Optimizing a company’s processes is essential to its growth. This work focused on the study of Aggregate Production Planning with the aim of optimizing a company’s a company’s production system. Aggregate Production Planning makes it possible to determine production and stock levels, hiring and firing of employees, number of overtime hours, delays and meeting demand in order to achieve the lowest possible costs. The aim of this work is to present and develop a mathematical model that can meet all the needs of the production, considering all aspects from staff productivity to the product or service. Subsequently, the developed PAP prototype was tested and compared with other planning methods such as Average Leveling, Excess Capacity and Adaptation to Demand, to determine which method resulted in the lowest costs. In order to confirm the results, static tests were carried out, which conclude that there is a significant difference between the means of the groups since the p-value (<.001) is much lower than the -value of 0.05.