Abstract:

Introduction. The mass-consumption industry faces increasing pressure to optimize its production processes; in Peru, most small and medium-sized manufacturing companies still operate with traditional systems characterized by low digitalization and strong dependence on human intervention. Objectives. To determine the impact of the design of autonomous systems for the improvement of industrial processes through artificial intelligence and simulation, proposing an integrated prototype for the predictive and efficient management of industrial operations in the mass-consumption industry. Materials and Methods. A quantitative, applied, and experimental study was conducted with a quasi-experimental design based on computational simulation and industrial validation, structured in five phases: process characterization, artificial intelligence model development, simulation system implementation, autonomous prototype integration, and validation. The prototype integrated deep neural networks, computational simulation, and statistical analysis, and was evaluated using Student's t-test, 95% confidence intervals, Pearson correlation, RMSE, MAE, and Monte Carlo simulation.