Status: This article has been WITHDRAWN at the formal request of the authors. Reason: The manuscript was withdrawn post-publication due to an administrative disagreement regarding the Article Processing Charge (APC). The Editorial Board notes that this withdrawal is purely administrative and is not related to any form of scientific or ethical misconduct, plagiarism, or unreliability of data. The full-text PDF has been removed from public access. Purpose. This paper aims to address the fragmentation between decarbonization strategies and closed-loop supply chain (CLSC) design in sustainable logistics by developing an integrated analytical framework that captures their interdependencies across operational and contextual dimensions. Methodology. A hybrid systematic–integrative review was conducted, combining PRISMA-based literature selection with thematic synthesis. A total of 59 peer-reviewed studies (2012–2026) were analyzed and coded across six dimensions: technological enablers, CLSC design architecture, policy mechanisms, organizational preconditions, geoecological accountability, and regional macroeconomic context. Quantitative findings were standardized using relative change metrics and synthesized through a quality-weighted approach. Results. The analysis indicates that integrated decarbonization – CLSC approaches are associated with cost reductions of up to 31% and emission reductions of 12%-40% in modeled scenarios. The results further demonstrate that technological solutions alone are insufficient; their effectiveness depends on organizational capabilities, policy alignment, and regional economic conditions. Significant variability across contexts suggests that integration outcomes are highly contingent on local constraints. Theoretical contribution. The study advances the literature by proposing a six-pillar framework that reconceptualizes sustainable logistics as a multi-level governance problem rather than a purely operational optimization task. It contributes a structured approach for integrating fragmented research streams and incorporating macroeconomic and geoecological dimensions into supply chain analysis. Practical implications. The findings highlight the need for coordinated implementation strategies combining technological investment, organizational readiness, and context-sensitive policy design. The framework provides actionable guidance for practitioners, policymakers, and financial institutions seeking to operationalize sustainable logistics transitions.
Purpose. This study aims to synthesise empirical and modelling evidence on inventory optimisation methods for raw materials, work-in-process, and finished goods in production and trading enterprises, and to translate that evidence into a practical, class-differentiated implementation framework deployable within standard warehouse management and enterprise resource planning systems. Methodology. A systematic review and meta-analytic synthesis of 31 peer-reviewed studies published between 2004 and 2025 was conducted following the PRISMA 2020 protocol. A random-effects model estimated by restricted maximum likelihood was applied to pool percentage cost-reduction effect sizes across 18 studies admissible to quantitative synthesis, complemented by a narrative synthesis of the remaining 13 studies. Pre-specified subgroup and moderator analyses examined the role of inventory class, demand pattern, and network complexity as effect-size moderators. Results. Distributional safety stock methods outperform classical normal approximations by a pooled mean of 9.3% (95% CI: 5.8–12.7%) at equivalent service levels, with the advantage being largest for high-variability SKU segments. Multi-echelon coordination yields a pooled mean cost reduction of 11.4% (95% CI: 6.9–15.9%), increasing significantly with network complexity and lead-time variability. Learning-based control methods deliver up to 16% cost reductions under complex network conditions but require substantial data and governance infrastructure. Commercial demand drivers systematically distort finished-goods inventory targets and require integration with sales-and-operations planning for accurate calibration. Theoretical contribution. The study provides the first cross-class synthesis covering raw materials, work-in-process, and finished goods within a unified evaluative framework, positioning machine learning and deep reinforcement learning methods alongside classical policy families and quantifying the boundary conditions for each approach. Practical implications. A six-phase, stepwise implementation framework is proposed, covering ABC-XYZ segmentation, forecast model selection, safety stock calibration, replenishment policy assignment, simulation-based parameter tuning, and KPI governance, enabling enterprises to achieve 9–16% reductions in inventory costs within existing WMS and ERP architectures.
Purpose. Road traffic fatalities claim approximately 1.19 million lives annually, with driver fatigue implicated in up to 50% of severe collisions in Europe, yet no published framework simultaneously integrates clinical physiological threshold derivation, wearable biosensor specification, machine learning architecture selection, and Internet of Things deployment within a unified, regulatory-compliant monitoring system. This study addresses that integrative gap by proposing the Physiologically-Grounded Driver Monitoring (PGDM) conceptual IoT framework. Methodology. A systematic literature review adhering to PRISMA 2020 guidelines was conducted across five databases, IEEE Xplore, ScienceDirect, PubMed/PMC, Web of Science, and arXiv, covering 2019 to May 2026. Of 2,847 initial records, 43 peer-reviewed studies met inclusion criteria following quality assessment using the adapted Mixed Methods Appraisal Tool (MMAT v.2018). Four open datasets provided empirical benchmarks: OpenDriver (81 drivers, ~4,600 hours, open-road ECG/IMU), WACHSens (n=62), DD-Database (n=10, EEG/EOG/ECG), and AdVitam (n=346, ECG/EDA/respiration). Results. EEG frontal theta power (4–8 Hz) anticipates behavioural drowsiness by 2–7 minutes; RMSSD below 20 ms and LF/HF above 2.0 constitute validated HRV impairment thresholds; PERCLOS exceeding 70% corresponds to severe drowsiness with 91% sensitivity. Obstructive sleep apnea, affecting 15–78% of professional drivers, is identified as the dominant uncontrolled physiological confound in existing detection algorithms. A hybrid CNN-LSTM-Attention architecture achieves 97.3% accuracy at 15–18 ms edge inference latency; Transformer-based multimodal fusion achieves the lowest cross-subject degradation (5.2 percentage points). Theoretical contribution. The PGDM framework constitutes the first published driver monitoring architecture that derives alert levels directly from validated clinical thresholds and achieves simultaneous compliance with EU GSR 2019/2144, EU AI Act 2024/1689, MDR 2017/745, and GDPR 2016/679, bridging the fragmented engineering, clinical, and regulatory research streams. Practical implications. The PGDM specification provides transport engineers, fleet operators, and regulatory bodies with an actionable, standards-compliant design blueprint for real-time wearable driver monitoring deployable across commercial vehicle fleets. Seven priority research gaps are identified, foremost a prospective cohort study stratifying fatigue algorithms by polysomnography-confirmed OSA status. Sustainable Development Goals (SDGs): SDG 3: Good Health and Well-Being; SDG 9: Industry, Innovation and Infrastructure; SDG 11: Sustainable Cities and Communities
Purpose. This study examines how industry-leading organizations operationalize Green Supply Chain Management (GSCM) practices to achieve measurable environmental performance improvements across manufacturing and service sectors. The research addresses critical gaps in understanding the mechanisms linking GSCM implementation to outcomes in carbon reduction, resource efficiency, and waste minimization. Methodology. The study employs a comparative case analysis of four industry leaders - Eastman Chemical Company (chemical manufacturing), Westpac Banking Corporation (financial services), The Coca-Cola Company (consumer goods), and Ernst & Young (professional services) - utilizing secondary data from corporate sustainability reports, Carbon Disclosure Project submissions, and third-party environmental certifications spanning 2008-2020. The analytical framework examines the organizational context, the specific GSCM practices implemented, the quantifiable environmental outcomes, and the enabling implementation mechanisms. Results. The findings reveal substantial environmental improvements: carbon emissions reductions ranging from 15% to 85% across sectors, water-use efficiency improvements of approximately 20% in water-intensive operations, and waste diversion rates exceeding 90% at leading facilities. Three critical success factors emerge: comprehensive carbon management systems integrated into business strategy, multi-stakeholder collaboration extending beyond traditional supplier relationships, and a sophisticated performance measurement infrastructure that enables accountability and continuous improvement. Theoretical Contribution. The study integrates Resource-Based View, stakeholder theory, and dynamic capabilities perspectives to explain how environmental competencies generate competitive advantage. It demonstrates that sector-specific institutional pressures shape GSCM configurations, requiring tailored rather than universal implementation approaches. Practical Implications. For managers, the research provides benchmarks for GSCM maturity assessment and identifies transferable best practices across sectors. For policymakers, findings inform regulatory design by illustrating how coercive, normative, and mimetic pressures drive corporate environmental action, supporting evidence-based sustainability policy development. Sustainable Development Goals (SDGs): SDG 12: Responsible Consumption and Production; SDG 13: Climate Action; SDG 9: Industry, Innovation and Infrastructure; SDG 17: Partnerships for the Goals
Purpose: This study examines the effect of cross-border logistics on shipping company performance in East Africa, specifically investigating how customs clearance efficiency, transportation management systems (TMS) adoption, last-mile delivery optimization, and supply chain coordination influence operational, financial, and market performance of maritime logistics firms in Mombasa County, Kenya. Methodology: The research employed a descriptive survey design with stratified random sampling of 172 shipping companies operating in Mombasa County. Data were collected through structured questionnaires from 149 respondents (86.63% response rate), yielding cross-sectional observations of logistics practices and performance outcomes. Multiple linear regression analysis examined the joint effects of independent variables on performance, controlling for firm size, years of operation, service scope, and customer profile. Results: All four hypothesized relationships were confirmed statistically (p < 0.05). Supply chain coordination demonstrated the strongest effect on performance (β = 0.458, standardized β = 0.338), followed by transportation management systems (β = 0.412, standardized β = 0.234), last-mile delivery optimization (β = 0.289, standardized β = 0.241), and customs clearance efficiency (β = 0.186, standardized β = 0.182). The regression model explained 77.2% of performance variance (R² = 0.772, F = 52.34, p < 0.001), indicating that cross-border logistics dimensions are critical performance determinants. However, technology adoption barriers limit TMS implementation to 34.2% of firms, representing untapped performance improvement opportunity. Theoretical Contribution: The study contributes to the logistics and supply chain management literature by providing firm-level empirical evidence on determinants of cross-border logistics performance in Sub-Saharan Africa, where such research remains limited. The integrated examination of multiple logistics dimensions reveals performance interdependencies. The findings extend institutional theory, the resource-based view, transaction cost economics, and network theory by demonstrating their complementary explanatory power in the context of logistics in developing economies. The dominant effect of supply chain coordination supports network theory predictions while highlighting limitations of technology-centered approaches in resource-constrained environments. Practical Implications: For shipping company executives, the research provides evidence-based guidance for strategic logistics investments, emphasizing that supply chain coordination and relationship development can yield higher returns than technology adoption alone. For policymakers, the findings support prioritizing customs reform, technology infrastructure investment, and regional harmonization within the East African Community and the African Continental Free Trade Area. For industry associations, the research identifies skills gaps in logistics management requiring capacity-building initiatives and suggests that collaborative problem-solving forums addressing inter-organizational coordination challenges would benefit industry competitiveness. Sustainable Development Goals (SDGs): SDG 9: Industry, Innovation, and Infrastructure; SDG 8: Decent Work and Economic Growth; SDG 17: Partnerships for the Goals
Purpose. This paper synthesizes empirical evidence on how to strengthen warehouse-centred supply chains in post-conflict economies facing damaged infrastructure, intermittent access, and volatile demand. Methodology. A systematic review of 15 protocol-verified empirical and simulation studies is conducted, coding contexts, warehouse interventions, and outcomes such as response time, service level/OTIF, inventory stability, delivery days, and unit logistics costs, with limited random-effects aggregation when comparable metrics are available. Results. Robust effects are identified for warehouse prepositioning, temporary or modular depots, lateral transshipment policies, and multi-echelon inventory control, which collectively reduce response times, increase service levels, and stabilise supply with moderate cost impacts. Digital enablers such as offline-capable warehouse management systems and energy‑autonomous facilities further enhance performance under grid and connectivity failures, though quantitative evidence remains sparse. Theoretical contribution. The review integrates the four-R flexibility perspective with the resilience capacities of absorption, adaptation, and recovery, showing how specific warehouse design and control levers operationalise resilience in the recovery phase of humanitarian and essential goods supply chains. Practical implications. The paper proposes a phased implementation roadmap for practitioners in post‑conflict settings, distinguishing quick wins in the first 0–3 months, network reconfiguration over 3–12 months, and longer‑term investments beyond 12 months to embed digital and energy autonomy in warehouse networks. Sustainable Development Goals (SDGs): SDG 2: Zero Hunger; SDG 3: Good Health and Well‑Being; SDG 8: Decent Work and Economic Growth; SDG 9: Industry, Innovation and Infrastructure; SDG 11: Sustainable Cities and Communities; SDG 12: Responsible Consumption and Production; SDG 16: Peace, Justice and Strong Institutions
Purpose: This study evaluates the total cost of ownership (TCO) for four distinct vehicle powertrain technologies - conventional vehicles (CV), hybrid electric vehicles (HEV), plug-in hybrid electric vehicles (PHEV), and battery electric vehicles (BEV)- in Saudi Arabia’s compact sport utility vehicle market. The research addresses a critical gap in regional transportation economics by examining technology competitiveness under current market conditions and alternative energy pricing scenarios aligned with Vision 2030 subsidy reform objectives. Methodology: A comparative TCO analysis was conducted using empirical pricing and technical specifications from Saudi dealerships (CV, HEV) and normalized regional data (PHEV, BEV). The analytical framework integrated depreciation modeling, discounted cash flow analysis, and sensitivity testing across seven energy pricing scenarios. Data sources included Saudi market sources, international academic literature, and validated research on battery degradation from fleet analyses covering over 10,000 electric vehicles. Results: Under baseline conditions (2024 pricing: gasoline USD 0.63/L, electricity USD 0.042/kWh), hybrid electric vehicles demonstrated lowest annual TCO at USD 6,395, with battery electric vehicles ranking second at USD 6,514 annually (1.9% difference). However, sensitivity analysis identified a critical inflection point at USD 0.85/L gasoline where BEVs transition to cost parity with HEVs. At USD 1.20/L gasoline - a plausible scenario within the decade - BEVs achieve decisive economic superiority. Depreciation emerged as the primary cost driver differentiating technologies, with BEV depreciation representing 62% of total TCO versus 23% for HEVs, reflecting infrastructure and market maturity constraints. Theoretical Contribution: This research extends TCO methodology to emerging markets characterized by energy subsidies, extreme climatic conditions, and nascent EV infrastructure. The study demonstrates that technology competitiveness in such contexts depends critically on interdependent factors spanning energy pricing, infrastructure investment, market maturity, and consumer behavior. The findings challenge the assumption that TCO analysis alone predicts adoption patterns, highlighting the need for complementary policy frameworks that address infrastructure, regulation, and market development. Practical Implications: For Saudi policymakers, the analysis indicates that aggressive investment in charging infrastructure and transparent sequencing of subsidy reform could accelerate EV adoption without substantial direct consumer subsidies. Automotive manufacturers and dealers require integrated service network development and consumer education programs. For fleet operators and consumers, the analysis provides evidence-based guidance on technology selection under heterogeneous ownership profiles and pricing assumptions. The research supports Vision 2030 objectives by demonstrating that electric mobility represents both an economically viable choice and a strategic imperative for economic diversification and environmental sustainability. Sustainable Development Goals (SDGs): SDG 7: Affordable and Clean Energy; SDG 9: Industry, Innovation, and Infrastructure; SDG 11: Sustainable Cities and Communities; SDG 12: Responsible Consumption and Production; SDG 13: Climate Action; SDG 17: Partnerships for the Goals
Purpose. This study investigates factors influencing lane choice behavior on the Addis Ababa–Adama Expressway, Ethiopia’s first controlled-access highway, to provide empirical evidence for transportation planning and highway design in sub-Saharan Africa, where such research is absent. Methodology. Video-based observational data were collected at five strategically selected sites across the 80-kilometer expressway corridor. A total of 45,924 vehicle observations were extracted through frame-by-frame manual analysis capturing lane choice, vehicle characteristics, traffic flow parameters, and lane-changing behavior. Multinomial logit models were developed for each site-direction combination (10 models in total) to quantify the relationships between explanatory variables and lane-choice probability, with Lane-3 serving as the reference category. Results. Analysis revealed a pronounced middle-lane bias, with 55.5% of traffic concentrated in Lane-2, while Lane-1 and Lane-3 received 24.7% and 19.1%, respectively. Average Speed Ratio exhibited consistently positive associations with outer lane selection (odds ratios: 3.01–66.19). Passenger cars demonstrated 3.00–60.14 times higher odds of selecting outer lanes compared to trucks, reflecting systematic vehicle stratification. Lane position of preceding vehicles showed negative associations (odds ratios: 0.12–0.36), indicating platoon avoidance behavior rather than following tendencies. Lane Utilization Factor demonstrated self-reinforcing effects exclusively for middle-lane selection. Theoretical contribution. This research provides the first empirical validation of utility-maximizing lane choice theory in sub-Saharan African expressway contexts, documenting platoon avoidance behavior and self-reinforcing lane utilization patterns with implications for traffic simulation model calibration. Practical implications. Findings inform lane-specific pavement design standards, capacity analysis methods that incorporate vehicle stratification effects, and traffic management strategies, including variable message signs and targeted enforcement, to improve operational efficiency and safety on Ethiopian expressways. Sustainable Development Goals (SDGs): SDG 3: Good Health and Well-being; SDG 9: Industry, Innovation and Infrastructure – Developing quality, resilient infrastructure; SDG 11: Sustainable Cities and Communities
Purpose. This study addresses the critical infrastructure gap in electric vehicle (EV) fast-charging networks along Ukraine’s M-06 international corridor by developing an optimization framework that simultaneously achieves European Union regulatory compliance and demonstrates commercial viability for private investors. Methodology. The research employs the Maximum Covering Location Problem (MCLP) methodology adapted for linear transport corridors, integrated with multi-criteria site evaluation and comprehensive financial modeling. Spatial analysis identifies coverage gaps relative to Alternative Fuels Infrastructure Regulation (AFIR) requirements, whilst discounted cash flow projections assess economic performance across baseline and sensitivity scenarios. Primary data sources include operator infrastructure inventories, traffic flow statistics, and grid capacity assessments spanning 2022 to 2025. Results. Analysis reveals five critical infrastructure gaps totaling 545 kilometers where inter-station distances exceed the 60-kilometer AFIR threshold. Optimization identifies seven strategically positioned 150-kW stations achieving full regulatory compliance with minimal deployment. Financial modeling demonstrates exceptional viability: 1.75-year payback periods, 52.3% internal rates of return, and positive net present values exceeding 64 million hryvnia across seven stations. Sensitivity testing confirms robustness under pessimistic utilization and cost scenarios, identifying retail tariff margins as the critical determinant of project viability. Theoretical contribution. This investigation advances facility location theory by adapting classical MCLP frameworks to the contexts of emerging-market transport infrastructure, characterized by regulatory transition and data constraints. The integrated optimization-economic methodology provides replicable approaches for systematic charging network planning across diverse geographic and institutional settings. Practical implications. Findings demonstrate that commercially viable EV charging networks can be deployed without substantial public subsidy, enabling capital-constrained governments to leverage private investment systematically. The seven identified priority locations provide actionable guidance for Ukrainian authorities and operators, whilst the methodology is scalable to additional corridors, both nationally and internationally. Results inform European Union integration negotiations by establishing Ukraine’s capacity for evidence-based infrastructure planning aligned with TEN-T standards. Sustainable Development Goals (SDGs): SDG 7: Affordable and Clean Energy; SDG 9: Industry, Innovation and Infrastructure; SDG 11: Sustainable Cities and Communities; SDG 13: Climate Action
Purpose. This paper evaluates the technical Efficiency of Dar es Salaam Port’s container terminal operations over the period 2019–2023, explicitly accounting for the influence of macroeconomic conditions and the COVID-19 pandemic on performance metrics. The study addresses a critical gap in port efficiency research by disentangling internal operational capability from external contextual factors in a developing-economy maritime gateway serving landlocked East and Central African countries. Methodology. A two-stage hybrid analytical framework integrates input-oriented Data Envelopment Analysis (DEA) with Contextual Value Added (CVA) regression. DEA efficiency scores are computed using a three-year rolling window approach with inputs (quay length, gantry cranes, terminal area) and outputs (container throughput, vessel calls). Second-stage ordinary least squares regression isolates the effects of GDP, trade volume, and pandemic disruption on measured efficiency. Quantitative findings are triangulated with qualitative stakeholder surveys (n=45) and semi-structured interviews to capture operational perceptions and institutional constraints. Results. DEA analysis reveals temporal efficiency variation ranging from 0.838 (2019) to 0.966 (2021), with post-pandemic decline to 0.890 (2023). CVA regression identifies a statistically significant negative relationship between trade volume and efficiency (β = −1.76×10⁻⁵, p = 0.03), indicating binding infrastructure constraints. The COVID-19 dummy exhibits a paradoxical positive coefficient (β = +0.090, p = 0.02), reflecting efficiency gains under suppressed demand rather than genuine productivity enhancement. Theoretical contribution. This study advances port efficiency assessment by demonstrating that unadjusted frontier methods can mask capacity deficits when external demand fluctuates. The hybrid DEA-CVA framework enables evidence-based attribution of efficiency sources, enhancing policy relevance. Practical implications. Findings underscore the urgent need for infrastructure expansion and procedural digitalization to accommodate regional trade growth under the African Continental Free Trade Area. Sustainable Development Goals (SDGs): SDG 8: Decent Work and Economic Growth; SDG 9: Industry, Innovation, and Infrastructure
Purpose. This study examines how strategic communication influences stakeholder support for renewable energy initiatives in urban mass rapid transit systems. Using PT MRT Jakarta as a case study, the research addresses a critical gap: despite technical feasibility and policy commitments to achieve decarbonization by 2040, renewable energy implementation remains constrained by insufficient stakeholder alignment and engagement. Methodology. A quantitative research design employing Structural Equation Modeling with Partial Least Squares (SEM-PLS) was utilized to test six hypotheses. Data were collected from 176 internal and external stakeholders of MRT Jakarta using a structured questionnaire that measured strategic communication, sense of importance, stakeholder ownership, stakeholder support, and perceived financial outcomes. Path analysis examined both direct and mediated relationships among these constructs. Results. Strategic communication demonstrated exceptionally strong effects on sense of importance (β = 0.989, p < 0.001) and stakeholder ownership (β = 0.978, p < 0.001). Both psychological constructs significantly mediated the relationship between communication and stakeholder support, with ownership exhibiting stronger influence (β = 0.677, p < 0.001) than importance (β = 0.316, p < 0.05). Stakeholder support strongly predicted perceived financial efficiency (β = 0.991, p < 0.001) and financial independence (β = 0.969, p < 0.001). All six hypotheses were empirically supported. Theoretical contribution. The study advances sustainability transitions scholarship by empirically validating communication as a strategic lever for institutional change. It demonstrates that renewable energy barriers in public infrastructure are fundamentally communicative rather than technical, extending strategic communication theory into sustainable transport contexts. Practical implications. Public transit agencies pursuing decarbonization must reposition communication from an auxiliary function to a strategic priority, employing participatory engagement platforms, values-aligned messaging, and transparent reporting mechanisms to accelerate renewable energy adoption and strengthen financial sustainability. Sustainable Development Goals (SDGs): SDG 7: Clean Energy; SDG 11: Sustainable Cities; SDG 13: Climate Action
Purpose. This study investigates how supply chain disruptions affect firm productivity and examines the differential mediating roles of proactive and reactive recovery strategies in manufacturing firms operating in emerging economies. Methodology. Drawing on the Resource-Based View and Dynamic Capabilities Theory, the research employs a cross-sectional survey design with data collected from 250 pharmaceutical and automotive manufacturing firms in Ghana. Structural Equation Modelling using SmartPLS 4.0 with bootstrapping procedures (5,000 subsamples) was applied to test direct effects and mediation hypotheses. Results. Supply chain disruptions negatively impact firm productivity (β = -0.247, p = 0.001). Proactive recovery strategies significantly mediate this relationship with a large positive indirect effect (β = 0.396, p < 0.001), indicating that anticipatory capabilities substantially buffer disruption-induced productivity losses. Reactive recovery strategies show no significant mediating effect (β = -0.039, p = 0.452), suggesting that post-disruption responses alone are insufficient for maintaining productivity. Theoretical contribution. The study advances supply chain resilience theory by reconceptualizing recovery strategies as dynamic capabilities with differential effectiveness. It provides empirical evidence distinguishing proactive from reactive mechanisms and demonstrates that the indirect effect of proactive recovery substantially exceeds the direct negative effect of disruptions, indicating that well-developed anticipatory capabilities can more than offset disruption impacts. Practical implications. Supply chain managers should prioritize investments in proactive recovery capabilities, including supply base diversification, contingency planning, scenario analysis, and real-time monitoring systems. For transport and logistics firms, proactive strategies such as alternative routing plans, carrier diversification, and fleet redundancy represent critical resilience investments with measurable productivity returns. Sustainable Development Goals (SDGs): SDG 9: Industry, Innovation and Infrastructure; SDG 12: Responsible Consumption and Production
The problem investigated in this paper originated from the distribution of beers by a Colombo-based company, Lion Brewery (Ceylon) PLC, in Sri Lanka. Currently, this company’s outbound logistics consist of a decentralized distribution model and a redistribution process for its beer bottles and cans in the Colombo region. Extra routing costs due to unreasonable consumption of additional distance have been noticed in the current decentralized redistribution process. Here, the problem is modeled as a variant of the vehicle routing problem with a heterogeneous fleet. Our objective is to minimize the routing costs by imposing constraints on the volume of company vehicles. Centralized heuristic and genetic algorithm solution procedures for the problem are presented. The superior performance of the proposed heuristic is demonstrated relative to existing heuristics through a beer distribution instance and 10 additional small-scale real-world application instances. The computational investigation highlights the cost savings that the proposed heuristic can accrue. The cost savings can be as significant as 19.84% compared to a company’s existing decentralized method, 4.34% compared to the genetic algorithm, and 6.73% and 2.47% compared to the two recent methods. This cost-saving has a practical impact on supplying customers with a necessary drink, beer, at a reduced price.
Purpose. This study examines the socio-economic impacts of urban road dualization in Ikare-Akoko, Ondo State, Nigeria, focusing on accessibility, economic activities, and quality of life for residents and business operators along the dualized corridor. Methodology. A multistage sampling procedure was employed, selecting five quarters through purposive and systematic random sampling. Data were collected from 170 household heads using structured questionnaires and analyzed through descriptive and inferential statistics (SPSS). Results. Road dualization generated mixed outcomes: positive effects included direct employment (22.4%), enhanced economic development (37.1%), reduced travel time and costs, and improved access to markets. However, significant negative consequences emerged, including property loss (62.9%), business displacement (81.2% lost customers), increased distance to destinations (92.9%), and elevated environmental costs (52.4%). Chi-square analysis confirmed a statistically significant relationship between road dualization and economic activities (χ² = 40.2, p < 0.05). Theoretical Contribution. This research contributes to urban transport geography and infrastructure development literature by demonstrating the dual nature of road dualization impacts in developing urban contexts, highlighting the need for integrated planning frameworks that balance infrastructure modernization with social equity. Practical Implications. Findings recommend complementary infrastructure development (shopping centers, utilities), improved urban planning standards to minimize property displacement, sustainable road maintenance policies, and public education programs on proper road usage to maximize socio-economic benefits while mitigating adverse effects. Sustainable Development Goals (SDGs): SDG 9: Industry, Innovation, and Infrastructure; SDG 11: Sustainable Cities and Communities; SDG 8: Decent Work and Economic Growth; SDG 10: Reduced Inequalities
Purpose: This study examines how Supply Chain Operations Reference (SCOR) metrics, specifically cycle time, cost, and flexibility metrics, influence the supply chain performance of Small and Medium Enterprises (SMEs) in Mombasa County, Kenya. Methodology: A descriptive research design was employed with data collected from 397 SMEs in Mombasa County using questionnaires. The Taro Yamane formula was used to determine the sample size. Data analysis involved both descriptive statistics and inferential analysis through correlation and regression. Results: The findings reveal statistically significant positive relationships between cycle time metrics (r=0.811), cost metrics (r=0.788), and flexibility metrics (r=0.848) with supply chain performance. Multiple regression analysis indicates that flexibility metrics substantially impact supply chain performance, followed by cost and cycle time metrics. Theoretical contribution: This study extends the application of SCOR model metrics to the context of Kenyan SMEs, addressing a significant research gap in supply chain performance measurement in developing economies. It validates the relevance of the SCOR framework in enhancing operational efficiency in resource-constrained environments. Practical implications: SMEs in Kenya should prioritize flexibility in their supply chain operations while optimizing cost structures and cycle times. The findings suggest that strategic allocation of limited resources should emphasize adaptability to market changes, which significantly impacts overall supply chain performance. Sustainable Development Goals (SDGs): SDG 8: Decent Work and Economic Growth; SDG 9: Industry, Innovation, and Infrastructure
Purpose: The devastation of Ukraine's transport infrastructure during the ongoing conflict has created immense challenges and a unique opportunity to rethink and rebuild the nation's logistics system for the twenty-first century. This paper investigates how integrating sustainable and smart logistics centers can catalyze Ukraine's economic recovery and future competitiveness. Methodology: Drawing on a mixed-methods approach, including satellite-based damage assessments, comparative analysis with leading European logistics models, and in-depth case studies of Ukrainian logistics hubs, the study explores both the obstacles and the transformative potential of digitalization, automation, and green technologies in the Ukrainian context. Results: The findings reveal that while Ukraine faces severe barriers, such as regulatory fragmentation, underinvestment in digitalization, and a legacy of war-related destruction, there are clear pathways forward. International best practices, particularly those from Germany, France, and Poland, demonstrate that multimodal, technology-driven logistics centers can accelerate recovery and foster sustainable growth. Case studies from Lviv and Odesa show that even amid a crisis, adopting solar energy and automated systems leads to measurable efficiency gains and cost reductions. The research also highlights the critical importance of harmonizing Ukrainian regulations with European Union standards, leveraging EU funding for corridor modernization, and fostering public-private partnerships to drive innovation. Theoretical Contribution: Theoretically, the paper advances the understanding of logistics center development in transition economies, proposing a framework synthesizing digital, environmental, and organizational innovations. Practical Implications: It offers policymakers and industry leaders actionable recommendations for regulatory reform, investment prioritization, and technology adoption to position Ukraine as a future leader in sustainable, smart logistics within the European transport network. Sustainable Development Goals (SDGs): SDG 9: Industry, Innovation and Infrastructure; SDG 11: Sustainable Cities and Communities; SDG 12: Responsible Consumption and Production; SDG 13: Climate Action
Purpose: This study investigates whether wines imported from the New World generate higher carbon emissions per 0.75L bottle during distribution to Germany than Old World wines and identifies key factors influencing the carbon footprint (CF) in the distribution process. Methodology: The research analyses a large dataset (over 220,000 records) from a primary European logistics provider, applying the EN 16258 standard and EcoTransIT World (ETW) tool to calculate CO2 equivalents for various distribution channels, shipment sizes, and modes of transport. Simulations for New World shipments complement empirical data. Results: Distribution channel, shipment size, and logistics handling significantly influence CF per bottle. Large, direct shipments to retailers have lower CF than small, multi-stop shipments. Contrary to common assumptions, New World wines do not always have a higher CF than Old World wines; logistics networks and shipment handling are critical determinants. Theoretical Contribution: This paper extends the literature by empirically quantifying the impact of logistics variables on wine CF, challenging the generalisation that New World imports are inherently less sustainable. Practical Implications: Findings inform wine importers, retailers, and policymakers on optimising logistics for lower emissions and provide a nuanced basis for consumer sustainability choices. SDG 7: Affordable and Clean Energy; SDG 9: Industry, Innovation and Infrastructure; SDG 11: Sustainable Cities and Communities; SDG 12: Responsible Consumption and Production; SDG 13: Climate Action
This study examines the implementation of lean-agile (leagile) supply chain strategies in Kenyan humanitarian organizations, assessing their impact on operational performance with organizational characteristics as moderators. A census survey of 330 humanitarian aid organizations in Kenya was conducted, achieving an 87.88% response rate (290 organizations). Using moderated multiple regression and Grey Incidence Analysis, the research tested five hypotheses linking supply chain responsiveness, resilience, efficiency, integration, and organizational characteristics to performance outcomes. Results revealed that leagile dimensions collectively explain 71.9% of performance variance (R²=0.719), with responsiveness (β=0.532), efficiency (β=0.415), and integration (β=0.458) showing strong positive effects. Organizational characteristics—size, structure, and age—moderated these relationships, increasing the explanatory power to 82.7% (R² = 0.827). The study validates Grey Incidence Analysis as a robust framework for managing uncertainties in humanitarian "grey systems" and integrates organizational theory into supply chain leagility research. Practically, it advocates for full leagile adoption, emphasizing decentralized decision-making, AI-driven forecasting, and technologies like IoT and blockchain to enhance coordination and resilience. Despite high donor funding, Kenya’s Arid and Semi-Arid Lands (ASALs) face persistent inefficiencies, underscoring the need for localized capacity-building and multi-stakeholder collaboration. Limitations include reliance on self-reported data and a cross-sectional design, suggesting future longitudinal studies and beneficiary feedback integration. This work contributes empirical evidence on hybrid supply chain strategies in humanitarian contexts, offering actionable insights for policymakers and practitioners to optimize disaster response in resource-constrained environments. Sustainable Development Goals (SDGs): SDG 1: No Poverty; SDG 2: Zero Hunger; SDG 3: Good Health and Well-being; SDG 9: Industry, Innovation and Infrastructure; SDG 10: Reduced Inequality; SDG 17: Partnerships for the Goals
Purpose: Kenya's strategic position on Africa's eastern coast makes its blue economy essential for national development. This study analyzes the relationship between sustainable maritime logistics and the potential of the blue economy to promote social equity, environmental sustainability, and economic growth. Methodology: A systematic literature review was conducted, synthesizing scholarly research, industry reports, and policy documents on maritime logistics and its contribution to Kenya's blue economy. The analysis focuses on key dimensions such as port infrastructure, regulatory frameworks, and technological innovation. Results: The study finds that efficient maritime logistics enhance trade efficiency, reduce transportation costs, and support sustainable practices, thus maximizing the blue economy's potential. However, challenges remain, including limited port capacity, inefficient customs processes, insufficient cold chain infrastructure, and gaps in workforce training. Addressing these issues requires policy reforms and investments in modern infrastructure. Theoretical Contribution: This research contributes to the sustainable development theory by positioning maritime logistics as a central element of blue economic resilience and environmental stewardship. It introduces a conceptual model viewing maritime logistics as an enabler of sustainable blue economy growth in Kenya. Practical Implications: The study recommends targeted investments in green technologies, improved maritime training, and the formation of multi-stakeholder partnerships to foster collaborative and resilient maritime logistics systems. It also highlights the need for more comprehensive data and encourages future research to include primary data for deeper insights. Sustainable Development Goals (SDGs): SDG 2: Zero Hunger; SDG 3: Good Health and Well-being; SDG 10: Reduced Inequalities; SDG 11: Sustainable Cities and Communities; SDG 12: Responsible Consumption and Production
Purpose: This study examines the relationship between Transport Management Systems (TMS) and the performance of Inter-Governmental Organizations (IGOs) in Nairobi City County. The research investigates how adopting TMS influences operational efficiency, cost reduction, and service delivery among IGOs. Methodology: A descriptive research design was employed, utilizing a census approach targeting 134 IGOs. Data was collected from supply chain managers using structured questionnaires, and descriptive and inferential statistical analyses were conducted. Pearson correlation and regression analysis were applied to determine the strength and significance of the relationship between TMS adoption and performance. Results: Findings revealed a significant positive correlation (r = 0.421, p < 0.001) between TMS adoption and IGO performance, indicating that organizations with well-integrated transport systems experience improved logistics efficiency and service delivery. Regression analysis further confirmed that TMS accounts for 53.7% of performance variations among IGOs. Theoretical Contribution: This study extends the application of Systems Theory by demonstrating the interdependence between transport management, logistics coordination, and organizational performance in an inter-governmental context. Practical Implications: IGOs in Nairobi City County can enhance operational efficiency by adopting advanced TMS solutions such as real-time tracking, route optimization, and automated reporting. Policymakers and stakeholders should also invest in infrastructure and regulatory frameworks that support effective transport management in IGOs. Sustainable Development Goals (SDGs): SDG 9: Industry, Innovation and Infrastructure; SDG 11: Sustainable Cities and Communities; SDG 13: Climate Action
