ml-supply-chain

Supply chain demand forecasting models are built using XGBoost, LightGBM, and TensorFlow. The process involves feature engineering on historical sales data, model training, prediction, and evaluation to accurately forecast future product demand using external factors like seasonality.

Computer vision for product defect detection in a supply chain is implemented using Convolutional Neural Networks (CNNs) via TensorFlow. This deep learning approach automates quality control by accurately identifying visual anomalies in products.

Segmenting customers or products using historical supply chain data is achieved through clustering techniques with Scikit-learn. This method groups items based on historical patterns, enabling targeted strategies and optimized inventory management.

Predicting shipment delays and lead times is supported through predictive modeling techniques. By applying machine learning algorithms to historical logistics data, you can forecast delivery timelines and anticipate potential supply chain disruptions.

Python libraries like Pandas and Numpy are required for supply chain predictive analytics to handle feature engineering and data manipulation. These libraries provide the foundational data structures needed before training models with XGBoost or Scikit-learn.