Conference Agenda

In the e-commerce environment, characterized by high order volumes and increasingly demanding customers, logistics processes must be optimized to ensure operational efficiency and service quality. This study comparatively analyzes continuous improvement methodologies and digital technologies applied to e-commerce logistics management, focusing on their contribution to operational performance. A systematic literature review following the PRISMA guidelines was conducted using the Scopus database and predefined eligibility criteria, resulting in 26 selected studies. The findings indicate that integrating digital technologies such as artificial intelligence, the Internet of Things (IoT), and blockchain with improvement methodologies is consistently associated with better process optimization outcomes, including reduced operating times, higher inventory accuracy, and strengthened logistics traceability. Reported validations rely on simulation, real-world implementations, and quantitative modeling, and several studies also highlight qualitative benefits related to customers’ perceived service experience. Overall, the evidence suggests that combining continuous improvement and digitalization provides a robust foundation for enhancing logistics competitiveness in e-commerce.

In the current context of global logistics, the optimization of operational costs is essential to maintain the competitiveness of supply chains. In particular, decisions related to load planning and unloading operations in multi-drop distribution systems play a crucial role in overall operational performance. In this regard, this paper addresses the Multi-Drop Container Loading Problem, integrating critical variables such as customer delivery sequences and the dimensional characteristics of the items. A mixed-integer programming model is proposed to efficiently organize the load, minimizing unnecessary rehandling during the unloading process through the incorporation of a last-in, first-out (LIFO) constraint. In addition, an auxiliary mathematical model is introduced to adjust the initial solution and ensure a physically coherent and stable load configuration. The proposed methodology is validated through case studies implemented in AMPL and Python Colab, demonstrating that the proposed approach not only optimizes the use of available space but also facilitates faster and more orderly unloading operations.

This paper examines how supply management, framed in lean logistics, can improve internal service level in a microenterprise producing artisanal fermented beverages. Using a mixed-method case study (documents, observation, interviews, demand analysis, and discrete-event simulation), the intervention integrates a lightweight S&OP cadence, pragmatic MRP configuration, standardized purchasing/receiving/dispatch routines, and competency-based training. Case evidence indicates fewer expedite purchases and coordination losses, investment recovery below 18 months, and an annual ROI close to 517%. The contribution is a replicable operating model that links supply decisions, process stability, and internal service performance under demand volatility and seasonal inputs

Rapid urban growth and vehicular congestion have driven the search for technological solutions to improve mobility in urban environments. This systematic review analyzed Machine Learning techniques applied to traffic monitoring and passenger demand prediction. Different approaches used in recent literature were explored, as well as their effectiveness compared to traditional methods.
The study was conducted following the PICO strategy and PRISMA methodology to ensure a rigorous and transparent process. A systematic search was carried out in scientific databases such as Scopus, IEEE Xplore and Web of Science, initially identifying 660 articles. Using the PICO strategy, the general question and keywords that helped the search for articles were generated. After applying the inclusion and exclusion criteria defined in the PRISMA protocol, 47 relevant studies were selected between 2021 and 2025.The findings were organized to answer four research questions: definition of the urban transportation system, techniques used, effectiveness versus traditional methods, and improvements achieved. It was identified that the most commonly employed techniques included deep neural networks, reinforcement learning, and hybrid models. These tools were applied to tasks such as vehicle flow prediction, incident detection and transportation user behavior analysis. In general, Machine Learning-based models demonstrated significant advantages in accuracy, adaptive capacity and real-time data processing, especially in dynamic urban contexts.It was concluded that Machine Learning represents a key tool for the transformation of urban transport systems, contributing to more efficient, resilient and data-driven planning.

The objective of this research was to determine the impact of Lean Manufacturing tools on inventory management at Agro Marsa S.A.C. in Chimbote during 2026. The identified problems included recurrent stockouts and a critical accumulation of obsolete products. Using a quantitative approach and a longitudinal pre-experimental design, a census population of 79 products was intervened. The implementation integrated ABC classification to prioritize the catalog, 5S methodology to standardize the warehouse, and a Kanban system to regulate the technical replenishment of high-turnover products. The results showed a significant reduction in product obsolescence, dropping from 15.39% to 6.43% (a 58.2% improvement). Similarly, the service level increased from 84.06% to 95.70%. Financial analysis validated the project's feasibility with a Net Present Value (NPV) of S/ 10,938.63 and a benefit-cost ratio of 1.64, achieving investment recovery in 11.15 months. It is concluded that the transition to technical management based on Lean significantly optimizes logistical efficiency and organizational profitability.

The purpose of this article is to determine the relationship between logistics management and exports in an agricultural export company specializing in figs in Nuevo Chimbote, 2024. To this end, the research adopted a quantitative approach, using a basic, descriptive-correlational, non-experimental cross-sectional design. The population consisted of 20 employees from the logistics department, who responded to a questionnaire in physical or virtual format. The data were processed using SPSS v.27 statistical software, and the results showed a significant positive correlation between the two variables (sig = 0.536, sig < 0.05). Therefore, it is concluded that the dimensions of supply, production, and distribution in logistics management are associated with the level of exports.