, development-oriented transition and practice-embedded modelling are comparatively underdeveloped. Conclusions: Simulation-supported humanitarian logistics is strongest for structured preparedness and response problems. Future research should connect decisions across phases and strengthen beneficiary-sensitive, operationally grounded modelling of recovery, localisation, service continuity and longer-term logistics capability.

Logistics doi: 10.3390/logistics10070149

Authors: Mohammad Hori Bernd Noche Abbas Horri

Background: Retail product recognition is difficult in practice because products often look similar, class distributions are unbalanced, and annotations usually contain only one observed product label, even though products belong to broader category structures. This study addresses this setting as classification under incomplete supervision rather than as a fully annotated multi-label problem. Methods: We evaluate a lightweight hierarchy-aware recognition framework on a laboratory-based retail dataset containing real supermarket products. Visual features are extracted with a pretrained ResNet-50, reduced by truncated singular value decomposition (TSVD), and classified using class-conditional kernel density estimation (KDE). A coarse-to-fine refinement step first assigns each sample to one of seven product groups and then applies a group-specific classifier. Results: The hierarchical TSVD-KDE model achieved 83.7% Top-1 accuracy, 95.8% Top-5 accuracy, and 71.8% macro-averaged F1-score, improving over the flat TSVD-KDE variant and the convolutional neural network (CNN) linear baseline. For rare classes, the model reached 62.4% Top-1 accuracy while maintaining an average inference time of 6.1 ms per sample. Conclusions: The results suggest that combining compact visual representations with probabilistic classification and a simple product hierarchy can improve balanced recognition performance without relying on a large end-to-end architecture.

Logistics doi: 10.3390/logistics10070148

Authors: Slim Belaid Houssein Ballouk

Background: Artificial intelligence (AI) is increasingly discussed as a means of improving procurement efficiency and supply chain agility, yet its role in supplier evaluation remains insufficiently understood, particularly when decisions depend on fragmented information, cross-functional coordination, explainability, and managerial accountability. This study examines how AI may augment decision-making agility in supplier evaluation. Methods: An exploratory qualitative single-case study was conducted in a large multinational manufacturing company. Data were collected through 18 semi-structured interviews with procurement, logistics, quality, operations, and ERP/process actors, and analyzed through a Gioia-inspired thematic analysis, complemented by a descriptive assessment of theme recurrence. Results: The findings show that supplier evaluation is constrained by informational fragmentation, weak organizational memory, limited explainability, and the need to preserve contextual human judgement. AI was not perceived as a substitute for procurement professionals but as a decision-support infrastructure that may reconnect dispersed supplier knowledge, detect recurring problems earlier, and support transparent recommendations. Conclusions: The study develops a preliminary conceptualization of AI-augmented procurement agility as a bounded, process-level capability composed of AI-enabled supplier sensing, AI-supported interpretive integration, explainable decision support, and human-supervised responsiveness. The findings remain context-dependent and require further validation through comparative and longitudinal research.

Logistics doi: 10.3390/logistics10070147

Authors: Najat Toufah Alaa Eddine El Moussaoui Taoufiq El Moussaoui Marc Ardizio Aymen Benkhouili

Background: This study examines how humanitarian supply chain cooperation in Morocco may evolve toward 2030, focusing on the roles of donors, volunteers, and digital technologies in strengthening coordination, operational performance, and resilience in crisis environments. Methods: A mixed-methods approach was adopted, combining qualitative data collected through interviews and focus groups with 57 humanitarian actors and quantitative data obtained from 1183 logistics professionals. Regression analysis, analysis of variance (ANOVA), and qualitative foresight techniques were employed to explore current cooperation patterns and future humanitarian supply chain dynamics. Results: The findings indicate that donors are increasingly expected to support digital coordination, predictive planning, and capacity-building initiatives rather than acting solely as financial contributors, while volunteers continue to play a critical role in adaptive and decentralized operations, particularly in rural areas. The results further suggest that digital technologies may enhance visibility, responsiveness, and collaboration when integrated into human-centered governance systems. Conclusions: The study proposes an integrated socio-technical framework for understanding humanitarian supply chain cooperation toward 2030 and provides practical insights for developing resilient, collaborative, and future-ready humanitarian logistics systems in emerging economies.

Logistics doi: 10.3390/logistics10070146

Authors: Klavdij Logožar

Background: High-technology supply chains are interdependent logistics infrastructures in which digital, energy, manufacturing, cloud and orbital layers are tightly coupled. This paper examines how such layering changes supply chain resilience and systemic vulnerability. Methods: The paper develops an analytical–conceptual framework linking supply chain resilience, interdependent infrastructure theory and network topology. It introduces Topological Phase Vulnerability (TPV), capturing proximity to structural fragility thresholds, and the Shock Amplification Coefficient (SAC), conceptualizing disruption amplification as a function of centrality concentration, cross-layer coupling and reconfiguration capacity. The framework is supported by a fuzzy-inspired diagnostic scorecard and stylized assessment of alternative infrastructure configurations. Orbital computing is an extreme illustrative context because it combines dependence on advanced semiconductor fabrication, hyperscale cloud orchestration, energy systems, launch capacity and logistics coordination. Results: Highly centralized configurations are more likely to transform local disruptions into cross-layer cascades, whereas modular and distributed configurations are more likely to contain disruption through redundancy, substitutability and rerouting. Conclusions: Resilience in next-generation logistics infrastructure depends not only on capacity or component reliability, but also on topology. Centrality dispersion, modularity and reconfiguration capacity are critical design principles for reducing shock amplification in high-technology supply chains.

Logistics doi: 10.3390/logistics10070145

Authors: Tawinan Simajaruk Jirapan Liangrokapart

Background: Strengthening, coordinating, and sustaining rail freight services is one of the most pressing yet neglected challenges for transport planners in developing economies. Focusing on the institutional context of Thai freight transport, we assess, contextualise, and rank the collaboration factors shaping rail freight competitiveness. Methods: MICMAC analysis was selected because it does not require a pre-defined hierarchical structure and it is computationally straightforward for small variable sets. A qualitative study comprising semi-structured interviews was conducted with participants from government transport agencies, the State Railway of Thailand (SRT), private logistics operators, and freight customers. Results: The analysis extracted six key factors: government policy, finance, infrastructure readiness, technology and innovation, information sharing, and human resource capacity. Government policy alone occupies the driving quadrant, exerting the strongest systemic influence. Finance and infrastructure readiness emerge as linkage factors, simultaneously shaping and being shaped by the wider network, whereas technology, information sharing, and human resources sit in the dependent quadrant and respond to change. Conclusions: This classification allows us to derive seven collaboration strategies matched with appropriate policy levers; the three highest-ranked are policy collaboration, economic instruments, and multimodal integration.

Logistics doi: 10.3390/logistics10070144

Authors: Alaa Eddine El Moussaoui Taoufiq El Moussaoui Najat Toufah Marc Ardizio

Background: The rapid digitalization of warehouse logistics has accelerated the adoption of smart industrial robots, AI-driven autonomous vehicles, and drones to enhance operational coordination and supply chain performance. However, empirical evidence regarding the combined operational implications of these autonomous technologies within emerging-market logistics environments remains limited. This study examines the relationships between autonomous warehouse technologies and perceived warehouse operational performance in Morocco. Methods: A quantitative cross-sectional research design was employed using survey data collected from 1753 logistics professionals operating in warehouses connected to the SoftLogistics platform. Exploratory Factor Analysis (EFA), Confirmatory Factor Analysis (CFA), and Structural Equation Modeling (SEM) were conducted to evaluate the proposed relationships and interaction effects among the study constructs. Results: The findings indicate that smart industrial robots, AI-driven autonomous vehicles, and drones are positively associated with perceived warehouse operational efficiency, operational accuracy, and warehouse resilience. The results further suggest that technological interoperability, workforce readiness, organizational adaptation, and AI-enabled coordination mechanisms may enhance the operational contribution of autonomous warehouse systems. Conclusions: Autonomous logistics technologies are associated with smart warehouse transformation and improved operational performance within emerging logistics environments. The findings also highlight the importance of organizational and socio-technical readiness for the implementation of autonomous warehouse technologies.

Logistics doi: 10.3390/logistics10070143

Authors: Aizhan Kamysbayeva Alisher Khussanov Botagoz Kaldybayeva Oleksandr Prokhorov Zhakhongir Khussanov Saule Bekzhanova Marat Sabyrkhanov Aikerim Issayeva

Background: Agricultural logistics in Kazakhstan is critical for export-oriented supply chains, but its resilience is limited by infrastructure constraints, fluctuating export demand, and insufficient coordination between market and logistics processes. Methods: This study develops a conceptual multi-level model of the agricultural logistics system and a hybrid simulation model combining system dynamics and discrete-event simulation to analyze intermodal transportation under demand and capacity constraints. The model integrates demand formation, storage, transport, and export operations, as well as feedback mechanisms between fulfilled demand, repeat orders, and logistics performance. The model is implemented in AnyLogic 8.9. Results: The conceptual model structures the interaction of key participants, logistics facilities, and infrastructure levels within Kazakhstan’s agricultural logistics system. Simulation experiments reproduce cyclic logistics behavior and show that reduced logistics capacity increases the demand gap and system pressure, while stronger market signals intensify demand and infrastructure load. The results confirm that resilience depends on the balance between demand activation, logistics capacity, and replenishment policy. Conclusions: The proposed approach provides a tool for analyzing the resilience of agricultural intermodal logistics in Kazakhstan and supports scenario-based evaluation of infrastructure and market factors. The novelty lies in combining a conceptual multi-level logistics model with hybrid simulation of demand and logistics flows.

Logistics doi: 10.3390/logistics10070142

Authors: Nicoletta González-Cancelas Beatriz Molina-Serrano Francisco Soler-Flores Javier Vaca-Cabrero

Background: Sustainable and smart logistics transition requires tools that connect environmental, energy, social and digital performance with transport structure. This study proposes an exploratory classification framework for European maritime–port logistics systems using Eurostat-based country-year observations. Methods: A composite transition profile was constructed from environmental, energy, social and digital indicators using min–max normalization, equal weighting and tercile classification into low, medium and high profiles. A shallow decision tree classifier was applied to identify transport, modal structure and maritime–port activity variables that discriminate between profiles. Results: Road freight transport intensity was the main discriminator, followed by inland passenger modal structure variables. Maritime–port activity variables were included in the initial predictor set but were not retained by the final tree, indicating that transition profiles are more strongly differentiated by inland logistics and modal configuration at the country-year level. The model showed moderate performance, with a five-fold cross-validated accuracy of 0.561, above the majority-class baseline. Conclusions: The framework provides an interpretable diagnostic tool for identifying logistics-related transition patterns and supporting sustainable logistics planning. Its exploratory scope and data limitations are explicitly acknowledged.

Logistics doi: 10.3390/logistics10060141

Authors: Nidal Mansouri Aziz Soulhi

Background: Evaluating centralized purchasing performance is a complex multi-criteria decision-making problem involving uncertainty, linguistic assessments, and subjective judgments from internal clients. Existing approaches provide limited support for handling these characteristics simultaneously. Methods: This study proposes a Mamdani fuzzy inference model integrating four criteria: Service Quality, Responsiveness, Compliance, and Collaboration. The fuzzy rule base was developed using expert knowledge and organizational evaluation practices. The model was applied to a real industrial case study based on an annual evaluation conducted collaboratively by four internal evaluators. Results: The model transformed qualitative assessments into an interpretable performance score while capturing interactions among evaluation criteria and handling uncertainty in the evaluation process. Conclusions: The proposed approach provides a structured decision-support framework for evaluating centralized purchasing performance. It enables the integration of linguistic assessments and expert knowledge, offering a flexible and coherent evaluation tool for industrial environments.

Logistics doi: 10.3390/logistics10060140

Authors: Neyfe Sablón Cossío Giselle Rodríguez Rudi Daniel Coq-Huelva Alexander Pulido-Rojano

Background: Given the disruption of the global market, technological factors are impacting food supply chains (FSCs). As a result, traceability is emerging as a potential solution for faster and more secure decision-making within FSCs. Methods: This research aims to define a traceability model for a shrimp food supply chain (FSC) in Ecuador using structural equation modelling which insists not only on the main factors that explain its overall performance but also on the effects of the changes in agents’ behaviours. The research was conducted between March and December 2025. A 41-item questionnaire was used for data collection and was administered to 73 stakeholders. The information was reduced and assembled in five main factors. A structural equation model was applied to explore the impact of agents’ coordination, digital transformations, and customer satisfaction on the traceability of the shrimp FSC. Results: The results show that customer satisfaction is broadly affected by the improvements in chain traceability. Furthermore, the results demonstrate the relevance of coordination, digitalization, and traceability as key factors for strengthening the FSC’s performance. Conclusions: The results could contribute to Sustainable Development Goals 12 and 17 and be applicable to other agri-food chains.

Logistics doi: 10.3390/logistics10060139

Authors: Mai S. Abdelaziz Tamer F. Abdelmaguid

Background: Egypt is one of the top steel producers in the Middle East and Africa, yet it faces acute water scarcity and rising energy costs, making it a critical context for studying trade-offs among carbon emissions, water ecological effects, and operational cost in steel supply chain. Methods: Using a multi-objective optimization model based on real data from a major Egyptian steel manufacturer, this study evaluates trade-offs among cost, tardiness, and environmental impact measured by carbon emissions and water ecological effects. Unlike prior studies, this study demonstrates that dedicated warehousing enables batch-level traceability of returned scrap while reducing material handling travel time and carbon emissions. The AUGMECON method generates Pareto-optimal solutions, and sensitivity analysis is conducted on six parameters: scrap take-back rate, demand variability, raw material price, energy cost, production capacity, and carbon tax. Results: Demand and raw material prices dominate performance: a 5% demand increase raises cost by 8.6%, and a 15% raw material price increase raises cost by 32.7%. The knee-point solution achieves 58.18 billion EGP, 0.99 months tardiness, and 2096 million kg CO2 over nine months. Conclusions: This study quantifies the impact of the circular economy and operational parameters on steel supply chain performance under a dedicated warehousing policy.

Logistics doi: 10.3390/logistics10060138

Authors: Patrik Eszes László Buics

Background: This study seeks to explore the link between smart city development and demand-responsive transport, analyze the ways of integrating demand-responsive transport into smart mobility concepts, estimate public perceptions about those phenomena, and review any international efforts aimed at promoting sustainable environmentally friendly transport options for cities in the EU. Methods: A systematic literature review was used as a basis for the current study; it is grounded in the analysis of scientific sources available in Scopus with the application of both PEO and PRISMA models. Survey analysis was also applied in order to examine public perception towards smart cities and DRT. Results: The results show that, even though the topics of smart cities and smart mobility have received significant attention in scholarly research, their links to demand-responsive transport still need more exploration. This paper describes the main features of smart mobility, urban mobility, and demand-responsive transport as well as the implications associated with each concept from an environmental, social, and operational perspective. In addition, the current international activities and examples of sustainable and flexible mobility systems are discussed. Conclusions: This paper presents a thorough examination of the relationships among smart mobility, urban mobility, and demand-responsive transportation.

Logistics doi: 10.3390/logistics10060137

Authors: Lei Jiang Anan Pongtornkulpanich Namphone Chaidee

Background: Although green supply chain management (GSCM) has been widely examined, prior studies have often treated it as a homogeneous construct and have paid limited attention to how different types of GSCM practices operate in logistics firms. This study addresses this gap by distinguishing between technical GSCM practices and behavioral GSCM practices and examining how both dimensions influence organizational performance through green innovation culture (GIC). Methods: Drawing on data from a cross-sectional survey of 426 logistics practitioners involved in supply chain, operations, and sustainability-related functions in Guangzhou, China, the study tested the proposed model using structural equation modeling (SEM). Results: Both technical and behavioral GSCM practices positively influence GIC, with behavioral practices having a stronger effect. GIC significantly improves organizational performance. Technical and behavioral GSCM practices also directly enhance organizational performance, indicating partial mediation. The indirect effect of behavioral GSCM practices through GIC is stronger, suggesting that behavioral governance is especially important for developing an innovation-oriented green culture. Conclusions: The study advances GSCM and green innovation literature and suggests logistics firms can achieve more sustainable performance improvements by combining technological upgrading with leadership support, employee involvement, stakeholder collaboration, and an innovation-oriented green culture.

Logistics doi: 10.3390/logistics10060136

Authors: Amal Belmabrouk Arij Lahmar Houssam Chouikhi Hatem Bentaher

Background: The Vehicle Routing Problem (VRP) is foundational to logistics optimization, yet its alignment with the Triple Bottom Line (TBL) and UN Sustainable Development Goals (SDGs) remains fragmented. This study conducts a strategic bibliometric audit of 301 peer–reviewed publications (1992–2025) to quantify the evolutionary progression and thematic maturity of sustainable routing research. Methods: A four–stage scientometric framework was employed, utilizing Scopus–based data retrieval, longitudinal mapping, and Python 3.14–driven text mining to visualize keyword co–occurrence networks, author collaborations, and regional research clusters. Results: Findings reveal a pronounced “Sustainability Asymmetry,” where 51.5% of studies prioritize economic efficiency, while only 2.6% address the social pillar. Additionally, social sustainability remains an “isolated island” with minimal cross–citation to the research core. Geographic analysis identifies a heavy concentration in China, the USA, and Western Europe, uncovering a critical North–South—collaboration gap. Conclusions: The study proves that while environmental themes reached maturity between 2018 and 2022, social indicators exhibit a significant maturity lag. This quantified social deficit, centered on the neglect of SDG 3 and SDG 10, mandates a fundamental paradigm shift toward a geographically inclusive and socially conscious research agenda to ensure global logistical equity.

Logistics doi: 10.3390/logistics10060135

Authors: Anjom Osman Rabaa Malik Esraa Abdel Azzem Salaheldin Salaheldin Amr Noureldin Samah Gouda

Background: Circular logistics translates circular economy principles into practical supply chain processes, but its adoption varies across firms because organizations differ in sustainability commitment, circular supply chain motivation, shared circular identity, and digital traceability capability. This study examines how supplier sustainability commitment and circular supply chain motivation influence circular logistics adoption through circular supply chain identity, while also testing the moderating role of digital traceability capability. Methods: Data were collected from 350 supply chain professionals in Saudi Arabia and analyzed using partial least squares structural equation modeling (PLS-SEM). Results: Supplier sustainability commitment and circular supply chain motivation positively influenced both circular logistics adoption and circular supply chain identity. Circular supply chain identity also positively affected circular logistics adoption and partially mediated the effects of both antecedents. Digital traceability capability acted as a selective moderator: it weakened the circular supply chain motivation–identity relationship, did not significantly moderate the supplier sustainability commitment–adoption relationship, but strengthened the circular supply chain identity–adoption relationship. It also moderated the indirect effect of circular supply chain motivation on circular logistics adoption through circular supply chain identity. Conclusions: Circular logistics adoption is driven not only by commitment and motivation, but also by shared circular identity and digitally enabled traceability.

Logistics doi: 10.3390/logistics10060134

Authors: Claudemir Leif Tramarico Antonella Petrillo Valério Antonio Pamplona Salomon

Background: Circular supply chains (CSC) have emerged as a strategic response to sustainability challenges, while adoption remains uneven. Supplier selection is a key driver of effectiveness, shaped by organizational capabilities, institutional support, and leadership. This study develops a structured framework for circular supplier selection (CSS) using a hybrid multi-criteria decision-making approach, addressing fragmented research and strengthening the link between methodological innovation and practice. Methods: The proposed framework integrates fuzzy DEMATEL, the Best-Worst Method (BWM), and the Analytic Hierarchy Process (AHP) within MCDM. Fuzzy DEMATEL identifies cause-and-effect relationships among criteria, distinguishing net causes from net effects. The most influential and dependent criteria serve as anchors for the BWM weighting, followed by AHP to evaluate sub-criteria and alternatives. Results: Environmental governance emerged as the most influential driver in the causal analysis, while circular performance received the highest weight in BWM. The final AHP evaluation ranked Alternative 5 as the most suitable, followed by A9 and A3, confirming the framework’s ability to deliver consistent, actionable insights for circular supplier selection. Conclusions: This integration enables a more granular and robust evaluation of supplier strategies within CSC, reinforcing their role in accelerating sustainability transitions. It establishes a structured framework for CSS, highlighting CSS performance and upstream supply chain decision-making.

Logistics doi: 10.3390/logistics10060133

Authors: Imane Dakhli Abdelfettah Sedqui Mostafa Derrhi

Background: Digital transformation is viewed as a lever of supply chain resilience, yet the intermediate pathways through which digital maturity relates to resilience remain underspecified, particularly in emerging-market contexts. Drawing on the Resource-Based View and the Dynamic Capabilities Framework, this study examines whether four dynamic capabilities (visibility, flexibility, risk management, and collaboration) mediate the relationship between digital maturity and supply chain resilience. Methods: Using a cross-sectional survey of 250 Moroccan firms and partial least squares structural equation modeling (PLS-SEM), we estimate a multi-mediator model and decompose the total association using variance accounted for (VAF). Results: The findings indicate that digital maturity is positively associated with resilience both directly (β = 0.219, p < 0.01) and indirectly through the four mediators, with the four capabilities jointly accounting for 63.7% of the total association (R2 = 0.523, SRMR = 0.027). Visibility (18.9%) and flexibility (15.9%) emerge as the strongest indirect channels. Conclusions: The study contributes by simultaneously testing four dynamic capabilities as mediators within a single specification, documenting evidence from an under-represented emerging-market context, and providing empirically grounded managerial recommendations and policy implications. Because the data are cross-sectional, all reported coefficients describe statistical associations.

Logistics doi: 10.3390/logistics10060132

Authors: Hrvoje Grofelnik Mladen Jardas Gorana Mudronja

Background: Container terminals are crucial nodes in global supply chains, but they also contribute significantly to environmental pollution. The analysis of sustainability in port logistics through carbon footprint offers crucial knowledge on how to reduce environmental impact in logistics. Methods: This systematic review uses a PRISMA-based research flow to extract key facts about energy consumption and greenhouse gas emissions, particularly CO2, which are still prevalent in terminal operations and logistics. Results: The paper analyses strategies and technologies adopted to reduce the carbon footprint, such as efficient infrastructure, electrification, automation, digitalisation, and AI-powered port logistics. It highlights the potential of sustainable logistics solutions, such as real-time cargo tracking, intelligent robotics and data analytics, to make container terminals more eco-friendly. Conclusions: Beyond analysing sustainability assessment models for the ecological efficiency and operational performance of container terminals, this paper highlights the need for future applied research into how investments in sustainable practices, as demonstrated by the most successful Asian port examples, can further reduce container terminal environmental footprint.

Logistics doi: 10.3390/logistics10060131

Authors: Anik Mazumder Bhaba R. Sarker

Background: The rapid expansion of wind energy systems has increased the need for reliable and cost-effective maintenance logistics, where the availability of critical spare parts is essential for sustaining turbine performance and reducing downtime. From this background, this study develops a three-echelon spare-parts inventory optimization model for wind-energy maintenance systems under disruption-dependent lead-time uncertainty. The model considers a hierarchical supply structure, consisting of a central warehouse, regional hub, and local maintenance base, where replenishment lead times are represented as a mixture of normal operating conditions and disruption-induced delays. Method: A total average cost function is formulated by incorporating ordering, holding, shortage, disruption penalty, and downtime costs, and a hybrid Nested Enumeration–Bisection Algorithm is developed as a method to determine the optimal order quantity, reorder points, and shipment multipliers. Results: Numerical experiments based on wind-turbine maintenance scenarios show that disrupted-condition lead-time distributions substantially affect total system cost. More variable disruption distributions increase total average cost, whereas more stable distributions produce lower-cost and more balanced inventory policies. Conclusions: The findings indicate that explicitly modeling disruption-sensitive lead times can improve spare-parts planning and provide decision support for enhancing cost efficiency and reliability in multi-echelon wind-energy maintenance logistics.

Logistics doi: 10.3390/logistics10060130

Authors: Carmen Bălan Maria-Cristiana Munthiu

In supply chains, the focus on last-mile logistics, environmental sustainability, and consumer behaviour became a priority for an increasing number of companies. There is a growing trend towards encouraging environmentally sustainable consumer behaviours by means of retail incentives and sales tactics. Background: This article analyses the research literature at the intersection of three fields: sustainable last-mile logistics, sustainable consumer behaviour, and retail contributions to the development of environmentally sustainable behaviours among consumers. Methods: The article is a systematic review which is based on a framework that assesses the current state of the literature, as regards the theories, methodologies, research contexts, and characteristics of the topics under study. Results: Three conceptual frameworks are suggested to analyse the process of encouraging sustainable consumer behaviours, as well as the types of retail incentives and sales tactics that stimulate specific consumer behaviours. Conclusions: At present, the literature is in an emerging stage of development. Distinct conclusions are drawn about the theories, methodologies, contexts and characteristics of the studied topics. Given the rapid development of e-commerce, omnichannel retailing, and sustainability in logistics and supply chains, further advancements in research are anticipated. On this basis, the paper concludes with a detailed agenda for future research.

Logistics doi: 10.3390/logistics10060129

Authors: Claudemir Leif Tramarico Miguel Angel Ortiz Barrios Valério Antonio Pamplona Salomon

Background: The transition from linear to circular supply chains (CSC) is critical for advancing sustainability, resilience, and resource efficiency, while supporting the UN Sustainable Development Goals (SDGs). However, existing studies rarely integrate internal operational performance with external PESTEL factors under the Benefits, Opportunities, Costs, and Risks (BOCR) perspective, limiting the ability to prioritize circular strategies holistically. Methods: This study develops a decision-making framework that combines the Best-Worst Method (BWM) and Fuzzy Technique for Order Preference by Similarity to Ideal Solution (FTOPSIS), enabling reliable prioritization of interdependent sustainability criteria. Results: A case analysis in the chemical industry demonstrates the applicability of the framework, enhancing transparency and reducing subjectivity in CSC evaluation. Findings highlight quality as the key operational attribute and social as the dominant PESTEL dimension, reinforcing the integration of internal and external factors toward SDG-oriented strategies. Conclusions: The study contributes theoretically by bridging operational and contextual dimensions in CSC evaluation under the BOCR perspective, and methodologically by advancing hybrid MCDM applications to address uncertainty. Managerially, the framework provides a structured tool for aligning circular supply chain strategies with organizational objectives and SDGs, supporting decision-making that strengthens environmental sustainability, stakeholder legitimacy, and resilience.

Logistics doi: 10.3390/logistics10060128

Authors: Yosi Agustina Hidayat Fariz Affandi Harahap

Background: The rise in global throughput has created major challenges for container terminals and depots, especially in managing limited storage space and unsynchronized truck pickup schedules from different companies. These conditions complicate the organization, retrieval, and relocation of containers, especially when target containers are located in the middle of stacks. This study aims to develop an integrated optimization model that combines Truck Appointment Scheduling (TAS) and Restricted Block Relocation Problem (RBRP) to minimize container relocations, coordinate pickup schedules, and improve operational efficiency. Methods: An integer programming model is formulated to integrate relocation and scheduling decisions by considering practical operational constraints, including maximum stack capacity, queue length, crane movement capacity, relocation validity, prevention of redundant movements, and efficient slot utilization. Two solution schemes are evaluated, simultaneous RPRP-TAS approaches and sequential TAS-then-RBRP approaches, to minimize relocations. Results: The results show that the simultaneous approach produces fewer relocation and more stable than the sequential approach. Sensitivity analysis also confirms that the simultaneous scheme is more robust to variations in the number of containers and crane movement capacity, while maintaining comparable pickup time-shift performance. Conclusions: The simultaneous integration of RBRP and TAS provides coordinated, practical, and efficient decision support for depot and terminal operations.

Logistics doi: 10.3390/logistics10060127

Authors: Maham Sohail Prosenjit Roy Sharfuddin Ahmed Khan Ashish Dwivedi Yasanur Kayikci

Background: Live-streaming e-commerce has emerged as a significant retail channel, especially in the apparel industry, characterized by high impulse-driven purchase rates and elevated product returns. Reverse logistics processes associated with these returns generate considerable environmental impacts that require systematic evaluation. Methods: This study performs a gate-to-gate Life Cycle Assessment (LCA) using SimaPro software, with a functional unit of 1 kg for one pair of returned jeans. Secondary inventory data were obtained primarily from the Ecoinvent database and supplemented with literature-based estimates for transport distances and packaging masses. Results: Key hotspots analyzed include transportation modes, packaging materials, and waste disposal pathways. Transportation mode selection was the dominant environmental hotspot, with air freight exhibiting the highest impacts across most midpoint and endpoint categories. Low-density polyethylene (LDPE) packaging and landfill disposal of textile waste were also major contributors to global warming, ozone formation, and resource depletion. Conclusions: The findings underscore the necessity of integrating Circular Supply Chain (CSC) principles into reverse logistics network design for live-streaming platforms. Optimizing transportation modes and packaging choices can effectively balance operational responsiveness with environmental sustainability. This study offers empirical evidence and practical decision-supporting insights for more sustainable return management in high-return digital retail environments.

Logistics doi: 10.3390/logistics10060126

Authors: Andrea C. Vazquez-Hernández Ruben H. Alvarez-Mirazo Ernesto A. Lagarda-Leyva

Background: This study evaluates the return on investment (ROI) in new transport equipment using a purpose-built graphical user interface (GUI), addressing whether acquiring additional vehicles for peak demand periods is economically viable compared to optimizing the existing fleet. The research focuses on agricultural product transportation—specifically potatoes—across four key routes. Methods: A system dynamics (SD) methodology was applied, combining simulation and data analysis through a GUI that enabled the adjustment of key variables, including operating costs, yields, and transportation expenses. Results: The analysis revealed notable differences in costs and profitability across the studied routes. Variables such as diesel costs and fuel efficiency proved particularly influential on outcomes. The GUI demonstrated clear value as a visualization tool, enhancing comprehension of simulated scenarios and supporting strategic decision-making. Conclusions: Investing in new transport equipment can be profitable under specific operational and economic conditions, providing a solid foundation for expansion and optimization decisions. Beyond its immediate operational contribution, the study offers a replicable profitability analysis model applicable to future projects within the company.

Logistics doi: 10.3390/logistics10060125

Authors: Atchara Dokkulab Duangpun Kritchanchai Kwanchai Pirojsakul Martin Crane

Background: Outpatient departments operate as interconnected service nodes through which patient and information flows must be coordinated across multiple handoffs. However, the role of patient value co-creation in shaping perceived outpatient process performance remains underexplored. Methods: This study examined how patient citizenship behavior (VCC_C) and participation behavior (VCC_P) are associated with patient satisfaction (SAT) across four outpatient processes and the overall outpatient pathway of a Thai university hospital. A process-level design was used, combining a cross-sectional survey of 400 patients with PLS-SEM, bootstrapping, multi-group analysis, Kruskal-Wallis tests, IPMA, and semi-structured interviews. Results: Across all processes, VCC_C showed greater explanatory importance for SAT than VCC_P and was strongly associated with VCC_P, indicating a citizenship-dominant pattern. Structural associations were statistically stable across processes, whereas satisfaction levels varied by operational context, with medication dispensing outperforming diagnosis and treatment. IPMA identified feedback and tolerance as high-importance, lower-performance priorities, whereas helping and advocacy emerged as strengths. Conclusions: Interpreted through a service logistics perspective, the findings suggest that queue visibility, handoff coordination, process transparency, and feedback management are important priorities for outpatient service improvement efforts.

Logistics doi: 10.3390/logistics10060124

Authors: Thi Minh Tam Nguyen Muhammad Azmat Reem Hadeed

Background: The rapid growth of e-commerce has intensified the tension between customer expectations for fast, convenient delivery and the need for more sustainable last-mile logistics. While existing studies have examined speed, price, sustainability, and convenience as separate delivery attributes, less attention has been given to how these dimensions are combined and presented in consumer-facing delivery options. Methods: This study adopts a mixed-methods approach, combining a systematic literature review with structured analysis of publicly available delivery offers on websites across the UK retail and logistics sectors. Results: The findings show that delivery design remains strongly shaped by speed, price visibility, and convenience, while sustainability signals are rarely embedded at the point of customer choice. Although the literature highlights growing interest in green logistics, observed delivery menus suggest a persistent gap between sustainability commitments and their implementation at checkout. Five delivery strategy archetypes are identified, illustrating how firms configure trade-offs among fast delivery, affordability, sustainability signalling, and convenience. Conclusions: The study contributes a four-pillar choice architecture framework for understanding online delivery design. It highlights the need for clearer sustainability communication, greener default options, and stronger alignment among firm strategy, consumer decision-making, and policy support in last-mile delivery.

Logistics doi: 10.3390/logistics10060123

Authors: Rodobaldo Martínez-Vivar Alexander Sánchez-Rodríguez Reyner Pérez-Campdesuñer Yailin Infante-Díaz Marcos Eduardo Valdés-Alarcón Gelmar García-Vidal

Background: Territorial logistics management has become increasingly important in turbulent environments, where digitalization, sustainability, resilience, and governance interact to shape regional logistics performance. This study aims to identify the strategic dimensions that structure territorial logistics management. Methods: A sequential mixed-methods design was adopted. First, relevant variables were identified through a structured literature review and expert judgment. Second, a survey of 376 specialists was analyzed using principal component analysis (PCA) to explore the empirical structure of the retained variables. Results: The analysis identified a four-dimensional structure comprising: (1) digital infrastructure and intelligent logistics, (2) sustainability and circular economy, (3) systemic resilience and risk management, and (4) territorial logistics, governance, and accessibility. Together, these dimensions explained more than 70% of the total variance. Conclusions: The findings suggest that territorial logistics management is a multidimensional phenomenon shaped by the interaction of technological, environmental, institutional, and spatial factors. The study provides an empirically grounded exploratory framework for understanding territorial logistics and supporting more integrated strategies for smart, sustainable, and resilient supply chains.

Logistics doi: 10.3390/logistics10060122

Authors: Padmaka Mirihagalla Gyula Vastag

Background: Lean management has been widely adopted, particularly in logistics operations. Achieving lean is not a discrete intervention but a continuous process of maturing in the integration of processes, work systems, and organizational capabilities within a coherent management philosophy. This maturation requires structured measurement instruments for tracking maturity progression. Although numerous lean maturity models (LMMs) have been proposed, none has achieved methodological standardization or acceptance as a measurement yardstick. Methods: This study addresses this gap by evaluating 27 qualified LMMs using a reproducibility-inspired assessment framework. The paper introduces the OVRGP framework (Opportunity, Validity, Reliability, Generalizability, and Process Integrity), comprising 17 rigor-based criteria. Independent raters with substantial lean expertise evaluated all 27 models using a six-point ordinal scale, achieving a pre-consensus inter-rater reliability of ICC(2,1) = 0.836. Results: Nine critical methodological weaknesses were identified, with average scores below 2.0 for criteria requiring empirical validation, structural integrity testing, and cross-context replication. Conclusions: The study offers targeted methodological guidelines for strengthening future LMM development in logistics and supply chain contexts, and introduces the OVRGP framework as a universal reference architecture for maturity model development across industries. It provides researchers, organizations, and consulting practitioners with a design reference standard for rigorous lean maturity instruments.

Logistics doi: 10.3390/logistics10060121

Authors: Dauren Turarov Zhumakul Abisheva Aiman Issayeva Madina Beisenova Stefan Dyrka

Background: This study aims to evaluate the impact of energy and logistics factors on the milk producer price index to support evidence-based policies that maintain price stability at an optimal level. Methods: Annual data for 2000–2023 are used, including the milk producer price index, milk production volume, transport CPI, diesel price, CO2 emissions from agriculture, and renewable energy consumption (percentage of total energy consumption). A log-linear ARDL model is applied to examine both short- and long-run asymmetric effects of diesel prices, transport costs, and agricultural CO2 emissions on milk production dynamics. Results: The research results indicate that energy expenses, logistics considerations, and environmental metrics have statistically significant asymmetric influences on milk production. This underscores the varying short-term adjustments and enduring long-term economic effects throughout the supply chain. Conclusions: Energy and cost factors on the supply side significantly influence the stability of milk markets. Therefore, improving transportation efficiency, encouraging the use of renewable energy sources, and addressing environmental impacts can contribute to consistent and sustainable pricing. Specific policies—including investments in transport infrastructure, subsidies for green energy targeting dairy producers, carbon pricing with support tailored to the sector, and digitalization of supply chains—can enhance resilience and ensure price stability.

Logistics doi: 10.3390/logistics10060120

Authors: Sakgasem Ramingwong Jutamat Jintana

Background: Vehicle routing optimization traditionally requires specialized software and technical expertise, limiting accessibility for small-to-medium enterprises. This study investigates whether generative AI (Claude 3.5 Sonnet via Claude.ai) can provide competitive vehicle routing solutions compared to traditional optimization methods while eliminating technical barriers. Methods: Fifty independent optimization trials were conducted across four methods—Claude.ai (generative AI), VRP Spreadsheet (Linear Programming), Routific (commercial heuristic), and genetic algorithm (evolutionary metaheuristic)—applied to a real-world case study of AED maintenance routing across 80 service locations in Chiang Rai, Thailand. Performance was evaluated across solution quality, ease of use, setup time, and implementation constraints. Results: The Genetic Algorithm achieved the best performance (908.34 km, −27.9% vs. manual routing), followed by Claude.ai best trial (941.64 km, −25.3%), VRP Spreadsheet (949.26 km, −24.7%), and Routific (964.36 km, −23.5%). Notably, Claude.ai’s best trial outperformed deterministic VRP Spreadsheet while requiring only 12 min setup versus 15 min. Probabilistic methods (Claude.ai, Genetic Algorithm) exhibited acceptable variability (CV: 2.24–2.28%), which was substantially lower than typical operational uncertainties. Conclusions: Generative AI provides accessible, competitive vehicle routing optimization, achieving 25%+ improvements with minimal technical expertise, democratizing advanced logistics planning for resource-constrained organizations.

Logistics doi: 10.3390/logistics10060119

Authors: Nattakorn Pinyanitikorn Rawida Wiriyakitjar Aannicha Thunyachairat

Background: Thailand is considered a logistics hub in Southeast Asia where the freight forwarding sector is essential for international trade and economic growth. This study aims to explore the relationships between logistics resources, strategic capabilities, competitive advantage, and organizational performance in Thailand’s freight forwarding sector. Methods: A quantitative cross-sectional survey was conducted with 250 management-level respondents from Thai freight forwarding companies. Partial Least Squares Structural Equation Modeling (PLS-SEM) was used to examine direct and mediating relationships among constructs. Results: Logistics resources significantly influenced competitive advantage, while strategic capabilities that integrate dynamic capabilities and green supply chain management exerted a stronger effect. Competitive advantage strongly predicted organizational performance, explaining 53.4% of its variance. Mediation analysis confirmed that competitive advantage mediates the effects of both logistics resources and strategic capabilities on organizational performance. Conclusions: Strategic capabilities exert greater impact on competitive advantage than static logistics resources, underscoring that organizational adaptability is more critical than resource possession alone. Freight forwarding firms should prioritize capability development and embed environmental management as a core competency rather than a compliance obligation.

Logistics doi: 10.3390/logistics10050118

Authors: Félix Díaz Nhell Cerna Rafael Liza Bryan Motta

Background: Blockchain and distributed ledger technologies are increasingly proposed to strengthen traceability, governance, visibility, and coordination in mining and mineral supply chains, but mining-specific evidence remains fragmented. Methods: We conducted a systematic mapping review of peer-reviewed articles indexed in Scopus and Web of Science to examine application contexts, functional roles, technical architectures, evidence types, and adoption constraints of blockchain-enabled systems in these settings. Results: The review shows that blockchain is used across five functional domains: traceability and provenance; governance and secure data control; operational monitoring and inspection; energy and market coordination; and sustainability and environmental surveillance. Permissioned and consortium-based architectures predominated and were commonly combined with sensors, external storage, identity mechanisms, and smart contracts. Evidence was strongest for technical feasibility under simulated, experimental, comparative, or bounded pilot conditions, whereas durable economic, social, and governance outcomes remained less substantiated. Conclusions: Blockchain is most credible in mining contexts when it supports controlled coordination, auditable recordkeeping, and process integrity. Its practical value depends on reliable physical-to-digital data capture, workable governance arrangements, interoperability, and validation under real institutional and operational conditions.

Logistics doi: 10.3390/logistics10050117

Authors: Evripidis P. Kechagias Sotiris P. Gayialis Nikolaos A. Panayiotou Georgios A. Papadopoulos

Background: Reconfigurable Manufacturing Systems (RMSs) address the deficiencies of previous manufacturing systems with expandable capacity and capability to respond to dynamic demand. While research on RMSs has been ongoing for decades, comprehensive classifications and categorizations of RMS research and their supply chain implications are sparse. Methods: The PRISMA 2020 guidelines were used for a systematic literature review on the Scopus database covering peer-reviewed publications from 1990 to 2025, and 247 papers were analyzed based on a four-stream classification framework (research scope, industry sectors, type of research, and RMS characteristics) that was inductively derived. Furthermore, a three-level conceptual model connecting RMS characteristics with manufacturing capabilities and supply chain resilience was established. Results: RMS research, particularly post 2020, has seen significant growth. However, RMSs are mainly oriented to heavy industries, while process industries and supply chain implications have been understudied. The dominance of theoretical research over experimental/practical research points to a theory-practice gap. Modularity is the most frequent RMS characteristic, underpinning the others, while diagnosability, despite its operational importance, is the least studied one. Conclusions: RMSs have significant potential as a supply chain resilience enabler through their characteristics. Nevertheless, this relationship is mostly theoretical and untested in practice, requiring interdisciplinary and application-oriented research.

Logistics doi: 10.3390/logistics10050116

Authors: Nattakan Jakkranuhwat Ravipim Chaveesuk Thanit Puthpongsiriporn

Background: Global food supply chains increasingly require sustainable and transparent operations; however, empirical evidence linking sustainability practices to firm performance remains inconsistent. This study examines how sustainability practices are translated into measurable business performance outcomes through traceability implementation in Thailand’s export-oriented food-processing sector. Methods: Grounded in Stakeholder Theory, traceability implementation was conceptualized as an accountability-oriented operational mechanism enabling the systematic verification of sustainability-related activities. Data were collected from 362 export-oriented food-processing firms in Thailand and analyzed using covariance-based Structural Equation Modeling (SEM). Results: The findings indicate that sustainability practices significantly influence both traceability implementation and business performance, while traceability implementation partially mediates the sustainability–performance relationship. The results further suggest that sustainability practices generate both direct and indirect performance benefits through structured monitoring, documentation, and verification routines. Conclusions: This study demonstrates that sustainability practices become more performance-relevant when institutionalized through traceable and verifiable operational processes. The findings highlight the importance of integrating traceability implementation into sustainability strategies to strengthen transparency, stakeholder confidence, and competitiveness within export-oriented food supply chain contexts.

Logistics doi: 10.3390/logistics10050115

Authors: Maria Karra Antonia Koumpoti Marina Saridi Foivos Anastasiadis

Background: Food waste at the consumption stage creates inefficiencies in food supply chains, with restaurants acting as downstream nodes where consumer behaviour affects operations. This study examines the behavioural drivers of plate waste in restaurants in Greece, within a Mediterranean context, and considers their implications for sustainable logistics. Methods: A cross-sectional online survey was conducted with 228 restaurant consumers in Greece. Data were analysed using descriptive statistics, Spearman’s rank-order correlation, and one-way ANOVA to examine associations between behavioural factors, restaurant type, socio-demographic characteristics, and self-reported food waste frequency. Results: Environmental awareness, intention to reduce food waste, and likelihood of taking leftovers home were negatively associated with self-reported food waste frequency. Perceiving food waste as a serious societal problem was not significantly associated with waste behaviour. Restaurant type had no significant effect, while socio-demographic effects were limited. Conclusions: Consumer-level behavioural factors explain restaurant food waste more consistently than structural restaurant characteristics. Behaviourally informed interventions, including portion flexibility and facilitated leftover retention, may improve both food waste reduction and restaurant efficiency.

Logistics doi: 10.3390/logistics10050114

Authors: Ilyas Masudin Dian Palupi Restuputri Dwi Iryaning Handayani Erly Ekayanti Rosyida

Background: The global transition to low-carbon energy systems has intensified the need for circular approaches in energy supply chains, yet studies on second-life EV battery ecosystems in emerging economies remain fragmented between barrier prioritization and efficiency assessment. Methods: This study addresses this gap by integrating the Best–Worst Method (BWM) and Data Envelopment Analysis (DEA) to connect subjective expert-based prioritization with objective efficiency benchmarking. Using expert panel inputs and scenario-based circular energy configurations representing emerging economy conditions, the results indicate that technical barriers (28.4%) and economic barriers (24.9%) dominate the priority structure, with battery performance uncertainty and high initial investment as the most critical constraints. Results: DEA results show that configurations with formal reverse logistics and certification mechanisms achieve frontier efficiency (θ = 1.000), whereas fragmented informal configurations exhibit the lowest efficiency (θ = 0.712). High-tech configurations with weak regulation demonstrate that technological investment alone is insufficient without institutional development. Conclusions: The novelty lies in developing a context-sensitive BWM–DEA framework that embeds barrier priorities into efficiency evaluation, an approach rarely explored in prior circular supply chain research. The study provides a holistic decision-support tool for policymakers and industry stakeholders seeking to accelerate circular energy transitions in emerging economies.

Logistics doi: 10.3390/logistics10050113

Authors: Oluwafemi Joshua Dele-Ijagbulu Progress Hove-Sibanda

Background: Although industrialisation of the 20th century advanced economic prosperity, it resulted in significant environmental deterioration and other industrialisation-related deleterious environmental impact. However, extant research has mainly focused on outcomes rather than on the underlying motives for sustainable supply chain management (SSCM) adoption, while limited studies focus on cross-country evidence from non-service small and medium enterprises (SMEs) in Africa. This study investigates the motives for adopting SSCM practices among SMEs operating in non-service sector-based industries in Ghana, Kenya, Nigeria, and South Africa. Methods: A quantitative approach was employed, drawing on instrumental, relational, and moral theoretical lenses. Data were collected through an online survey of 378 SME owners/managers using purposive and convenience sampling and analysed using comparative statistical techniques. Results: The findings reveal that relational and moral motives are the strongest drivers of SSCM adoption, with moral motives strongest in Ghana and Nigeria, moderate in South Africa, and weakest in Kenya. Significant cross-country differences and a notable motive-adoption gap were identified, highlighting the role of institutional and operational constraints. Conclusions: The study contributes novel cross-country empirical evidence from Africa and highlights the need for context-specific SSCM strategies that strengthen governance and capacity to translate ethical intent into practice.

Logistics doi: 10.3390/logistics10050112

Authors: Sudhir Rana Umesh Bamel

Background: Research on what promotes agility and resilience in the supply chain from an organizational strategy perspective is limited. This paper profiles the factors that can enable supply chain agility and resilience, with a special emphasis on organizational strategy. Method: Using an exploratory approach, the study first identifies the enablers of supply chain agility and resilience and then applies Fuzzy Total Interpretive Structural Modeling (Fuzzy TISM) to rank them. Data were collected from experts using a literature-derived knowledge base. Results: The findings reveal key resource and knowledge-based enablers (Integration, both internal and external; Knowledge Management; Culture for Flexibility, Risk Management, Innovation, Organizational Ambidexterity, Absorptive Capacity, and Collaborative Communication) that strengthen resilience and agility, offering insights into mitigating disruptions caused by macro- and micro-level factors and global interdependencies. Conclusions: The study contributes by exploring the enablers of supply chain agility and resilience through an organizational strategy lens. By applying a rent-yielding mechanism grounded in resource and dynamic capability theories, the study advances theoretical maturity in this domain from an emerging-country context.

Logistics doi: 10.3390/logistics10050111

Authors: Kariyawasam Pinikahana Gamage Lahiru Sandaruwan Robert Jeyakumar Nathan Shavindya Laksirini Sumanasekara Thomas Ntangere Maria Fekete Farkas

Background: Studies of fish supply chain efficiency often rely on price spreads or frontier-based measures, which do not fully capture actor-level coordination performance in heterogeneous, informal supply chains. This study addresses this gap by developing a composite Market Efficiency Index (MEI) that integrates financial performance, operational quality, service equity, and relational governance. Methods: The MEI, a multidimensional alternative to frontier-based measures, was developed and applied to data collected from 250 supply chain actors in Sri Lanka. Results: The results show a clear efficiency gradient along the supply chain, with fishers scoring the lowest (MEI = 0.44), intermediaries moderate (MEI = 0.54), and retailers the highest (MEI = 0.67), yielding an overall system efficiency of 0.55 and relational governance emerging as the weakest system-level dimension. These results indicate persistent structural differences in value distribution and in how well the fish supply chain functions as a cohesive network, driven by liquidity constraints, information asymmetry, and weak cold-chain infrastructure. Conclusions: A multidimensional supply chain assessment provides a more effective basis for diagnosing coordination constraints and enables targeted digital interventions that offer feasible pathways to improve transparency, liquidity, and inclusiveness in smallholder-dominated fish supply chains.

Logistics doi: 10.3390/logistics10050110

Authors: Apostolos Panagiotopoulos Vasileios Karyotis Georgios N. Dimitrakopoulos Vassiliki Choleva-Tsiringkaki Panos Kourouthanassis

Background: Humanitarian campaigns are more popular with more initiatives organized than ever by public and private bodies. Simultaneously, more peculiar challenges, such as zero-waste and transparency, are required compared to traditional supply chains. This work investigates the current technological landscape for shaping the next generation of humanitarian aid management systems, focusing on algorithmic and operational aspects, matching them to a new architecture for a target-platform, called AidTech. Methods: The methodology follows a systematic literature coverage, identifying cross-disciplinary trends from operations research, computer science and sustainable logistics. We examine the convergence of optimization, artificial intelligence, and blockchain-enabled traceability toward efficient and transparent humanitarian logistics. The employed methods include adaptive scheduling, resource allocation algorithms and privacy–preserving collaboration for meeting special constraints imposed by the humanitarian scope. Results: The findings focus on the architectural perspective of humanitarian supply chains and highlight that modern humanitarian infrastructures will increasingly rely on hybrid optimization methods integrating graph theory, dynamic routing under stochastic demand, multi-criteria decision analysis and distributed ledger technologies. Conclusions: We conclude that these paradigms, when combined under a unified cyber–physical architecture, e.g., AidTech, can substantially improve responsiveness, equity and sustainability in crisis management, further shaping future humanitarian logistics.

Logistics doi: 10.3390/logistics10050109

Authors: Aizhan Kamysbayeva Alisher Khussanov Botagoz Kaldybayeva Oleksandr Prokhorov Zhakhongir Khussanov Aibarsha Dosmakanbetova Baurzhan Korganbayev Aikerim Issayeva

Background: In the context of the digital transformation of transport systems and the increasing complexity of logistics flows, the role of intelligent route forming methods capable of accounting for the spatial structure of transport networks, time constraints and resource limitations is growing. This issue is particularly relevant for the Republic of Kazakhstan, which is characterized by a vast territory, a distributed network of transport nodes and significant transit potential. Methods: This article presents an integrated model for the intelligent optimization of freight transportation based on the combined use of the Google OR-Tools library and simulation modeling in the AnyLogic environment with the application of geographic information technologies. The main variants of vehicle routing problems are implemented, including VRPTW, CVRP, and MDVRP. Results: The developed model enables both identification of optimal routes and simulation of their execution in a dynamic environment, forming the basis for a digital twin of the transport system. Experimental studies demonstrate the impact of time constraints, capacity limitations, and spatial structure on routing solutions. Conclusions: The results confirm the effectiveness of the proposed approach for logistics flow distribution in a distributed transport system and its potential for decision support in the digital transport sector.

Logistics doi: 10.3390/logistics10050108

Authors: Laura Valentina Bocanegra-Villegas Cuauhtémoc Sánchez-Ramírez Jorge Luis García-Alcaraz

Background: A system dynamics model was developed to assess how United States (U.S.) tariff policies impact production, exports, labor, and Gross Domestic Product (GDP) in the Mexican automotive sector. Methods: Beginning with a baseline scenario without tariffs, the model introduces increases of up to 25% in exports to the U.S. Additionally, it analyzes a tariff increase alongside variations in demand elasticity (−0.5 to −1.6) over a 24-month period. Results: The results indicate that a 25% tariff leads to a sustained decrease in production, GDP, and exports, although adjustments in employment occur with some delay. Specifically, production volume contracts by 34.9%, and exports contract by 31.3%. Furthermore, a greater absolute elasticity of demand corresponds to a more significant decrease in production. Conclusions: The findings indicate that geographic diversification is not an immediate substitute for the current export structure, as it requires significant logistical, organizational, and capital adjustments. Reducing dependence on the U.S. market would involve reconfiguring supply chain management, diversifying suppliers, and adapting distribution networks to gain greater flexibility. The sector’s vulnerability therefore lies in its high concentration in a single destination market, which restricts production adaptability in Mexico and weakens the capacity to respond efficiently to disruptions in the supply chain.

Logistics doi: 10.3390/logistics10050107

Authors: Pattama Lenuwat

Background: This study examines how information technology (IT) capability contributes to manufacturing flexibility (MF) through knowledge acquisition (KA) and customer–supplier integration (CSI), drawing on dynamic capability theory. Methods: Survey data were collected from 137 manufacturing firms in Thailand and analyzed using structural equation modeling to test the proposed relationships. Results: The findings show that IT capability significantly enhances KA, which subsequently strengthens CSI. While IT does not directly influence MF, its effect is transmitted through a sequential pathway in which IT improves KA, KA facilitates CSI, and CSI is the only construct directly associated with MF. These results identify CSI as the central conversion mechanism through which IT-enabled information and externally acquired knowledge are translated into operational adaptability. Conclusions: The study demonstrates that externally acquired knowledge generates value only when embedded in collaborative interorganizational routines, such as joint planning and synchronized decision-making. It further highlights the complementary roles of learning-oriented and integration capabilities in enabling manufacturing flexibility. Managerially, firms should prioritize IT investments that strengthen customer–supplier integration rather than focusing solely on technological sophistication.

Logistics doi: 10.3390/logistics10050106

Authors: Napat Harnpornchai Worrawat Saijai

Background: Location decision plays a key role in strategic logistics and business success. The beauty business in Thailand has continuously grown, and medical aesthetics clinic location is one of the critical factors for business success. The problem is also related to sustainable urban service accessibility. Methods: This paper presents, for the first time, a systematic selection of medical aesthetics clinic location as a multi-criteria decision-making (MCDM) problem. The Simple Multi-Attribute Rating Technique (SMART) and Single-Valued Neutrosophic TOPSIS (SVN-TOPSIS) are combined to solve the location selection problem. SMART determines criterion weights, whereas SVN-TOPSIS evaluates alternatives using linguistic terms understandable to non-technical decision makers. Results: The proposed SMART SVN-TOPSIS is applied to a real investment problem in which two investors select the best clinic location from five alternatives with nine criteria. Siam Square—the heart of shopping, fashion, and youth culture in Bangkok—is recommended as the top location. Conclusions: The results indicate that the proposed method is capable of generating a consistent ranking of alternatives and differentiating between locations that exhibit similar evaluation characteristics. The findings may also support sustainable urban service planning and healthcare-related facility location decisions.

Logistics doi: 10.3390/logistics10050105

Authors: Sara Elzarka Islam El-Nakib

Background: Supply chain management (SCM) research has expanded across the Middle East and North Africa (MENA), yet the field remains fragmented. Limited synthesis exists on how regional conditions shape research themes, theories, and methods. Methods: This study applies the PRISMA 2020 protocol to review SCM articles indexed in Scopus and Web of Science from January 2000 to March 2025. After screening and eligibility assessment, 512 peer-reviewed studies were retained. Bibliometric mapping and thematic coding were used to identify publication trends, research streams, theoretical lenses, and methodological patterns. Results: SCM research increased sharply after 2015, reflecting national diversification agendas, logistics reform, digitalization, and exposure to global supply chain disruptions. Three dominant streams were identified: resilience, sustainability, and digital transformation. Research output is concentrated in Saudi Arabia and the United Arab Emirates, while cross-country comparative studies remain scarce. Empirical studies rely mainly on cross-sectional surveys and SEM-based analysis, with limited longitudinal, qualitative, mixed-method, and comparative work across the region. Conclusions: The study develops an integrative SCM capability framework linking regional structural conditions, capability development, and supply chain outcomes. The findings support managers and policymakers seeking resilient, sustainable, and digitally enabled supply chains, and define clear future research priorities for the MENA region.

Logistics doi: 10.3390/logistics10050104

Authors: Sura Alayed Sultan Alateeg

Background: Rapid adoption of artificial intelligence (AI) in logistics enhances operational efficiency and firm performance; however, empirical evidence on its capability-driven impact remains limited, particularly in Saudi Arabia’s e-commerce sector. This study’s purpose is to examine the influence of AI on logistics firm performance through the mediation role of supply chain consistency and logistics capabilities. Methods: A quantitative study was conducted and data were collected using a convenience sampling technique from 275 employees working in the Saudi Arabian logistics firms. Partial Least Squares Structural Equation Modeling was used to perform data analysis. Results: The study findings indicated that AI usage has significant and positive influence on supply chain consistency (β = 0.290) and logistics capabilities (β = 0.303). Furthermore, supply chain consistency (β = 0.115) and logistics capabilities (β = 0.171) play mediating role between AI usage and firm performance. The research model exhibits substantial predictive capability, explaining 74.6% (R2 = 0.746) of the variance in firm performance, while AI usage explains a smaller portion of the variance in supply chain consistency 8.4% (R2 = 0.084) and logistics capabilities 9.2% (R2 = 0.092). Conclusions: The findings demonstrate that AI-based logistics operations provide extensive support to streamline operations and reduce costs.

Logistics doi: 10.3390/logistics10050103

Authors: Laquanda Leaven Johnson Oghenetejiri Ebakivie

Background: Healthcare inventory management is critical for ensuring timely access to supplies and reducing stockouts. As supply chains grow more complex, algorithms, AI, and analytics techniques have emerged as tools for forecasting, tracking, classification, and procurement. Yet empirical validation across diverse contexts remains inadequate, and existing reviews treat these approaches as separate streams rather than an integrated system. Methods: To evaluate these capabilities, a systematic review of 64 peer-reviewed articles published between 2011 and 2025 was conducted using a descriptive and content analysis approach on the use of Triple A (Analytics, AI, and Algorithms) techniques in inventory frameworks across various healthcare contexts, such as hospitals, pharmaceutical supply chains, and humanitarian supply chains. Results: Integrating multiple Triple A approaches consistently outperforms single-method strategies, particularly with RFID and IoT tools. Key findings often overlooked are: emergency procurement and classification, which remain neglected despite the highest patient safety stakes, and key procurement drivers—organizational conditions, supplier reliability, and team capacity. Data quality, interoperability, and cybersecurity further constrain generalizability. Conclusions: Bridging these gaps requires integrated Triple A approaches rather than single methods. Phased implementation, cloud-based platforms, and privacy-by-design offer practical pathways for building resilience under real-world constraints.

Logistics doi: 10.3390/logistics10050102

Authors: Dilek Gümüş Öner Gümüş

Background: This study develops a transparent, decision-focused framework that integrates the World Health Organization’s defined daily dose (DDD) standard with the planned-backorder economic order quantity (EOQ) model to manage nursing and pharmacy workflows within a unified economic and operational scale. Method: Demand was expressed in DDD per year, and process-based costs were monetized according to National Health Service (NHS) workflow steps, where the holding cost was computed as H = r × cu and the delay cost B was derived from the target fill rate via a closed-form shadow-price relationship. The model was calibrated for a typical NHS acute-care hospital with 600 beds (D ≈ 130,305 DDD/year). Results: Calibration resulted in an ideal order quantity of 7554 DDD, an inter-order interval of 21 days, and a minimum annual total cost of £451. In the national conceptual scenario, the fill rate is about 99.4%, and the minimum annual total cost is £26,366. At this optimum, cost components are symmetrically balanced, with order cost and combined holding–delay cost contributing equally. Conclusions: This repeatable framework, based on the DDD scale, enhances management visibility regarding the cost–service balance, thereby confirming the policy’s robustness.

Logistics doi: 10.3390/logistics10050101

Authors: Emre Tokgoz Iddrisu Awudu Theodore Trafalis

Background. Ethanol distribution in the energy supply chain can be maximized by solving a Location Routing Problem (LRP). Manifold LRP (MLRP) expands on the classic domain assumptions of LRP to manifold surfaces, and it can be applied to profit maximization in ethanol supply chains. Methods. In this work, a hybrid MLRP (H-MLRP) is introduced as a new mixed integer nonlinear programming NP-hard problem assuming discrete facility allocation that requires a mix of truck and train transportation for ethanol distribution from the facility to its customers. Ethanol supply chain profit can be maximized by solving a stochastic nonlinear integer programming problem (SNLP) using ethanol raw materials, production quantity, logistics, railcar shipments, and transit times as the decision variables. H-MLRP and SNLP are combined as a two-stage optimization methodology to design a biofuel energy distribution system for making optimal decisions to maximize ethanol profit. Results. A case study demonstrated the effectiveness of the proposed method on the relocation of an ethanol producer that is currently located in North Dakota (ND) to Oklahoma (OK). In this case study, customer demand destinations and suppliers of raw materials are located in different regions of the United States. Conclusions. The results indicate a good use of the new model for decision-making.

Logistics doi: 10.3390/logistics10050100

Authors: Mohammad Hori Najafabadi Paria Mahmoudi Bernd Noche

Background: Automated identification of logistics units is a critical requirement in high-volume warehouse operations, particularly in retails that handle millions of cartons annually. Although barcode-based systems are widely used, they generate recurring costs for labeling, printing, quality control and readability issues, often leading to manual intervention and delays. Methods: This study presents a low-cost and flexible vision-based identification system that directly reads carton identifiers using optical character recognition (OCR). This system designed for edge deployment on resource-constrained hardware and incorporates a rotation-invariant preprocessing pipeline to support robust recognition under real conditions. Proposed approach was tested in two German retails. Results: Tests show recognition accuracies 96% to 98% under operational conditions, with real-time processing performance in the range of 58 to 125 ms per scan, depending on the hardware. These indicate that the system can be integrated into high-throughput logistics workflows. Additionally, the study provides insights into the economic implications of replacing barcode-based identification. Based on site-specific observations and labeling costs, the system shows the potential to reduce manual intervention and lower operational expenses in large-scale retails. Conclusions: Findings suggest that OCR can serve a cost-efficient alternative to barcode systems in environments where flexibility, robustness, and low deployment cost are critical.

Logistics doi: 10.3390/logistics10050099

Authors: Jonathan Vieira Alvaro Neuenfeldt Júnior Paulo Carteri Coradi Olinto Araújo Vanessa Alves

Background: In the grain logistics context, pre-processing operations such as reception, pre-cleaning, drying, storage, and shipping are performed at farm, collecting, intermediate, sub-terminal, and terminal storage units to preserve quality, reduce losses, and add value in the products. However, high transportation costs and limited static storage capacity reduce the selling prices. The objective of this article is to maximize profit associated with pre-processing, storage, and transportation along the grain flow in Brazil. Methods: A generic post-harvest logistics network is represented as a graph connecting producers, multi-level storage units, agribusiness facilities, and ports. A multi-period, multi-level mathematical model is applied in a case study framework explored in three scenarios, covering pre-cleaning, drying, storage, and transportation costs from production areas to commercialization nodes. Results: In all three scenarios, road transport resulted in transportation costs ranging from approximately US$ 49 million to US$ 492 million, mainly over long distances. Conclusions: The location and static capacity of collecting and intermediate storage units strongly influenced transport, storage use, CO2 emissions, and post-harvest efficiency. Also, the flow concentration increased heavy-vehicle traffic, reducing overall logistics performance.

Logistics doi: 10.3390/logistics10050098

Authors: Gábor Nagy Akylbek Umetaliev Szabolcs Szentesi

Background: Green logistics requires decision-support approaches that jointly address cost efficiency, emissions reduction, service reliability, and reporting transparency under dynamic operating conditions. Existing studies often treat optimization, predictive updating, stakeholder coordination, and emissions traceability separately, limiting integration. Methods: This study develops a simulation-based integrated decision-support framework that combines multi-objective mixed-integer linear programming (MILP), machine learning-based travel-time prediction in a rolling-horizon setting, cooperative allocation using a Shapley value mechanism, and ISO 14083:2023-aligned emissions accounting. A permissioned blockchain layer is included as a post-decision governance mechanism to support traceability. The framework is evaluated using industry-calibrated synthetic scenarios over a 30-day planning horizon with 50 independent simulation runs. Results: Under the tested scenarios, the integrated configuration reduced average CO2 emissions per route by 27.6% (±2.4%), improved the cost index by 17.3% relative to the baseline, and increased on-time delivery to 96.8%. Robustness analyses showed average key performance indicator (KPI) deviations below 5%. Component-level analysis suggests that the main operational gains arise from the interaction between predictive updating and prescriptive optimization, while the blockchain layer mainly improves auditability. Conclusions: The framework improves environmental and operational performance under the tested simulation scenarios, although real-world validation remains necessary before deployment-level conclusions can be drawn.

Logistics doi: 10.3390/logistics10050097

Authors: Svitlana Onyshchenko Oleksiy Melnyk Martin Jurkovič Piotr Gorzelanczyk Viktor Berestenko Eva Tvrdá Terézia Debnárová

Background: Efficient carrier selection and routing in multimodal logistics networks is increasingly complex due to operational uncertainty, fluctuating service reliability, and the need for disturbance-aware decision-support tools. This study develops an integrated optimization framework for simultaneous carrier selection and routing under operational disturbances. Methods: A disturbance-aware multi-criteria network optimization model is proposed that incorporates transportation cost, delivery time, reliability, and economic risk within a unified mathematical formulation. Operational disturbances are represented as parametric perturbations of network costs, enabling recalculation of routing decisions. Computational experiments were conducted on an illustrative multimodal network and additional synthetic networks. Results: The results show that routing decisions remain stable under disturbance levels up to 5% and reconfigure under higher perturbations. Comparative analysis with a classical cost-minimization routing model illustrates a potential reduction in expected economic risk exposure of approximately 25–30% within the illustrative experimental setting while maintaining comparable transportation time. Conclusions: The proposed framework integrates carrier selection and routing decisions in multimodal logistics systems and supports risk-aware decision-making under operational uncertainty.

Logistics doi: 10.3390/logistics10050096

Authors: Muhamad Safuan Shamshol Bahri S. Sarifah Radiah Shariff Nazry Yahya Chang Won Lee Nur Farizan Tarudin

Backgrounds: Ports are critical nodes in global logistics and supply chains, yet their operations generate substantial environmental and social externalities. Existing evaluation frameworks have limited capability to address uncertainty, ambiguity, and expert hesitation. Moreover, prior studies frequently examine isolated performance dimensions, overlooking the interconnected roles of port authorities as landlords, regulators, operators, and community stakeholders. Methods: This study proposes an integrated evaluation framework using the Intuitionistic Fuzzy Analytical Hierarchy Process (IF-AHP) to assess environmentally sustainable port performance under uncertain decision environments. By incorporating membership, non-membership, and hesitation degrees, the approach improves the robustness of expert judgments and applies a dual consistency check to reduce bias. Empirical data are obtained from Malaysian port management professionals, enabling the development of a comprehensive framework that includes four main functions and twenty sub-functions. Results: Results reveal that the landlord function holds the highest priority, while operational sustainability dimensions receive the greatest emphasis, with a global weight of approximately 0.105. In contrast, community engagement and social initiatives are assigned relatively lower importance. Conclusions: The IF-AHP framework offers an uncertainty-aware tool that prioritizes sustainability functions, especially environmental mitigation and energy efficiency, enabling informed resource allocation, strategic planning, and policy formulation for balanced, sustainable port overall performance.

Logistics doi: 10.3390/logistics10050095

Authors: Wipaporn Kitthiphovanonth Chalermchai Chaikittiporn Arroon Ketsakorn Korn Puangnak

Background: To address the critical challenges of hazardous material (HAZMAT) incidents in dense urban areas, this study develops a hybrid framework for spatial emergency response optimization tailored for Intelligent Transport Systems (ITSs). Methods: Our approach integrates the Fuzzy Analytic Hierarchy Process (FAHP) with a rigorous technical benchmarking of multiple navigation APIs to improve routing decisions under volatile Bangkok traffic. By employing a normalized cost function (scale 0–1), we evaluated the performance of localized (Longdo Map) versus global (Google Maps and OpenStreetMap) platforms across day and night scenarios. Results: Experimental results, yielding normalized costs between 0.464 and 0.748, identified Bon Kai as the optimal response node, whereas Chan Road showed the lowest efficiency. Interestingly, OpenStreetMap provided the highest temporal consistency for emergency logistics. Conclusions: These findings offer a practical decision-support tool for authorities, proving that integrated API assessment is essential for building resilient and responsive urban mobility infrastructures.

Logistics doi: 10.3390/logistics10040094

Authors: Vipulesh Shardeo Anchal Patil

Background: The COVID-19 pandemic triggered unprecedented mobility disruptions worldwide as governments imposed strict lockdowns to contain the spread of the virus. In India, prolonged restrictions severely affected economic activity, particularly for migrant workers, leading to a large-scale and unplanned exodus from urban employment centres to native places. This sudden population movement undermined containment efforts and contributed to the spatial diffusion of infections. Understanding evacuees’ behavioural responses during such crises is therefore critical for effective emergency logistics and evacuation planning. Methods: This study examines the determinants of transport mode and shelter choice decisions made by migrants during the COVID-19-induced evacuation in India. Using primary survey data, a multinomial logistic regression model is developed to analyze how socio-economic characteristics influence evacuees’ choices of travel mode and shelter type. Results: The results reveal significant heterogeneity in decision-making, highlighting the role of economic vulnerability and accessibility constraints in shaping evacuation behaviour. Conclusions: The findings offer actionable insights for policymakers and emergency planners to design inclusive evacuation strategies, improve crisis-responsive transportation planning, and enhance shelter provisioning in future pandemics or large-scale disruptions. The study contributes to the logistics and humanitarian operations literature by providing empirical evidence on evacuation behaviour under public health emergencies.

Logistics doi: 10.3390/logistics10040093

Authors: Vida Davidaviciene Bassel Diab Mohamad Al Majzoub

Background: The growing emphasis on sustainability has increased interest in understanding how environmentally oriented supply chain practices translate into organizational outcomes. However, empirical research examining how circular economy principles, technological integration, and sustainable supply chain management (SSCM) practices jointly influence green supply chain performance remains limited, particularly in developing economies. Methods: A quantitative research design was employed using survey data collected from 333 professionals in the Lebanese consumer goods industry through structured Likert-scale questionnaires. The proposed conceptual model was analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) to evaluate the measurement model and test the relationships among circular economy practices, technological integration, SSCM practices, green supply chain performance, and organizational performance. Results: The findings indicate that technological integration, circular economy practices, and SSCM practices collectively enhance green supply chain performance. The results further show that improved green supply chain performance supports stronger organizational outcomes. Conclusions: This study contributes to sustainable supply chain literature by integrating circular economy principles, technological capabilities, and SSCM practices within a unified framework. It highlights the strategic role of green supply chain performance in linking sustainability initiatives to organizational outcomes and provides insights for managers seeking to implement integrated sustainability strategies.

Logistics doi: 10.3390/logistics10040092

Authors: Panagiotis G. Giannopoulos Thomas K. Dasaklis

Background: The rapid evolution of omnichannel retailing has reshaped retail supply chains (SCs) by coupling replenishment, fulfillment, and service decisions across multiple demand channels under inventory, lead-time, and capacity constraints. These interdependencies create coordination challenges, particularly when demand shocks interact with limited operational capacity. Methods: To address these challenges, this study develops a centralized Hierarchical Reinforcement Learning (HRL) control framework that makes decision timing explicit: replenishment and allocation are optimized weekly, while fulfillment and lateral inventory rebalancing are controlled daily. Policies are learned using Proximal Policy Optimization (PPO) in an actor–critic architecture, with bounded stochastic policies for constrained action spaces. To mitigate the curse of dimensionality in HRL, we introduce a capacity-aware state–action encoding mechanism that compresses the control interface into structured summary signals. Demand shocks are modeled using two specifications: a mixed profile, where half the products follow a uniform demand process and the rest a Merton-type jump-diffusion process, and a fully shock-driven profile. Results: The framework is evaluated against forecast-driven base-stock and greedy fulfillment heuristics, and a perfect-information oracle, with pairwise differences examined through Wilcoxon signed-rank tests. Conclusions: Overall, the proposed framework improves learning efficiency and scalability, outperforming heuristic baselines while remaining below the oracle bound.