arXiv subjects

Fabricio Orlando Sanchez Varretti

Publications and source records attributed to Fabricio Orlando Sanchez Varretti.

4 recordsLinked to original sources

arXiv2026

A categorical error sensitivity index (ISEC): A preventive ordinal decision-support measure for irrecoverable errors in manual data entry systems

Data entry systems remain structurally vulnerable to categorical misclassifications, particularly in small and medium sized enterprises (SMEs). When nominal categories exhibit semantic or morphological proximity, human machine interaction may produce errors that are irrecoverable ex post. In the absence of automated input controls, manual data entry frequently generates irrecoverable categorical distortions that propagate into Key Performance Indicators (KPIs), thereby misleading managerial decision making. State of the art normalization tools typically evaluate semantic and morphological dimensions in isolation and rely heavily on standard dictionaries, rendering them ineffective for SME master data rich in custom SKUs, abbreviations, and domain-specific technical jargon. This paper introduces the Categorical Error Sensitivity Index (ISEC), an ordinal composite score designed to rank category pairs according to their structural susceptibility to confusion. ISEC integrates semantic distance (via word embeddings), custom weighted morphological transformation costs (through an adapted Damerau Levenshtein algorithm), and empirical frequency into a unified, mathematically robust preventive framework. By leveraging vector database architectures, ISEC reduces computational complexity, achieving approximately a 195x performance improvement over brute-force methods. Validated across three heterogeneous datasets: governmental judicial records, retail inventory, and a synthetic ISO coded metalworking catalog, ISEC provides a scalable and proactive data governance instrument that enables SMEs to detect latent structural risk embedded within their categorical data assets.

cs.CL↗

arXiv2025

Modelado y gemelos digitales en el contexto fotovoltaico

The photovoltaic industry faces the challenge of optimizing the performance and management of its systems in an increasingly digitalized environment. In this context, digital twins offer an innovative solution: virtual models that replicate in real time the behavior of solar installations. This technology makes it possible to anticipate failures, improve operational efficiency and facilitate data-driven decision-making. This report analyzes its application in the photovoltaic sector, highlighting its benefits and transformative potential.

physics.soc-ph↗

arXiv2025

A physical model approach to order lot sizing

The growing need for companies to reduce costs and maximize profits has led to an increased focus on logistics activities. Among these, inventory management plays a crucial role in minimizing organizational expenses by optimizing the storage and transportation of materials. In this context, this study introduces an optimization model for the lot-sizing problem based on a physical system approach. By establishing that the material supply problem is isomorphic to a one-dimensional mechanical system of point particles connected by elastic elements, we leverage this analogy to derive cost optimization conditions naturally and obtain an exact solution. This approach determines lot sizes that minimize the combined ordering and inventory holding costs in a significantly shorter time, eliminating the need for heuristic methods. The optimal lot sizes are defined in terms of the parameter $ γ= 2C_O / C_H $, which represents the relationship between the ordering cost per order ($ C_O $) and the holding cost per period for the material required in one period ($ C_H $). This parameter fully dictates the system's behavior: when $ γ\leq 1 $, the optimal strategy is to place one order per period, whereas for $ γ> 1 $, the number of orders $ N $ is reduced relative to the planning horizon $ M $, meaning $ N < M $. By formulating the total cost function in terms of the intensive variable $ N/M $, we consolidate the entire optimization problem into a single function of $ γ$. This eliminates the need for complex algorithms, enabling faster and more precise purchasing decisions. The proposed model was validated through a real-world case study and benchmarked against classical algorithms, demonstrating superior cost optimization and reduced execution time. These findings underscore the potential of this approach for improving material lot-sizing strategies.

physics.app-ph↗

arXiv2024

Nuevo modelo para el dimensionamiento de lotes de pedidos en función del volumen de compra y deterioro temporal de los artículos

This research presents the development of a new simulation model to determine the optimal order lot sizes in Material Requirements Planning, based on purchase volume and the temporal deterioration of items. The scientific novelty lies in the exhaustive enumeration of all supply strategies, considering when and how much raw material and/or inputs to acquire while simultaneously managing multiple factors. The developed model enables obtaining and visualizing all feasible solutions, though a significant challenge arises when dealing with larger planning horizons. The methodology includes formulating a mathematical equation to calculate the total cost of all supply strategies, taking into account quantity discounts and the maximum allowable shelf life of inventory items. The results demonstrate that the model thoroughly explores the entire search space and identifies the optimal solution. Validation is conducted using the tabu search heuristic, a widely recognized optimization technique. While the heuristic converges toward the global minimum, it requires a significantly higher computational load. In contrast, the developed model identifies the global minimum with fewer calculations, showcasing its efficiency and accuracy.

math.OC↗