| dc.description.abstract | NPT is a corporation that specializes in E-commerce cooperation. Their primary lines of
business include retail sales and operations, digital marketing, customer service,
warehousing, and fulfillment.
The company profit is generated by addressing customers' needs for beauty products such
as cosmetics, skincare products, etc. Because the cost of holding stock grows
exponentially with increased availability which will never reach 100%. This effectively
means that there will be times when an order is placed for an item that is currently out of
stock, resulting in a backorder. While not all backorders may be totally avoided,
anticipating them allows for proactive efforts to be made, potentially lowering lead times
and costs.
To address the problem of backorder prediction, this study compared various machine
learning models for binary classification. There are three machine learning models (Light
Gradient Boosting machine, Random Forest, and CatBoost) that are evaluated based on
their ability to predict backorders. Additionally, one data transformation technique is used
(Normalization) comes along with AUC score and performance metrics comparison.
Among these models, RF performed the best after scrutinizing the sensitivity. The
features’ importance score analysis highlighted the significance of inventory stock,
product delivery volume, imminent demand (sales), and accurate future demand
prediction in correctly predicting backorders. | en_US |
