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.
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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.
What You Will Learn
- Calcualte safety stock using SARIMA predictions combined with manipulaitng lead times.
Course 4 of 4 in the Machine Learning for Supply Chains Specialization
Syllabus
WEEK 1
Exploratory Data Analysis Using Pandas and Groupby
In this module, we'll get acquainted with our dataset by exploring some of the most obvious groupings and identifying the variation in products. We'll discover which products sell where and prepare ourselves to use timeseries forecasts and safety stock predictions.
WEEK 2
Demand Predictions Using SARIMA
In this module, we'll use the SARIMA model to make predictions on future sales. We'll then visualize some of these predicted sales before evaluating the accuracy and viability of our chosen model.
WEEK 3
Calculating Safety Stock
In this module, we'll finish the project by calculating safety stock from monthly usage and lead times. We'll start by grouping products in order to find more accurate usage numbers. Then, we'll conclude by using the known formula along with our insights from the data in the calculation of safety stock for each product.
