The purpose of this research work is to evaluate the use of statistical tools, specifically Ordinary Least Squares (OLS) and Artificial Neural Networks (ANN) and with the help of these tools to be able to independently and effectively predict the costs. of production in the context of supply logistics in the Peruvian paper industry. Both models that turn out to be different in their analysis, however, turn out to be complementary for a more exact and precise result, highlighting the ANNs for their superior performance in the precision of the evaluated metrics, where they managed to achieve an RMSE of 0.0171 and a MAE of 0.0122 compared to the OLS statistical model that achieved an RMSE of 0.0181 and a MAE of 0.2070. Likewise, the analysis between the dimensions studied, purchasing management stands out with a negative coefficient of -0.4978, which shows that its optimization will generate a positive impact on production costs, contrary to the case with the other two dimensions, which are: storage management and inventory management, which resulted in positive coefficients (0.7457 and 0.4667), which shows that their optimization does not necessarily generate a positive impact on production costs, but quite the opposite, that their inadequate management On the contrary, it can harm production costs. These results highlight the inherent need that Peruvian paper companies must have in being able to implement updated logistics systems, capable of integrating advanced statistical tools such as the use of ANN and MCO, which can scientifically help better decision making, allowing thereby improving your supply processes and thus being able to reduce your production costs.

Our research used a SVAR to analyze the underlying copper price shocks, that is, a commodity shock for the Junín department in Peru. The results of a short- and long-run SVAR were based on traditional matrix constraints that capture the fact that domestic shocks do not affect international prices. The main conclusion is that before the pandemic a shock in the international price of copper decreases economic growth and inflation in the department of Junín in Peru, after the pandemic the opposite happens. As a result of the model, the short and long-run effects of the international copper price on the main macroeconomic variables of Junín in Peru are statistically significant. Before the pandemic, the dynamics of the international copper price reflected the existence of the curse of mining resources in copper institutions. Before the pandemic, a percentage increase in the international price of copper decreased economic growth, reflecting the existence of the mining resource curse in copper institutions. After the pandemic, a percentage increase in the price of copper increases economic growth by up to 0.0488%, then decreases over time, noting the transitory effect of economic recovery and poor management of mineral resources.