Similar Datasets
Problem Statement
Material backorder is a common problem in a supply chain system, impacting an inventory system's service level and effectiveness. Identifying parts with the highest chances of shortage prior to their occurrence can present a high opportunity to improve an overall company’s performance. In this project, we will train classifiers to predict future back-ordered products and generate predictions for a test set.
File descriptions
Here we have two CSV files (Training_BOP.csv and Testing_BOP.csv)
Training_BOP.csv - the training set
Testing_BOP.csv - the testing set
Each file has 23 columns; the last column (went_on_backorder) is the target column.
Data fields
sku - sku code
national_inv - Current inventory level of component
lead_time - Transit time
in_transit_qty - Quantity in transit
forecast_x_month - Forecast sales for the net 3, 6, and 9 months
sales_x_month - Sales quantity for the prior 1, 3, 6, and 9 months
min_bank - Minimum recommended amount in stock
potential_issue - Indicator variable noting a potential issue with the item
pieces_past_due - Parts overdue from the source
perf_x_months_avg - Source performance in the last 6 and 12 months
local_bo_qty - Amount of stock orders overdue
x17-x22 - General Risk Flags
went_on_back_order - Product went on backorder
Validation - indicator variable for training (0), validation (1), and test set (2)
