Conference Agenda

This study examines nanostore owners’ perceptions of how consumers evaluate the adaptive capabilities of these traditional retail formats in Honduras. A quantitative, descriptive, cross-sectional design was used. Data were collected in 2025 via structured surveys applied to a probabilistic sample of 468 nanostore owners. The instrument measured five dimensions: adaptability to market demands, perceived product quality, services offered, diversification of offerings, and innovation. Analysis included descriptive statistics and Spearman correlation coefficients. Results show a moderate overall perception, primarily driven by perceived product quality and adaptability, followed by services offered. Diversification and innovation have weaker associations. Findings suggest that consumer loyalty to nanostores relies more on relational and emotional factors (proximity, trust, personalized service) than on technological or innovative practices. The study highlights opportunities for service diversification and gradual digitalization to enhance competitiveness.

The research aimed to diagnose the state of informal commerce and its relationship to the deterioration of the human landscape in the Chacra a la Olla market in the city of Chimbote in 2025. It was conducted using a quantitative, basic, and non-experimental cross-sectional design. Questionnaires were administered to 90 vendors and users, validated by expert judgment using Aiken's V coefficient (1.0), which confirmed their validity. Reliability was determined using SPSS software, yielding a Cronbach's alpha of 0.88, demonstrating high internal consistency. Observation sheets were also used to record the physical, social, and functional conditions of the public space. The results revealed a significant and uncontrolled presence of the informal sector, which affects spatial organization, cleanliness, and safety. High levels were diagnosed: 86% informal occupation, 74% physical deterioration, and 72% negative impact on the perception of space, confirming the need for strategies to organize and revitalize the human landscape. In conclusion, the study shows that inadequate management of informal trade significantly deteriorates the quality and functionality of the human landscape.

This paper proposes a hybrid decision-making model for optimizing the material supply chain innatural gas installation projects, with a focus on companies providing natural gas installation services for condominiums, residential complexes, and large-scale residential civil works in Peru. In a context characterized by constrained budgets and critical delivery times, the model integrates Deterministic Dynamic Programming (DDP) for optimal supplier selection and Markov Chains to assess operational resilience. The results indicate that, after adjusting demand to a feasible operational scenario, the DDP model achieved a cost reduction of 28.35%, optimizing the standard budget for a 100-unit residential condominium from USD 29,700 to USD 21,280, while ensuring a supply lead time of 20 days. Furthermore, the Markov chain analysis yielded a steady-state compliance probability of 70.9%, identifying a failure risk of 6.0%, which highlights the need for contingency planning. The proposed approach enables small and medium-sized enterprises (SMEs) in the natural gas installation services sector to transition from empirical decision-making to a data-driven and scientific management framework, enhancing both financial sustainability and operational reliability in large-scale residential projects.

This research aimed to demonstrate how combining engineering tools with Machine Learning models can help reduce the percentage of unserved customers in a restaurant. To achieve this, a methodology was designed that integrated process standardization through BPMN diagrams, the development of a digital reservation registration system, and the implementation of a predictive model based on linear regression. This approach made it possible to connect process improvement with data analytics, resulting in a practical solution adapted to the company’s real operational conditions. Additionally, a custom code has been developed to make the most of the available information, despite the initial limitations related to the lack of digitalization. The results show significant improvements. The service level increased from 86.6% to 95.8% using the program Arena simulator, while demand planning accuracy improved from approximately 85% to 95%. Furthermore, a system guaranteeing 100% traceability of reservations was implemented, something the company did not have before. These findings demonstrate that integrating BPMN, process standardization and Machine Learning is a viable, efficient and replicable alternative for demand planning in environments with limited resources. Finally, the study concludes that digitalization plays a key role in enhancing operational efficiency within the food service sector. It is recommended to strengthen a data-driven organizational culture and progressively advance toward the adoption of digital predictive systems.