Background: This study examines how information technology (IT) capability contributes to manufacturing flexibility (MF) through knowledge acquisition (KA) and customer–supplier integration (CSI), drawing on dynamic capability theory. Methods: Survey data were collected from 137 manufacturing firms in Thailand and analyzed using structural equation modeling to test the proposed relationships. Results: The findings show that IT capability significantly enhances KA, which subsequently strengthens CSI. While IT does not directly influence MF, its effect is transmitted through a sequential pathway in which IT improves KA, KA facilitates CSI, and CSI is the only construct directly associated with MF. These results identify CSI as the central conversion mechanism through which IT-enabled information and externally acquired knowledge are translated into operational adaptability. Conclusions: The study demonstrates that externally acquired knowledge generates value only when embedded in collaborative interorganizational routines, such as joint planning and synchronized decision-making. It further highlights the complementary roles of learning-oriented and integration capabilities in enabling manufacturing flexibility. Managerially, firms should prioritize IT investments that strengthen customer–supplier integration rather than focusing solely on technological sophistication.

Background: Modular and autonomous rail-based transport concepts promise increased flexibility and efficiency, but their feasibility strongly depends on reliable and scalable terminal handling operations. In such systems, transport units must be safely and rapidly coupled to carrier units without manual intervention. Methods: This study presents a structured pod-handling concept for a modular rail transport system, covering transport unit preparation, crane-based lifting and positioning, mechanical coupling via twist-lock interfaces, and automated electrical and media connections. To evaluate operational performance, a discrete-event simulation model was developed in AnyLogic that represents the complete loading process from order reception to pod dispatch. Results: Simulation results show that a single crane is sufficient under low-demand conditions, maintaining an average processing time of approximately 12 min per order. As demand increases, system performance becomes highly sensitive to crane availability; insufficient resources lead to excessive waiting times. For high-frequency demand, scalable crane allocation is required to preserve stable throughput. Conclusions: The results confirm that automated pod-handling mechanisms, combined with demand-adaptive terminal resources, are essential for the viability of modular rail pod systems. The proposed process model and simulation framework guide terminal design and support the integration of decentralised rail pods into future multimodal mobility and logistics networks.

Background: The rapid growth of e-commerce has intensified the need for packaging strategies that reduce logistics costs and environmental impact. Traditional box recommendation methods select the best-fitting box from a fixed set of options, which limits their ability to minimize unused space and total costs. Methods: This study formulates the Shipping Box Configuration Problem (SBCP), which aims to determine an optimal set of box types and dimensions for multi-product orders. To solve this problem, we propose a Particle Swarm Optimization (PSO)-based heuristic that dynamically designs box configuration rather than selecting from predefined sizes. Results: The proposed method is evaluated using real order data from two South Korean e-commerce companies with different product characteristics and existing box configurations. Computational results show that the PSO-based approach reduces total packaging and shipping costs and improves space utilization compared to current box configurations. The analysis also indicates that increasing the number of box types and reducing safety ratios generally lead to cost savings, although these effects must be balanced against operational complexity. Conclusions: The results suggest that adaptive box configuration design can improve both economic efficiency and environmental performance, providing practical guidance for e-commerce logistics managers seeking to optimize packaging strategies under operational constraints.

Background: Combinatorial optimization problems (COPs) are central to Logistics and Supply Chain decision making, yet their NP-hardness prevents exact optimal solutions in reasonable time. Methods: This work addresses that limitation by developing a novel ternary network flow linear programming (LP) model of the assignment problem (AP) polytope. The model is very large scale (with Θ(m9) variables and Θ(m8) constraints, where m is the number of assignments). Although not intended to compete with conventional two-dimensional formulations of the AP with respect to solution procedures, it enables hard COPs to be solved exactly as “strict” (integrality requirements-free) LPs through simple transformations of their cost functions. Illustrations are given for the quadratic assignment problem (QAP) and the traveling salesman problem (TSP). Results: Because the proposed LP model is polynomial-sized and there exist polynomial-time algorithms for solving LPs, it affirms “P=NP.” A separable substructure of the model shows promise for practical-scale instances due to its suitability for large-scale optimization techniques such as Dantzig–Wolfe Decomposition, Column Generation, and Lagrangian Relaxation. The formulation also has greater robustness relative to standard network flow models. Conclusions: Overall, the approach provides a systematic, modeling-barrier-free framework for representing NP-complete problems as polynomial-sized LPs, with clear theoretical interest and practical potential for medium to large-scale Logistics and other COP-intensive applications.

Background: Liquefied natural gas (LNG) distribution in archipelagic regions involves complex trade-offs between transportation, infrastructure investment, and contractual arrangements. While most optimisation studies focus on seller-managed Delivery Ex-Ship (DES) schemes, limited research addresses buyer-managed Free on Board (FOB) frameworks that extend decision responsibility upstream. Methods: This study develops a two-stage integrated optimisation model for long-term LNG supply chain planning under an FOB contractual scheme with time-dependent deterministic demand. Stage 1 determines hub selection, port clustering, vessel sizing, fleet configuration, and endogenous infrastructure capacities using a genetic algorithm, while Stage 2 optimises cluster-level routing sequences. Robustness is assessed through multiple independent runs and sensitivity analysis. Results: A case study of the Nusa Tenggara region identifies Sumbawa as the optimal hub. The upstream segment consistently selects a 65,000 m3 vessel under terminal service capacity constraints, while downstream clusters are served by 3500 m3 and 10,000 m3 vessels depending on distance and demand aggregation. Infrastructure requirements are derived from peak-demand conditions, and the resulting levelised logistic cost is 4.66 USD/MMBtu. Conclusions: The findings demonstrate that FOB arrangements fundamentally reshape network configuration, fleet segmentation, and infrastructure sizing, providing a robust strategic planning framework for buyer-managed LNG supply chains in archipelagic contexts.

Background: We study one of the most important problems in production and warehouse management: the problem of determining how to allocate the initial stock and the quantity produced to bins, and then how to manage picking operations from these bins. The objective is to minimize the total cost of the bins used. Methods: We formulate an integer linear programming model able to manage the two time periods related to assignment and picking together, and to handle the FIFO picking logic. We prove that it is NP-hard, and solve it to optimality. Then, we design a tailored heuristic algorithm, inspired by the current rule of thumb used by one of the main Italian mineral water bottling companies. Results: An extensive computational experiment allows us to show that this problem can be solved to optimality in a reasonable computational time based on real-world instances, and that the heuristic provides near-optimal solutions. Conclusions: Our approach provides a contribution to modeling and solving this problem when FIFO picking operations are taken into account. Moreover, it contributes by building important bridges between theoretical understanding and practical applications.

Background: Amid global sustainability imperatives, the logistics sector serves as a key economic enabler while remaining a major contributor to greenhouse gas emissions. This study investigates the causal relationships between green logistics practices and sustainable business performance in Vietnamese small- and medium-sized enterprises, mediated by competitiveness, and forecasts future trends to inform transitions aligned with net-zero goals. Methods: A mixed-methods design integrates structural equation modeling with the gray model. Primary data were collected via Likert-scale questionnaires administered to 350 managers to measure latent variables. Secondary financial metrics (revenue, costs, assets, profits) from 15 firms spanning 2021–2024 enabled forecasting. Results: SEM, employing bootstrapping for path estimation, revealed positive direct effects, with the strongest effects for green transportation and weaker effects for technology, packaging, and warehousing. Mediation via competitiveness yielded mixed indirect effects: positive for warehousing and transportation, but negative for technology. GM(1,1) projected moderate performance growth under conditions of data uncertainty. Conclusions: The hybrid framework advances the resource-based view in emerging market contexts, recommending prioritization of transportation and technology initiatives alongside policy incentives to align with sustainable development goals and enhance resilience in Vietnam’s logistics sector.

Background: this study aims to investigate the impact of supply chain risk management (SCRM) on supply chain resilience and robustness providing empirical evidence from an underexplored emerging economy. Methods: drawing on empirical data collected through a survey of 110 Moroccan manufacturing firms, the study tests a conceptual framework proposed using SmartPLS. Results: the results show that SCRM practices do not significantly reduce disruption impacts, which contrasts with several previous studies. However, supply chain robustness and resilience are significantly improved by SCRM practices. In examining the direct effects of disruption impacts, results indicate a significant negative influence on robustness, while no significant effect is observed on resilience. Furthermore, the association between supply chain outcomes and SCRM is not supported by the mediation effect of disruption impacts. Conclusions: to the best of the author’s knowledge, few studies have examined SCRM, resilience, and robustness simultaneously. Furthermore, no prior research has investigated the mediating role of disruption impacts, and almost no studies have focused on the Moroccan context. This study therefore bridges these gaps, providing new theoretical insights and practical implications for improving supply chain performance under uncertainty and disruptions.

Background: The rapid diffusion of large language models (LLMs) such as Claude, ChatGPT, Gemini, LLaMA, and Mistral is reshaping logistics and supply chain management by embedding generative intelligence into planning, coordination, and governance processes. While prior studies emphasize algorithmic capability, far less is known about how differences in diffusion pathways shape productivity outcomes, managerial cognition, and institutional control. Methods: This study develops and applies an integrative analytical framework—the AI Diffusion Triad—comprising Productivity, Perspective, and Power. Using comparative qualitative analysis of five leading LLM ecosystems, the study examines how technical architecture, access models, and governance structures influence adoption patterns and operational integration in logistics contexts. Results: The analysis shows that diffusion outcomes depend not only on model performance but on socio-technical alignment between AI systems, human workflows, and institutional governance. Proprietary platforms accelerate productivity through centralized integration but create dependency risks, whereas open-weight ecosystems support localized innovation and broader participation. Differences in interpretability and access significantly shape managerial trust, learning, and decision autonomy across supply chain tiers. Conclusions: Sustainable and inclusive AI adoption in logistics requires balancing operational efficiency with interpretability and equitable governance. The study offers design and policy principles for aligning technological deployment with workforce adaptation and ecosystem resilience and proposes a research agenda focused on diffusion governance rather than algorithmic advancement alone.

Background: The rapid digital transformation in logistics requires the adaptation of transport companies to electronic information management, particularly through the implementation of electronic consignment notes (e-CMR). This study examines how the digitization of transport documentation affects the competitiveness, operational efficiency, and environmental performance of road freight transport companies. Methods: A questionnaire survey was conducted among Slovak and Czech carriers to analyze their experience and readiness for adopting e-CMR. The collected data were evaluated using descriptive and comparative methods to quantify economic and ecological impacts, focusing mainly on invoicing efficiency and paper consumption. Results: The results show that only a small share of carriers currently use e-CMR, primarily due to high software costs and the lack of partner participation. Nevertheless, digitization can significantly shorten the average invoicing delay by approximately 11.5 days, releasing around 7% of tied-up working capital and improving cash flow. From an environmental perspective, the replacement of paper CMR forms could save millions of sheets annually, leading to a substantial reduction in CO2 emissions and paper waste within the V4 region. Conclusions: The findings confirm that the adoption of e-CMR improves economic performance, increases transparency, and contributes to sustainability, representing a crucial step toward a more competitive and environmentally responsible road freight transport sector in Europe.