Conference Agenda

Continuous improvement programs are a cornerstone of operational excellence in manufacturing and logistics. Firms rely on structured idea management systems to channel employee suggestions for process improvements into tangible efficiency gains. Yet realizing the operational value of these ideas depends not only on selecting the right ones, but on doing so promptly. Delayed evaluations erode the value of approved improvements and, as research on procedural justice suggests, undermine the cooperative behavior of employees who perceive the process as slow or unfair. Despite this practical importance, prior research has focused almost exclusively on the outcome of idea evaluation — which ideas get selected — leaving the process largely unexplored. This paper asks: what factors drive idea evaluation time in operational improvement systems, and how can we explain their impact?

We draw on longitudinal data from the internal idea management system of a large European manufacturer in the mobility industry. The dataset covers more than 1.2 million evaluation activities from over 30,000 unique evaluators for roughly 240,000 submitted kaizen ideas spanning 14 years (2004–2018), with precise timestamps for each evaluation step. Ideas range from small operational upgrades (e.g., tool mounting improvements) to significant process changes (e.g., restructuring manufacturing workflows).

Given that theory on evaluation speed is nascent, we follow a two-stage approach. First, we apply algorithm-supported induction: we train a gradient boosting model (XGBoost) and use Shapley values (SHAP) to surface patterns in the data, which we then embed in existing theory to formulate hypotheses. Second, we test these hypotheses on a held-out sample using a two-way fixed-effects OLS regression.

Results reveal several robust drivers of evaluation time. Evaluations take roughly one-third longer when evaluator and ideator share the same organizational unit, pointing to coordination overhead or heightened local scrutiny. Involvement of higher-hierarchy evaluators in prior steps cuts evaluation time by around one-fifth, suggesting senior participation resolves operational ambiguity or raises process priority. A higher average duration of preceding activities increases the focal evaluation time — consistent with collective shirking — while longer prior-evaluator time reduces it, suggesting effort substitution. Evaluator workload increases delays; ideator experience and recent dual-role activity (having submitted an idea oneself) reduce them.

The findings offer actionable levers for operations managers seeking to accelerate improvement cycles, reduce procedural drag, and sustain employee engagement in continuous improvement programs.

Inland waterways offer a good opportunity to expand the inland traffic routes. Current standards involve transferring goods from seaports to the hinterland by road or rail. However, road transhipment is especially unreliable and depends on traffic. In addition, there are high emissions. Another way for inland transhipment is by train, which is often less well connected than road traffic, leading to a combined use of train and road traffic, with each depending on the other. A third possibility here is the usage of inland waterways. These are currently used sparsely, but have high potential to reduce road traffic and lower emissions. Especially in the context of container transhipment. Unfortunately, there is a lack of decentralised, conveniently located, transhipment points, which could reduce the last mile of container transport by truck and thus create incentives for the increased use of waterways.

This paper proposes a conceptual planning approach for decentralised transhipment points by leveraging small, modular facilities built into existing infrastructure, such as bridges. The installation of these so-called “MicroPorts” aims to lower the threshold for accessing inland waterway transport. The proposed approach supports the early planning phase of MicroPorts. First, potential locations are evaluated using suitability criteria, including available space, access to existing infrastructure, and local demand for container handling. Second, the required services of the MicroPorts are defined. These are core functions, such as container handling and temporary storage, as well as potential additional services, such as long-term storage or container repair. Then, the technical concept defines a basic layout including areas for transhipment, container handling, storage, and other services. Furthermore, the technical concepts define the required equipment for container handling and storage, e.g., mobile handling systems such as reach stackers or fixed solutions such as gantry cranes. Last, a discrete-event simulation should assess how an additional transhipment option affects container transport costs and emissions, for instance, in cases where customers currently relying on truck transport may shift to inland waterway transport.

The planning approach is illustrated through an exemplary use case on an existing inland waterway route, where a potential MicroPort site is identified. At the selected example location, existing infrastructure can be repurposed, reducing the need for investment. The simulation results indicate that using a MicroPort can lead to lower transport costs and emissions than truck-based container transport. While the approach supports conceptual planning and evaluation of decentralised transhipment points, further steps are required before realising the concept, including detailed cost assessment, construction planning, and administrative approval processes.

Introduction:
Port calls constitute a central component of global maritime logistics, as they influence vessel turnaround times, cargo handling efficiency, and the reliability of the overall supply chain. Despite international guidelines such as ITPCO (International Transport and Port Call Optimization), operational procedures vary considerably between ports. The aim of this study is to systematically analyse port call processes across selected representative ports in Africa, the Americas, Asia, Australia, and Europe in order to identify differences as well as potential areas for optimisation.

Methodology:
The present study examines port call processes through a comparative analysis of ten selected ports, comprising one small and one large port per continent: Durban and Casablanca in Africa, Los Angeles and Honolulu in the Americas, Shenzhen and Basuo in Asia, Melbourne and Broome in Australia, as well as Rotterdam and Aalborg in Europe. The analysis is based on publicly available information from the respective port websites and the specialist publication Guide to Port Entry. Five key aspects were investigated: port size, port call guidelines, the application of ITPCO-defined timestamps, communication structures, and operational coordination between involved stakeholders. Based on these analyses, a classification framework was developed to systematically capture similarities and differences between the ports. The methodology therefore combines a structured comparison of current practices with a critical evaluation in order to clearly structure the main aspects of port processes and identify optimisation potential.

Results:
The analysis demonstrates that the level of detail in process descriptions is heterogeneous, ranging from limited to highly detailed, thereby indicating considerable differences in the degree of standardisation of operational procedures across the examined ports. With regard to temporal process structures, the findings reveal only a partial implementation of standardised timestamps. While the Estimated Time of Arrival (ETA) is communicated consistently in all ports, other relevant timestamps, including process-related milestones, are recorded systematically only to a limited extent. In addition, the communication structures can predominantly be characterised as analogue to hybrid, as operational coordination is still largely conducted via VHF radio or telephone. Digital systems are generally employed only as supplementary tools, primarily in the preliminary stages of service booking and administrative coordination. Consequently, the overall level of operational coordination can largely be classified as low to moderate, as integrated and continuous coordination between all involved stakeholders is only partially achieved. Overall, the findings suggest that the incomplete standardisation of timestamps, combined with only partially digitalised and fragmented communication structures, reduces the predictability, transparency, and operational efficiency of port call processes.

Originality:
This study provides a cross-continental comparative analysis of port call processes, an area that has received only limited attention in the existing literature to date. In doing so, key aspects relevant to port calls are integrated within a unified analytical framework. On this basis, specific efficiency deficits can be identified, and potential avenues for process and operational optimisation can be derived. Overall, the study establishes a robust foundation for further research as well as for the development of approaches aimed at enhancing the efficiency of port call processes.

Background: Logistics resilience theory has long assumed that efficiency and redundancy trade off against each other. Humanitarian pharmaceutical supply chains in low- and middle-income countries where temperature-sensitive, life-critical commodities move through donor-funded, multi-tier networks under extreme demand uncertainty, severe last-mile constraints, cold-chain requirements, and recurrent disruption constitute a natural laboratory for examining how organizational capabilities generate resilience under fundamental uncertainty. This systematic review asks which capability clusters characterize these chains, how they operate, and what transferable lessons they offer logistics and supply-chain management.

Methods: We conducted a complete search of electronic databases from inception to April 2026. Inclusion criteria: peer-reviewed empirical studies set in LMICs, focused on organizational capabilities or resilience practices in health-commodity supply chains, and reporting at least one supply-chain performance indicator or health-access outcome. Exclusion criteria: general humanitarian logistics without a medical-commodity focus; non-empirical work; high-income-country-only settings. Quality appraisal used MMAT (2018), and findings were synthesized through CIMO extraction and thematic analysis and interpreted through dynamic capabilities theory.

Results: 41 studies met inclusion criteria, with evidence concentrated in Ethiopia, Ghana, Nigeria, Uganda, Kenya, and Tanzania. Six recurring capability clusters emerged: coordination and collaboration, agility and resilience, technology-enabled logistics, procurement and financing, workforce and adaptive learning, and governance and regulation.

Coordinated routines were the dominant mechanism (37/41), while information sharing and interorganizational trust each appeared in (25/41). Capabilities operated under recurring constraints, including institutional and regulatory weakness, infrastructure limitations, donor funding volatility, and limited health system capacity. The strongest empirical performance finding was a 43% reduction in procurement lead times reported by Ethiopia's Pharmaceuticals Supply Agency following procurement and supply-chain reforms. Only indirect health-outcome linkages were identified. These findings indicate a capability architecture for logistics systems operating under fundamental uncertainty. Across studies, organizational capabilities generated resilience through three recurring mechanisms: coordinated routines, information sharing, and interorganizational trust, suggesting how capabilities are converted into resilient performance under conditions of disruption. The evidence further indicates that sustained performance improvements depend less on the adoption of individual technologies than on their integration within broader coordination and governance structures.

These findings offer a new perspective on the efficiency–resilience trade-off by suggesting that coordination and information sharing help organizations balance redundancy and agility under crisis conditions. The absence of evidence linking capabilities to population-level outcomes reveals a persistent capability-to-value gap and informs the development of the Sustainability–Capability–Resilience–Catastrophic Health Expenditure framework as a future research agenda.

Conclusions: This review reframes humanitarian pharmaceutical supply chains as a natural laboratory for examining how organizational capabilities generate resilience under fundamental uncertainty. It identifies a transferable capability architecture comprising six capability clusters and shows that resilience emerges through coordinated routines, information sharing, and inter-organizational trust. The findings suggest that sustained performance improvements depend on institutionalized capabilities rather than stand-alone interventions and reveal a persistent capability-to-value gap due to the lack of direct evidence linking capabilities to population-level outcomes. The study contributes to logistics and supply-chain management by clarifying how capabilities translate into resilient performance and highlighting the need for context-specific capability measurement and resilience assessment.

Traditional supply chain strategy models have long provided valuable frameworks for aligning operational configurations with market and product characteristics. Seminal contributions such as Fisher’s product-based classification (Fisher 1997) and Lee’s uncertainty matrix (Lee 2002) have guided managerial decision-making by linking supply chain strategy to demand and supply uncertainty. However, the growing frequency of global disruptions, increasing regulatory pressure, and the urgent need for environmental responsibility have exposed important limitations in these models. While resilience and sustainability have become central strategic imperatives, existing supply chain strategy frameworks rarely integrate these dimensions in a systematic manner.

This research addresses this gap by proposing an extension of Lee’s uncertainty matrix through the introduction of environmental impact strategic relevance as an additional dimension. The objective is to develop a more comprehensive framework capable of supporting supply chain design in contexts characterized not only by uncertainty, but also by increasing sustainability requirements. The study investigates how traditional supply chain strategy models can be adapted to better reflect the realities of contemporary business environments, where competitiveness increasingly depends on the ability to balance efficiency, resilience, and environmental responsibility.

The research adopts a qualitative multiple case study methodology and was conducted in two steps. The first step is based on published literature. Five sustainability-oriented companies operating in different industries were selected for analysis: Patagonia (apparel), Lush (cosmetics), Fairphone (electronics), Toyota (automotive), and EcorNaturaSì (agri-food). These organizations were chosen due to their strong commitment to environmental and social sustainability and the availability of extensive secondary data. The second step is an empirical investigation conducted through semi-structured interviews with managers from five additional companies (different from those selected for the first step) actively implementing ESG-related supply chain practices. A within-case and cross-case analyses were elaborated with focus on uncertainty management, ethical sourcing, traceability, circular product design, carbon footprint reduction, logistics strategies, and the interaction between sustainability and business performance.

The findings reveal that sustainability is no longer a peripheral operational concern but a strategic driver that fundamentally influences supply chain configuration. Companies increasingly adopt practices that simultaneously enhance resilience and environmental performance, including supplier diversification, transparency mechanisms, circularity initiatives, local sourcing, and collaborative governance structures. At the same time, the study identifies important trade-offs related to costs, technological constraints, and regulatory compliance.

Building on these insights, this study proposes two complementary strategic matrices that integrate environmental impact with demand uncertainty and supply uncertainty, respectively. The resulting framework introduces new interpretative quadrants and operational guidelines that better reflect contemporary supply chain realities. The study contributes to supply chain management literature by extending traditional strategic fit theories and offering a practical tool for organizations seeking to incorporate resilience and sustainability objectives. More broadly, it advances the discussion on how supply chains can evolve from efficiency-oriented systems toward adaptive, responsible, and long-term value-generating networks, despite not encompassing the social dimension, which remains an avenue for future research.

Resilience has attracted growing attention in both academia and industry. Despite the increasing focus and the growing body of literature dedicated to protective disciplines, a lack of conceptual clarity persists, regarding which elements constitute resilience and which factors are most critical for its effectiveness, particularly in industry. While organizations face numerous barriers to implementing resilience, academic research has paid limited attention to these barriers in a comprehensive and systematic manner.

This paper is based on the author’s ongoing doctoral research on resilience in industrial organizations. Resilience is conceptualized as being shaped by four interacting forces within a “resilience nexus” model: resilience constituents, IT and digital technologies as enabling factors, and internal and external barriers. Based on an extensive literature review and validation through expert interviews and focus groups, resilience constituents and barriers have been identified, described, and assessed. This paper presents a proposed functional classification of resilience constituents based on their role in building, maintaining, and executing resilience, and illustrates a new framework that extends prior temporal models by integrating a functional perspective that considers not only when resilience constituents become relevant during a disruption, but also how they contribute to resilience. This approach contributes to a more structured and holistic understanding of resilience across disruption phases.

Furthermore, the paper presents the framework developed for classifying resilience barriers, a four-cluster matrix generated by combining the categorization across two dimensions: attitudinal versus implementation barriers, and internal versus external barriers. Attitudinal barriers refer to how resilience is perceived, understood, and prioritized, whereas implementation barriers hinder the deployment of resources, tools, strategies, and processes for resilience. A total of 36 identified constraints were evaluated by a panel of experts, who largely confirmed both the categorization framework and the relevance of the identified barriers.

The findings address an important research gap by providing a more systematic and integrated perspective on resilience constituents and implementation barriers. In addition, the proposed frameworks may provide practical guidance for organizations seeking to strengthen resilience, particularly in industrial environments.

The integration of autonomous vehicles into airport cargo ground operations faces regulatory and other challenges that hinder progress. Testing and deploying emerging technologies at airports (as critical infrastructure) require approvals that are difficult to obtain because airport ground operations are dynamic, largely unstructured, and characterized by mixed traffic. Despite the growing number of prototypes and solutions, many stakeholders remain hesitant to invest, resulting in missed opportunities to improve efficiency.

This paper presents a survey concept for 30 semi-structured interviews with international aviation experts, focusing on processes, technologies, safety and security, economics, and sustainability. The interviewees are members of a working group contributing to the revision of IATA’s Airport Handling Manual, Section 908 (AHM908), on autonomous ground support equipment (GSE). They are based in 13 countries across four IATA regions - Americas, Europe, Middle East and Asia-Pacific - and they represent seven IATA organization types - aircraft manufacturers, airports, civil aviation authorities, ground handling service providers, ground support equipment manufacturers, IATA airline members, and solution providers. Data collection and analytics are sensitive to safeguard anonymity and to preserve IATA’s neutrality in policymaking.

Main results will include structured perceptions of relevant stakeholder groups, organized by topic clusters, focus airports, and regulatory scenarios. We will explore strategies, approval processes, best practices, investment calculations, technical capabilities, barriers, and hesitation, among other topics. Several local automation initiatives should be mentioned by interviewees, providing valuable perspectives as well as different shades of uncertainty and hesitation. Our holistic approach contributes a novel perspective to automation research in airport operations. Having exclusive access to such a specialized industry working group allows for in-depth discussions with early movers from carefully selected visionary stakeholder organizations. This work will inform policymakers about frameworks to support the development and implementation of autonomous solutions, aiding adaptation to evolving demands and labor shortages in airport logistics. It is complemented by an exemplary investment calculation concept to explore the technology-innovation-business nexus as a central element linking technology development and innovation with operations and business realities at airports.

Purpose:

This study proposes an explainable machine learning workflow for taxi-in time prediction at Frankfurt Airport to support transparent, data-driven ground operations. Accurate taxi-in time prediction is important for the scheduling of ground operations. Given the persistent labor shortages in this sector avoiding waiting times based on more accurate scheduling optimizes ground crew utilization but also avoids delays at the beginning of the aircraft turnaround.

Prior studies have investigated aircraft taxi times at various airports using ADS-B (Automatic Dependent Surveillance – Broadcast), A-CDM (Airport Collaborative Decision Making) and AODB (Airport Operational Databases) data. Most of the studies thereby focus on the taxi-out-times or the full turnaround from the perspective of an airline or airport with the objective of reducing delays or maximizing system efficiency. However few studies have predicted taxi-in-times specifically for optimized ground operations.

Methodology:

The study uses ADS-B-based aircraft movement data from Frankfurt Airport, covering approximately 67,000 arrival events between July 2025 and April 2026, combined with operational and contextual information. Compared with prior studies using broader feature sets, the workflow refines the input space to a compact set of 17 meaningful variables by retaining core operational predictors and adding a few relevant new features. This design improves interpretability, reduces redundancy, and relies on information that is practically available around landing, including landing runway, parking position, aircraft type, local traffic load, time-related variables, weather conditions, and airline-related information. Several supervised regression models are trained and compared, including Linear Regression, MLP, Random Forest, XGBoost, LightGBM, and CatBoost. CatBoost achieved the best overall performance and was selected for detailed evaluation. Model performance is assessed using mean absolute error and operationally interpretable accuracy thresholds. A bootstrap-based uncertainty estimation approach is used to estimate prediction confidence, while subgroup analyses identify operational conditions where taxi-in time is harder to predict.

Findings:

The CatBoost model achieves a mean absolute error of about 105 seconds (≈1.75 min). Around 86% of taxi-in events are predicted within 3 minutes and 96% within 5 minutes, showing useful accuracy under real airport operating conditions. However, subgroup analysis reveals substantial variation, suggesting that specific runway-stand combinations and traffic conditions are systematically more difficult to predict. SHAP-based explanation identifies landing runway and parking position as the most influential predictors. In addition to point predictions, the model provides a confidence score for each flight, indicating how reliable the prediction is expected to be.

Originality:

This study contributes to airport surface operations research in two main ways. First, it extends taxi-in time prediction beyond point estimates by introducing a confidence-aware workflow. Each prediction is accompanied by a confidence score that indicates its expected reliability. Second, the study examines prediction difficulty across operational subgroups, making it possible to identify the conditions under which larger errors are more likely. This provides a more operational view of model reliability than aggregate accuracy measures alone. Together with the explainable ADS-B-based modelling approach, these contributions can support practical planning decisions, such as dispatching ground crews to assigned stands under specific runway, parking, and traffic conditions.

In rail systems, maintenance activities in one subsystem may influence incidents in other subsystem. However, these relationships are rarely analyzed using integrated operational and maintenance data. Besides, existing predictive maintenance studies mainly focus on individual components or sensor-based monitoring, while event-based operational data and maintenance decision support remain underexplored. Thus, this research proposes a machine-learning (ML) based predictive maintenance framework for rail systems using integrated event-based operational and maintenance data. The framework processes include data preprocessing, descriptive analysis, feature engineering and predictive modelling, as well as model performance evaluation. Operational incident and maintenance records from an urban railway signalling system consisting of 98 train sets over the period of 2024–2025 was used as a case study in this study. Incident records, maintenance data, and operational information are firstly integrated. Since maintenance and incident records are largely text-based and subjective depending on maintainer interpretation, preprocessing techniques were applied to transform unstructured records into structured and interpretable datasets suitable for machine-learning based analysis and modelling. Furthermore, integrated operational and maintenance-related datasets provide opportunities to investigate hidden relationships and operational dependencies between railway subsystems. Descriptive analysis is subsequently performed to identify recurring operational incident patterns and relationships between maintenance activities and incident occurrences, especially wheel re-profiling activities and overspeed incidents. Linear Regression and several machine learning techniques including Support Vector Regression (SVR), Random Forest, and Gradient Boosting, were applied to build rail-incident forecasting models. Model performance evaluation results showed that Gradient Boosting achieved the best predictive performance, with a Mean Absolute Error (MAE) of 8.14 days and an R-squared (R²) value of 0.82. Moreover, feature importance analysis also indicated that wheel position was the most influential feature in the predictive model. It significantly supported that maintenance-related and event-based operational data contain meaningful information in forecasting cross-system incidents in rail systems for predictive maintenance.

Exporters can access foreign markets through various channel arrangements, including direct exporting, Export Management Companies (EMCs), Export Trading Companies (ETCs), piggybacking arrangements, and foreign distributors. While existing research has largely focused on control, resource commitment, and market uncertainty as determinants of export channel choice, the role of logistics conditions remains underexplored. This study examines how logistics performance influences the suitability of alternative export intermediaries across different international market environments.
A multi-criteria decision-making framework is developed to evaluate five export channel alternatives using nine criteria: market access capability, local market intelligence, degree of control, resource commitment, risk exposure, logistics coordination capability, customs and regulatory support, adaptability to low-performance logistics environments, and supply chain resilience. The World Bank Logistics Performance Index (LPI) is used to classify target markets into high-, medium-, and low-performance logistics environments. Expert judgments are collected to assess the relative importance of the evaluation criteria and the suitability of export channel alternatives under each scenario. The CRITIC-BWM method is employed to determine criterion weights and rank the alternatives.
The study contributes by introducing logistics performance as a determinant of export intermediary selection and by integrating export channel and logistics management perspectives. The findings provide a decision framework that helps exporters align intermediary choices with the logistics realities of target markets. Furthermore, the study offers insights into how changing logistics environments influence the effectiveness of alternative export channels and supports more resilient international market entry strategies.

Purpose - When employees give robots or AI agents names, talk to them, or feel anxious when they malfunction, they are revealing a fundamental human response to technology. As autonomous systems become increasingly integrated into retail logistics, an important question arises: How does working with robots and AI affect the people who interact with them? This article examines psychological and physiological reactions to human-technology collaboration (HTC), ranging from anthropomorphism and emotional attachment to technostress, social isolation, and feelings of surveillance or displacement (Cadario et al., 2021; Schlund & Zitek, 2024; Sapkota et al., 2025; Kayas et al., 2025). Although cobots and AI agents differ technologically, both can reduce perceived autonomy, blur the boundaries of agency, and trigger similar affective reactions. We therefore advocate for an integrated, human-centric perspective on technology acceptance and hybrid work systems in future retail logistics.

Approach - This paper proposes a two-pronged framework for action. Employees can form parasocial relationships with robots by attributing social characteristics to the machines and even giving them names (Schömbs et al., 2023; Leichtmann et al., 2025; Berretta et al., 2023). Conversely, collaboration between humans and technology can undermine autonomy, social connectedness, and professional identity. AI-supported systems can further exacerbate these effects through novel stressors - such as unpredictability, social erosion, and job insecurity - thereby creating new occupational health risks in retail logistics (Sapkota et al., 2025).

Design/methodology - Building on previous research on psychophysiological stress in logistics work (Hagemann et al., 2021; Keil et al., 2025; Keil & Klumpp, 2025), this paper develops a conceptual model of human reactions to robots and AI systems, ranging from attachment and trust to alienation and stress. By integrating findings from the fields of anthropomorphism (Schömbs et al., 2023; Tang et al., 2023; Glikson & Woolley, 2020), parasocial relationships (Gambino et al., 2020), and psychophysiological stress research (Orlando et al., 2025; Pereira et al., 2025), the model establishes a human-centric perspective on hybrid work systems in retail logistics. Picking in the warehouse serves as an illustrative context for human-technology collaboration, due to the close interaction between employees and autonomous systems (Klumpp et al., 2022; Tudisco et al., 2026).

Practical implications - What happens when employees form an emotional attachment to technologies that are later replaced, or when they feel increasingly monitored by AI? Research links AI-supported work, job insecurity caused by automation, and digital surveillance to emotional exhaustion and depressive symptoms. Since cobots and AI are viewed as technologies that reshape both work processes and employee experiences, this article advocates for a human-centric research agenda. This agenda places employee well-being - on an equal footing with operational efficiency - at the center as a key design goal for Industry 5.0 and Warehousing 5.0 (Rieth et al., 2024; Hagemann et al., 2026).

Third-party conformity assessment under ISO/IEC 17065 represents a pivotal governance mechanism for railway safety assurance. While non-accredited assessments may overlook critical system hazards leading to catastrophic failures and losses, mandating rigorous ISO-compliant processes demands additional financial overhead and procedural burdens. Thus, evidence guiding these accreditation mandates remains scarce, particularly in emerging railway markets. To address this gap, this study proposes a comprehensive probabilistic framework that quantitatively evaluates the systemic and economic impacts of accreditation rigour conditioned by institutional enforcement quality across four Safety Integrity Levels (SILs) and a Basic Integrity baseline.

Employing a quantitative risk evaluation methodology with stochastic parameter ranges, the framework can determine residual hazard rates as a function of SIL-specific baselines, an Intrinsic Rigour Score (K₀), the instructional effectiveness of the railway authority (β) and enforcement-conditioned certification effectiveness. , extracted from ISO/IEC 17065, critically imposes verifiable requirements for technical competence, impartiality and procedural rigour on certification bodies. serves as a weight-sum representation of the railway regulatory maturity index, government effectiveness and control of corruption. Finally, to translate technical safety outcomes into an economic justification metric, the derived residual hazard rates are converted into lifecycle expected losses by factoring in total operational hours and heavy-tailed consequence costs per event.

A 2 × 2 × 5 factorial design (accreditation status, enforcement profile, and SILs) was evaluated through extensive computational simulations. The proposed framework establishes a quantitative link between institutional regulatory inputs and high-consequence safety outcomes. The model is formulated, demonstrating how variations in safety integrity requirements alter the financial breakeven financial thresholds for mandatory accreditation and technical competence for certification bodies. By capturing the divergence between expected-value economic metrics and heavy-tailed risk distributions, the model directly supports the ALARP principle and precautionary regulatory decision-making. Ultimately, this research delivers a replicable, multi-tiered evaluation methodology. It provides policy-makers and railway authorities with a robust, evidence-based structural tool to confidently justify and integrate mandatory ISO/IEC 17065 technical qualification directly into infrastructure procurement specifications.

In maritime logistics, container terminals serve as critical nodes for global trade but are facing intense pressure to mitigate their environmental footprint due to stringent climate policies in context of climate warming. Traditional transport and handling equipment heavily rely on diesel engines, making container terminal operations a primary source of localized greenhouse gas emissions and air pollutants. To address these challenges, transitioning toward electrified equipment has emerged as a key pathway to increasing energy efficiency while achieving localized zero-emission operations.

This paper provides a systematic literature review that outlines the basic framework for the electrification of container terminals. Given the recent breakthroughs in heavy-duty battery storage, smart grid integration, and automated charging systems, the academic and practical landscape has evolved rapidly. This study maps out newly emerged technological pathways, evaluates current operational challenges, and provides strategic recommendations for container terminal operators.

Following the PRISMA framework, a systematic review was conducted across major academic databases. The literature is classified based on key operational units (e.g., Rubber-Tyred Gantry cranes, Automated Guided Vehicles, and mobile yard equipment) and primary electrification dimensions, including grid-connected power supplies, hybrid powertrains, and fully battery-electric energy storage systems.

The analysis reveals a significant acceleration in research addressing fully battery-powered mobile equipment and dynamic charging strategies. While grid-connected solutions remain dominant for heavy cranes, recent publications increasingly focus on decentralized charging station placement within transport zones to maximize operational uptime. Furthermore, the review identifies key research streams concerning peak shaving via ultracapacitors, vehicle-to-grid (V2G) capabilities providing primary frequency control, and automated battery swapping.

The paper concludes with an integrated action framework that aligns equipment electrification with port automation and digitalization. Terminal operators are provided with strategic insights regarding infrastructure dimensioning, the utilization of off-peak electricity tariffs, and layout-dependent charging optimization. Ultimately, this study bridges current research gaps by providing a holistic, up-to-date guide for transitioning to sustainable, low-carbon container terminal operations.

Containerized maritime transport is a core pillar of global trade, yet persistent imbalances between import and export flows continuously generate spatial mismatches in container availability. To manage these imbalances, liner shipping companies rely on empty container repositioning (ECR), a complex and cost-intensive process that ensures equipment availability across global networks. In recent years, this task has become increasingly challenging due to recurring global disruptions, including pandemics, geopolitical conflicts, and supply chain volatility.

ECR decisions depend on accurate forecasts to estimate future container demand and support operational planning. Increased uncertainty during trade crises gives leverage to human planners during the forecasting process, as they review and adjust system-generated predictions using contextual and operational knowledge. Understanding how users interact with these forecasts, particularly under varying levels of uncertainty and across different forecast horizons, is therefore crucial for improving decision quality in operational logistics environments.

This study examines user adjustment behavior and its effectiveness in an operational empty container repositioning forecasting environment at a major liner shipping company through a large-scale field study. The analysis is based on a comprehensive dataset covering 2020–2024 with more than 1.3 million adjusted forecasts across multiple container types. It includes system-generated forecasts, predominantly produced by AI-based and statistical models, realized demand, and user-adjusted forecasts submitted by practitioners for empty container releases at depots and terminals serving customer export shipments.

The empirical setting distinguishes between a crisis period (2020–2022), marked by severe disruptions such as the COVID-19 pandemic and a more stable period (2023–2024). Methodologically, the study combines established Forecast Value Added metrics (e.g., RelAME, MAPE, bias) with regression-based models that examine the determinants of forecast adjustment behavior and its impact on forecast accuracy across forecast horizons and different levels of environmental uncertainty. The paper aims to answer the following question: (1) Do human planners improve forecast accuracy greater during times of crises (and if so, in which means) (2) does the forecast horizon influence adjustment behavior and performance, and (3) does the influence of the forecast horizon change between stable and unstable times?

This study contributes to research in several ways. First, it extends existing research by providing a large-scale empirical analysis of human adjustment behavior in maritime demand forecasting. Second, it builds on prior work by examining human responses to AI-generated forecasts in an ECR setting across multiple forecast horizons. Finally, the findings offer insights into the role of human judgment in expert-supported forecasting processes under conditions of operational disruption and uncertainty.

Results show a pronounced shift in user behavior during crisis conditions, particularly in the magnitude of forecast adjustments. While forecast value added is higher at shorter forecast horizons, overall human interventions tend to reduce forecast accuracy on average. During crisis periods, however, planners contribute value primarily through downward adjustments, indicating that human judgment can partially offset systematic over forecasting by system forecasts under uncertain conditions.

Housekeeping is a key process in terminals, where containers are rearranged during off-peak hours to minimize future reshuffles during retrieval. The classical Container Pre-Marshalling Problem (CPMP) assumes a deterministic retrieval sequence, an assumption that rarely holds in practice, particularly in rail-road terminals where retrieval times of import containers are largely unknown. This paper introduces the Stochastic Container Pre-Marshalling Problem (SCPMP), which explicitly models uncertainty in container retrieval times through pairwise retrieval probabilities.

Unlike existing robust approaches that require non-overlapping time windows or convert the problem back into a deterministic formulation, the SCPMP encodes uncertainty directly at the container level by delivering a more realistic representation of the operations. Pairwise probabilities P(i,j), representing the likelihood that container i is retrieved before container j, are derived from heterogeneous sources including Machine Learning-based dwell time forecasts, truck appointment systems, and train schedules. These probabilities naturally accommodate overlapping retrieval windows and serve as direct input parameters for both the optimization model and the solution algorithm.

We formulate the SCPMP as a binary integer program that minimizes the expected number of badly placed items under a fixed move budget, reflecting the limited crane capacity available during housekeeping periods. A threshold parameter τ controls which blocking probabilities are classified as critical, enabling operators to calibrate the trade-off between reshuffling effort and residual risk.

For the solution approach, we adapt the Iterative Deeping A* (IDA*) algorithm, established as state-of-the-art for the deterministic CPMP, to the stochastic setting. A central contribution is the development of improved heuristic bounds that tighten the search. Beyond the direct lower bound based on τ-classified badly placed items, we introduce a blocking bound (counting well-placed containers that physically obstruct access to badly placed ones) and a destination-deficit bound (identifying badly placed containers lacking any safe target stack). Both bounds are proven admissible and combined via a maximum operator, preserving optimality guarantees while significantly reducing the search space. Computational experiments show a median speedup factor of 2.6× in node expansions compared to the baseline heuristic.

A computational study on synthetic instances calibrated with real operational data from a German intermodal terminal demonstrates that the IDA* approach solves typical terminal configurations (up to 5 stacks, 4 tiers) within seconds. We further compare solution quality against the exact BIP formulation, analyze the impact of uncertainty levels on algorithmic performance, and evaluate final layouts using CV@R-based risk metrics. Results show that probabilistic information significantly reduces expected reshuffles and tail-risk compared to deterministic and robust reference approaches.

Modern robotics applications increasingly demand heterogeneous multi-robot fleets to simultaneously handle diverse, specialized tasks such as collaborative material transport, manipulation, and sorting that single-purpose automation systems cannot manage alone. However, executing these complex logistical processes and scientific experiments requires advanced process control systems capable of managing highly dynamic and intricate workflows. While existing fleet management solutions often lack the flexibility to handle diverse robotic capabilities, this paper introduces a comprehensive process control concept specifically designed for the orchestration and monitoring of diverse robotic agents. This concept directly addresses the challenge of dynamically coordinating interdependent, multi-robot tasks without relying on hardcoded workflows. Rather than focusing on the underlying software architecture, the primary emphasis is placed on the core order-modeling and control mechanisms that enable the seamless definition and coordination of complex, multi-robot workflows.

At the heart of the proposed concept is a highly flexible order definition framework. The foundational order structure is broadly oriented toward the VDA 5050 standard, adopting core concepts like nodes, edges, and actions for order creation, transmission, and monitoring. To meet the demands of advanced robotic operations, such as tightly coupled multi-robot collaboration, real-time reactive behaviors, and environment-dependent task execution, this structure is significantly extended by a suite of powerful control mechanisms. These include flexible start and end conditions, spatial zones for coordination, and the use of runtime placeholders. These placeholders allow action parameters to be dynamically assigned during execution using real-time data or results from other system components.

To manage the temporal and logical dependencies between multiple robots, the concept incorporates a dedicated cross-order coordination backbone. This framework supports synchronization actions, such as sync lists and specific triggers, as well as the capacity for repeating orders that loop dynamically based on defined events, robot states, or manual user confirmations via a graphical interface. The capabilities of this process control framework are demonstrated through complex, multi-robot use cases, such as the collaborative transport of large loads and the boarding process of a delivery robot into an autonomous shuttle.

Finally, the paper addresses current limitations regarding the ongoing expansion efforts of existing standards and outlines future conceptual pathways to scale the process control system for next-generation robotic research environments.

Urban railways are critical logistics systems that move large passenger flows through time-sensitive and capacity-constrained networks. When disruptions occur, their effects can propagate rapidly across stations, trains, operators, and passenger journeys, requiring coordinated decisions among operations, maintenance, and customer-service teams. For advanced logistics management, disruption records should therefore support not only retrospective reporting but also evidence-based severity assessment, operational learning, and future integration with simulation or digital-twin environments. However, in Thailand, rail disruption narratives are often recorded as mixed Thai–English free text with inconsistent spacing, abbreviations, and operator-specific expressions, limiting the usefulness of structured incident codes alone for large-scale analytics.

This study proposes a machine-learning-based natural language processing workflow for classifying disruption severity using daily records from the Department of Rail Transport dataset during 2021–2025 (N = 770). Severity labels were deterministically derived from delay minutes and grouped into three operational classes: A for delays below 5 minutes, B for delays from 5 to below 15 minutes, and S for delays of 15 minutes or more. These labels are interpreted as a proxy for passenger and operational impact. The DETAIL field was used as the main narrative input. Text preprocessing included removal of repeated spaces and Thai word segmentation using PyThaiNLP with a custom dictionary developed from station names, station codes, and disruption-cause terms to improve recognition of railway-specific expressions in mixed Thai–English text.

The experiment compared four text feature representations: Bag-of-Words, TF-IDF, Trigram features, and WangchanBERTa sentence embeddings. Each representation was evaluated with Logistic Regression and XGBoost. Two validation settings were used. A stratified split divided the data into 70% training, 20% validation, and 10% testing while preserving severity-class proportions. A chronological split used the same proportion but sorted records by date to evaluate temporal generalization for future deployment.

The dataset was imbalanced, with Class S accounting for 429 records (55.71%), Class B for 268 records (34.81%), and Class A for 73 records (9.48%). The average delay was 30.02 minutes, with a median of 15 minutes and a maximum of 4,320 minutes. Under the stratified split, BoW + XGBoost achieved the best balanced performance, with accuracy = 0.870 and F1-score = 0.859. BoW + Logistic Regression also performed strongly, with accuracy = 0.844 and F1-score = 0.838. Under the chronological split, TF-IDF + XGBoost achieved the highest accuracy of 0.857, while WangchanBERTa embeddings + XGBoost achieved the highest F1-score of 0.607. However, the chronological test set did not contain Class A examples; therefore, chronological results must be interpreted carefully and should be considered together with the stratified-split results.

These findings show that mixed Thai–English disruption narratives contain meaningful severity signals and that lightweight, interpretable NLP models can provide a practical baseline for disruption monitoring, severity validation, escalation support, and future rail logistics decision-support to an operator or digital-twin applications.

Ports are central nodes in multimodal transport networks, linking maritime or inland waterway transport with rail and road-based hinterland connections. Within these nodes, rail shunting operations are operationally critical but difficult to plan digitally because transport orders, infrastructure states, locomotive availability, wagon movements, and timing information are often distributed across heterogeneous systems. Existing digital platforms in port logistics primarily support information exchange and process visibility, while less attention is given to the transformation of local operational states into executable shunting decisions under time, resource, and infrastructure constraints.

This paper presents a reference architecture developed within the KIRBI project, which investigates AI-supported shunting and dispatching processes in port rail operations. The architecture is currently being developed and prototyped in two German port railway contexts, Dortmunder Hafen and Hafen Hamm, and targets the transition from conceptual design toward prototypical implementation. The contribution focuses on the system-level integration required to couple operational data, digital infrastructure representation, analytical services, heuristic optimization, and dispatcher-oriented decision support.

The architecture follows a modular, event-driven design. Heterogeneous inputs such as transport orders, telematics or GPS data, train arrival notifications, master data on locomotives and wagons, infrastructure information, and camera-based observations are harmonized into a common operational state model. This state model represents the current planning situation, including available resources, pending tasks, relevant infrastructure elements, operational dependencies, and timing constraints. Analytical services then provide decision-relevant parameters for dispatching. In this context, AI-based components are used as supporting services, for example for estimating travel or availability times and for camera-based validation of wagon sequences. By replacing static planning assumptions with situation-dependent estimates, these services allow the heuristic optimizer to react to changing operational conditions such as delayed arrivals, varying resource availability, or deviations in wagon sequences.

The dispatching decision itself is generated by a heuristic optimization component. The current prototype uses a constructive planning logic with iterative improvement: initial locomotive-task sequences are derived from operational priorities and feasibility constraints and are subsequently refined with neighborhood-based adjustments. The optimizer considers task readiness, due times, shift structures, setup times, travel times, resource availability, operational dependencies, and return-to-base requirements. Generated proposals are presented through a decision-support interface where dispatchers can review, adjust, or reject suggested plans. Changed operational states, dispatcher feedback, and execution events can trigger repeated replanning and create a basis for future model calibration and empirical evaluation.

The architecture differs from generic port information systems by focusing on local, time-critical rail shunting decisions rather than inter-organizational information exchange alone. Its transferability is addressed through a separation between site-specific elements, such as infrastructure topology and local process rules, and reusable architectural components, such as data adapters, event-based communication, operational state modeling, analytical services, and heuristic dispatching logic. As the current development stage lies between concept and prototype, the paper does not claim quantitative performance improvements. Instead, it defines the architectural design rationale and an evaluation framework for subsequent empirical assessment, including indicators such as delay, empty movements, resource utilization, plan stability, dispatcher workload, and replanning responsiveness.

Reverse logistics in the form of product returns severely impact ecological systems, deplete natural resources and exacerbate climate change. Approximately 25% of all returned goods ultimately end up in landfills. In e-commerce, return rates can soar to 30%, presenting a critical financial and operational challenge for online retailers.

Because generous return policies serve as vital risk-reduction mechanisms for consumers and offer retailers a distinct competitive advantage, outright banning returns is unfeasible. Therefore, the viable path forward is to mitigate the negative environmental externalities of these policies. While existing reverse logistics literature predominantly adopts an operations- or retailer-centric perspective, this study addresses a critical gap by integrating the under-researched consumer standpoint into sustainable supply chain discourse.

Grounded in the Techniques of Neutralization Theory, this study examines how consumers utilize neutralization techniques, cognitive rationalizations that justify deviant behaviors and mitigate guilt, to excuse frequent product returns despite being aware of their environmental degradation. Incorporating return policy type (lenient vs. restrictive) as a categorical moderator, empirical data was collected via two distinct questionnaires from 348 consumers with active product return experience (160 under lenient policy conditions; 188 under restrictive conditions).

Partial Least Squares (PLS) structural equation modeling reveals that personal return rationales (e.g., impulse buying, change of mind) trigger a wider array of neutralization techniques than product-related rationales (e.g., defects, poor quality). Across both policy frameworks, personal rationales led consumers to actively invoke condemning the condemners (arguing that the accusers have ulterior motives and thus in no position to make the accusation), denial of injury (arguing that no harm is associated with the return), metaphor of the ledger (arguing that return is fine because enough good deeds on environment protection have already been done), and defense of necessity (arguing that the return is necessary) techniques. Conversely, product-related rationales only prompted the defense of necessity technique under restrictive policy environments.

Furthermore, PLS-multigroup analysis (PLS-MGA) demonstrates that the influence of personal rationales on condemning the condemners and denial of injury is significantly stronger under lenient return policies than restrictive ones.

This study contributes to sustainability and behavioral literature by extending the Techniques of Neutralization Theory to the domain of green reverse logistics. It shifts the analytical focus from macro-level supply chain mechanics to micro-level consumer psychology, illustrating how corporate policy environments influence moral disengagement. By identifying policy leniency as a structural catalyst for cognitive rationalization, the study clarifies the socio-psychological barriers to sustainable consumer behavior and eco-responsible supply chains.

For e-retailers and supply chain managers aiming to build environmental resilience, these findings demonstrate that overly lenient return policies inadvertently alleviate consumer guilt regarding carbon footprints and waste. To counter this, managers should design targeted digital nudges, such as real-time environmental impact prompts during the online return process, to disrupt consumer rationalizations. Furthermore, strategically tightening policies for impulse-driven purchases while maintaining flexibility for genuine product defects can structurally reduce return volumes without eroding core customer goodwill.

Corporate Purpose has gained substantial attention as a strategic lever for employee engagement and organizational resilience. Yet a critical question remains underexplored: how do employees actively contribute to shaping Corporate Purpose, and what role does motivation play in driving this contribution? This question carries particular urgency for logistics and supply chain organizations, where persistent workforce shortages, high turnover rates, and structurally fragmented workforces make employee identification with organizational values both a strategic imperative and a persistent challenge.

Existing research has predominantly treated Corporate Purpose as a top-down managerial declaration, examining its effects on employees rather than employees' active role in shaping it. Where co-creation has been studied, it has remained conceptually underdeveloped, with employee involvement treated as a monolithic construct without differentiation between its individual and collective dimensions. This paper addresses this gap by examining how employee motivation drives two distinct forms of Corporate Purpose co-creation and how these in turn contribute to Purpose Strength at the organizational level.

Drawing on a quantitative survey of 188 employees across industries and hierarchical levels, the study tests a structural model. Corporate Purpose Strength is operationalized through the Purpose Strength Index (PSI) by Lleo et al. (2021). The model differentiates between individual co-creation (employees' personal contributions to shaping Corporate Purpose) and collective co-creation (collaborative, cross-hierarchical processes through which Corporate Purpose is jointly developed and reinforced).

The findings reveal that motivation is the central driver of Purpose Strength, both directly and through two sequential co-creation pathways. Motivation first activates individual co-creation, which in turn strongly predicts collective co-creation - suggesting that individual engagement may function as a precondition for collective purpose-shaping to emerge. Both co-creation forms contribute independently to the Purpose Strength. Together, these results indicate a sequential logic: motivation activates individual co-creation, which enables collective co-creation, both of which reinforce Corporate Purpose Strength. For organizations seeking to strengthen Corporate Purpose, this implies that collective participation cannot be mandated top-down, rather it must be preceded by individual motivation and personal engagement.

For organizations with structurally diverse and geographically dispersed workforces, such as those in logistics and supply chain management, the sequential model offers actionable guidance, building individual motivation and engagement is the necessary first step before collective Purpose co-creation mechanisms can take effect.

Motivation - Retail supply chains are increasingly characterized by volatility, complexity, and susceptibility to disruptions on demand and supply sides, particularly in the globalized and complex domain of grocery products. Macro-level crises such as geopolitical uncertainties, extreme weather events, and pandemics have become the new normal, amplifying vulnerabilities associated with micro-level supply chain disruptions. The importance of resilience management is highlighted by supply chain disruptions affecting 85.7% of German grocery retailers in 2024 and global out-of-stock costs of USD 1.2 trillion.

Background - To efficiently maintain high levels of resilience, grocery retail logistics requires a detailed understanding of relevant mechanisms. While extant studies have primarily concentrated on organizational and structural mechanisms, such as technological systems, standardized processes, and strategic sourcing practices, these factors alone cannot fully explain resilience outcomes. Recent research indicated that human-level factors constitute a further key determinant. Employees contribute to resilience through disruption sensing, decision-making, information sharing, and adaptive action. However, effective transmission of human-level factors into resilience outcomes is highly contingent upon organizational conditions in which they are embedded. Organizational conditions may unlock or constrain the extent to which human-level factors contribute to supply chain resilience. How this interplay between organizational conditions and human-level factors shapes supply chain resilience has so far received limited attention.

Methodology - The present study follows a qualitative, exploratory research design and draws on 41 semi-structured empirical interviews conducted with managers, employees, and further key actors across German grocery supply chains. The interviews have been analyzed using the Gioia methodology.

Results - Findings reveal that human-level factors constitute a central foundation of supply chain resilience, while organizational conditions shape whether these human-level factors can be effectively translated into resilience. Specifically, organizational conditions influence the development, maintenance, and activation of relevant human-level factors, including situational awareness, adaptive decision-making, coordination, and learning capabilities. The results further indicate that resilience is not determined by the mere presence of favorable organizational conditions. Rather, a small number of unfavorable organizational conditions can overshadow the cumulative impact of all favorable conditions by constraining relevant human-level factors. Favorable organizational conditions are characterized by high information availability and quality, clear responsibilities, decision-making autonomy, effective coordination mechanisms, and a strong learning culture. In contrast, rigid structures, ambiguous roles, restricted discretion, technological shortcomings, resource limitations, and weak error- and learning-oriented cultures inhibit employees' ability to perceive disruptions, coordinate responses, and adapt effectively, thereby reducing overall supply chain resilience.

Implications - This study advances theoretical understanding of supply chain resilience by highlighting a human-centric and context-sensitive perspective. It demonstrates that resilience is shaped not only by the presence of human-level factors, but also by organizational conditions through which these factors become effective. It offers an integrated perspective linking human-level factors with workplace environments, managerial orientations, and communication practices, contributing to a more integrated perspective on resilience in grocery retail logistics towards conceptual foundations in supply chain management. From a managerial perspective, organizations should design supportive organizational conditions that allow human-level factors to contribute effectively to resilience during disruption situations.

Efficiently managing supply chains is vital for modern defense and space operations to ensure security, rapid deployment, and resource maximization (Grala et al., 2024; Wang and Jin, 2020). This paper examines two critical planning layers: macro-level transport selection and micro-level cargo space optimization. Specifically, we investigate the integration of Data Envelopment Analysis (DEA)—a mathematical method used to calculate the relative efficiency of different options (Charnes et al., 1978; Lepchak and Voese, 2020)—and intelligent three-dimensional bin packing problems (3D-BPP), which involve placing items of different sizes into fixed containers (Lodi et al., 2002; Gonzalez-San-Martin, 2024). Crucially, we extend this model to the domain of space logistics, where extreme weight and volume limits, tight rocket launch windows, and massive transport costs amplify supply chain risks (Bacon et al., 2022; Grogan et al., 2018). Following a rigorous PRISMA-compliant screening protocol (Page et al., 2021) of peer-reviewed papers published between 2016 and 2026, we systematically map how multi-criteria efficiency tools interact with automated loading plans across earth and space-based environments.

Our systematic literature review (SLR) reveals a deep disconnect between these two planning layers. On one hand, DEA models are highly effective at establishing efficiency baselines to balance costs, orbital cargo risks, and strategic priorities when selecting transport modes (Chao and Yu, 2022). However, DEA outputs are rarely linked dynamically to physical loading constraints (Chu et al., 2025). On the other hand, computer-driven approaches to 3D-BPP—such as Deep Reinforcement Learning (DRL)—excel at generating tight loading layouts under strict safety regulations, including load stability, center-of-gravity calculations, and hazardous material separation (Gao et al., 2025; Kaleta, 2025). Yet, these packing algorithms lack the broad, multi-layered risk and strategic fleet evaluation inherent to DEA frameworks, particularly when navigating civil-military shared infrastructure under dynamic uncertainty (Pishvaee et al., 2011; Verma and Dynamic Group, 2023).

Based on our synthesis, we identify three critical research gaps in the current state-of-the-art:

1. A severe lack of real-time data integration under sudden operational and space-weather uncertainty (Ivanov et al., 2010; Szymczak, 2017).
2. Insufficient attention paid to shared civil-military fleet capacity allocation for space launch vehicles (Habibie, 2026).
3. A shortage of explainable AI (XAI) frameworks tailored to provide transparent justifications to human operators in safety-critical transportation environments (Gunning et al., 2019; Hu et al., 2026).

To bridge these gaps, we propose a new conceptual framework that feeds DEA efficiency scores directly as input states into reinforcement learning packing agents. This review establishes a comprehensive academic baseline for future empirical research, offering a structured, actionable pathway toward building autonomous, resilient, and eco-efficient earth and space-based defense supply chains (McKinnon, 2018; Sbihi and Eglese, 2010).

The increasing diffusion of Smart Connected Products (SCPs) is transforming how firms create, capture, and leverage data throughout product lifecycles. While SCPs have been extensively studied in the context of product innovation, servitization, and customer value creation, their implications for supply chain management (SCM) remain insufficiently understood. Existing SCM research widely focuses on improving information visibility through transactional data, such as point-of-sale information, to mitigate inefficiencies such as the bullwhip effect. SCPs introduce a new source of information by generating real-time usage and performance data during the post-purchase phase, potentially reshaping supply chain structures, information flows, and planning processes. Yet little is known about how organizations currently exploit this potential and how SCPs may transform supply chains in the future.

This study investigates the impact of SCPs on SCM through an exploratory multiple-case study. Four case studies were conducted in industries characterized by long product lifecycles and growing digital connectivity, including home appliances, building automation, and elevator systems. Data were collected through interviews with managers responsible for supply chain, service, and digitalization activities. Interviews were analyzed to identify current practices, challenges, and opportunities associated with the use of SCP-generated data in supply chain processes.

The findings reveal a substantial gap between the technological potential of SCPs and their current utilization in SCM. Although all companies recognize the value of SCP-generated data, none fully integrate these data streams into core demand planning processes. A major barrier is the activation gap between product purchase and customer activation of connected functionalities, which limits the availability and reliability of usage data for forecasting purposes. Consequently, traditional demand signals continue to dominate production and inventory planning. However, SCPs are already creating value in post-transaction activities, particularly predictive maintenance, fault detection, and spare-parts forecasting. Companies report improved visibility into component usage, more accurate service planning, and opportunities to reduce stockouts and excess inventory in after-sales operations. These findings are especially relevant given the persistence of bullwhip-effect dynamics in spare-parts supply chains.

Building on the empirical insights, the research illustrates how SCPs may reshape supply chains. SCPs enable the emergence of servitized supply chains, create new information flows from product usage back to manufacturers, and potentially alter power relationships among supply chain actors. Beyond supporting maintenance activities, SCP-generated data may enable forecasting of complementary products and services, thereby connecting previously independent supply chains. Such developments suggest a transition from transaction-driven supply chains toward data-driven supply chain ecosystems.

This study contributes to the growing literature on digital supply chains by highlighting SCPs as a novel source of supply chain management enhancement and by identifying the organizational and technological barriers that currently constrain their impact. The findings provide a foundation for future research on SCP-enabled forecasting, supply chain reconfiguration, and even data-centric business models, while offering managers practical insights into how SCP may enhance supply chain performance and resilience.

Background: Contemporary maritime logistics increasingly operates in close proximity to environmentally sensitive areas and regions highly dependent on tourism, where deficiencies in crisis management may lead to serious environmental and economic consequences. Despite the growing body of literature on maritime accidents and risk management, relatively little attention has been paid to the role of institutional non-action and delayed response as factors contributing to the escalation of risk.

Methods: This study is based on a qualitative case study of the VSG Glory incident off the coast of El Quseir in the Red Sea. The analysis draws on AIS data, secondary sources, and field observations conducted at the site of the event. The applied approach enabled the reconstruction of the sequence of events and the identification of key decision points at which the lack of institutional response influenced the further development of the situation.

Results: The findings indicate that the lack of coordinated action among key stakeholders—including the shipowner, the flag state, and coastal authorities—significantly contributed to the increase in environmental risk and threats to the tourism sector. The analysis shows that prolonged periods without intervention amplified potential damage and reduced the effectiveness of mitigation efforts. The case reveals a structural gap in existing governance frameworks, where responsibility for early intervention remains fragmented and ambiguous.

Conclusion: The study highlights the need to redefine crisis management approaches in maritime logistics by recognizing culpable omission as a distinct risk factor. The findings emphasize the importance of developing integrated, cross-sectoral coordination mechanisms linking logistics, environmental protection, and tourism management, particularly in ecologically sensitive regions. The study also indicates directions for future research on the role of institutional responsibility in preventing and mitigating the consequences of maritime incidents.

Machine learning approaches are increasingly discussed in port logistics, but practical value depends on whether they can improve real operational decisions. This paper presents an applied machine learning use case for a port terminal operator: forecasting container flow volumes in order to support resource planning, equipment readiness, and operational preparation. The main goal is to investigate the impact of machine learning based forecasts for container volumes under real-world operational conditions and to show that forecasting of container volumes is feasible in a real terminal environment, and to study under which data conditions and forecast horizons the results become useful for practice.

The work focuses on shift-level forecasts of inbound and outbound full and empty container volumes—an area where planning still relies on manual estimates, spreadsheets, and simple historical averages. In practice, workloads are shaped by multiple concurrent drivers (e.g., internal moves, planning changes, slot deviations, pre-announcements, weather, holidays, traffic, and disruptions), making the task both highly relevant for machine learning and methodologically challenging.

The paper follows a classical machine learning workflow. First, raw operational movement logs and related terminal data are transformed into analytical datasets. Second, extensive feature engineering is applied to represent the operational state of a shift. This includes lag variables, rolling aggregates, calendar and weekday effects, yard-related indicators, planning signals, and selected external variables. A key research question is the extent to which forecast quality improves when internal terminal movement data are combined with external data and planning-related information, instead of relying on movement data alone.

Several machine learning approaches are compared, including statistical baseline models, autoregressive learning setups, and tree-based ensemble methods. The paper discusses which model class performs best under volatile port conditions and how feature importance can be used to better understand the drivers of predicted volumes. Special attention is given to model validation in a real operational setting. Historical rolling backtests and holdout-based black-box validation are used to assess not only general forecast error, but also the practically useful forecast horizon. In other words, the paper examines whether the forecast quality is sufficient only a few hours ahead, for the next 24 hours, or even for several days.

The expected benefits are both operational and economic. More accurate machine learning-based forecasts can improve staffing and equipment planning, reduce under- and over-planning, stabilize terminal operations, and lower coordination effort with operators and service units. This, in turn, can enhance service reliability for shipping lines and hinterland partners and, over time, strengthen overall port performance and competitiveness.

Port and crane operations are safety critical activities where human error and inadequate training can contribute substantially to operational accidents. Previous research has shown that over 70% of terminal accidents are associated with human factors, such as inadequate training, communication failures and insufficient supervision. A review of 245 studies revealed that only around 2% investigated immersive technologies as a potential approach for reducing accident risks associated with training deficiencies and human factors. Existing safety interventions therefore continue to be focused on primarily on recurrent instruction, procedural compliance, and modifications of training content. Although the adoption of immersive technologies in training has grown considerably in recent years, their use for adaptive workforce training in port operations remains relatively limited.

Building on these findings, this study proposes an adaptive retraining framework for safety critical crane and port operations. Crane-related activities were selected due to their accident relevance, their procedural complexity, and the increasing evidence supporting the use of immersive technologies for training in such environments. The proposed framework focuses on adapting the training process according to trainee performance. Procedural errors trigger different retraining paths, corrective feedback mechanisms, and varying levels of instructional support. Based on the type and frequency of errors, the training workflow can evolve into alternative learning scenarios while also supporting performance based difficulty adjustments. In addition, predefined intervention points allow supervisors to monitor progress and provide guidance when required.

This study focuses on the practical application of mixed reality in training the port operations workforce, moving beyond the development of a retraining framework. Particular attention was given to understanding the opportunities and limitations of using mixed reality for training. This included examining the extent to which operational errors, unsafe actions, and corrective learning situations could be realistically represented in a virtual environment. The developed demonstrator was used to examine how training scenarios can be adapted according to trainees' behaviour and to compare simulated learning experiences with the challenges typically encountered in conventional training settings. Furthermore, the framework generates structured performance and feedback data, which can be used to inform the development of future AI-supported training and feedback systems. Thus, this research contributes to the development of more adaptive, data-driven, learner centred approaches to training the maritime logistics workforce.

Purpose:
Shipbuilding supply chain includes many stages, multiple suppliers, complex material inflows and often changing schedules. To improve visibility of supply chain, various material tracking technologies such as UWB, GPS, BLE, RFID, and QR can be utilized. This study examines how combination of GPS and QR methods can be used to improve supply chain visibility and management.

Objective of material tracking was to assess whether right materials were in right place at right time. Tracking pilot was used to examine delivery reliability, lead times, storage times, transportation routes, as well as duration of the various stages of supply chain. Study aimed to identify deviations related to material inflows, such as deliveries to incorrect locations, unnecessary material movements, and material loss during supply chain.

Methodology:
Study applied a case study research approach to examine implementation of hybrid tracking solution in shipbuilding supply chain. Empirical data were collected from 40 tracked units by combining location-based data and QR-code event recordings. Additional data was collected from different stakeholders involved in the supply chain. Tracking methods criteria were based on cost-efficiency, tracked units and indicators, tolerance to different weather conditions and applicability to logistics processes and operations.

Tracked material consisted of ceiling panels used in ship’s interior. Digital Matter Yabby NB-IoT trackers were installed on the tracked units. Trackers combine GNSS (Global Navigation Satellite System), such as GPS, for location calculation and NB-IoT (Narrowband Internet of Things) for data transmission to a cloud service. Trackers were programmed to transmit a signal every 3 minutes while in motion and every 12 hours when stationary. In addition to these trackers, QR code stickers were attached to the units, enabling users to record events using smartphones, including location updates and shipment statuses such as received and dispatched.

Findings:
Results indicate that neither of tested tracking technologies used were able to provide complete visibility across entire supply chain. It was concluded that a lot of QR mis-tracks did occur as a consequence of insufficient user activity. Signal inaccuracies occurred in ship and warehouse environments due to interference caused by metal structures. We found out that 3-minute transmission interval during movement was too long to capture route data with full accuracy. Because of the time interval and GPS signal interference, a remarkable number of missing or false GPS timestamps were observed. Study revealed that transportation delivery accuracy throughout supply chain was good and the transit time was within normal limits. Storage time at the supplier's warehouse extended to weeks, which can be interpreted as non-value adding time and waste within supply chain.

Originality:
Study contributes to the openness of logistics visibility by providing an empirical outlook and evaluation of selected hybrid tracking solution in a complex shipbuilding supply chain context. Our results suggest that combining two different tracking methods can provide reasonable information about cargo transport. Paper extends existing research on logistics tracking technologies by focusing on practical end-to-end implementation challenges and solutions in project-based industrial logistics involving multiple stakeholders and logistics stages.

Purpose:

Offshore wind energy is a key component in achieving the decarbonization goals set by the German government. These goals are 40 GW of installed offshore wind energy capacity by 2035 and 70 GW by 2045 in German waters. In order to reach these ambitious goals, the levelized cost of energy must be reduced. Logistics and thus the associated costs represent a major challenge especially during the O&M phase due to the distances and harsh wind and wave conditions.

The Operation & Maintenance (O&M) phase accounts for about 25% of the levelized cost of energy and is one of the levers to reduce the costs of offshore wind. Standard O&M agreements are set up for an initial period of 5 to 10 years during which the original equipment manufacturer (OEM) is responsible for O&M. After this period the owner of an offshore wind farm has to decide whether to extend this contract, do the O&M itself or contract another company to do the O&M.

For external third-party providers of O&M it is thus difficult to decide what prices they should offer for the new contract since the real O&M processes are only known to the OEM. A method to mitigate this disadvantage by analyzing historical AIS (Automatic Identification System) data will be proposed in this paper.

Methodology:

In this paper a method was developed to analyze historical AIS data and weather data to identify O&M processes of an offshore wind farm including the movements of the vessels and the duration of the visits to each turbine using Python. AIS data transmits every vessel’s position, speed, and course over ground at certain intervals based on said speed, which can allow the routes of O&M vessels to be traced.

Findings:

The findings are that this method can be used to analyze the O&M processes ex post and thus can give external entities valuable insights into the inner workings of O&M processes in offshore wind farms. It is possible to see which vessels were used and for how long they were at the wind turbines and thus determine the O&M processes.

Originality:

The use of AIS data is a new field in offshore wind logistics and has mainly been used to identify collision risks in the past but not to identify O&M processes and thus provide a basis for improving them. The paper is original because it analyzes the AIS data and uses it as a basis for future business decisions.

Inland waterway transport on the Rhine corridor forms a critical component of European freight logistics, moving high volumes of bulk cargo under increasingly variable operating conditions. Traffic along the corridor is frequently disrupted by hydrological extremes, infrastructure failures, meteorological events, and operational incidents. However, the relationship between disruption frequency and actual traffic impact remains insufficiently researched. This gap is significant, as infrastructure investment and resilience planning are often based on event-frequency statistics, implicitly assuming that the most frequent disruptions also cause the greatest impacts.Recent studies have examined this issue using Notices to Skippers data from the EURIS portal and negative binomial regression to estimate the effects of short- and long-term events on daily vessel passages at lock systems along the Rhine, Main, and Danube. Related AIS data-based research has addressed vessel emissions, typhoon-induced network resilience, and Rhine water-level forecasting. However, these studies do not integrate disruption taxonomies with corridor-wide AIS-derived traffic measurements.Existing analyses have two main limitations: they assess impacts only at locks rather than across the full navigable corridor, overlook threshold effects and interactions between concurrent disruptions. Consequently, the relative contribution of each disruption category to traffic impacts on the Rhine remains unclear. Hence, a disruption attribution framework that links classified disruption events with corridor-wide traffic performance indicators is proposed. Disruptions are grouped into hydrological, meteorological, infrastructural, and operational categories and represented through time- and location-specific features. Traffic conditions are measured continuously along river segments using AIS and Notice to Skippers data, enabling the estimation of speed deviations and accumulated delays. A data-driven modelling approach is used subsequently to estimate segment-level traffic impacts and attribute these impacts to individual disruption categories. This enables a transparent assessment of how different disruption types contribute to corridor-wide traffic delays. The final results quantify the contribution of different disruption categories to traffic delays and reveal that disruption frequency does not necessarily correspond to traffic impact, suggesting that frequency-based monitoring may misrepresent risk priorities. The resulting category-level attribution weights may provide waterway authorities, port operators, and policymakers with a transparent, data-driven basis for prioritizing infrastructure investment, maintenance scheduling, and targeted resilience measures.

Despite Thailand’s substantial investments in rail infrastructure over the past decade, the freight modal shift from road to rail remains severely limited. As of 2024, rail freight accounts for less than 2% of total cargo volume, while road transport continues to dominate nearly 80% of the national logistics system. This Chronic imbalance drive persistent logistics inefficiencies, traffic congestion, rapid infrastructure deterioration, severe environmental externalities, and inflated supply chain costs. Traditional modal shift research relies on static econometrics or optimization models, such as discrete choice logit / probit or optimization models, that fail to capture the endogenous feedback mechanisms, infrastructure degradation cycles, and stakeholder behavioral delays inherent to transport systems. Consequently, these static approaches are unable to demonstrate the long-term, dynamic evaluation of freight transport systems, risking suboptimal policy recommendations.

This study develops a System Dynamics framework to integrate the complex, time-dependent interactions between road and rail freight transport. The proposed model evaluates modal choice behavior, generalized transport costs, operational capacity, congestion, infrastructure investment deterioration within a unified dynamic system driven by policy feedback loops. Notably, the framework incorporates perception delays through perceived generalized costs to reflect the gradual behavioral shift of freight operators and shippers

The framework was initially calibrated using data from the strategic Northeastern–Eastern Thailand freight corridor that connects inland production hubs to Laem Chabang Port. The model simulates the long-term interactions among freight demand, traffic congestion, infrastructure performance, rail utilization, terminal handling delays, and maintenance cycles. In particular, the study emphasizes the joint role of externalities internalization policies (such as truck enforcement and road pricing measures) and rail infrastructure enhancements (including capacity expansion, terminal development, and service quality upgrades). This framework enables a rigorous analysis of policy coordination, execution timing and latent capacity bottlenecks.

Preliminary simulation results demonstrate that rail infrastructure investment alone is insufficient to induce a substantial modal shift if road transport externalities remain underpriced. Conversely, road enforcement policies alone increase overall logistics costs if rail networks lack the capacity to absorb the shifting freight demand. Effective modal shift emerges from coordinated policy combinations implemented under appropriate timing conditions. Furthermore, the model reveals highly dynamic system behaviors, including nonlinear modal transitions, supply-chain spill-back effects from rail capacity shortage, balancing feedback from terminal congestion, and long-term reductions in road deterioration and congestion. Ultimately, this research introduces an integrated dynamic policy-feedback framework that unifies modal competition, infrastructure degradation, externalities, and transport system performance within a single endogenous system. The proposed framework serves as a robust policy simulation tool for evaluating sustainable freight transport strategies in developing economies.