Supply Chain Management for Business Process Optimization using Decision Tree Regression Model

Abstract

Business process optimization and increases supply chain is the practice of increasing organizational efficiency by improving optimized processes, and supply chain lead to optimized business goals. Any business model supply chain can be improvised by optimizing the process between two or more parties. In our application, there is a need to optimize and classify a large amount of data between clients and enterprises, then classify requirements and purchase update details between employees and the purchasing team. Thus we propose a Decision tree regression model. Decision trees are powerful machine learning algorithms that can be used for classification and regression tasks. They work by splitting the data up multiple times based on the category that they fall into or their continuous output in the case of regression. In the base paper Linear regression is used to predict output but for a linear relationship between dataset and output variable.