Conference Agenda

This paper addresses a practical operational challenge at Nongkhai Station of the State Railway of Thailand, where both passenger and freight services operate within a shared station environment. The station plays an important role not only in domestic railway transportation, but also in the broader logistics network of the Greater Mekong Subregion due to its proximity to the Thailand–Lao PDR border. In recent years, cross-border railway logistics between Thailand, Lao PDR, and China have become increasingly significant under regional economic connectivity initiatives. Nongkhai Station therefore functions as an important gateway for international freight movement, particularly for agricultural products, consumer goods, and containerized cargo transported through the Laos–China Railway connections. This growing logistics role increases operational complexity within the station, where freight activities must coexist with regular passenger services under limited infrastructure capacity.

In such an operational environment, shunting operations are essential for managing trainset and locomotive movements, including coupling and decoupling activities, passenger boarding and alighting, freight loading and unloading, as well as the immigration process. These processes are highly interdependent and constrained by limited track availability, requiring proper coordination to avoid operational conflicts, delays, and disruptions that may propagate across the wider railway network.

From a methodological perspective, the study first reviews existing railway timetabling and visualization tools to support the analysis of shunting operations. However, it is found that current tools are not fully suitable for representing detailed station yard-level shunting processes in a manner that is both operationally meaningful and sufficiently practical for the case study. While some tools provide timetable visualization or microscopic simulation capabilities, they either lack detailed focus on shunting activities or require extensive infrastructure and operational data that are not currently available. Consequently, the study adopts a conceptual analytical approach rather than relying on a specific software tool.

The paper further proposes a graphical representation of shunting operation patterns within the station along with the shunting activity list which provides standardization of the activity as a norm acting as checklist for the scheduler to reassure the overall schedule. This visualization illustrates train movement sequences, coupling and decoupling activities, and track occupancy over time in a structured format. The approach enhances understanding of dynamic station yard behavior and assists in identifying potential operational conflicts more clearly, particularly in the absence of suitable existing visualization tools. Overall, the study highlights the importance of railway shunting operations as a logistics-related scheduling problem closely connected to timetable reliability, operational efficiency, and cross-border freight transportation performance.

This paper provides the first contribution on track utilization via track occupancy chart and the second contribution on standardization of the shunting activities. Track utilization gives insight data to scheduler in term of current and statistics data to know the utilization rate of each track to do further data analysis. Standardized shunting activity list helps schedulers as checklist of the activity within the station more systematically Both contributions are to enhance proper shunting operation management within the station yard and for foundation of future disruptions.

Cross-border railway connectivity is often examined from an infrastructure perspective, with emphasis placed on the construction of railway links between countries. However, international rail services also require train path allocation mechanisms to coordinate the use of railway infrastructure across national boundaries. This suggests that physical connectivity alone is insufficient to support cross-border rail operations and must be complemented by mechanisms that facilitate operational coordination among multiple countries. This study therefore investigates cross-border railway corridors to examine how corridor-specific characteristics influence international train path allocation arrangements and to explore policy implications for the development of the Malaysia-Thailand-Laos corridor.

This study adopts a multiple case study approach. Case studies were selected based on the following inclusion criteria: the existence of regular commercial cross-border rail services, identifiable train path allocation mechanisms, and sufficient reference materials. The selected cases were then compared using four factors expected to influence train path allocation arrangements: track gauge compatibility, the number of infrastructure managers, institutional frameworks, and transport type.

The findings identify two principal models of international train path allocation: (1) allocation coordinated by a central entity across the entire corridor, and (2) allocation based on cooperation between neighboring countries. The study further finds that the choice of allocation model is shaped by the interaction among the continuity of cross-border rail operations, infrastructure governance structures, and institutional frameworks governing international cooperation. These factors influence the extent to which train path allocation can be coordinated across an entire corridor or must be managed through segmented arrangements.

The Malaysia-Thailand-Laos corridor exhibits varying operational characteristics across its segments, particularly with respect to the continuity of cross-border rail operations and transshipment requirements. As a result, train path allocation is more likely to be managed on a segmented basis rather than through a corridor-wide allocation mechanism. The findings suggest that train path allocation models adopted in other cross-border railway corridors may not be directly transferable to this corridor and that a coordination framework tailored to its specific operational context is required. This study contributes to a better understanding of the factors shaping international train path allocation arrangements and provides a foundation for developing coordination mechanisms suited to the unique characteristics of the Malaysia-Thailand-Laos corridor.

Rail transit has continuously evolved and plays an important role in improving the quality of life for urban residents. With 4.88 billion smartphone users worldwide in 2024, and reports that up to 90% of public transport passengers use smartphones during their journeys, journey planning applications have become a widely used tool for rail travel. The quality of these applications directly affects the travel experience. Developing effective journey planning applications is not straightforward. Applications must meet traveler needs across multiple dimensions, including real-time information, route guidance, fare calculation, and connections to other transport modes. At the same time, application features are shaped by the operational and governance context of the rail system. The governance model, whether state-operated, privately managed, or structured as a public-private partnership, determines who develops the application, what data can be accessed, and which features are prioritized. Understanding the relationship between application features and rail governance context is therefore important for identifying the current limitations of journey planning tools and improving the travel experience for urban residents. This study examines how rail governance models relate to the features of journey planning applications. It presents a comparative analysis of applications across nine cities, using a three-step analytical framework: rail governance model, open data policy, and application features across three categories: route planning, ticketing, and navigation. The findings reveal patterns between governance models and application features. Some applications have limited feature scope due to fragmented governance or restricted data sharing. This suggests that application limitations are not purely technical but reflect the governance structures of each city. In cities with a centralized model where a single authority controls both data and application development, applications tend to offer broader and more integrated features. In contrast, cities operating under concession-based models, where multiple operators function independently without shared data mechanisms, often produce applications with narrower feature coverage. Open data policies also play a role by determining whether transit data, such as static timetables, real-time arrival information, and fare data, is published in interoperable standards such as GTFS or in proprietary formats that limit third-party access. These findings are intended to support application developers in designing features that are realistic within the data constraints of each context. They also aim to support policymakers in considering how restructuring governance arrangements or adopting open data standards can create conditions for more capable journey planning applications, particularly in cities that are restructuring their rail systems or considering open data policies for the first time.

The growing demand for batteries has increased the importance of secure, sustainable, and resilient battery value chains. At the same time, battery manufacturers face new challenges arising from regulatory requirements, sustainability objectives, critical raw material dependencies, and operational disruptions. These challenges require companies to consider compliance, transparency, circularity, and supply chain risks alongside traditional objectives such as cost, quality, and delivery performance.

Although large volumes of compliance, sustainability, and operational data are becoming available, limited research has examined how this information can support battery value chain reconfiguration. Existing studies often address compliance and resilience separately, providing limited guidance on how assessment results can be translated into value chain adaptation and reconfiguration decisions.

Meanwhile, Manufacturing as a Service (MaaS) provides the mechanism for discovering and selecting alternative actors and capabilities within the value chain. Extending upon this concept, this paper proposes a conceptual framework for sustainable and compliant battery value chains integrating MaaS as an enabling mechanism for reconfiguring distributed manufacturing resources. The framework consists of assessment, filtering and evaluation of value chain actors using compliance information and regulations, sustainability indicators, and operational data such as geographic location, logistics, and disruptions. This enables decision-makers to evaluate and compare different viable reconfiguration alternatives.

The applicability of the proposed framework is demonstrated through a proof-of-concept decision support tool, applied within a battery value chain context. Using lithium-ion batteries as the unit of analysis, the tool illustrates how regulatory compliance, sustainability requirements, and operational performance can be integrated to reconfigure the value chain. Following the framework, the tool first applies compliance-based filtering and subsequently evaluates feasible configurations according to criteria such as cost, lead time, capacity, distance, emissions, and resilience.

In conclusion, this research contributes a structured approach for transforming compliance, sustainability, and operational information into value chain reconfiguration decisions. This approach supports the development of sustainable, resilient, and compliant battery value chains.

In both the present and the future's increasingly competitive environment, operations managers need to prioritize sustainability (Swink et al., 2020). Thus, this study focuses on identifying the dimensions of sustainable operations management (SOM). The study, through a systematic literature review, identifies three core practices of sustainable operations management: sustainable product design, sustainable manufacturing processes, and sustainable supply chain management. The study further investigates the indicators of SOM for each sustainable operations management practice. Hence, through a rigorous exploratory factor analysis (EFA) of 220 large manufacturing firms in Ethiopia, the study validates measures of SOM practices. Although the systematic literature review identified 6, 8, and 15 indicators for sustainable product design, sustainable manufacturing processes, and sustainable supply chain management, respectively, the EFA yielded 5, 8, and 9 indicators for these areas, respectively. Subsequently, to determine the most influential sustainable operations management practices and the key indicators within each, five experts from both academia and industry were engaged. A multicriteria decision-making approach—specifically, the Analytic Hierarchy Process—was applied to rank the practices and identify the indicators with the highest priority for operations and supply chain managers. The findings reveal that sustainable manufacturing Process is the most influential practice, followed by sustainable supply chain management and Sustainable Product Design. The robustness of the results, confirmed through sensitivity analysis, demonstrates the reliability of the analytic hierarchy process model for sustainability-related decision-making. The findings offer valuable guidance for manufacturing managers and policymakers on prioritizing sustainability efforts and allocating resources efficiently, as we have identified and ranked sustainability indicators specific to manufacturing firms in an emerging-economy logistics setting.

Despite intensifying environmental sustainability pressures, industrial firms continue to face challenges in embedding sustainability-oriented practices within their supply chain and logistics operations and translating them into tangible competitive and performance gains. Although Sustainable Supply Chain Management (SSCM) is increasingly adopted as a strategic response, its operational effectiveness in generating sustained competitive advantage and improving organizational performance remains insufficiently understood within emerging economies. This study addresses this gap by examining the direct and mediated relationships between SSCM practices, competitive advantage, and organizational performance in Ethiopian manufacturing firms, with a focus on key sustainable supply chain mechanisms, including environmental conservation, supplier collaboration, logistics optimization, green product design ensured with green warehousing, and material reuse. A quantitative research design was employed using survey data collected from 221 managers and supervisors through structured questionnaires. Partial Least Squares Structural Equation Modeling (PLS-SEM) using SmartPLS 4.0 was applied to assess both measurement and structural models. The measurement model demonstrated satisfactory reliability and validity, with all factor loadings exceeding 0.70, composite reliability values above 0.80, and average variance extracted (AVE) values above 0.50. The structural model results revealed that SSCM practices have a significant positive effect on competitive advantage (β = 0.702, p < 0.05) and organizational performance (β = 0.301, p < 0.05). Competitive advantage also exhibited a strong positive influence on organizational performance (β = 0.331, p < 0.05). Mediation analysis using bootstrapping confirmed that competitive advantage partially mediates the relationship between SSCM and organizational performance, indicating both direct and indirect effects of SSCM on performance outcomes. The model further demonstrated satisfactory explanatory power (R² = 0.493 for competitive advantage and R² = 0.536 for organizational performance). From a theoretical standpoint, the findings extend the Resource-Based View (RBV) by empirically validating SSCM practices (logistics optimization, supplier collaboration, material reuse, and green product design ensured with green warehousing) as eco-centric supply chain capabilities that serve as strategic drivers of competitive advantage within an underexplored emerging economy context. Practically, the study provides actionable insights for supply chain professionals, demonstrating how operationalizing logistics optimization, collaborative supplier management, green warehousing, and reuse of materials serves as a critical mechanism to drive superior competitive positioning and organizational outcomes.

Purpose: Access to essential medicines remains a major challenge in developing countries due to inefficient supply chains and weak Logistics Management Information Systems (LMIS) (Stevens & Huys, 2017; WHO, 2010). In Ethiopia, persistent problems related to data quality, infrastructure, workforce capacity, and system integration continue to hinder health commodities supply chain performance despite ongoing health sector reforms. This study employed a grounded theory approach to develop a conceptual model for evaluating LMIS performance in public healthcare facilities in Ethiopia. The model aims to strengthen digital logistics data management, improve supply chain efficiency, and support evidence-based healthcare delivery aligned with Ethiopia’s Health Information Revolution agenda.

Methodology: An institution-based concurrent exploratory mixed-methods design with a qualitative emphasis was employed. The study was conducted across 102 public healthcare facilities in the Amhara Region. Eighty-three key informants, including health facility heads, medical directors, pharmacy heads, supply chain coordinators, district coordinators, and chief executive officers, were purposively selected based on their experience in health commodities supply chain management. Data were collected through in-depth interviews using open-ended questions. Qualitative data were analyzed using grounded theory methodology, including open, axial, and selective coding based on the coding paradigm model of Corbin and Strauss (Corbin J & Strauss A, 2015). MAXQDA version 24.4.1 was used for coding and analysis. Ethical approval was obtained from Addis Ababa University, and participant confidentiality was maintained throughout the study.

Findings and Analysis: The analysis generated 1,427 coding segments, 187 first-level concepts, 64 second-level categories, and 21 high-level categories. The resulting conceptual model identified key factors influencing LMIS performance and health commodities supply chain efficiency. Major causal factors included budget limitations, poor data quality, inadequate workforce training, and LMIS implementation challenges. Contextual conditions such as government commitment, infrastructure availability, public health emergencies, and security concerns also significantly affected system performance. The model further highlighted strategic interventions including digital innovation, LMIS integration, workforce development, and enhanced implementation support. Overall, the model demonstrated that an effective LMIS improves healthcare delivery through timely, reliable, and high-quality data for informed decision-making.

Originality: This study contributes original evidence by developing a grounded theory-based conceptual model for evaluating health commodities LMIS performance in a low-resource healthcare setting. Unlike previous studies that primarily focused on isolated operational challenges, this study integrates causal, contextual, and strategic dimensions into a comprehensive logistics performance framework. The model provides a practical foundation for strengthening digital logistics platforms, improving supply chain data systems, and supporting evidence-based decision-making in healthcare logistics management.

Conclusion and Recommendations: The developed conceptual model provides a practical framework for evaluating and improving LMIS performance in Ethiopian healthcare facilities. The study recommends piloting the model prior to large-scale implementation and conducting further quantitative validation studies to establish measurable key performance indicators for routine LMIS monitoring and evaluation.

Abstract:

Purpose: The purpose of this study is to operationalize Digital Twin (DT) technologies into real-life project-based supply network environments by investigating how practical implementation can improve visibility, enable real-time decision support, and enhance coordination across operations (Ivanov, 2021) (Zaidi, 2024). The study emerges from recurring operational issues in the supply network, such as missing or incorrect materials, and poor adherence to schedules, all of which lead to significant resource waste and delays. The study therefore aims to identify and structure a set of relevant KPIs that capture lean performance, e.g., value creation, waste reduction. These KPIs will be examined in terms of how they can be integrated into a Digital Shadow artifact, enabling real-time monitoring, analytics, and decision support.

Methodology/approach: The study adopted a qualitative approach, and the Design Science Research (DSR) is considered a research methodology which is appropriate for research aimed at creating and evaluating innovative artifacts. DSR methodology combines real-life challenges (relevance) and scientific knowledge (rigor) or expertise in design, development, and evaluation (design) of the artifact (Hevner et al. 2004).

Findings: The results of this study indicate that digital shadow can significantly enhance visualization and real-time situational awareness of material location over supply network. The developed digital shadow enables both real-time material location tracking and detailed pallet-level traceability, thereby improving transparency and control over supply network. However, the findings also highlight that the accuracy and precision requirements for material location may constrain freedom to select IoT solutions. These constraints are particularly influenced by the operational environment and surrounding conditions where artifact is deployed. During the design phase, the digital shadow was conceptualized and iteratively refined. Resulting artifact integrates multiple technologies, including IoT-sensors, wireless communications 5G/GPS networks, cloud-based data platforms, Geographical Information System (GIS), and visualization dashboards. Furthermore, the rigorously developed dashboard incorporates performance analytics, supporting both lean value stream analysis and the monitoring of key performance indicators (KPIs).

Originality: This study focuses on identified a lack of empirical implementation of network level material tracking and dashboard solutions within a digital twin (DT) of supply network. The managerial perspective, study introduces practically relevant KPIs to support lean waste reduction and supply networks operational targets in project business context and provides a dashboard level visibility with real-time situational awareness for decision making. At the network level, study contributes not only to the development of the artifact but also to broader knowledge creation regarding digital shadow applications in material tracking. From a scientific perspective, it contributes by empirically testing Digital Twin (DT) operationalization in a real-life supply network context, bridging the gap between conceptual DT models and their practical implementation.

Verifying engineering requirements is a critical step in railway Signalling projects because delayed or inconsistent compliance assessment can slow certification, postpone system handover, and affect operational readiness. Engineers use a Requirements Verification Matrix to compare stated requirements with submitted evidence and confirm whether each requirement has been satisfied. This process remains labor-intensive, error-prone, and difficult to scale across large projects. The challenge increases when expert-labeled compliance records are scarce, especially for non-compliant cases that require specialist judgment.

This study proposes a semi-supervised adversarial transformer framework for railway compliance verification when only a small number of records have been checked and labeled by compliance experts. The framework uses 636 confirmed compliant records from a railway signalling project and applies six controlled perturbation strategies to construct 636 synthetic non-compliant records. These perturbations include negation insertion, evidence mismatch, scope substitution, meaning inversion, numerical alteration, and standard reference swapping. The resulting 1,272-record dataset serves as a proof-of-concept evaluation setting for testing whether artificial intelligence can distinguish compliant evidence from non-compliant evidence under this controlled setting. Each record is represented in a natural language inference format that combines verification context, the stated requirement, and the submitted evidence.

The proposed model follows a GAN-BERT-inspired training strategy. A transformer encoder learns representations from real compliance records, while a generator produces artificial feature vectors. The discriminator learns from three types of training signals, including expert-labeled real records for compliance classification, real records without expert labels for distribution learning, and generated feature vectors for adversarial regularization. This design enables learning from both labeled and unlabeled records without requiring full expert annotation.

Five transformer architectures are evaluated within this pipeline, namely BERT-base, RoBERTa, DeBERTa, SciBERT, and DistilBERT. The experiment tests nine training settings, where only 8 to 64 records are provided with expert labels. The results show that BERT-base and SciBERT provide stable performance across different training settings, while SciBERT performs strongly when more expert-labeled records are available. DistilBERT reaches a practical safety-oriented threshold with only 14 expert-labeled records, achieving non-compliant recall of 0.901 without training collapse. Within the controlled perturbation-based setting, DistilBERT achieved very high internal performance at 48 expert-labeled records, with macro F1 of 0.998 and non-compliant recall of 1.000. RoBERTa and DeBERTa show higher sensitivity to instability under extreme expert-label scarcity.

The findings suggest that semi-supervised adversarial transformer learning can reduce the number of records that compliance experts need to label while supporting digital assurance workflows for railway Signalling projects. The study contributes an AI-based verification pipeline for scarce-label environments and highlights the importance of matching model capacity to the number of available expert-labeled records. Because the non-compliant cases in this experiment are synthetically constructed, future validation with real project non-compliance records is needed before operational deployment.

Due to their complex material composition, the recycling of cable components is a technical and economic challenge. This study investigates the potential of establishing a circular economy for automotive wire harnesses. The objective is to systematically examine the technological processes and socio-economic structures relevant to wire harness recycling. Methodologically, a case study of an industrial recycling supply chain in Germany is conducted. The case study involved multiple interviews with the actors at each stage of the supply chain. As a result, the case shows that copper recovery is feasible at a high technical level, whereas plastic fractions are predominantly downcycled. We conclude that an effective circular economy is impeded less by technological limitations than by the lack of harmonization among regulatory standards, digital constraints, and the inadequate integration of supply chain practices. Thus, the new EU End-of-Life Vehicles Regulation demands ongoing managerial efforts.

Over the past few decades, e-commerce has evolved into a dynamic economic sector with steadily increasing revenue (Statista Research Department, 2026). Given the consistent expansion of online retail and the rising number of orders, demand for packaging is expected to increase persistently (Harwardt, 2023). In particular, products shipped via B2C e-commerce are often packaged in oversized boxes (Jaoua, Elarbi and Jaoua, 2024). Moreover, most of this packaging is discarded after delivery, resulting in significant resource use and large volumes of waste (Lin et al., 2023; Zimmermann and Hauschke, 2024). This issue is further exacerbated by the high return rates characteristic of online retail (Yang, Habib and Wood, 2023). At the same time, public and political interest in redesigning these systems sustainably is growing. Optimising packaging solutions has been identified as a lever for promoting environmental sustainability in e-commerce (Harwardt, 2023). Furthermore, the transition to reusable packaging is increasingly considered crucial for reducing environmental impacts (Zimmermann and Hauschke, 2024). Consequently, reusable transport packaging (RTP) is becoming an increasingly important topic, particularly in light of stricter legal requirements, such as the EU Packaging and Packaging Waste Regulation (PPWR). While RTP is well-established in B2B logistics, its adoption in B2C e-commerce remains nascent (Xu et al., 2025). Existing research, however, predominantly examines individual stakeholders in isolation or adopts a dual-stakeholder approach involving no more than two perspectives, leaving a comprehensive, cross-stakeholder understanding largely absent from the literature (Afif, Rebolledo and Roy, 2022; Cano, Londoño-Pineda and Rodas, 2022).

This study addresses this gap by investigating how external factors shaping RTP implementation in B2C e-commerce can be translated into positive outcomes through the leveraging of stakeholders' internal capabilities. A qualitative case study was conducted using 22 semi-structured interviews across five stakeholder groups. Packaging manufacturers, online retailers, logistics service providers, research institutes, and policy actors were interviewed to systematically capture and compare their perspectives. The central research question asks:

“How do external influencing factors and stakeholders' internal capabilities shape the adoption of RTP systems in B2C e-commerce?”

The results demonstrate that legal requirements, particularly the PPWR, significantly influence the adoption of RTP. All stakeholder groups acknowledge the need for action, although the sources of motivation vary, and shortcomings in existing single-use packaging systems are increasingly cited as a driving factor. Crucially, stakeholders' existing competencies, networks, and organisational capacities represent underutilised resources for converting regulatory and societal pressure into concrete progress. To implement RTP in a way that is more profitable than single-use systems, a supportive EU policy framework is considered essential. Pilot projects by major online retailers are identified as critical catalysts for expanding RTP systems in B2C e-commerce. Interviewees further emphasise the need for standardised, shared industry solutions at European and national levels. Alongside robust packaging designs capable of withstanding a high number of cycles, a shared return infrastructure, and digital processes for seamless tracking are necessary.

Based on this multi-stakeholder analysis, the study derives concrete recommendations for action to mobilise stakeholders' internal capabilities, accelerate RTP adoption, and advance sustainability in B2C e-commerce.

The rapid growth of e-commerce has indicated the importance of last-mile delivery as a key determinant of customer satisfaction and competitive advantage. At the same time, last-mile logistics are among the most costly and environmentally impactful components of online retail operations. While retailers increasingly invest in sustainability initiatives to reduce delivery-related emissions, many of these measures require customers to accept lower levels of service convenience, such as longer delivery lead times or increased delivery flexibility. Consequently, the success of sustainable logistics strategies depends not only on operational feasibility but also on consumer acceptance. This research investigates how sustainability communication and economic incentives influence customers’ willingness to adjust their delivery time to support environmentally friendly delivery options in the online grocery sector. Assessing on a large-scale choice-based conjoint experiment involving 1500 U.S. online grocery shoppers on Prolific Academic in Jan 2026 (pre-register at AsPredicted: https://aspredicted.org/xd6w3j.pdf), the study examines trade-offs consumers make between delivery time window, discount incentives, and notification lead time. Participants were randomly assigned to either a generic delivery scenario or a sustainability-focused communication scenario that explicitly explained the environmental benefits of delivery adjustments. Individual preferences were estimated using Hierarchical Bayes methods, enabling the identification of heterogeneous responses to sustainability initiatives. Beyond understanding consumer behavior, our study also integrates customer preference data into a vehicle routing optimization model. Individual-level utility estimates are incorporated into sequential decision process models to evaluate the impact of customer choice on economic and ecological consequences of shifting demand toward more flexible delivery schedules. This integrated approach allows the assessment of potential reductions in logistics costs and CO₂ emissions under alternative combinations of sustainability communication and incentive schemes. Preliminary results indicate that financial incentives remain the strongest driver of customer choice; however, sustainability-focused communication significantly increases consumers’ willingness to accept delivery-time adjustments. The findings suggest that carefully designed communication strategies can enhance customer participation in sustainable logistics programs while reducing the need for costly incentives. The optimization analysis will further quantify the extent to which consumer trade-offs can contribute to more efficient route consolidation and lower environmental impacts. In conclusion, by linking targeted marketing messages with logistics optimization, this research contributes to the literature on sustainable logistics and last-mile delivery. The study offers actionable insights for online retailers seeking to balance customer service, operational efficiency, and environmental responsibility in increasingly competitive e-commerce markets.

The ongoing shift toward containerization and the continuous increase in vessel sizes place growing demands on the design and dimensioning of container terminals. Worldwide, most container terminals use yard cranes to stack containers in the yard. The most common type are Rubber-Tired Gantry Cranes (RTGs) due to their flexible deployment within the yard and the comparatively high stacking density. Despite their widespread use, empirical knowledge on regional differences in RTG model preferences and storage block designs remains limited.

This study addresses this research gap by first providing an overview of the most frequently used container stacking equipment types in the Top 100 container ports worldwide using satellite imagery from Google Earth Pro. The results reveal significant regional disparities: While RTGs are used most frequently worldwide, rail-mounted gantry cranes and straddle carriers are also quite frequently used, especially in Northern Europe and North America, followed by specialized container stacking solutions and a combination of several equipment types. This context is essential for interpreting the subsequent analysis of design parameters.
In the next stage, it is investigated whether overarching design principles for RTG-based container terminals can be identified. The analysis is based on a comprehensive dataset of RTG-dominated container terminals across all nine global regions. The data collection relies on systematic measurements using Google Earth Pro and is complemented by vessel data from MarineTraffic to examine potential relationships between terminal design and vessel sizes. For each terminal, key parameters such as block lengths, crane spans, lifting heights, terminal sizes, and the lengths of calling vessels are collected and consolidated into comparable metrics. The findings indicate that, despite these regional differences, consistent and globally prevalent patterns exist in the dimensioning of RTG terminals, particularly regarding block lengths and crane spans. These patterns suggest the presence of established design principles that extend beyond individual regions. At the same time, no direct relationship can be identified between vessel size and block length. Instead, stable ratio-based relationships between these variables emerge, pointing to a relative scaling logic in terminal design.

Overall, this study contributes to a deeper understanding of the global design logic of RTG terminals and provides an empirical basis for planning, benchmarking, and optimization in terminal management. It also highlights the limitations of simplified planning assumptions and identifies avenues for further research, particularly regarding the integration of throughput and capacity-related metrics.

Twistlocks are mechanical locking devices to secure containers on vessels. They must be removed and installed during loading and unloading operations. Despite increasing automation in container terminals, twistlock handling is still done manually during port operations. The activity is time-consuming and poses significant occupational safety risks. Despite the obvious advantages that automated twistlock handling (ATS) promise, there are no comprehensive studies of ATS. As a result, twistlock handling remains a manual activity in the otherwise already highly automated functional area of waterside handling processes at container terminals. The paper is a part of the SIM-TWIST project (Project number: 19H24006B) funded by Federal Ministry of Transport (BMV) of Germany under IHATECH from Jan, 2025 till Dec, 2026.

This paper addresses the need for a systematic assessment of automated twistlock handling technologies at container terminals. First, an overview of existing and emerging approaches for automated twistlock handling systems is provided. Based on this overview, a classification scheme for ATS is developed. As a result, four ATS scenarios are derived based on the application area: On the crane ATS, under the crane ATS, drive-through ATS, and ATS in dedicated operation zones.

Second, the paper analyses how the application of ATS influences the terminal operations, specially on the waterside container handling process. It highlights the impacts of identified ATS technologies on the process cycle times through the synchronisation of crane and horizontal transport configurations, mapping different terminal design concepts, with the objective of improving productivity, a key performance indicator of the container terminals. Furthermore, different ATS scenarios place different demands on process design and resources. For example, regarding transport processes, transport capacities, quay crane capacities, and area capacities. These differences directly affect the entire port performance such as vessel turnaround times, terminal throughput and crane productivity.

As a result, the paper shows that discrete-event simulation is suitable for evaluating and quantifying the influence of ATS on terminal processes and for comparing different ATS scenarios. In this context, the paper presents a basic concept for developing a simulation environment for container terminal operations, including automated twistlock handling. The simulation environment focuses on processes required for vessel handling, including container storage and transportation, twistlock handling operations, and loading resp. unloading of vessels by crane. The environment allows simulation and comparison of the ATS scenarios mentioned above.

Passenger crowding in urban rail networks creates severe operational challenges and safety risks for transit stations, often causing platform overcrowding and train delays. These crowding issues are most critical at major transfer stations where crossing pedestrian flows disrupt the movement of passengers. At these locations, a large number of passengers get off trains while bi-directional escalators continuously move people from lower platform to the upper platform. This creates a major conflict point between passengers entering the platform and those trying to use the escalators. To reduce these conflicts and improve passenger flow, this study evaluates different physical barrier layouts near escalators in the peak hours. Physical barriers play an important role in guiding pedestrian movement and organizing queues, but how the layout of these barriers affect passenger movement has not been widely studied.

This research develops a pedestrian model of BTS Siam Station, one of the busiest interchange stations in the Bangkok Mass Transit System network, by using PTV VISSIM pedestrian simulation. Seven distinct physical barrier layouts applicable to escalator entry zones are designed and comparatively analyzed under peak-hour operational conditions. This research evaluates passenger density for each physical barrier layout to determine which configuration performs the best. The study further validates simulation outcomes through real-world observation of passenger behavior at BTS Siam Station, establishing the practical applicability of the proposed design interventions.

Results demonstrate that barrier configuration significantly influences passenger distribution, queue organization, and boarding efficiency at escalator interfaces. The findings identify that center long openside is the optimal barrier configuration, which successfully reduces maximum passenger density in the upper platform by 18.27% and reduces average density by 22.08% compared to the current baseline, effectively mitigating crowd accumulation while maintaining orderly and safe boarding behavior. This research shows how modifications to physical barrier configurations can improve pedestrian flow in high-demand urban rail systems. The study also highlights the value of simulation-based approaches for evaluating and optimizing pedestrian infrastructure before physical implementation.

Women’s underrepresentation in operations leadership is not a talent pipeline issue. It is a system design issue. The organizations that close the leadership gap will be those that examine and change how they structure roles, govern career pathways, manage culture, and define leadership - not those that only invest in developing individual women to fit systems that weren’t built for them. This brief highlights four structural barriers that consistently influence who gains credibility, visibility, and decision-making authority in operational roles: safety is not integrated into role design; career pathways assume a male life cycle; organizational culture perpetuates gender norms through informal gatekeeping; and leadership evaluation favors visibility over impact. These barriers work together and reinforce each other. The findings are based on ten in-depth interviews with senior women leaders from five different sectors and a participatory workshop with seventeen participants in Amman. The main strength of the study is the consistent results across sectors: barriers that appear in humanitarian, public, and private sector settings are systemic, not specific to one sector. The request is straightforward: view leadership system design as a governance duty. Review your role structure, career standards, sponsorship methods, and evaluation systems against the four barriers in this brief. Ensure accountability for addressing identified gaps.

Read Full: HELP-TL-Brief-Fix_the_System_Not_the_Women-Digital-20260323.pdf

Despite growing circular economy mandates across Europe, flat glass from the construction and demolition (C&D) sector remains one of the most underrecycled building materials (Glass for Europe, 2021). This paper investigates the systemic barriers that prevent effective reverse logistics for flat glass, drawing on a review of academic and grey literature to synthesise the current state of knowledge and identify priority areas for policy and industry intervention.

The study identifies four systemic failure points that collectively impede closed-loop supply chains for flat glass. First, collection and acquisition processes remain fragmented: flat glass is frequently co-mingled with mixed demolition waste, sharply reducing purity and recovery rates. Second, sorting and inspection infrastructure is underdeveloped Barriers are classified across technical, economic, organisational, and behavioural dimensions (Dekker et al., 2004). surface coatings, lamination layers, and contaminants make quality assessment technically demanding and economically marginal. Third, stakeholder coordination is structurally weak glazing contractors, waste handlers, recyclers, and float glass manufacturers operate in disconnected value chain segments with misaligned economic incentives. Fourth, behavioral barriers including low recycling intention among site contractors and limited awareness of available collection schemes produce an intention-action gap that persists even where physical infrastructure exists. These findings are interpreted through the closed-loop supply chain (CLSC) framework (Guide & Van Wassenhove, 2009), the operational taxonomy of reverse logistics (Dekker et al., 2004), and nudge theory applied to recycling behaviour (Thaler & Sunstein, 2008; Miafodzyeva & Brandt, 2013).

This paper offers the first structured, multi-dimensional barrier synthesis specific to flat glass in the construction sector. Prior research has addressed glass recycling in broader contexts container glass and post-consumer cullet but has not systematically mapped reverse logistics constraints for architectural flat glass. The four failure point framework provides a structured diagnostic lens that can guide extended producer responsibility (EPR) policy design, reverse logistics network configuration, and targeted stakeholder engagement strategies.

The findings call for mandatory material separation requirements in demolition contracts, subsidised collection and sorting infrastructure for glazing waste, and nudge-based communication campaigns targeting contractors and site managers. Policymakers are encouraged to extend existing EPR frameworks currently focused on container glass to cover flat glass from the built environment.

Circular economy is increasingly discussed as a means of improving the environmental performance of logistics and supply chain systems. Yet, circular economy approaches in both research and practice are still frequently associated with recycling, recovery, waste management, and end-of-life treatment. This perspective risks underrepresenting non-recycling R-strategies that are deployed earlier in the product life cycle and seek to prevent resource use, extend product lifetimes, or maintain the functionality and value of products and components before material recovery becomes necessary.

This paper examines selected non-recycling R-strategies in circular supply chains, while recycling and recovery serve as reference points within the broader R-strategy spectrum. The paper conducts an integrative literature review, synthesizing selected research on circular economy, R-strategies, value retention, circular supply chain management, and environmental sustainability. First, the review consolidates the current state of knowledge on the R-strategy spectrum by bringing together different perspectives from the literature, including life-cycle stages, value-retention levels, and circularity logics.

Building on this conceptual overview, the main analysis links selected non-recycling R-strategies to supply chain processes. For this purpose, the paper uses the Supply Chain Operations Reference (SCOR) model as a structuring framework and examines how the selected strategies relate to the process areas Plan, Source, Make, Deliver, Return, and Enable. This allows the analysis to identify which supply chain processes and operational capabilities are particularly relevant for the practical application of different non-recycling R-strategies.

Finally, the review provides a concise ecological reflection on the proposed classification. Rather than conducting an empirical impact assessment or life cycle assessment, it discusses how the identified supply chain processes and operational requirements may help explain why circular strategies are not automatically environmentally beneficial and why their relevance depends on the specific context of application.

By connecting the current state of knowledge on the R-strategy spectrum with a SCOR-based analysis of selected non-recycling R-strategies, the paper contributes to a more differentiated understanding of circular economy beyond recycling and recovery. It outlines two directions for future research: the empirical environmental assessment of non-recycling R-strategies and the analysis of strategy-process combinations across circular supply chain contexts.

Abstract: With the emergence of large language models such as ChatGPT, the use of artificial intelligence (AI) has become firmly established in both private and business contexts. The use of AI applications is now considered a decisive factor for companies’ competitiveness. Logistics and supply chain management (SCM) offer promising areas of application in this context. Small and medium-sized enterprises (SMEs), in particular, face the challenge of identifying suitable fields of application and successfully integrating AI into their existing business processes due to limited resources.

This paper aims to develop an AI implementation model that supports SMEs in identifying suitable application areas in logistics and SCM and adopting them in a structured way.

First, expert interviews are conducted to identify requirements for an AI implementation model for SMEs in SCM and logistics with regard to technology, organization, and environment. Subsequently, an extensive literature analysis is undertaken to compare existing AI and digitalization implementation models against these requirements. The analysis shows that although many models exist, they often lack a specific focus on AI and do not sufficiently address the previously identified requirements.

Based on these findings, an AI implementation model is developed that systematically integrates organizational conditions and implementation measures considering the environment SMEs are operating in. The model structures the implementation process into six phases with several sub-steps, which are carried out iteratively. Finally, the AI implementation model is validated with practice partners.

The model presented in this paper supports SMEs in SCM and logistics by providing a practical, phase-based approach to AI implementation. It supports organisations in evaluating AI use cases, prioritizing them, and implementing them.

Cutting stock problems are still considered within the class of NP-hard problems that continue to be actively studied in the field of combinatorial optimization. These problems encompass processes such as material utilization and waste generation, and their effective resolution plays a crucial role in managing operations that directly influence efficiency. From a sustainability perspective, the reduction of unused waste directly contributes to lowering environmental impacts, while improvements in material utilization enhance supply chain resilience by reducing dependency on raw material inputs. Consequently, unnecessary waste, overproduction, excessive ordering, and redundant transportation processes can be effectively minimized. Therefore, these problems are of significant importance in the context of sustainable manufacturing and logistics.

This study addresses a variant of the two-dimensional cutting stock problem with the objective of improving material efficiency within supply chains. The proposed approach is developed based on benchmark instances in literature.

While existing benchmark studies predominantly focus on two-stage and three-stage guillotine cutting strategies, this study expands the solution space by investigating randomized non-guillotine cutting patterns that eliminate traditional guillotine constraints. A mixed-integer goal programming model is formulated with the dual objectives of minimizing total material usage (objective cost) and maximizing the recoverable leftover area (leftover value). The proposed methodology aims to generate cutting patterns at an optimal level by integrating an evaluation framework that simultaneously considers minimal waste and maximum recovery potential.

Computational experiments were conducted, and the obtained results were systematically compared with those reported in the literature. The findings indicate that the proposed non-guillotine approach achieves superior performance, yielding lower objective costs and higher reusable leftover values compared to prior studies. In particular, it is observed that in cases where guillotine constraints restrict pattern diversity, the non-guillotine strategy provides a significant advantage by enabling more flexible and efficient cutting configurations.

Overall, this study contributes to the literature by demonstrating that relaxing structural cutting constraints can lead to both economic and environmental benefits. Furthermore, it provides a robust decision-support framework for practitioners seeking to optimize cutting operations under sustainability and resilience considerations.

This research presents an analysis of electromagnetic interference (EMI) affecting Radio-based positioning readers deployed in logistic rail transport systems, where they serve as critical components of the train position determination subsystem. As modern rail operations continue to evolve toward higher levels of automation, reliable positioning becomes increasingly essential to ensure operational safety, traffic flow stability, and overall system efficiency. However, the electromagnetic environment within rail infrastructure is inherently complex due to dense electrical installations, extensive metallic structures, and the coexistence of multiple overlapping communication and control subsystems. These systems include signaling networks, wireless communications, and onboard electronics. As a result, the railway environment is highly susceptible to electromagnetic interference (EMI), where unwanted electromagnetic energy can disrupt the normal operation of equipment. This complex interaction of emissions increases the risk of performance degradation, signal corruption, or even system malfunction making effective electromagnetic compatibility (EMC) design and mitigation strategies essential for ensuring safe and reliable operations.

The research integrates full-wave electromagnetic simulation using advanced 3D numerical solvers to model the complete coupling pathway from interference sources to the positioning reader antenna. The modeling incorporates multilayer dielectric boundaries, conductive enclosures, and realistic rail vehicle underbody geometries, enabling accurate characterization of both near-field and far-field interference phenomena. Electromagnetic field (EM field) extraction is employed to quantify coupling intensities under various installation configurations and structural alignments. Furthermore, simulation results are systematically validated through parametric studies examining material conductivity, geometric separation, and relative positioning, allowing the identification of worst-case EMI scenarios likely to occur under real-world operating conditions.

By combining numerical modeling with EMI pathway identification, this study aims to significantly enhance the reliability of Radio-based positioning systems. The results establish a robust engineering framework that supports consistent system performance, reduces susceptibility to electromagnetic disturbances, and improves the stability of localization algorithms. Furthermore, unresolved EMI effects can introduce system-level disruptions, including repeated signal reprocessing, misread recovery cycles, and delayed decision-making, which collectively contribute to increased latency and reduced efficiency within overall logistics operations. Ultimately, this work contributes to safer and more efficient operations by ensuring accurate and reliable position detection even in electromagnetically challenging environments.