Conference Agenda

This study analyzes the relationship between logistics service quality and supply chain management in the export of blueberries by an agricultural exporter located in Ica, Peru, 2025. The study examines the logistical challenges faced by these companies in maintaining their products in good condition, complying with quality standards, and reducing costs, as required by global markets. The research uses a quantitative methodology with a non-experimental, correlational, and descriptive design. The main technique used was a survey with structured Likert-type questionnaires, applied to a sample of 132 workers in the areas of logistics, operations, and foreign trade. This instrument made it possible to measure key dimensions such as delivery time, product conditions, customer service, flexibility, coordination with suppliers, demand planning, inventory management, and international transportation. The results show a direct and significant relationship between logistics service quality and supply chain management, demonstrating better performance in these areas by increasing operational efficiency, reducing costs, and improving compliance with export deadlines. In practical terms, the findings suggest improvements in coordination with suppliers, inventory management, and international transport. In conclusion, improving logistics quality not only streamlines internal processes, but also contributes to the international competitiveness of Peruvian blueberries, stabilizing Ica as a key exporting region.

The processed meat industry faces critical challenges in balancing competitiveness with sustainability, particularly in emerging economies. This study analyzes waste management practices in a leading Panamanian meat processing company, integrating circular economy principles and reverse logistics strategies to strengthen supply chain resilience. Using semi-structured interviews and non-participant observations, the research identifies key waste streams—organic residues, packaging materials, and non-conforming products—and evaluates their operational, economic, and environmental impacts. Findings reveal partial valorization of organic by-products but significant gaps in packaging recovery, traceability, and performance monitoring. To address these deficiencies, the study proposes a framework that embeds reverse logistics into the supply chain, incorporating industrial symbiosis, biorefinery applications, and lean protocols. This approach positions waste not as a liability but as a resource, aligning with Sustainable Development Goals (SDG 12 and SDG 9) and advancing supply chain sustainability in Latin America. The contribution lies in offering the first qualitative evidence for Panama’s meat industry, providing actionable insights for policymakers and firms seeking innovation, competitiveness, and alignment with international standards.

The accelerated expansion of Panama’s automotive sector has intensified the generation of used lubricating oils, classified as hazardous waste due to their heavy metal and hydrocarbon content. Although regulatory frameworks exist, compliance among workshops and service centers remains limited, creating significant environmental and public health risks. This research conducts an exploratory case study of a representative automotive service company in Panama City to examine logistical, economic, and regulatory barriers in oil waste management. Building on best international practices and circular economy principles, a strategic reverse logistics model is developed to optimize collection, treatment, and final disposal processes. The proposed model demonstrates how reverse logistics can transform hazardous oil waste management into a driver of environmental protection, regulatory compliance, and economic efficiency. The findings highlight the potential of sustainable reverse logistics systems to strengthen Panama’s automotive sector while contributing to regional sustainability goals.

This quantitative, non-experimental, cross-sectional study was conducted with 110 interprovincial public transport drivers in La Libertad, Peru, to estimate the influence of mental health (MH) on traffic accidents (TA). Both constructs were operationalized and recoded at three ordinal levels, prioritizing traceability between measurement and analysis. Internal consistency was adequate (MH: α=0.853, ω=0.884; TA: α=0.803, ω=0.769). The contrast between mental health (MH) and work-attention (WA) was assessed using ordinal logistic regression (ORR) and systemic evaluative mechanics (SEM) (AMOS): ORR yielded an odds ratio (OR) of 1.69 (95% CI 0.50–5.76; p =
0.399), and SEM estimated a path with a βstd = −0.210 (p = 0.094) with moderate adjustment (χ²/df = 1.93; CFI = 0.861; RMSEA = 0.092; SRMR = 0.112). It is concluded that MH contributes as a partial modulator of risk, and screening, well-being, self-regulation, and shift/rest
management are recommended.

Efficiency in the collection of recyclable waste is a critical component for strengthening circular economy
models in emerging markets. In this context, Traperos de Emaús faces increased operating costs and unnecessary extensions to
its paper collection routes, resulting from empirical routes and limited logistical planning. This study develops a nonlinear
programming model to optimize these routes, integrating vehicle capacity constraints, operating times, and sustainability
criteria, with the aim of evaluating its impact on reducing distances and costs. The proposed methodology employs the
Evolutionary algorithm from the Solver plugin, selected for its ability to solve complex global search problems and its
suitability for non-convex objective functions. Comparative scenarios between the current operation and the optimized
sequence were modeled, allowing for the precise quantification of logistical improvements. The results show significant
reductions in transportation costs: 36.90% for unit TM1 and 12.95% for TM2, in addition to a streamlining of routes that
reduces overlaps, redundancies, and unproductive time. Taken together, the findings demonstrate that optimization through
nonlinear programming is an effective tool for enhancing collection efficiency, consolidating data-driven logistics decisions,
and strengthening operational sustainability in recycling organizations.

This study examines the implementation of predictive, cognitive, and generative artificial intelligence associated with operational efficiency within the supply chain. It aims to analyze the need to overcome the limitations of current systems that are not effectively adapted to the highly changeable logistics environment through systems based on artificial intelligence. A mixed approach combining quantitative and qualitative methods is used. The design is non-experimental and correlational, with the aim of identifying the magnitude of their relationship in a real-world context. The unit of analysis consists of companies from various sectors, while the unit of response includes professionals from different fields, technology managers, and supply chain managers through interviews as instruments. The techniques used include document analysis and correlation analysis to determine the strength of the relationship between variables, and interviews that allowed for contextualizing the interpretation of results. Findings indicate that cognitive and predictive artificial intelligence enable greater adaptability, more efficient data interpretation, and significant improvement in decision-making processes. However, the study recognizes the low level of adoption of these technologies in Honduras, as well as their economic limitations, as a constraint. In conclusion, the integration of advanced artificial intelligence technologies has a positive and significant impact on the operational efficiency of the supply chain.