How to Create a Global Supply Chain Risk Tracker

To build a Global Supply Chain Risk Tracker, diverse data sources such as shipping logs, news feeds, labor strikes, and tariff updates are aggregated and analyzed. News articles are collected via Google News RSS and filtered by keywords like “port delay,” “labor strike,” and “tariff hike.” Each article is tagged with industry and region labels using rule-based and machine learning classification. TF-IDF vectorization and logistic regression help predict risk categories, while classification models evaluate accuracy across industry and region. Time-based aggregation allows weekly monitoring of disruption trends, visualized through heatmaps and line charts. A normalized risk score is computed based on article frequency per week, industry, and region. Sentiment analysis is applied to titles using a transformer model to distinguish negative, neutral, and positive news. Risk models are refined using sentiment-weighted scores rather than raw counts, enhancing accuracy and trend insights. Alerts are triggered when disruption signals exceed predefined thresholds. This end-to-end pipeline supports real-time, data-driven risk tracking across global supply chains.

Видео How to Create a Global Supply Chain Risk Tracker канала Analytics in Practice