Alexander Pulido-Rojano, Juan Carlos Calabria-Sarmiento, Oscar Osorio-Marín, Tatiana Prada-Ballestas, Ronny Ariza-Corro, Dilan Atencia-Colina, Juan Molina-Londoño
A simulation model allows observing the behavior of systems over time and supporting decision-making. Its application helps to study mathematical models and logical relationships between variables to understand how the system works. This document proposes a novel simulation model to predict the behavior of products or businesses in the market, applying the principles of the BCG matrix and Markov chains. For this, starting from the historical sales of a product, the growth rate and the participation rate were calculated as input variables to the model. The designed algorithm of the model classified the product according to the BCG matrix and, through simulations, allowed calculating the probabilities of transition from one classification to another to finally obtain the steady state probabilities of the product, as a measure of success or failure. The results of the model validation allowed knowing the behavior of a real product over time and helped to propose suitable strategies to improve the company's participation in the market. The outputs of the algorithm show its efficiency to obtain the probabilities of the stable state of the evaluated product, confirming that, in the long term, the product has a 68.64% of achieving a weak participation and low sales in the market. In addition, only 14.41% of maintaining itself as a product with high participation and 4.24% of achieving high levels of growth and positive cash flow. In conclusion, this research shows the utility of the model to efficiently combine the principles of the Markov processes and the BCG Matrix.