Citation : TY - BOOK, AU - Santis, Rodrigo ,AU - P. de Aguiar, Eduardo,AU - Goliatt, Leonardo PY - 2017/11/08 T1 - Predicting Material Backorders in Inventory Management using Machine Learning DO - 10.1109/LA-CCI.2017.8285684
Data Description A backorder is the order which could not be fulfilled by the company. Due to high demand of a product, the company was not able to keep up with the delivery of the order. The backordering can lead to upsetting customer as they couldn't get what they ordered and the loyalty will decrease. Also, company cannot overstock every product in their inventory to avoid such situation. There has to be a way for the company to know for which products they can face this problem. So, the company has shared a data file with different input features for each product and it hopes to find a pattern inside this data which can give them some insight. The data file contains the historical data for some weeks prior to the week we are trying to predict. The data has 23 columns including 22 features and one target column. To model and predict the target, we’ll use the features columns, which are:
sku – Random ID for the product national_inv – Current inventory level for the part lead_time – Transit time for product (if available) in_transit_qty – Amount of product in transit from source forecast_3_month – Forecast sales for the next 3 months forecast_6_month – Forecast sales for the next 6 months forecast_9_month – Forecast sales for the next 9 months sales_1_month – Sales quantity for the prior 1 month time period sales_3_month – Sales quantity for the prior 3 month time period sales_6_month – Sales quantity for the prior 6 month time period sales_9_month – Sales quantity for the prior 9 month time period min_bank – Minimum recommend amount to stock potential_issue – Source issue for part identified pieces_past_due – Parts overdue from source perf_6_month_avg – Source performance for prior 6 month period perf_12_month_avg – Source performance for prior 12 month period local_bo_qty – Amount of stock orders overdue deck_risk – Part risk flag oe_constraint – Part risk flag ppap_risk – Part risk flag stop_auto_buy – Part risk flag rev_stop – Part risk flag went_on_backorder – Product actually went on backorder. This is the target value.
Problem Statement
- Do a complete EDA in the python notebook file
- Build a solution design architecture for end to end solution starting from data ingestion to deployment with a detail documentation.
On the basis of the given data in the features column and target column, you have to build a model which will be able to predict whether an order for a given product can go on backorder or not.