MIT SCM Symposium 2024

There is a growing need to leverage data for improvement of processes in the procurement organization. Understanding the interaction of stakeholders with procurement structures and processes through advanced data analytics benefits the entire organization as significant efficiency and optimization can be realized.
This capstone project explores the procurement buying channels process in the sponsor company with the aim of providing insights on variables that influence the use of those buying channels by internal customers and improving the channel planning process. Clustering for the features was carried out using K-means algorithm. Regression and Classification Machine learning models were then built to identify the most relevant variables in the use of buying channels, and to predict buying channel behaviors.
Implementing this approach to data analysis revealed that predicting the completion time of purchases through various channels had low accuracy due to significant variability in cycle times Additionally, there was confusion in channel usage for similar purchase categories, leading to misclassification of channels. This underscores the necessity for a clearer remapping of channels and the empowerment of requesting parties to better understand and utilize the process.