Logistics

Background: The growing emphasis on sustainability has increased interest in understanding how environmentally oriented supply chain practices translate into organizational outcomes. However, empirical research examining how circular economy principles, technological integration, and sustainable supply chain management (SSCM) practices jointly influence green supply chain performance remains limited, particularly in developing economies. Methods: A quantitative research design was employed using survey data collected from 333 professionals in the Lebanese consumer goods industry through structured Likert-scale questionnaires. The proposed conceptual model was analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) to evaluate the measurement model and test the relationships among circular economy practices, technological integration, SSCM practices, green supply chain performance, and organizational performance. Results: The findings indicate that technological integration, circular economy practices, and SSCM practices collectively enhance green supply chain performance. The results further show that improved green supply chain performance supports stronger organizational outcomes. Conclusions: This study contributes to sustainable supply chain literature by integrating circular economy principles, technological capabilities, and SSCM practices within a unified framework. It highlights the strategic role of green supply chain performance in linking sustainability initiatives to organizational outcomes and provides insights for managers seeking to implement integrated sustainability strategies.

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: 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: 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.