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: Location decision plays a key role in strategic logistics and business success. The beauty business in Thailand has continuously grown, and medical aesthetics clinic location is one of the critical factors for business success. The problem is also related to sustainable urban service accessibility. Methods: This paper presents, for the first time, a systematic selection of medical aesthetics clinic location as a multi-criteria decision-making (MCDM) problem. The Simple Multi-Attribute Rating Technique (SMART) and Single-Valued Neutrosophic TOPSIS (SVN-TOPSIS) are combined to solve the location selection problem. SMART determines criterion weights, whereas SVN-TOPSIS evaluates alternatives using linguistic terms understandable to non-technical decision makers. Results: The proposed SMART SVN-TOPSIS is applied to a real investment problem in which two investors select the best clinic location from five alternatives with nine criteria. Siam Square—the heart of shopping, fashion, and youth culture in Bangkok—is recommended as the top location. Conclusions: The results indicate that the proposed method is capable of generating a consistent ranking of alternatives and differentiating between locations that exhibit similar evaluation characteristics. The findings may also support sustainable urban service planning and healthcare-related facility location decisions.

Background: To address the critical challenges of hazardous material (HAZMAT) incidents in dense urban areas, this study develops a hybrid framework for spatial emergency response optimization tailored for Intelligent Transport Systems (ITSs). Methods: Our approach integrates the Fuzzy Analytic Hierarchy Process (FAHP) with a rigorous technical benchmarking of multiple navigation APIs to improve routing decisions under volatile Bangkok traffic. By employing a normalized cost function (scale 0–1), we evaluated the performance of localized (Longdo Map) versus global (Google Maps and OpenStreetMap) platforms across day and night scenarios. Results: Experimental results, yielding normalized costs between 0.464 and 0.748, identified Bon Kai as the optimal response node, whereas Chan Road showed the lowest efficiency. Interestingly, OpenStreetMap provided the highest temporal consistency for emergency logistics. Conclusions: These findings offer a practical decision-support tool for authorities, proving that integrated API assessment is essential for building resilient and responsive urban mobility infrastructures.

Background: The COVID-19 pandemic triggered unprecedented mobility disruptions worldwide as governments imposed strict lockdowns to contain the spread of the virus. In India, prolonged restrictions severely affected economic activity, particularly for migrant workers, leading to a large-scale and unplanned exodus from urban employment centres to native places. This sudden population movement undermined containment efforts and contributed to the spatial diffusion of infections. Understanding evacuees’ behavioural responses during such crises is therefore critical for effective emergency logistics and evacuation planning. Methods: This study examines the determinants of transport mode and shelter choice decisions made by migrants during the COVID-19-induced evacuation in India. Using primary survey data, a multinomial logistic regression model is developed to analyze how socio-economic characteristics influence evacuees’ choices of travel mode and shelter type. Results: The results reveal significant heterogeneity in decision-making, highlighting the role of economic vulnerability and accessibility constraints in shaping evacuation behaviour. Conclusions: The findings offer actionable insights for policymakers and emergency planners to design inclusive evacuation strategies, improve crisis-responsive transportation planning, and enhance shelter provisioning in future pandemics or large-scale disruptions. The study contributes to the logistics and humanitarian operations literature by providing empirical evidence on evacuation behaviour under public health emergencies.

Background: The rapid development of e-commerce has led to significant changes in last-mile logistics, where innovative delivery solutions such as parcel lockers are increasingly considered to improve efficiency and flexibility. Methods: This study analyzes user attitudes and behavior toward traditional delivery and parcel locker usage through a quantitative survey conducted in November 2024 in Serbia, on a sample of 420 respondents with diverse demographic characteristics. Results: The findings indicate that, despite recognized advantages such as flexibility, accessibility, and reduced risk of missed deliveries, parcel lockers remain underutilized. This is mainly due to limited user awareness, insufficient infrastructure, and a strong preference for traditional home delivery. Statistically significant differences were identified across demographic groups, including gender, age, education level, occupation, and place of residence. Conclusions: The results suggest that improving infrastructure, increasing user awareness, and implementing targeted communication strategies could significantly enhance the adoption of parcel lockers. The study contributes to a better understanding of user behavior and supports the development of more efficient and user-oriented last-mile delivery solutions.

Background: Production systems are complex environments where logistics processes play a crucial role alongside manufacturing. Although the digitalisation of value-creating processes is increasingly important, production-supporting logistics activities are often missing from digital models. Their absence reduces the accuracy of digital representations and may lead to suboptimal operational decisions. Methods: This study reviews digitalisation solutions in manufacturing systems with a focus on integrating production logistics activities. Relevant research articles are analysed, and integration problems are organised into a problem tree supported by practical experience. Based on these findings, an extended process modelling methodology and related indicators are applied to quantify digital transparency. The methodology is demonstrated through tests on a physical laboratory model. Results: The literature review and practical observations highlight several issues that hinder the integration and quantification of production logistics activities in digital models. The proposed modelling approach addresses these challenges by defining appropriate modelling depth and placement of logistics processes, enabling a clearer evaluation of digital transparency. Conclusions: Experiments conducted on the physical model confirm the feasibility of the methodology. The approach provides an important initial step toward the digital integration of production logistics and supports the development of more effective digital twin models for industrial applications in future research.

Background: The rapid evolution of omnichannel retailing has reshaped retail supply chains (SCs) by coupling replenishment, fulfillment, and service decisions across multiple demand channels under inventory, lead-time, and capacity constraints. These interdependencies create coordination challenges, particularly when demand shocks interact with limited operational capacity. Methods: To address these challenges, this study develops a centralized Hierarchical Reinforcement Learning (HRL) control framework that makes decision timing explicit: replenishment and allocation are optimized weekly, while fulfillment and lateral inventory rebalancing are controlled daily. Policies are learned using Proximal Policy Optimization (PPO) in an actor–critic architecture, with bounded stochastic policies for constrained action spaces. To mitigate the curse of dimensionality in HRL, we introduce a capacity-aware state–action encoding mechanism that compresses the control interface into structured summary signals. Demand shocks are modeled using two specifications: a mixed profile, where half the products follow a uniform demand process and the rest a Merton-type jump-diffusion process, and a fully shock-driven profile. Results: The framework is evaluated against forecast-driven base-stock and greedy fulfillment heuristics, and a perfect-information oracle, with pairwise differences examined through Wilcoxon signed-rank tests. Conclusions: Overall, the proposed framework improves learning efficiency and scalability, outperforming heuristic baselines while remaining below the oracle bound.

Background: This study explores sourcing risk in supply chains by identifying key risk categories, trends, and management strategies. It responds to increased vulnerabilities exposed by recent global disruptions such as the COVID-19 pandemic and geopolitical conflicts. Methods: The research applies a Systematic Literature Network Analyses (SLNA) combined with textual analysis to examine 687 peer-reviewed publications over the past three decades using the PRISMA protocol. Citation network analysis, keyword co-occurrence mapping, and main path analysis were conducted to map intellectual developments. Additionally, textual analysis using the Semantic Brand Score (SBS) approach revealed thematic relevance, novelty, and impact. Results: A shift exists from foundational supplier optimization models to resilience-building/strengthening, ethical sourcing, and technology-enabled strategies. Responsible sourcing and modern slavery were found to be the most innovative and underexplored areas. Research on sector-specific challenges, particularly for small and medium-sized enterprises, remains limited.; Conclusions: Sourcing risk has become a systemic challenge requiring resilience, ethics, and data-driven coordination across supply networks.

Background: Last-mile logistics is one of the most complex and cost-intensive segments of supply chains, particularly in densely populated urban environments where rising customer expectations, sustainability requirements, and operational constraints increasingly intersect. Despite growing academic interest, empirical evidence remains limited regarding how convenience-related last-mile service attributes influence customer satisfaction, while the sector is undergoing a revolutionary transformation. Methods: This study applies a refined Kano model to classify last-mile convenience services according to their differentiated effects on customer satisfaction. Data were collected through a structured questionnaire administered to active e-commerce users in a metropolitan area. The methodological approach modifies and extends the traditional Kano framework. Results: The findings reveal clear patterns among last-mile service attributes. Online tracking and preferred payment options function as One-dimensional attributes, proportionally influencing customer satisfaction. Time-based delivery, flexible pickup options, and sustainability-oriented service features appear as Attractive attributes, generating additional increases in service value. In contrast, advanced technological solutions such as drone or autonomous vehicle delivery were perceived as Indifferent attributes. These interpretations are further nuanced by the fuzzy approach. Conclusions: The results provide important insights and validation for consumer-centered service design and support the prioritization of investments aimed at developing sustainable and customer-oriented last-mile logistics systems.

Background: The COVID-19 pandemic created an urgent need to understand how supply chains can withstand and adapt to severe disruptions. While prior research has highlighted the importance of supply chain resilience and robustness in managing disruptions, less attention has been given to the mechanisms through which firms transform these capabilities into financial outcomes. Drawing on the Resource Orchestration Perspective (ROP), this study proposed that absorptive capacity acts as a cognitive orchestration mechanism that enables firms to more effectively translate resilience and robustness capabilities into financial performance during periods of major disruption. Methods: Using a quantitative approach, this research employed partial least squares structural equation modeling to analyze data from 66 supply chain managers who experienced varying levels of supply chain disruption following the COVID-19 pandemic. Results: Both supply chain resilience and robustness affect organizational absorptive capacity, which, in turn, enhances performance. Conclusions: This study extends ROP and provides new insights into how firms can strategically leverage disruption-related knowledge to enhance performance in turbulent environments by identifying absorptive capacity as a key mechanism linking resilience capabilities to financial outcomes. In practice, it provides managers with valuable insights to prioritize AC development and reduce financial risks associated with disruptions.