Search Results (555)

Recent disruptions have led to a growing interest in studying the social dimension of sustainability and its relationship to resilience within supply chains. Social sustainability is characterized as complex, often offering anomalous data and confounding variables that are impossible to categorically define as true or false axioms. This work starts from an epistemological premise, in which non-parametric statistical methodologies and mathematical analytics are complementary perspectives to comprehensively understand the same social phenomenon. Second-generation predictive statistics, such as the PLS-SEM algorithm, have demonstrated robustness in treating multivariate social information, making it feasible to prepare data for knowledge transfer with mathematical techniques specialized for fuzzy data. This research aimed to analyze evolutionary fuzzy knowledge transfer pre-, during-, and post-pandemic COVID-19, and its effect on the relationship between social sustainability and supply chain resilience in representative cases from Mexico. Based on empirical data collected from supply chain managers in 2019 (n = 153), 2021 (n = 159), and 2023 (n = 119), the methodological technique involved three phases: (1) PLS-SEM modeling, (2) fuzzy-evolutionary predictive evaluation based on knowledge transfer between latent data, and (3) comparative analysis of the predictive effects of social attributes (labor rights, health and safety, inclusion, and social responsibility) on supply chain resilience. The results found a moderate significant variance in the pre-in-post-COVID-19 effect of social dimensions on supply chain resilience. Social and management implications are presented. Full article

Background: The adoption of information and communication technology (ICT) is transforming supply chains in the European Union, affecting logistical performance, economic integration and sustainability. This study examines the extent to which ICT adoption affects logistics efficiency in the 27 EU Member States. Methods: Using panel data from the World Bank and UNCTAD (2008–2018), the analysis applies the Arellano–Bond Generalized Method of Moments estimator to assess the impact of ICT indicators, broadband penetration, mobile connectivity and digital skills on logistics performance. GDP per capita and trade openness are included as control variables. Results: The results show that a 1% increase in ICT usage correlates with a 0.12-point increase in the Logistics Performance Index. Higher ICT usage leads to more efficient supply chains, lower costs and higher customer satisfaction. However, there are still differences in digitalization: the ICT usage rate of SMEs is 28% in Bulgaria and 27% in Romania, compared to the EU average of 59%. Conclusions: Bridging the digital divide requires targeted investments in ICT infrastructure, harmonized regulatory frameworks and stronger public–private cooperation to foster regional economic cohesion. This study provides policy recommendations to drive digital transformation, strengthen the resilience of logistics and improve the sustainability of supply chains in the EU. Full article

Background: Recent global disruptions have exposed the vulnerability of international supply chains, prompting a shift toward regionalization to enhance economic resilience. As a European Union (EU) member, Croatia has an opportunity to strengthen its integration into EU regional value chains (RVCs), fostering economic stability and competitiveness. This study examines Croatia’s integration into EU RVCs and its economic impact. Methods: Using panel data from the UNCTAD–Eora database (2000–2019), this study applies panel data quantile regression (PDQR) to analyse Croatia’s trade relationships with EU Member States. Unlike traditional regression models, PDQR captures variations in trade dynamics across different levels of economic activity, providing a more detailed understanding of Croatia’s trade resilience. Results: The findings show that Croatia’s trade integration strengthens at higher economic quantiles (τ = 0.75–0.85), reflecting its ability to scale exports during economic expansions. Lower quantiles (τ = 0.05–0.25) display stable but less dynamic trade patterns, suggesting a need for targeted policy interventions to enhance supply chain resilience. Strong trade linkages with Germany, Austria, Slovenia, Hungary, and Italy highlight Croatia’s comparative advantage in high-value trade sectors. Conclusions: Croatia’s integration into EU RVCs supports economic resilience and competitiveness. These findings provide insights for policymakers to optimize trade participation and mitigate vulnerabilities. By demonstrating the benefits of quantile-based trade analysis, this study advances the discourse on regional economic integration. Full article

Background: The global supply chain landscape is undergoing a significant transformation with the increasing adoption of digital tools. Despite the potential benefits, many organizations struggle to effectively integrate these technologies due to a lack of systematic understanding and frameworks. At the same time, the academic literature on supply chain digitalization lacks a clear taxonomy and analysis of research paradigms that guide scholarly investigations. Methods: To address these gaps, this paper conducts a comprehensive literature review utilizing an analytic approach, based on abductive reasoning, that establishes an analytical framework to identify, assess, and examine the application of various digital technologies in supply chain management. Results: Based on this analysis, the authors propose new systematic dimensions for digitalization in supply chains, alongside emerging research paradigms in this field. Conclusions: The findings provide valuable insights into the current research landscape, offering a foundation for future investigations. Additionally, practical recommendations are presented for advancing research, education, and management practices, with the goal of promoting innovation and the effective implementation of digital technologies in supply chain management. Full article

Background: The literature on measuring the complexity of production systems employs the graph and information theory. This study analyzes these systems and their coordination under varying states of control, with a focus on the probability of unfavorable events and their temporal characteristics. Methods: Coordination systems are represented as temporal networks, using entropy and node influence metrics. Two case studies are presented: a factory operating under the principles of the Toyota Production System (TPS) with adjacent (local) coordination and andon (global) coordination and a university obstetrics clinic with only adjacent (local) coordination. Results: Adjacent coordination leads to zero entropy in 38.40% of all situations in the TPS example, contrasted to 76.62% in the same system with andon coordination. Degree centrality of nodes outside of zero-entropy situations exhibits higher average and maximum values in andon coordination networks, compared to those with adjacent coordination in TPS. Entropy values in the university obstetric clinic range from 0.92 to 2.23, average degrees vary between 3 and 4.08, and maximum degrees range from 7 to 9. Conclusions: Coordination systems modeled as temporal networks capture the evolving nature of centralizing and decentralizing coordination in production systems. Full article

Background: Integrating artificial intelligence in unmanned aerial vehicle systems may enhance the surveillance process of outdoor expansive areas, which are typical in logistics facilities. In this work, we propose methods to optimize the training of such high-performing systems. Methods: Specifically, we propose a novel approach to tune the training hyperparameters of the YOLOv3 model to improve high-altitude object detection. Typically, the tuning process requires significant computational effort to train the model under numerous combinations of hyperparameters. To address this challenge, the proposed approach systematically searches the hyperparameter space while reducing computational requirements. The latter is achieved by estimating model performance from early terminating training sessions. Results: The results reveal the value of systematic hyperparameter tuning; indicatively, model performance varied more than 13% in terms of mean average precision (mAP), depending on the hyperparameter setting. Also, the early training termination method saved over 90% of training time. Conclusions: The proposed method for searching the hyperparameter space, coupled with early estimation of model performance, supports the development of highly efficient models for UAV-based surveillance of logistics facilities. The proposed approach also identifies the effects of hyperparameters and their interactions on model performance. Full article

Background: Ensuring cost-efficient and high-quality processes for logistics tasks is a significant competitive factor for companies. This includes not only improving existing processes but also examining outsourcing opportunities. Current trends, such as the increasing variety of products, shorter product life cycles, and a dynamically changing economic environment, necessitate frequent reviews and, if needed, the reorganization of logistics activities. Methods: Modern digitalization technologies (e.g., digital twins, artificial intelligence, etc.) open new possibilities for (re)evaluating outsourcing decisions, such as improving process transparency and leveraging optimization opportunities. The currently applied solutions are fragmented and, in many cases, do not integrate digitalization technologies and standardized examination processes, necessitating the development of a new process development framework concept. The research follows an inductive–deductive methodology, combining practical industrial experience with a thorough literature review. Results: The framework presented in this study enables a faster and more efficient evaluation compared to previous approaches by incorporating the application of digitalization technologies. The validity of the developed concept is demonstrated through a case study. Conclusions: The findings highlight the importance of integrating digitalization technologies into logistics process development to enhance decision-making and efficiency. The proposed framework provides a structured approach that facilitates a more effective evaluation of outsourcing decisions and process improvements. Full article

Background: Emergency scenarios present unprecedented challenges for supply chains worldwide, particularly in the management and distribution of critical supplies, where timely delivery and maintaining integrity are crucial. Methods: This article explores an innovative approach to enhance the emergency management of supply chains using blockchain technology and simulation-based modelling. The proposed methodology aims to tackle issues such as transparency, efficiency, and security, which are vital for managing logistics during crises. A case study involving a vaccine rollout is used to demonstrate how blockchain can optimise supply chain operations, reduce bottlenecks, and ensure better traceability and accountability throughout the process. The case study is specifically developed based on the distribution of COVID-19 vaccines in Italy. Results: The integration of blockchain technology not only enhances data integrity and security but also facilitates real-time monitoring and decision-making. Conslusions: The findings suggest that the proposed blockchain-based model can significantly improve supply chain resilience in emergency situations compared to traditional methods, thereby offering valuable insights for policymakers and supply chain managers facing future crises. Full article

Background: Facility location is a key challenge in humanitarian logistics, particularly in disaster response, where rapid and efficient resource deployment is crucial. Temporary facilities offer a cost-effective solution due to their rapid deployment and flexibility in addressing increased demand and the dynamic conditions of post-disaster environments. Methods: This study conducts a systematic literature review following PRISMA guidelines to analyze facility location problems involving temporary or modular facilities in humanitarian logistics. A total of 65 articles from Scopus and Web of Science were analyzed. Results: Most studies focus on temporary facilities like shelters and medical centers in earthquake-affected areas, with most applications in Asia. Despite being temporary, only 6% of the studies consider closure decisions. Recent research explores modular facilities that enhance adaptability through module relocation and capacity adjustments. Conclusions: Temporary facilities after sudden-onset disasters require advanced modeling approaches that include multi-period planning, modular design, and complex decision-making, requiring solutions through heuristics or relaxations. However, there is a lack of research on their application in slow-onset and human-induced disasters. Moreover, considering geographical, cultural, and political factors is essential to ensure effective solutions. Further studies are also needed on facilities functioning as collection and processing centers, given their critical role in the humanitarian supply chain. Full article

Background: Each disaster has its specific resource requirements, varying based on its size, location, and the affected region’s socio-economic level. Pre-disaster planning and post-disaster dynamic resource allocation including material and human resources is essential. Methods: To address the resource allocation challenges in disaster response, a multi-objective two-stage stochastic programming model is developed for search and rescue and first aid activities. The model aims to minimize the total unmet human demand, the number of resources transferred between regions, and the total unmet material demand. The proposed model was solved for a real case of an expected earthquake in Istanbul’s Kartal district. The augmented epsilon constraint 2 algorithm was employed using the CPLEX solver. A sensitivity analysis was made. Results: Most of the unmet demand occurs in the first period. After that period, the unmet demand decreases with interregional transfers and additional resources. The model is robust to scenario probability and penalty value changes in the objectives. Conclusions: This is the first study that simultaneously and dynamically allocates renewable and non-renewable material resources and human resources, including the official rescue units and volunteers, for disaster response. Volunteers’ inclusion in teams considering their training and quitting behavior are unique aspects of the study. Full article

Background: In today’s business world, where competition lies between supply chains, customer expectations are changing dynamically. Effective order picking in warehouses has become a top concern given expectations for rapid delivery, a larger product range, and continuous support. Methods: In this study, it is aimed to find a simultaneous solution to the problems of picker routing and order batching, which have an important place in order picking. A genetic algorithm-based solution with group-based coding is proposed to minimize the travel time of pickers. Results: A new set of equations for rectangular warehousing systems with three or more blocks (multi-blocks) is presented to directly determine the shortest distances between order points. It is found that the proposed solution methodology gives better results than traditional approaches. Conclusions: The study is expected to contribute to the improvement of order picking, which is the most costly and repetitive activity in warehouses, within the scope of practical and academic applications. Full article

Background: Governments face critical decisions regarding road remediation projects, requiring careful economic evaluation, especially in countries like Slovakia where road infrastructure is crucial for attracting foreign investment. These decisions are complex, involving short-term and long-term costs and revenues, along with inherent uncertainty about future outcomes. Traditional economic assessments often fail to capture the full scope of these factors, potentially leading to suboptimal choices. Methods: This study proposes four probability-based models: the Short-term Model (SM), Long-term-Short-term Model (LSM), Social Long-term-Short-term Model (SLSM), and Long-term-Short-term Model with a Time Aspect (TLSM). These models incorporate probabilistic functions to calculate expected costs and profits, considering various factors such as reparation costs, financial compensations, social costs, and time-related costs, as well as long-term benefits like increased investment and lives saved. Results: The proposed models were partially validated through an ex post analysis of a past road remediation project on road 1/18 (E50) under the Strecno castle cliff in Slovakia. The analysis demonstrated the models’ utility for multi-criteria decision-making in transportation problems, highlighting their ability to capture the complex interplay of economic and societal factors. Conclusions: The models enable governments to maximize societal benefit while mitigating potential risks, contributing to a more sustainable and efficient transportation sector. Future research could focus on refining the models and adapting them to other sectors beyond transportation. Full article

Background: Defining the optimal fleet portfolio is a crucial process in airline planning. The published efforts in literature provide ways to anticipate the disruption effects on the passenger demand; however, the proposed solution in this paper provides visibility on the impact of sustainable disruption and the way an airline can resist it. Methods: This paper proposes a two-stage methodology to find the best portfolio for airline operational requirements under the impact of disruption. The first stage considers optimization for normal airline operations under a specific fleet portfolio using an Integer Linear Programming (ILP) model. The second stage of the analysis is a mapping for the scenario-based methodology to find a way out for an airline subjected to some given disruption in operations. Results: The result of the two-stage analysis shall define the best fleet portfolio to withstand sustained disruptions by mapping the results in a disruption funnel and showing the impact of the supply and demand gap on the airline’s sustainable profitability. Conclusions: This paper provides a novel, practical way of evaluating strategic decisions to choose the best fleet portfolio and make airlines rely on the mapping of the disruption funnel to modify their network while increasing supply chain resilience. Full article

Background: Cargo theft represents a persistent challenge to last-mile logistics in the peripheral regions of São Paulo, Brazil, compromising transportation security and increasing operational costs. These high-crime areas disrupt supply chain stability and hinder e-commerce growth. Traditional security methods often fail to address the complexity and uncertainty present in these environments, necessitating adaptive approaches. Methods: This study applies an Expert System based on Paraconsistent Annotated Evidential Logic Eτ to assess the effectiveness of security interventions. Logic Eτ is particularly suited for analyzing uncertain, incomplete, and contradictory data in complex logistics settings. A mixed-methods approach was employed, integrating evaluations from nine experts representing different hierarchical levels within a logistics company. Six key security measures, including GPS tracking, armed escorts, optimized delivery windows, and the hiring of local drivers, were analyzed using favorable degrees and unfavorable degrees for each parameter. Results: The results demonstrated that five measures were effective, contributing to a 58% reduction in security costs in Arujá and 75% in Cajamar, two major distribution hubs. Conclusions: This study highlights the potential of combining Expert Systems and Eτ Logic to enhance cargo transport security, offering a scalable decision support framework for companies operating in high-risk urban regions. Full article

Background: The present study investigates Industry 4.0’s (I4.0) impact on supply chain capabilities (SCCs), supply chain innovation (SCI), and supply chain performance (SCP). The influence of SCCs and SCI on SCP is also explored. Additionally, the mediating impacts of SCCs and SCI on the I4.0-SCP relationship are analyzed. Methods: The study’s population consisted of manufacturing companies located in Amman, Jordan. A purposive sample of 211 companies was selected. Self-administered questionnaires were completed by targeted managers in the participating companies. Results: The outcomes indicated that the total impact of I4.0 on SCP was significant and positive. I4.0 positively affected both SCCs and SCI. Additionally, SCCs and SCI were found to positively affect SCP. Finally, the results demonstrated a full mediating impact of SCCs and SCI on the I4.0-SCP relationship, with over two-thirds of the mediation impact attributed to SCCs. Conclusions: This research is among the earliest to examine I4.0’s impact on SCP. It also fills a research gap by exploring I4.0’s influence on both SCCs and SCI. To the best of our knowledge, the present study is the first to investigate the mediation effect of SCCs and SCI on the I4.0-SCP relationship, thus providing a valuable contribution to the existing literature. Full article