Capstone Project

This course is a part of Machine Learning for Supply Chains, a 4-course Specialization series from Coursera.

In this course, we'll make predictions on product usage and calculate optimal safety stock storage. We'll start with a time series of shoe sales across multiple stores on three different continents. To begin, we'll look for unique insights and other interesting things we can find in the data by performing groupings and comparing products within each store. Then, we'll use a seasonal autoregressive integrated moving average (SARIMA) model to make predictions on future sales. In addition to making predictions, we'll analyze the provided statistics (such as p-score) to judge the viability of using the SARIMA model to make predictions. Then, we'll tune the hyper-parameters of the model to garner better results and higher statistical significance. Finally, we'll make predictions on safety stock by looking to the data for monthly usage predictions and calculating safety stock from the formula involving lead times.