Logistics

22 pages, 445 KB

Open AccessArticle

AI Integration in Fundamental Logistics Components: Advanced Theoretical Framework for Knowledge Process Capabilities and Dynamic Capabilities Hybridization

by Zsolt Toth, Alexandru-Silviu Goga and Mircea Boșcoianu

Logistics 2025, 9(4), 140; https://doi.org/10.3390/logistics9040140 - 5 Oct 2025

Abstract

Background: Despite significant technological advances, many logistics organizations in emerging markets struggle to realize the transformative potential of artificial intelligence, with reported success rates below 65% and limited theoretical understanding of the organizational capabilities. This study develops and proposes an integrated theoretical [...] Read more.

Background: Despite significant technological advances, many logistics organizations in emerging markets struggle to realize the transformative potential of artificial intelligence, with reported success rates below 65% and limited theoretical understanding of the organizational capabilities. This study develops and proposes an integrated theoretical framework examining how knowledge process capabilities and dynamic capabilities interact to enable successful artificial intelligence adoption in logistics organizations within emerging market contexts. Methods: Through comprehensive literature review and theoretical synthesis, we propose a hybrid capability framework that integrates knowledge-based view perspectives with dynamic capabilities theory. Results: Theoretical analysis suggests that knowledge combination capabilities may be the strongest predictor of artificial intelligence implementation success, while dynamic reconfiguring capabilities could mediate the relationship between artificial intelligence adoption and performance outcomes. The proposed framework indicates that organizations with hybrid capability architecture may achieve superior implementation success compared to traditional approaches. Environmental uncertainty is theorized to strengthen the knowledge process capabilities—artificial intelligence adoption relationship. Conclusions: The framework suggests that successful artificial intelligence integration requires simultaneous development of knowledge-based and adaptive capabilities rather than sequential capability building. The hybrid capability framework provides theoretical guidance for managers in emerging markets, while highlighting the critical role of environmental context in shaping transformation strategies. Full article

(This article belongs to the Topic Sustainable Supply Chain Practices in A Digital Age)

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24 pages, 2318 KB

Open AccessArticle

From Chaos to Coherent Structure (Pattern): The Mathematical Architecture of Invisible Time—The Critical Minute Theorem in Ground Handling Operations in an Aircraft Turnaround on the Ground of an Airport

by Cornel Constantin Tuduriu, Dan Laurentiu Milici and Mihaela Paval

Logistics 2025, 9(4), 139; https://doi.org/10.3390/logistics9040139 - 1 Oct 2025

Abstract

Background: In the dynamic world of commercial aviation, the efficient management of ground handling (GH) operations in aircraft turnarounds is an increasingly complex challenge, often perceived as operational chaos. Methods: This paper introduces the “Critical Minute Theorem” (CMT), a novel framework [...] Read more.

Background: In the dynamic world of commercial aviation, the efficient management of ground handling (GH) operations in aircraft turnarounds is an increasingly complex challenge, often perceived as operational chaos. Methods: This paper introduces the “Critical Minute Theorem” (CMT), a novel framework that integrates mathematical architecture principles into the optimization of GH processes. CMT identifies singular temporal thresholds,

t_{k}^{*}

at which small local disturbances generate nonlinear, system-wide disruptions. Results: By formulating the turnaround as a set of algebraic dependencies and nonlinear differential relations, the case studies demonstrate that delays are not random but structurally determined. The practical contribution of this study lies in showing that early recognition and intervention at these critical minutes significantly reduces propagated delays. Three case analyses are presented: (i) a fueling delay initially causing 9 min of disruption, reduced to 3.7 min after applying CMT-based reordering; (ii) baggage mismatch scenarios where CMT-guided list restructuring eliminates systemic deadlock; and (iii) PRM assistance delays mitigated by up to 12–15 min through anticipatory task reorganization. Conclusions: These results highlight that CMT enables predictive, non-technological control in turnaround operations, repositioning the human analyst as an architect of time capable of restoring structure where the system tends to collapse. Full article

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17 pages, 1041 KB

Open AccessArticle

The Impact of Technological Innovations on Digital Supply Chain Management: The Mediating Role of Artificial Intelligence: An Empirical Study

by Ali F. Dalain, Mohammad Alnadi, Mahmoud Izzat Allahham and Mohammad Ali Yamin

Logistics 2025, 9(4), 138; https://doi.org/10.3390/logistics9040138 - 27 Sep 2025

Abstract

Background: This study examines the impact of technological innovations on digital supply chain management, with a focus on the mediating role of artificial intelligence. With global supply chains increasingly relying on digital platforms, the integration of advanced technologies has become essential for [...] Read more.

Background: This study examines the impact of technological innovations on digital supply chain management, with a focus on the mediating role of artificial intelligence. With global supply chains increasingly relying on digital platforms, the integration of advanced technologies has become essential for achieving efficiency and competitiveness. Methods: The research employs a mixed-methods approach, combining survey data and expert interviews with professionals from Jordan’s industrial sector. It investigates how emerging digital innovations influence supply chain performance and examines the extent to which artificial intelligence contributes to automation, predictive analytics, and data-driven decision-making. Results: The findings reveal that artificial intelligence plays a pivotal role in enhancing the effectiveness of technological innovations within digital supply chain systems. Specifically, AI improves adaptability to market fluctuations, increases operational efficiency, and strengthens strategic flexibility. These outcomes suggest that organizations adopting AI-enabled innovations are better equipped to respond to uncertainty and achieve superior supply chain performance. Conclusions: The study concludes that technological innovations significantly advance digital supply chain management when supported by artificial intelligence as a mediating factor. The integration of AI not only magnifies the value of digital innovations but also enables sustainable performance improvements and reinforces competitiveness in dynamic industrial environments. Full article

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20 pages, 745 KB

Open AccessArticle

Fuzzy–Monte Carlo-Based Assessment for Enhanced Urban Transport Planning in Amman, Jordan

by Reema Al-Dalain and Dilay Celebi

Logistics 2025, 9(4), 137; https://doi.org/10.3390/logistics9040137 - 26 Sep 2025

Abstract

Background: Cities worldwide face continuous challenges in sustainable development, particularly in transportation systems where decisions have long-lasting impacts on urban infrastructure and quality of life. The evaluation of sustainable transportation alternatives requires careful consideration of multiple indicators, making it a complex multi-criteria [...] Read more.

Background: Cities worldwide face continuous challenges in sustainable development, particularly in transportation systems where decisions have long-lasting impacts on urban infrastructure and quality of life. The evaluation of sustainable transportation alternatives requires careful consideration of multiple indicators, making it a complex multi-criteria decision-making process. Existing multi-criteria decision-making (MCDM) frameworks often overlook the dual uncertainties introduced by both fuzzy expert judgments and probabilistic performance measures, hindering robust evaluation of transportation alternatives in developing countries. Methods: In response, this study introduces a novel hybrid methodology combining fuzzy set theory and Monte Carlo simulation to evaluate transportation alternatives through 14 comprehensive sustainability indicators. Addressing the critical need for sustainable public transportation assessment in rapidly urbanizing developing countries, where existing assessment frameworks frequently prove inadequate, we present a case study from Amman, Jordan. Results: The results reveal that a Bus Rapid Transit (BRT) system outperforms both conventional automobiles and small buses in 87.06% of simulation scenarios, underscoring its robust sustainability profile. The sensitivity analysis highlights that a BRT system is highly robust, with minimal sensitivity to changes in most criteria and strong responsiveness to critical factors such as land usage. Conclusions: This research provides decision-makers with a comprehensive, evidence-based tool for evaluating public transport investment under uncertainty. The methodology’s ability to account for multiple stakeholder perspectives while handling uncertainty makes it particularly valuable for urban planners and policymakers facing complex transportation infrastructure decisions in rapidly evolving urban environments. Full article

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18 pages, 1003 KB

Open AccessArticle

Weathering the Storm: Dynamic Capabilities and Supply Chain Agility in Supply Chain Resilience

by Marie Legg, Reginald A. Silver and Sungjune Park

Logistics 2025, 9(4), 136; https://doi.org/10.3390/logistics9040136 - 25 Sep 2025

Abstract

Background: Despite growing interest in supply chain resilience (SCRes), theoretical overlap between dynamic capabilities (DC) and supply chain agility (SCA) has complicated empirical analysis of their distinct roles. Additionally, the contextual role of information asymmetry in shaping resilience remains underexplored. This study [...] Read more.

Background: Despite growing interest in supply chain resilience (SCRes), theoretical overlap between dynamic capabilities (DC) and supply chain agility (SCA) has complicated empirical analysis of their distinct roles. Additionally, the contextual role of information asymmetry in shaping resilience remains underexplored. This study addresses both issues by modeling DC hierarchically and examining IA as a moderator. Methods: Data were collected through a cross-sectional survey of 157 U.S.-based supply chain professionals. Partial least squares structural equation modeling (PLS-SEM) was used to examine the relationships among DC, SCA, IA, and SCRes. Results: SCA was a strong, direct predictor of SCRes. In contrast, DC showed no direct effect in the full model; however, in a hierarchical component model (HCM), DC, a higher-order construct, emerged as significant predictor of SCRes. IA exerted a dual negative influence: it directly weakened SCRes and negatively moderated the relationship between DC and SCRes. Conclusions: This study makes two novel contributions. First, it resolves ambiguity between DC and SCA by empirically modeling DC as a higher-order construct that encompasses but remains distinct from SCA. Second, it introduces IA as a multidimensional barrier to resilience, demonstrating its direct and interactive effects. These findings provide new insight into capability design and contextual adaptation for SCRes in uncertain, information-constrained environments. Full article

(This article belongs to the Special Issue Advancements in Building Resilient Reverse Supply Chains: Strategies, Technologies, and Sustainable Practices)

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37 pages, 503 KB

Open AccessArticle

A Holistic Human-Based Approach to Last-Mile Delivery: Stakeholder-Based Evaluation of Logistics Strategies

by Aleksa Maravić, Vukašin Pajić and Milan Andrejić

Logistics 2025, 9(4), 135; https://doi.org/10.3390/logistics9040135 - 23 Sep 2025

Abstract

Background: The growing complexity of last-mile logistics (LML) in urban environments has created an urgent need for sustainable, efficient, and stakeholder-inclusive solutions. This study addresses these challenges by exploring a holistic, human-centered approach to evaluating LML strategies, recognizing the diverse expectations of [...] Read more.

Background: The growing complexity of last-mile logistics (LML) in urban environments has created an urgent need for sustainable, efficient, and stakeholder-inclusive solutions. This study addresses these challenges by exploring a holistic, human-centered approach to evaluating LML strategies, recognizing the diverse expectations of logistics service providers, delivery personnel, customers, and local authorities. Methods: To capture both subjective and objective factors influencing decision-making, the study employs a Multi-Criteria Decision-Making (MCDM) framework that integrates the Fuzzy Analytic Hierarchy Process (FAHP) and Evaluation based on Distance from Average Solution (EDAS). Evaluation criteria encompass operational efficiency, environmental impact, social acceptance, and technological feasibility. Results: Six LML solutions were assessed and ranked using this approach. The results indicate that the cargo bike (A₂) emerged as the most favorable alternative, while electric freight vehicles (A₅) ranked lowest. These findings reflect significant trade-offs between stakeholder priorities and the varying performance of different delivery strategies. Conclusions: The proposed methodology offers practical guidance for designing balanced and socially responsible urban logistics systems. By emphasizing inclusivity in decision-making, this approach supports the development of LML solutions that are not only operationally effective but also environmentally sustainable and broadly accepted by stakeholders. Full article

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27 pages, 788 KB

Open AccessArticle

Extending the DBQ Framework: A Second-Order CFA of Risky Driving Behaviors Among Truck Drivers in Thailand

by Supanida Nanthawong, Panuwat Wisutwattanasak, Chinnakrit Banyong, Thanapong Champahom, Vatanavongs Ratanavaraha and Sajjakaj Jomnonkwao

Logistics 2025, 9(3), 134; https://doi.org/10.3390/logistics9030134 - 22 Sep 2025

Abstract

Background: Truck drivers are a vital workforce sustaining Thailand’s freight transport, particularly in Northeastern Thailand (Isan), a major logistics hub connecting with Laos, Vietnam, and Cambodia via Highway No. 2 and the AEC network. However, these drivers face disproportionately high risks of [...] Read more.

Background: Truck drivers are a vital workforce sustaining Thailand’s freight transport, particularly in Northeastern Thailand (Isan), a major logistics hub connecting with Laos, Vietnam, and Cambodia via Highway No. 2 and the AEC network. However, these drivers face disproportionately high risks of severe road accidents due to occupational factors such as fatigue, time pressure, and long-distance driving. Methods: This study developed and validated a second-order confirmatory factor analysis (CFA) model to examine the multidimensional structure of risky driving behavior among Thai truck drivers. Grounded in the Driver Behavior Questionnaire (DBQ), the framework was extended to include seven dimensions: traffic violations, errors, lapses, aggressive behavior, substance use, technology-related distractions, and pedestrian-related risks. Results: Data were collected from 400 truck drivers in Isan using a structured questionnaire. CFA results confirmed the model’s structural validity, with satisfactory fit indices (X²/df = 2.122, CFI = 0.913, TLI = 0.897, RMSEA = 0.053, SRMR = 0.079). Conclusions: The findings reveal that risky driving behavior in this group extends beyond traditional DBQ categories, incorporating emerging risks specific to the commercial transport environment. This framework can be effectively utilized for risk assessment, behavioral screening, and the development of targeted safety interventions for this high-risk occupational group. Full article

(This article belongs to the Section Sustainable Supply Chains and Logistics)

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24 pages, 769 KB

Open AccessArticle

An Inventory Model with Price-, Time- and Greenness-Sensitive Demand and Trade Credit-Based Economic Communications

by Musaraf Hossain, Mostafijur Rahaman, Shariful Alam, Magfura Pervin, Soheil Salahshour and Sankar Prasad Mondal

Logistics 2025, 9(3), 133; https://doi.org/10.3390/logistics9030133 - 22 Sep 2025

Abstract

Background: Price is the most authoritative constituent among the factors shaping consumer demand. Growing consciousness among global communities regarding environmental issues makes greenness one of the key factors controlling demand, along with time, which drives demand in markets. This paper addresses such issues [...] Read more.

Background: Price is the most authoritative constituent among the factors shaping consumer demand. Growing consciousness among global communities regarding environmental issues makes greenness one of the key factors controlling demand, along with time, which drives demand in markets. This paper addresses such issues associated with a retail purchase scenario. Methods: Consumer’s demand for products is hypothesized to be influenced by pricing, time and the green level of the product in the proposed model. Time-dependent inventory carrying cost and green level-induced purchasing cost are considered. The average cost during the decision cycle is the objective function that is analyzed in trade credit phenomena, involving delayed payment by the manufacturer to the supplier. The Convex optimization technique is used to find an optimal solution for the model. Results: Once a local optimal solution is found, sensitivity analysis is conducted to determine the optimal value of the objective function and decision variables for other impacting parameters. Results reveal that demand-boosting parameters, for instance, discounts on price and green activity, result in additional average costs. Conclusions: Discounts on price and green activity advocate a large supply capacity by boosting demand, creating opportunities for the retailer to earn more revenue. Full article

(This article belongs to the Section Sustainable Supply Chains and Logistics)

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19 pages, 1000 KB

Open AccessArticle

Multi-Criteria Decision Support for Sustainable Supplier Evaluation in Mining SMEs: A Fuzzy Logic and TOPSIS Approach

by Joachim O. Gidiagba, Modestus Okwu and Lagouge Tartibu

Logistics 2025, 9(3), 132; https://doi.org/10.3390/logistics9030132 - 22 Sep 2025

Abstract

Background: Improving operational efficiency in the mining industry increasingly de-pends on a mature asset management framework and the careful selection of reliable, sustainable suppliers for systems, personnel, equipment, and services. Given the complexity of mining operations and the growing use of digital [...] Read more.

Background: Improving operational efficiency in the mining industry increasingly de-pends on a mature asset management framework and the careful selection of reliable, sustainable suppliers for systems, personnel, equipment, and services. Given the complexity of mining operations and the growing use of digital tools, choosing the right maintenance management system requires a robust decision-making process that considers economic, environmental, and social sustainability factors. Methods: This study develops and compares two multi-criteria decision-making approaches, a ranking method and a fuzzy logic-based model to evaluate four maintenance management systems against fifteen sustainability-related criteria. Expert opinions from executives and operational managers in the South African mining sector were gathered, focusing on factors such as cost, integration, reliability, ease of use, inventory control, and predictive capabilities. Results: The ranking method produced a clear, quantitative order of preference, while the fuzzy model addressed uncertainty and subjectivity in expert judgments. Both methods identified the same top choice: UPKEEP, followed by SAP, FIIX, and LIMBLE. Conclusions: This comparison shows that combining fuzzy logic with sustainability-focused evaluation can improve the flexibility and reliability of supplier selection in asset management. The proposed approach offers practical guidance for aligning maintenance system choices with broader sustainability goals in mining operations. Full article

(This article belongs to the Topic Sustainable Supply Chain Practices in A Digital Age)

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25 pages, 567 KB

Open AccessArticle

Impact of Container Reverse Logistics on the Maritime Sector: Economic and Environmental Factors

by Joaquim Jorge Vicente, Lurdes Neves and Catarina Marques

Logistics 2025, 9(3), 131; https://doi.org/10.3390/logistics9030131 - 17 Sep 2025

Abstract

This paper investigates the growing problem of abandoned maritime containers and the lack of effective reverse logistics to manage them: Background: The research highlights the significant environmental impact and economic burdens caused by the imbalance of container inflow and outflow, which leads to [...] Read more.

This paper investigates the growing problem of abandoned maritime containers and the lack of effective reverse logistics to manage them: Background: The research highlights the significant environmental impact and economic burdens caused by the imbalance of container inflow and outflow, which leads to the accumulation of containers in storage yards; Methods: The study used the Delphi Method, gathering insights from a panel of experts in container transport and maintenance. The goal was to identify key challenges and potential solutions for improving container reverse logistics in Portugal; Results: The results confirm the urgent need for efficient reverse logistics strategies to address the container imbalance. The experts reached over 60% consensus on the importance of developing logistics systems and improving communication between ports. Implementing these strategies would not only reduce economic costs but also significantly lower environmental pollution; Conclusions: The paper concludes that a strategic shift toward effective reverse logistics is essential for enhancing the sustainability and operational efficiency of the maritime transport sector. Full article

(This article belongs to the Section Maritime and Transport Logistics)

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17 pages, 3917 KB

Open AccessArticle

A Data-Driven Approach Using Recurrent Neural Networks for Material Demand Forecasting in Manufacturing

by Jorge Antonio Orozco Torres, Alejandro Medina Santiago, José R. García-Martínez, Betty Yolanda López-Zapata, Jorge Antonio Mijangos López, Oscar Javier Rincón Zapata and Jesús Alejandro Avitia López

Logistics 2025, 9(3), 130; https://doi.org/10.3390/logistics9030130 - 12 Sep 2025

Abstract

Background: In the current context of increasing competitiveness and complexity in markets, accurate demand forecasting has become a key element for efficient production planning. Methods: This study implements recurrent neural networks (RNNs) to predict raw material demand using historical sales data, [...] Read more.

Background: In the current context of increasing competitiveness and complexity in markets, accurate demand forecasting has become a key element for efficient production planning. Methods: This study implements recurrent neural networks (RNNs) to predict raw material demand using historical sales data, leveraging their ability to identify complex temporal patterns by analyzing 156 historical records. Results: The findings reveal that the RNN-based model significantly outperforms traditional methods in predictive accuracy when sufficient data is available. Conclusions: Although integration with MRP systems is not explored, the results demonstrate the potential of this deep learning approach to improve decision-making in production management, offering an innovative solution for increasingly dynamic and demanding industrial environments. Full article

(This article belongs to the Special Issue Machine Learning and Optimization for Warehouse Inventory Management in Agile Supply Chains)

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30 pages, 983 KB

Open AccessArticle

An Integrated DEA–Porter Decision Support Framework for Enhancing Supply Chain Competitiveness in the Muslim Fashion Industry: Evidence from Indonesia

by Jilly Ayuningtias, Marimin Marimin, Agus Buono and Arif Imam Suroso

Logistics 2025, 9(3), 129; https://doi.org/10.3390/logistics9030129 - 12 Sep 2025

Abstract

Background: The competitiveness of Indonesia’s Muslim fashion industry requires evaluation through both internal efficiency and external strategic factors, yet existing approaches often assess these dimensions separately. Methods: This study develops a Weighted Efficiency Competitive Score (WECS) that integrates Data Envelopment Analysis (DEA) to [...] Read more.

Background: The competitiveness of Indonesia’s Muslim fashion industry requires evaluation through both internal efficiency and external strategic factors, yet existing approaches often assess these dimensions separately. Methods: This study develops a Weighted Efficiency Competitive Score (WECS) that integrates Data Envelopment Analysis (DEA) to measure operational efficiency and Porter’s Five Forces to capture market pressures. The weights of α and β were calibrated through sensitivity analysis under the constraint α + β = 1, with values ranging from α = 0.3 to 0.7 and β = 0.7 to 0.3, using data from 23 Muslim fashion businesses in Jakarta. Results: The analysis identified α = 0.6 and β = 0.4 as the most stable configuration, and only 30% of firms achieved both high efficiency and strong market positioning. Strategic leaders such as JT. Co and PM. Co demonstrated that digital transformation, disciplined cost structures, and strong supply chain partnerships foster sustainable competitiveness. Conclusions: The WECS framework offers a replicable method to quantitatively integrate micro and macro determinants of competitiveness, contributes to the literature by bridging efficiency and strategy evaluation, and provides practical guidance for managers and policymakers to enhance decision support systems in strengthening the Muslim fashion industry’s global positioning. Full article

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28 pages, 1538 KB

Open AccessArticle

Optimal Inventory Planning at the Retail Level, in a Multi-Product Environment, Enabled with Stochastic Demand and Deterministic Lead Time

by Andrés Julián Barrera-Sánchez and Rafael Guillermo García-Cáceres

Logistics 2025, 9(3), 128; https://doi.org/10.3390/logistics9030128 - 11 Sep 2025

Abstract

Background: Inventory planning in retail supply chains requires balancing cost efficiency and service reliability under demand uncertainty and financial limitations. The literature has seldom addressed the joint integration of stochastic demand, deterministic lead times, and supplier-specific constraints in multi-product and multi-warehouse settings, [...] Read more.

Background: Inventory planning in retail supply chains requires balancing cost efficiency and service reliability under demand uncertainty and financial limitations. The literature has seldom addressed the joint integration of stochastic demand, deterministic lead times, and supplier-specific constraints in multi-product and multi-warehouse settings, particularly in the context of small- and medium-sized enterprises. Methods: This study develops a Stochastic Pure Integer Linear Programming (SPILP) model that incorporates stochastic demand, deterministic lead times, budget ceilings, and trade credit conditions across multiple suppliers and warehouses. A two-step solution procedure is proposed, combining a chance-constrained approach to manage uncertainty with warm-start heuristics and relaxation-based preprocessing to improve computational efficiency. Results: Model validation using data from a Colombian retail distributor showed cost reductions of up to 17% (average 15%) while maintaining or improving service levels. Computational experiments confirmed scalability, solving instances with more than 574,000 variables in less than 8800 s. Sensitivity analyses revealed nonlinear trade-offs between service levels and planning horizons, showing that very high service levels or short planning periods substantially increase costs. Conclusions: The findings demonstrate that the proposed model provides an effective decision support system for inventory planning under uncertainty, offering robust, scalable, and practical solutions that integrate operational and financial constraints for medium-sized retailers. Full article

(This article belongs to the Special Issue Logistics and Supply Chain Challenges and Solutions in the Turbulent World)

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17 pages, 889 KB

Open AccessArticle

App-Based Logistics for Residual Biomass Recovery: Economic Feasibility in Fire Risk Mitigation

by Tiago Bastos, Leonor Teixeira and Leonel J. R. Nunes

Logistics 2025, 9(3), 127; https://doi.org/10.3390/logistics9030127 - 8 Sep 2025

Abstract

Background: Rural fires, worsened by climate factors such as drought, biomass buildup, and ignition sources, threaten sustainability. Recovering residual biomass (RB) presents a promising way to lower fire risk by reducing fuel loads and generating renewable energy; however, logistical costs in the [...] Read more.

Background: Rural fires, worsened by climate factors such as drought, biomass buildup, and ignition sources, threaten sustainability. Recovering residual biomass (RB) presents a promising way to lower fire risk by reducing fuel loads and generating renewable energy; however, logistical costs in the RB supply chain—due to poor stakeholder coordination—limit its feasibility. App-based models can help solve these issues by improving information sharing, but their economic viability remains largely unexplored. This study suggests that such models work well when large amounts of biomass are involved and moisture content is low. Still, they might need external incentives for widespread use and fire risk reduction. Methods: The study modeled recovery scenarios by comparing costs (harvesting, retrieval, transport, and pre-processing) with biomass market value, using expert inputs and sensitivity analysis on variables like fuel prices and wages. Results: The economic feasibility is possible for large volumes (e.g., 10-ton loads) with low moisture (<30%), allowing transportation distances up to 459 km; however, small-scale or high-moisture situations often are not viable without support. Conclusions: App-based models need external support, like subsidies, to overcome owner and RB challenges, ensuring effective fire mitigation and sustainability benefits. Full article

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17 pages, 536 KB

Open AccessArticle

Leveraging Household Food Waste Consumer Behaviour to Optimise Logistics

by Sotiris Ntai, Maria Kontopanou and Foivos Anastasiadis

Logistics 2025, 9(3), 126; https://doi.org/10.3390/logistics9030126 - 2 Sep 2025

Abstract

Background: This study explores how consumer behaviour influences household food waste and its ripple effects on the efficiency of the agri-food supply chain. Methods: Using survey data, we applied regression analysis to analyse the links between shopping habits, household demographics, waste reduction [...] Read more.

Background: This study explores how consumer behaviour influences household food waste and its ripple effects on the efficiency of the agri-food supply chain. Methods: Using survey data, we applied regression analysis to analyse the links between shopping habits, household demographics, waste reduction goals, and disposal practices. Results: Results show that purchasing driven by promotions significantly boosts household waste, while waste reduction goals strongly reduce disposal behaviours. These results illustrate how irregular consumer purchasing patterns create upstream demand fluctuations, making inventory management and production planning more complex. The findings highlight opportunities for logistics improvements, such as demand-based inventory systems, optimised purchasing routines, adjusted promotional strategies, and consumer-involved forecasting models to cut waste and promote resource sustainability. Conclusions: This research connects consumer behaviour with supply chain management, offering practical insights for building more sustainable and efficient food supply chains through targeted logistics actions. Full article

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19 pages, 271 KB

Open AccessArticle

Conceptualizing Warehouse 4.0 Technologies in the Third-Party Logistics Industry: An Empirical Study

by Erika Marie Strøm, Julie Amanda Busch, Lars Hvam and Anders Haug

Logistics 2025, 9(3), 125; https://doi.org/10.3390/logistics9030125 - 2 Sep 2025

Abstract

Background: Industry 4.0 (I4.0) has gained significant attention in recent years, with the term Logistics 4.0 (L4.0) emerging in the logistics industry. However, L4.0 remains vague and lacks a unified definition or classification of related technologies. Existing studies defining L4.0 are mainly [...] Read more.

Background: Industry 4.0 (I4.0) has gained significant attention in recent years, with the term Logistics 4.0 (L4.0) emerging in the logistics industry. However, L4.0 remains vague and lacks a unified definition or classification of related technologies. Existing studies defining L4.0 are mainly conceptual and speculative, rather than grounded in empirical research. To address this gap, this study contributes to defining L4.0 through the sub-area of Warehouse 4.0 (W4.0), focusing on the challenges of adopting I4.0 technologies in warehouses. Methods: Through the I4.0 and L4.0 literature, an initial classification of W4.0 technologies in third-party logistics (3PL) was developed. This was refined using a case study of a global logistics service provider (LSP) in the 3PL industry, through semi-structured interviews with stakeholders. Results: The empirical findings identify new application areas for I4.0 technology in 3PL warehouses, including horizontal and vertical system integration, big data, and cybersecurity, technologies that can enhance 3PL competitiveness. Conclusions: This study offers a structured classification of W4.0 technologies and insights into the application areas of W4.0 in 3PLs. It contributes practical insights into which I4.0 technologies are relevant for the 3PL warehouse industry and their potential application areas. Full article

22 pages, 2039 KB

Open AccessArticle

ML and Statistics-Driven Route Planning: Effective Solutions Without Maps

by Péter Veres

Logistics 2025, 9(3), 124; https://doi.org/10.3390/logistics9030124 - 1 Sep 2025

Abstract

Background: Accurate route planning is a core challenge in logistics, particularly for small- and medium-sized enterprises that lack access to costly geospatial tools. This study explores whether usable distance matrices and routing outputs can be generated solely from geographic coordinates without relying [...] Read more.

Background: Accurate route planning is a core challenge in logistics, particularly for small- and medium-sized enterprises that lack access to costly geospatial tools. This study explores whether usable distance matrices and routing outputs can be generated solely from geographic coordinates without relying on full map-based infrastructure. Methods: A dataset of over 5000 Hungarian postal locations was used to evaluate five models: Haversine-based scaling with circuity, linear regression, second- and third-degree polynomial regressions, and a trained artificial neural network. Models were tested on the full dataset, and three example routes representing short, medium, and long distances. Both statistical accuracy and route-level performance were assessed, including a practical optimization task. Results: Statistical models maintained internal consistency, but systematically overestimated longer distances. The ANN model provided significantly better accuracy across all scales and produced routes more consistent with map-based paths. A new evaluation method was introduced to directly compare routing outputs. Conclusions: Practical route planning can be achieved without GIS services. ML-based estimators offer a cost-effective alternative, with potential for further improvement using larger datasets, additional input features, and the integration of travel time prediction. This approach bridges the gap between simplified approximations and commercial routing systems. Full article

(This article belongs to the Section Artificial Intelligence, Logistics Analytics, and Automation)

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25 pages, 526 KB

Open AccessArticle

Integrating CRM, Lean Practices, and Use of IT to Enhance Operational Performance: The Mediating Role of Quality Information Sharing

by A. H. M. Yeaseen Chowdhury, M. M. Hussain Shahadat, Saurav Chandra Talukder, Arnold Csonka and Maria Fekete Farkas

Logistics 2025, 9(3), 123; https://doi.org/10.3390/logistics9030123 - 1 Sep 2025

Abstract

Background: This study explores the relationship among various supply chain management practices, including customer relationship management, lean practices, use of information technology, and quality of information sharing with operational performance in the readymade garments industry of Bangladesh. It also examines the mediating [...] Read more.

Background: This study explores the relationship among various supply chain management practices, including customer relationship management, lean practices, use of information technology, and quality of information sharing with operational performance in the readymade garments industry of Bangladesh. It also examines the mediating role of quality of information sharing in these relationships. Methods: Data were collected from 80 readymade garment companies across five different geographical locations, with companies of varying sizes (large, medium, and small), involving 365 respondents with a response rate of 65%. A self-administered questionnaire survey was conducted, and Partial Least Squares Structural Equation Modeling (PLS-SEM) was applied for the analysis. Results: The results indicate that all four practices significantly enhance operational performance, while customer relationship management and use of information technology also improve performance indirectly through quality of information sharing, unlike lean practices. Conclusions: The findings suggest that supply chain managers and stakeholders can improve operational performance by implementing supply chain management practices and understanding the complexities of their interrelationships. Full article

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27 pages, 1506 KB

Open AccessArticle

Port Performance and Its Influence on Vessel Operating Costs and Emissions

by Livia Rauca, Catalin Popa, Dinu Atodiresei and Andra Teodora Nedelcu

Logistics 2025, 9(3), 122; https://doi.org/10.3390/logistics9030122 - 1 Sep 2025

Cited by 1

Abstract

Background: Port congestion contributes significantly to operational inefficiency and environmental impact in maritime logistics. With tightening EU regulations such as the Emissions Trading System (EU ETS) and FuelEU Maritime, understanding and mitigating the economic and environmental effects of vessel delays is increasingly [...] Read more.

Background: Port congestion contributes significantly to operational inefficiency and environmental impact in maritime logistics. With tightening EU regulations such as the Emissions Trading System (EU ETS) and FuelEU Maritime, understanding and mitigating the economic and environmental effects of vessel delays is increasingly critical. This study focuses on a single bulk cargo pier at Constanta Port (Romania), which has experienced substantial traffic fluctuations since 2021, and examines operational and environmental performance through a queuing-theoretic lens. Methods: The authors have applied an M/G/1/∞/FIFO/∞ queuing model to vessel traffic and service time data from 2021–2023, supplemented by Monte Carlo simulations to capture variability in maneuvering and service durations. Environmental impact was quantified in CO₂ emissions using standard fuel-based emission factors, and a Cold Ironing scenario was modeled to assess potential mitigation benefits. Economic implications were estimated through operational cost modeling and conversion of CO₂ emissions into equivalent EU ETS carbon costs. Results: The analysis revealed high berth utilization rates across all years, with substantial variability in waiting times and queue lengths. Congestion was associated with considerable CO₂ emissions, which, when expressed in monetary terms under prevailing EU ETS prices, represent a significant financial burden. The Cold Ironing scenario demonstrated a substantial reduction in at-berth emissions and corresponding cost savings, underscoring its potential as a viable mitigation strategy. Conclusions: Results confirm that operational congestion at the studied berth imposes substantial environmental and financial burdens. The analysis supports targeted interventions such as Just-In-Time arrivals, optimized berth scheduling, and Cold Ironing adoption. Recommendations are most applicable to single-berth bulk cargo operations; future research should extend the approach to multi-berth configurations and incorporate additional operational constraints for broader generalizability. Full article

(This article belongs to the Section Sustainable Supply Chains and Logistics)

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