Use of a Machine Learning Model for the Reduction of Backorders in the Cross Docking Sales Process for the Homecenter Order Service

In this work, it is necessary to analyze the increase of Back Order in the attention of cross-docking orders in the attention of Homecenter customers due to the need for more definition of purchase planning processes, resulting in logistics costs, fill rate charges, and low service level. Thus, companies that handle high volumes of inventory and constant orders should have a forecast plan to cover possible stock-outs. The primary purpose of the research is to explain a way to prevent stock-outs using an artificial intelligence model based on historical sales data of a medium-sized company that manages inventories, as well as to determine the machine learning model to predict and reduce backorders. The Orange software was used for the data analysis. The data was trained with different artificial intelligence models such as Decision Trees, Support Vector Machines, Random Forests, and neural networks. The most accurate model was defined according to numerical indicators such as the confusion matrix, the area under the curve (AUC), and the ROC curve analysis. Thus, we opted for the neural network model, which presented the most accurate data. Finally, the results are presented, and a suggestion is made at the management level regarding decision-making in the supply process. For this purpose, it's considered relevant to delve into the subject of the variables that influence the accumulation of backorders.