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Volume 10
Issue 3
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Logistics, Volume 10, Issue 3 (March 2026) – 18 articles

Cover Story (view full-size image): Biomass accounted for 60% of U.S. renewable energy in 2023, yet its viability depends on optimizing a complex supply chain challenged by feedstock seasonality and geographic dispersion. Traditional biomass logistics rely on road transport. Multimodalism offers a flexible, reliable alternative; however, it introduces significant complexities in coordinating rail, barge, and road synchronization. AI offers a solution to these dynamic barriers, yet a gap remains in applying AI specifically to multimodal biomass networks. This study fills that gap using a four-tiered framework: (1) physical feedstock constraints, (2) structural network design, (3) functional AI branches for decision-making, and (4) strategic insights. This review examines how AI-driven multimodal integration boosts efficiency in biomass supply chain management. View this paper
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17 pages, 980 KB  
Article
Real-Time Supply Chain Wave Analytics: A Framework for KPI Monitoring in Non-Food Retail
by Paria Mahmoudi, Mohammad Hori Najafabadi, Bernd Noche and André Terharen
Logistics 2026, 10(3), 69; https://doi.org/10.3390/logistics10030069 - 23 Mar 2026
Viewed by 1053
Abstract
Background: Modern supply chains (SC) are increasingly difficult to manage as they become more complex and interconnected. This encourages companies to rely more on real-time data analysis and analytical tools on operational processes. This study aims to develop and evaluate a Supply [...] Read more.
Background: Modern supply chains (SC) are increasingly difficult to manage as they become more complex and interconnected. This encourages companies to rely more on real-time data analysis and analytical tools on operational processes. This study aims to develop and evaluate a Supply Chain Wave Report for a non-food retail that represents goods movement across logistics stages as a continuous analytical flow. Methods: Proposed framework integrates multiple operational phases—Booked Orders, Main Transit, On-Carriage, Warehouse Operations, Store Delivery, and Sales—into a unified monitoring structure. This model can combine operational data with advanced analytics, including Artificial Intelligence-, cloud computing-, and Internet of Things-based technologies. Through cloud-based data infrastructures, System enables data integration and near real-time visibility across organizational functions, allowing continuous monitoring through key performance indicators and predictive simulations. Results: This framework enables dynamic performance of supply chain management and generates real-time signals as goods move across logistics network. This enables managers to detect irregularities earlier and respond before operational deviations propagate further along the chain. Wave-based monitoring approach highlights interdependence between SC stages and illustrates how small disruptions may propagate over time, potentially contributing to effects like bullwhip effect. Conclusions: Findings suggest that a cloud-enabled wave analytics framework can enhance coordination, reduce information gaps, and support informed decision-making in retail. Full article
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22 pages, 2006 KB  
Article
PSO-Based Optimization of Shipping Box Configurations: An Empirical Study with South Korean Enterprise Data
by Changsoo Ok, Heesu Ahn and SeJoon Park
Logistics 2026, 10(3), 68; https://doi.org/10.3390/logistics10030068 - 17 Mar 2026
Viewed by 650
Abstract
Background: The rapid growth of e-commerce has intensified the need for packaging strategies that reduce logistics costs and environmental impact. Traditional box recommendation methods select the best-fitting box from a fixed set of options, which limits their ability to minimize unused space [...] Read more.
Background: The rapid growth of e-commerce has intensified the need for packaging strategies that reduce logistics costs and environmental impact. Traditional box recommendation methods select the best-fitting box from a fixed set of options, which limits their ability to minimize unused space and total costs. Methods: This study formulates the Shipping Box Configuration Problem (SBCP), which aims to determine an optimal set of box types and dimensions for multi-product orders. To solve this problem, we propose a Particle Swarm Optimization (PSO)-based heuristic that dynamically designs box configuration rather than selecting from predefined sizes. Results: The proposed method is evaluated using real order data from two South Korean e-commerce companies with different product characteristics and existing box configurations. Computational results show that the PSO-based approach reduces total packaging and shipping costs and improves space utilization compared to current box configurations. The analysis also indicates that increasing the number of box types and reducing safety ratios generally lead to cost savings, although these effects must be balanced against operational complexity. Conclusions: The results suggest that adaptive box configuration design can improve both economic efficiency and environmental performance, providing practical guidance for e-commerce logistics managers seeking to optimize packaging strategies under operational constraints. Full article
(This article belongs to the Section Artificial Intelligence, Logistics Analytics, and Automation)
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24 pages, 1672 KB  
Article
Quantum Computing for Supply Chain Optimization: Algorithms, Hybrid Frameworks, and Industry Applications
by Fayçal Fedouaki, Mouhsene Fri, Kaoutar Douaioui and Amellal Asmae
Logistics 2026, 10(3), 67; https://doi.org/10.3390/logistics10030067 - 16 Mar 2026
Viewed by 3427
Abstract
Background: This paper investigates hybrid quantum–classical optimization approaches for addressing core supply chain management (SCM) problems. A unified hybrid framework is implemented and evaluated across five representative domains: vehicle routing, scheduling, facility location, inventory optimization, and demand forecasting. Methods: The framework [...] Read more.
Background: This paper investigates hybrid quantum–classical optimization approaches for addressing core supply chain management (SCM) problems. A unified hybrid framework is implemented and evaluated across five representative domains: vehicle routing, scheduling, facility location, inventory optimization, and demand forecasting. Methods: The framework integrates quantum algorithms—namely the Quantum Approximate Optimization Algorithm (QAOA), Quantum Annealing (QA), and the Variational Quantum Eigensolver (VQE)—with classical constraint-handling and local refinement procedures in an iterative workflow. Quantum solvers are employed for global solution exploration, while classical optimization ensures feasibility and convergence stability. Results: Experiments conducted on standardized synthetic benchmarks demonstrate that the proposed hybrid framework consistently outperforms classical-only and quantum-only baselines, achieving 12–18% reductions in operational costs and 20–35% faster convergence. In routing and fulfilment tasks, quantum-generated candidate solutions provide effective warm starts for classical refinement. Robustness analysis based on stochastic SCM simulations further indicates lower performance variance under uncertainty. Conclusions: These results demonstrate that hybrid quantum–classical optimization constitutes a practical and scalable strategy for near-term SCM decision-making under current Noisy Intermediate-Scale Quantum (NISQ) hardware constraints. Full article
(This article belongs to the Topic Sustainable Supply Chain Practices in A Digital Age)
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24 pages, 1157 KB  
Article
Emergent Competitiveness in Artisanal Furniture: A Case Study from Misantla, Mexico
by Luis Enrique García-Santamaría, Eduardo Fernández-Echeverría, Gregorio Fernández-Lambert, Nora Amalia Parra-Hernández, Elizabeth Delfín-Portela, Areli Brenis-Dzul, José Aparicio-Urbano and Juan Manuel Carrión-Delgado
Logistics 2026, 10(3), 66; https://doi.org/10.3390/logistics10030066 - 15 Mar 2026
Viewed by 802
Abstract
Background: This study examines the competitive dynamics of the artisanal wooden furniture industry in Misantla, Veracruz, Mexico, a predominantly informal productive system characterized by family-managed production units and strong territorial embeddedness. Methods: A mixed-methods research design was employed. Quantitative data were collected from [...] Read more.
Background: This study examines the competitive dynamics of the artisanal wooden furniture industry in Misantla, Veracruz, Mexico, a predominantly informal productive system characterized by family-managed production units and strong territorial embeddedness. Methods: A mixed-methods research design was employed. Quantitative data were collected from 187 family-managed production units (86 woodworking units and 101 workshops) using a structured questionnaire based on five-level Likert scales assessing external efficiency, collective efficiency, and innovation. Statistical analyses included descriptive measures and chi-square tests to examine associations between competitiveness and collective strategies, while qualitative validation and thematic interpretation based on expert assessments were used to contextualize sectoral practices and structural constraints. Results: The findings indicate a low overall competitiveness score (1.92/5), associated with informal practices, limited technical training, and weak supply chain integration. Despite these constraints, the sector maintains a strong cultural identity and contributes to its local economy. Conclusions: Artisanal supply chains can achieve functional levels of logistics performance through internal coordination dynamics. Strengthening collaboration mechanisms is a viable strategy for improving logistics performance in artisanal manufacturing systems in emerging economies. These findings provide empirical evidence to support the design of collaborative strategies that integrate traditional craftsmanship with modern supply chain practices in artisanal micro-industries. Full article
(This article belongs to the Section Sustainable Supply Chains and Logistics)
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20 pages, 879 KB  
Article
Willingness to Implement Logistics and Supply Chain Resilience Strategies Amid COVID-19: Insights from Japanese Manufacturing Firms
by Rajali Maharjan, Hironori Kato and Sunkyung Choi
Logistics 2026, 10(3), 65; https://doi.org/10.3390/logistics10030065 - 13 Mar 2026
Viewed by 803
Abstract
Background: The COVID-19 pandemic has underscored the critical importance of supply chain resilience. However, little is known about firms’ willingness to implement logistics and supply chain resilience strategies (SCRESTs), and how this willingness varies across contexts. This study investigates the willingness of [...] Read more.
Background: The COVID-19 pandemic has underscored the critical importance of supply chain resilience. However, little is known about firms’ willingness to implement logistics and supply chain resilience strategies (SCRESTs), and how this willingness varies across contexts. This study investigates the willingness of Japanese manufacturing firms to implement SCRESTs and examines how the pandemic has influenced this willingness. Methods: Using survey data from 549 Japanese manufacturing firms collected from March to April 2022, we employed binary choice models and the average treatment effect on the treated (ATET) analysis to examine the factors influencing the willingness to implement SCRESTs before and during/after the pandemic. Results: Firms demonstrated significantly higher willingness to implement SCRESTs during/after the pandemic compared with before. Company size, industry sector, logistics strategy, implementation obstacles, and past SCREST implementation significantly influenced willingness across both periods. The ATET analysis confirmed that past SCREST implementation positively affects future willingness. Conclusions: The pandemic served as a catalyst for enhanced supply chain resilience awareness among Japanese manufacturers. Sector-specific interventions addressing both informational and structural barriers are essential to sustain and strengthen the willingness to implement SCRESTs, particularly in strategically important sectors where financial incentives alone may prove insufficient. Full article
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27 pages, 3523 KB  
Article
Optimizing Inventory in Convenience Stores to Maximize ROI Using Random Forest and Genetic Algorithms
by Kelly Zavaleta-Zarate, Jesus Escobal-Vera and Eliseo Zarate-Perez
Logistics 2026, 10(3), 64; https://doi.org/10.3390/logistics10030064 - 13 Mar 2026
Viewed by 1276
Abstract
Background: Convenience stores face volatile demand and a direct trade-off between stock-outs and overstocking, both of which affect service levels and profitability. This study aims to optimize inventory management through a reproducible forecasting-and-optimization workflow, assessing its impact on return on investment (ROI) [...] Read more.
Background: Convenience stores face volatile demand and a direct trade-off between stock-outs and overstocking, both of which affect service levels and profitability. This study aims to optimize inventory management through a reproducible forecasting-and-optimization workflow, assessing its impact on return on investment (ROI) and operational metrics, such as fill rate and stockouts. Methods: The workflow integrates daily, store-level transactions with external covariates, constructs temporal and lag features, and trains a Random Forest (RF) model using chronological splitting and time-series validation. Daily forecasts are then aggregated to the monthly level and used as inputs to an inventory simulation and an ROI-based economic model. Building on this simulation, a Genetic Algorithm (GA) optimizes the parameters of a monthly replenishment policy, incorporating minimum-coverage constraints. Results: In testing, the forecasting model achieved a mean absolute percentage error (MAPE) below 13%, and the RF+GA scheme outperformed the 28-day moving average baseline (MA28) in ROI across all five stores, with an average improvement of 4.52 percentage points; statistical significance was confirmed using the Wilcoxon test. Conclusions: Overall, the RF+GA approach serves as a decision-support tool that generates monthly order quantities consistent with demand and operational constraints, delivering verifiable improvements in both economic and service metrics. Full article
(This article belongs to the Special Issue Multi-Criteria Decision-Making and Its Application in Sustainable Smart Logistics—2nd Edition)
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40 pages, 608 KB  
Article
A Θ(m9) Ternary Minimum-Cost Network Flow LP Model of the Assignment Problem Polytope, with Applications to Hard Combinatorial Optimization Problems
by Moustapha Diaby
Logistics 2026, 10(3), 63; https://doi.org/10.3390/logistics10030063 - 12 Mar 2026
Viewed by 641
Abstract
Background: Combinatorial optimization problems (COPs) are central to Logistics and Supply Chain decision making, yet their NP-hardness prevents exact optimal solutions in reasonable time. Methods: This work addresses that limitation by developing a novel ternary network flow linear programming (LP) model of the [...] Read more.
Background: Combinatorial optimization problems (COPs) are central to Logistics and Supply Chain decision making, yet their NP-hardness prevents exact optimal solutions in reasonable time. Methods: This work addresses that limitation by developing a novel ternary network flow linear programming (LP) model of the assignment problem (AP) polytope. The model is very large scale (with Θ(m9) variables and Θ(m8) constraints, where m is the number of assignments). Although not intended to compete with conventional two-dimensional formulations of the AP with respect to solution procedures, it enables hard COPs to be solved exactly as “strict” (integrality requirements-free) LPs through simple transformations of their cost functions. Illustrations are given for the quadratic assignment problem (QAP) and the traveling salesman problem (TSP). Results: Because the proposed LP model is polynomial-sized and there exist polynomial-time algorithms for solving LPs, it affirms “P=NP.” A separable substructure of the model shows promise for practical-scale instances due to its suitability for large-scale optimization techniques such as Dantzig–Wolfe Decomposition, Column Generation, and Lagrangian Relaxation. The formulation also has greater robustness relative to standard network flow models. Conclusions: Overall, the approach provides a systematic, modeling-barrier-free framework for representing NP-complete problems as polynomial-sized LPs, with clear theoretical interest and practical potential for medium to large-scale Logistics and other COP-intensive applications. Full article
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21 pages, 1931 KB  
Article
Transport of Immunobiologicals in Brazil: A Multiple Case Study
by Thayane Ingrid Xavier de Andrade, Selma Maria da Fonseca Viegas, Gabriela Gonçalves Amaral, Larissa Carvalho de Castro, Wiara Viana Ferreira, Francieli Fontana Sutile Tardetti, Ione Carvalho Pinto, Eliete Albano de Azevedo Guimarães and Valéria Conceição de Oliveira
Logistics 2026, 10(3), 62; https://doi.org/10.3390/logistics10030062 - 11 Mar 2026
Viewed by 756
Abstract
Background: Immunobiologicals are thermolabile products that require strict storage and transportation conditions to maintain their immunogenic efficacy, particularly in countries where logistical and operational challenges are evident, such as Brazil. Methods: A holistic multiple case study, carried out in five regions [...] Read more.
Background: Immunobiologicals are thermolabile products that require strict storage and transportation conditions to maintain their immunogenic efficacy, particularly in countries where logistical and operational challenges are evident, such as Brazil. Methods: A holistic multiple case study, carried out in five regions of Brazil, in 2022, with 42 workers from different instances of the cold chain was conducted. As a source of evidence, data were collected through interviews and analysis of printed documents and analyzed using Thematic Content Analysis, using the analytical technique of cross-case synthesis. Results: The influence of geoclimatic diversity and transportation modes on immunobiological logistics was highlighted. Challenges and requirements were identified, as well as aspects of monitoring during transportation and distribution. Among the main challenges were long distances, poor road conditions, seasonality and the need to share vehicles due to the unavailability of exclusive transportation. Conversely, positive practices were highlighted, such as the use of air-conditioned vehicles, dataloggers and properly prepared thermal boxes. Conclusions: It is necessary to adopt mitigation strategies that consider regional inequalities and promote equity, through raising awareness among managers, investing in logistical infrastructure and expanding good practices in order to guarantee the universal and qualified distribution of immunobiologicals in the country. Full article
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42 pages, 1981 KB  
Article
An Integrated Optimisation Model for LNG Supply Chain Planning and Infrastructure Under FOB Scheme with Time-Dependent Demand
by Firmanto Hadi, Heri Supomo, Tri Achmadi and Imam Baihaqi
Logistics 2026, 10(3), 61; https://doi.org/10.3390/logistics10030061 - 10 Mar 2026
Viewed by 1025
Abstract
Background: Liquefied natural gas (LNG) distribution in archipelagic regions involves complex trade-offs between transportation, infrastructure investment, and contractual arrangements. While most optimisation studies focus on seller-managed Delivery Ex-Ship (DES) schemes, limited research addresses buyer-managed Free on Board (FOB) frameworks that extend decision [...] Read more.
Background: Liquefied natural gas (LNG) distribution in archipelagic regions involves complex trade-offs between transportation, infrastructure investment, and contractual arrangements. While most optimisation studies focus on seller-managed Delivery Ex-Ship (DES) schemes, limited research addresses buyer-managed Free on Board (FOB) frameworks that extend decision responsibility upstream. Methods: This study develops a two-stage integrated optimisation model for long-term LNG supply chain planning under an FOB contractual scheme with time-dependent deterministic demand. Stage 1 determines hub selection, port clustering, vessel sizing, fleet configuration, and endogenous infrastructure capacities using a genetic algorithm, while Stage 2 optimises cluster-level routing sequences. Robustness is assessed through multiple independent runs and sensitivity analysis. Results: A case study of the Nusa Tenggara region identifies Sumbawa as the optimal hub. The upstream segment consistently selects a 65,000 m3 vessel under terminal service capacity constraints, while downstream clusters are served by 3500 m3 and 10,000 m3 vessels depending on distance and demand aggregation. Infrastructure requirements are derived from peak-demand conditions, and the resulting levelised logistic cost is 4.66 USD/MMBtu. Conclusions: The findings demonstrate that FOB arrangements fundamentally reshape network configuration, fleet segmentation, and infrastructure sizing, providing a robust strategic planning framework for buyer-managed LNG supply chains in archipelagic contexts. Full article
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32 pages, 1232 KB  
Article
Procurement Literacy Capability Theory (PLCT): Development and Validation
by Priscilla Boafowaa Oppong and Anokye M. Adam
Logistics 2026, 10(3), 60; https://doi.org/10.3390/logistics10030060 - 9 Mar 2026
Viewed by 1028
Abstract
Background: Ethical challenges in public procurement are often addressed through compliance approaches that stress rule awareness. These perspectives, however, offer limited insight into how ethical intentions form before professional practice. This study develops and empirically validates the Procurement Literacy Capability Theory (PLCT), which [...] Read more.
Background: Ethical challenges in public procurement are often addressed through compliance approaches that stress rule awareness. These perspectives, however, offer limited insight into how ethical intentions form before professional practice. This study develops and empirically validates the Procurement Literacy Capability Theory (PLCT), which conceptualises procurement literacy as a sequenced, interdependent set of capabilities that produce ethical readiness. Methods: Survey data were collected from 776 undergraduates in procurement-related programmes at four accredited Ghanaian universities. Structural equation modelling tested capability interdependence, sequencing, and behavioural translation. Mediation was examined via bootstrapped indirect effects, with sensitivity analysis using reduced structural models. Results: The findings support PLCT. Digital and E-Procurement Literacy predicts planning and decision-making capability, which then predicts supplier and contract management literacy. This literacy strongly influences Ethical Procurement Practice Literacy, the strongest predictor of Ethical Behavioural Intention. Legal and policy knowledge literacy has no direct effect on ethical intention but acts indirectly through ethical procurement practice capability. Models excluding ethical practice capability have much lower explanatory power. Conclusions: Ethical Behavioural Intention in procurement is shaped by sequenced capability development and applied ethical competence rather than rule awareness alone, confirming ethical practice literacy as the central behavioural mechanism within PLCT. Full article
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19 pages, 1030 KB  
Article
A Quantitative–Qualitative Framework for Evaluating Blockchain Adoption in PI-Oriented Logistics Systems
by Qian Huang, Takeshi Kawase, Sirawadee Arunyanart and Shunichi Ohmori
Logistics 2026, 10(3), 59; https://doi.org/10.3390/logistics10030059 - 9 Mar 2026
Viewed by 742
Abstract
Background: Blockchain has emerged as a promising enabler for improving transparency, trust, and operational efficiency in logistics systems. In PI-oriented logistics environments, where openness, interoperability, and streamlined information exchange are emphasized, blockchain offers a decentralized alternative to conventional coordination methods. However, its [...] Read more.
Background: Blockchain has emerged as a promising enabler for improving transparency, trust, and operational efficiency in logistics systems. In PI-oriented logistics environments, where openness, interoperability, and streamlined information exchange are emphasized, blockchain offers a decentralized alternative to conventional coordination methods. However, its economic feasibility remains uncertain due to substantial system development and operational costs. Existing literature largely isolates qualitative benefits from quantitative cost structures. Methods: This study proposes a quantitative–qualitative evaluation framework to assess blockchain adoption in PI-oriented logistics systems. Two Mixed-Integer Linear Programming (MILP) cost-minimization models were constructed to represent alternative coordination approaches: PI–BC (blockchain-enabled coordination) and PI–Human (traditional human-centered coordination). The results of the optimization analysis were integrated into an Analytic Hierarchy Process (AHP) evaluation alongside qualitative criteria such as interoperability, reliability, and transparency. Results: Numerical findings show that although PI–BC incurs higher operational costs, it performs considerably better in qualitative dimensions related to information visibility and robustness. Conclusions: These results suggest that blockchain provides particular value in PI-oriented contexts at the adoption stage. However, the framework does not provide a universal recommendation, as the relative advantage of PI–BC is highly contingent on decision-makers’ subjective criterion weight assignments, as revealed by the sensitivity analysis. Full article
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23 pages, 908 KB  
Systematic Review
Traditional and Innovative Managerial Adaptations in Dairy Supply Chains During COVID-19: A Comprehensive Review
by Fachri Rizky Sitompul and Csaba Borbély
Logistics 2026, 10(3), 58; https://doi.org/10.3390/logistics10030058 - 9 Mar 2026
Viewed by 1041
Abstract
Background: The COVID-19 pandemic disrupted global dairy supply chains and threatened business continuity from farms to retail outlets. There is limited understanding of how operational-level managerial decisions supported resilience in this perishable sector. Methods: This study applies a systematic literature review [...] Read more.
Background: The COVID-19 pandemic disrupted global dairy supply chains and threatened business continuity from farms to retail outlets. There is limited understanding of how operational-level managerial decisions supported resilience in this perishable sector. Methods: This study applies a systematic literature review based on PRISMA 2020 guidelines. It analyses 21 peer-reviewed studies published between 2019 and 2025 across 19 countries. Results: The findings identify 8 primary supply chain challenges. Adaptive responses are classified into traditional and innovative managerial adaptations. Traditional adaptations rely on established practices such as production adjustments, cross-training, and product reallocation to stabilise short-term performance. Innovative adaptations involve structural and analytical approaches such as network optimisation, digital coordination, and scenario planning to support long-term resilience. The results also reveal differences between developed and developing economies. Conclusions: Resilient dairy supply chains require both operational continuity and structural innovation. This study proposes a sector-specific classification of managerial adaptations and highlights directions for future research. Full article
(This article belongs to the Special Issue Logistics and Supply Chain Challenges and Solutions in the Turbulent World)
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36 pages, 3273 KB  
Systematic Review
Integrating IoT and Blockchain for Real-Time Inventory Visibility and Traceability: A Bibliometric–Systematic Review
by Blessing Takawira and Babra Duri
Logistics 2026, 10(3), 57; https://doi.org/10.3390/logistics10030057 - 9 Mar 2026
Cited by 4 | Viewed by 2780
Abstract
Background: The accelerated convergence of the Internet of Things (IoT) and Blockchain is reconfiguring logistics, yet knowledge regarding their operationalisation for real-time inventory management remains fragmented. Methods: A Bibliometric–Systematic Literature Review (B-SLR) was conducted on peer-reviewed sources from Scopus and Web of Science [...] Read more.
Background: The accelerated convergence of the Internet of Things (IoT) and Blockchain is reconfiguring logistics, yet knowledge regarding their operationalisation for real-time inventory management remains fragmented. Methods: A Bibliometric–Systematic Literature Review (B-SLR) was conducted on peer-reviewed sources from Scopus and Web of Science (2019–2025), utilising science mapping to visualise intellectual and conceptual structures. Results: The analysis reveals a steep rise in publications during 2024–2025, identifying traceability, smart contracts, and integrity mechanisms as central themes. The synthesis supports a layered theoretical model linking transparency (sensing) and trust (ledger validation) to efficiency and supply chain resilience in Industry 5.0. The review highlights unresolved issues, including interoperability and privacy-by-design, alongside emerging directions such as digital twins. Conclusions: While scholarship has expanded rapidly, it remains weighted toward adoption mapping, underscoring the need for empirical, context-aware models that explain socio-technical integration and its measurable impacts on logistics performance. Full article
(This article belongs to the Section Artificial Intelligence, Logistics Analytics, and Automation)
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22 pages, 898 KB  
Article
An Enhanced Composite Green Logistics Performance Index for MENA: Methodology, Drivers and Hybrid Forecasting to 2030
by Islam El-Nakib and Sara Elzarka
Logistics 2026, 10(3), 56; https://doi.org/10.3390/logistics10030056 - 5 Mar 2026
Cited by 1 | Viewed by 1401
Abstract
Background: Amid rising trade, urbanization, and carbon emissions in MENA countries, sustainable logistics faces major constraints. This study develops an enhanced Green Logistics Performance Index (GLPI) using min-max normalization and Principal Component Analysis (PCA) to integrate the World Bank’s Logistics Performance Index (LPI) [...] Read more.
Background: Amid rising trade, urbanization, and carbon emissions in MENA countries, sustainable logistics faces major constraints. This study develops an enhanced Green Logistics Performance Index (GLPI) using min-max normalization and Principal Component Analysis (PCA) to integrate the World Bank’s Logistics Performance Index (LPI) and Yale’s Environmental Performance Index (EPI). The study uses fixed-effects panel regression on data from 20 MENA countries (2018–2024), identifies key drivers, and applies ARIMA and LSTM models for 2030 projections. The prior ratio-based GLPI suffered from scale sensitivity and volatility; this refined version provides improved stability and predictive utility for Green Supply Chain Management (GSCM). Methods: Panel data from 20 MENA countries (2018–2024) were analyzed. The enhanced GLPI normalizes and weights LPI and EPI scores via PCA. Fixed-effects regression identifies drivers, while ARIMA and LSTM enable scenario-based forecasting (baseline, optimistic, and pessimistic). Results: Renewable energy share positively influences GLPI, while trade openness has a negative effect. Projections indicate the regional GLPI will reach about 0.65 by 2030, with Saudi Arabia potentially achieving 25% higher under optimistic conditions. Conclusions: The refined GLPI advances GSCM theory by operationalizing triple bottom line trade-offs through a robust, predictive metric. It bridges descriptive limitations in prior literature, enabling forward-looking insights into sustainable logistics in emerging economies, with potential applicability beyond MENA. Full article
(This article belongs to the Section Sustainable Supply Chains and Logistics)
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32 pages, 3592 KB  
Systematic Review
Mapping the Landscape of Healthcare Supply Chain Management Through an NLP-Driven Systematic Review
by Andrea Tomassi, Antonio Javier Nakhal Akel, Andrea Falegnami and Federico Bilotta
Logistics 2026, 10(3), 55; https://doi.org/10.3390/logistics10030055 - 4 Mar 2026
Viewed by 1096
Abstract
Background: Healthcare supply chains (HSCs) are critical socio-technical systems that ensure the timely delivery of pharmaceuticals, medical devices, and electromedical equipment, yet they face increasing complexity due to regulatory constraints, demand uncertainty, and the growing digitalization of healthcare systems. This study aims [...] Read more.
Background: Healthcare supply chains (HSCs) are critical socio-technical systems that ensure the timely delivery of pharmaceuticals, medical devices, and electromedical equipment, yet they face increasing complexity due to regulatory constraints, demand uncertainty, and the growing digitalization of healthcare systems. This study aims to systematically map the HSC literature and identify its main thematic structures and research gaps. Methods: A systematic literature review was conducted following PRISMA guidelines, analyzing 705 peer-reviewed articles retrieved from the Web of Science database (PROSPERO registration: CRD42024605761). Natural language processing techniques were applied to support the analysis, including topic modeling, term frequency–inverse document frequency for keyword relevance, and Keyword in Context analysis for semantic interpretation. Results: The analysis identified six main thematic clusters and revealed a fragmented research landscape, characterized by limited integration across supply chain tiers, uneven attention to technological innovations, and marginal consideration of sustainability and implementation issues. The findings also highlight a gap between conceptual developments and real-world applications. Conclusions: This study provides a data-driven overview of the HSC research domain, highlighting key gaps and opportunities for more integrated, resilient, and efficient supply chain management. Full article
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25 pages, 2662 KB  
Review
Optimizing Biomass Feedstock Logistics Using AI for Integrated Multimodal Transport in Bioenergy and Bioproduct Systems: A Review
by Johanna Gonzalez and Jingxin Wang
Logistics 2026, 10(3), 54; https://doi.org/10.3390/logistics10030054 - 2 Mar 2026
Viewed by 1515
Abstract
Background: The constant growth in demand for sustainable energy products and the development of the circular economy have created a critical need for an efficient supply chain for biomass. However, the inherent challenges of biomass make its harvesting, collection, storage, and transport [...] Read more.
Background: The constant growth in demand for sustainable energy products and the development of the circular economy have created a critical need for an efficient supply chain for biomass. However, the inherent challenges of biomass make its harvesting, collection, storage, and transport difficult, impacting logistical efficiency and the viability of bioenergy and bioproduct production. This study analyzes how combining artificial intelligence (AI) with multimodal transport can optimize and improve efficiency, as well as reduce costs, in biomass logistics. Methods: The study uses a tiered research framework that encompasses the physical domain (biomass limitations), the structural domain (mathematical modeling for multimodal transport), the intelligence domain (AI-based decision making), and the strategic approach. Results: The outcomes indicate that while truck transport is ideal for short distances, integrating rail and water transport through AI-driven optimization reduces costs and greenhouse gas emissions for long-distance travel. AI technologies, such as digital twins and machine learning, improve demand forecasting, real-time routing, and cargo consolidation, leading to enhanced prediction accuracy for transport costs. Conclusions: The integration of AI and multimodal networks builds resilient and sustainable biomass supply chains. However, full implementation requires addressing data fragmentation and investing in digital infrastructure to enable seamless coordination between supply chain stakeholders. Full article
(This article belongs to the Special Issue Artificial Intelligence and Business Analytics Applications in Supply Chain Operations)
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20 pages, 1305 KB  
Article
The Stock Allocation Problem in a Production System with FIFO Picking Operations
by Luca Bertazzi and Felice Pedersoli
Logistics 2026, 10(3), 53; https://doi.org/10.3390/logistics10030053 - 1 Mar 2026
Viewed by 770
Abstract
Background: We study one of the most important problems in production and warehouse management: the problem of determining how to allocate the initial stock and the quantity produced to bins, and then how to manage picking operations from these bins. The objective [...] Read more.
Background: We study one of the most important problems in production and warehouse management: the problem of determining how to allocate the initial stock and the quantity produced to bins, and then how to manage picking operations from these bins. The objective is to minimize the total cost of the bins used. Methods: We formulate an integer linear programming model able to manage the two time periods related to assignment and picking together, and to handle the FIFO picking logic. We prove that it is NP-hard, and solve it to optimality. Then, we design a tailored heuristic algorithm, inspired by the current rule of thumb used by one of the main Italian mineral water bottling companies. Results: An extensive computational experiment allows us to show that this problem can be solved to optimality in a reasonable computational time based on real-world instances, and that the heuristic provides near-optimal solutions. Conclusions: Our approach provides a contribution to modeling and solving this problem when FIFO picking operations are taken into account. Moreover, it contributes by building important bridges between theoretical understanding and practical applications. Full article
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Article
Assessing the Impacts of Green Logistics on Sustainable Business Performance: An Application of a Hybrid SEM-GM(1,1) Approach
by Khanh Han Nguyen and Tin Van Vo
Logistics 2026, 10(3), 52; https://doi.org/10.3390/logistics10030052 - 24 Feb 2026
Viewed by 2448
Abstract
Background: Amid global sustainability imperatives, the logistics sector serves as a key economic enabler while remaining a major contributor to greenhouse gas emissions. This study investigates the causal relationships between green logistics practices and sustainable business performance in Vietnamese small- and medium-sized [...] Read more.
Background: Amid global sustainability imperatives, the logistics sector serves as a key economic enabler while remaining a major contributor to greenhouse gas emissions. This study investigates the causal relationships between green logistics practices and sustainable business performance in Vietnamese small- and medium-sized enterprises, mediated by competitiveness, and forecasts future trends to inform transitions aligned with net-zero goals. Methods: A mixed-methods design integrates structural equation modeling with the gray model. Primary data were collected via Likert-scale questionnaires administered to 350 managers to measure latent variables. Secondary financial metrics (revenue, costs, assets, profits) from 15 firms spanning 2021–2024 enabled forecasting. Results: SEM, employing bootstrapping for path estimation, revealed positive direct effects, with the strongest effects for green transportation and weaker effects for technology, packaging, and warehousing. Mediation via competitiveness yielded mixed indirect effects: positive for warehousing and transportation, but negative for technology. GM(1,1) projected moderate performance growth under conditions of data uncertainty. Conclusions: The hybrid framework advances the resource-based view in emerging market contexts, recommending prioritization of transportation and technology initiatives alongside policy incentives to align with sustainable development goals and enhance resilience in Vietnam’s logistics sector. Full article
(This article belongs to the Section Sustainable Supply Chains and Logistics)
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