How machine learning is helping to predict critical events among thousands of shipment routes

The challenge

The client, a leading company in risk management for the transportation and logistics sector, faced the challenge of efficiently managing and prioritizing a vast number of workflows. Their system handled approximately 25 million events daily, with about 2 million workflows per month and 350,000 shipments tracked monthly.

Operators managed 100 to 200 shipments simultaneously, but only 1 to 2% of these shipments resulted in the incidents they aimed to prevent. The challenge was to use machine learning to predict which shipments were at the highest risk and prioritize them accordingly, thereby improving operational efficiency and reducing the workload on human operators.

The solution: machine learning for risk prediction

Machine learning techniques were applied to predict risks and prioritize workflows based on the likelihood of critical events. The solution involved identifying the types of incidents that should be prioritized and selecting appropriate algorithms considering factors like model complexity, problem-solving capacity, and predictive effectiveness.

The problem was addressed using an ensemble method — a machine learning technique that builds a sequence of decision tree models and combines their outputs to create a robust and accurate model. Given the naturally imbalanced nature of the problem, machine learning techniques were used to correct data distribution and minimize the impact on predictions.

The strategic focus was on minimizing false negatives (shipments with critical risks that the model incorrectly predicts as safe), as untreated incidents could lead to financial losses. The approach allowed for an increase in false positives (shipments without critical risks that the model predicts as risky), ensuring that more potential incidents were captured in the workflow management system.

The Results

The implementation of the machine learning model significantly improved the client’s ability to predict and prioritize high-risk shipments. The model demonstrated a high capacity to distinguish between shipments with and without critical risks, achieving an AUC of 94%, indicating reliability and effectiveness in risk prediction.

The false negative rate was 11.5%, a manageable figure given the challenge of identifying incidents. The model correctly classified 88.5% of shipments with critical risks, proving its efficiency in the task.

These results reflect the model’s effectiveness in making precise risk predictions, enhancing the reliability of its applications in logistics risk management.

3. Results

• High Predictive Accuracy: The model achieved a 94% AUC, indicating strong discriminatory power in predicting risks.
• Reduction of False Negatives: The false negative rate was reduced to 11.5%, allowing better focus on high-risk shipments.
• Operational Efficiency: Prioritizing shipments with predicted risks reduced the operational burden on human operators, enabling them to concentrate on the most critical cases.