Utilizing Machine Learning Models to Forecast Pricing on Mechanical Components and Automate Sourcing

In supply chain management, price quotations on machine components can be crucial in determining accurate pricing that may suit a business’s reliability and improve cost control. This process emphasizes price comparisons and negotiations to enable well-informed decisions that do not compromise quality or cost. With the emergence of machine learning and artificial intelligence, companies can leverage these tools to discern reasonable prices and establish the price quotations they can present to suppliers. This study evaluates machine learning models that employ previous material prices and properties such as part family, material grade, dimensions, thread type, and coating to determine the most optimal model. However, the prototype designed within this research expands further by adjusting the hyperparameters of the chosen model to enhance its efficacy. Ultimately, the model analysis determined that Gradient Boosting had a significant predictive accuracy and thus was the best-fit model to forecast pricing on mechanical components.