Using the Decision Tree for MRO What-if Analysis

Once we have our Decision Tree model, we can simulate different maintenance scenarios with greater granularity:

What if we extend the A-check interval by 10% and there are part delivery delays?

new_scenario = pd.DataFrame({
    'A_Check_Interval_Days': [90 * 1.1],  # Extend by 10%
    'Technicians_Assigned': [8],
    'Part_Delivery_Delay_Hours': [10],
    'Additional_Safety_Checks': [1],
    'Fuel_Price_Impact': [1.5],
    'Technician_Availability': [0.95]
})
predicted_downtime = model.predict(new_scenario)
print(f"Predicted Aircraft Downtime with extended A-check interval and part delivery delays: {predicted_downtime[0]} hours")

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What if fuel prices increase and fewer technicians are available?

new_scenario = pd.DataFrame({
    'A_Check_Interval_Days': [80],
    'Technicians_Assigned': [6],
    'Part_Delivery_Delay_Hours': [5],
    'Additional_Safety_Checks': [0],
    'Fuel_Price_Impact': [1.8],  # Increased fuel price
    'Technician_Availability': [0.8]  # Fewer technicians available
})
predicted_downtime = model.predict(new_scenario)
print(f"Predicted Aircraft Downtime with increased fuel prices and reduced technician availability: {predicted_downtime[0]} hours")

These scenarios help MRO planners determine the optimal balance between maintenance frequency, technician allocation, spare part logistics, regulatory requirements, and other operational complexities, ensuring minimal disruption to flight schedules.

Feature Importance in MRO Decision-Making

Understanding which factors have the greatest impact on aircraft downtime is crucial for MRO decision-making. Feature importance in a Decision Tree helps identify these key variables, enabling MRO teams to focus their efforts where they matter most.

# Feature importance analysis
importances = model.feature_importances_
feature_importance_df = pd.DataFrame({
    'Feature': features.columns,
    'Importance': importances
}).sort_values(by='Importance', ascending=False)

print(feature_importance_df)

This analysis provides insights into whether increasing the number of technicians, adjusting the maintenance interval, addressing part delivery delays, focusing on safety checks, or managing fuel price impacts has the greatest influence on minimizing downtime, thus informing strategic decisions.

Advanced Scenario Exploration for MRO Operations

For MRO staff who may not be familiar with reading complex decision charts, advanced machine learning can be integrated with AI chat technology to bridge this gap. Instead of interpreting visual decision trees, users can simply interact with different variables, and receive straightforward answers like "Predicted Aircraft Downtime with increased fuel prices and reduced technician availability: 115.0 hours". This makes powerful analysis accessible to all, regardless of technical expertise.

What-if Analysis becomes even more impactful when integrated with visualization tools like Plotly or Power BI. By creating interactive dashboards, MRO planners can adjust variables like maintenance intervals, technician assignments, spare part availability, and safety checks in real time, immediately visualizing the impact on aircraft availability and operational costs.

At ePlaneAI, we build custom ML models, data-intensive dashboards that allow granular exploration of scenarios or integrate with existing cloud-based dashboard solutions to bring all your data into one place. Our goal is to provide MRO teams with powerful tools for decision-making that drive efficiency and minimize costs.

For example, using Dash by Plotly or Qlik, you could create a web application that allows MRO managers to explore different maintenance schedules, technician allocations, fuel price impacts, and other variables while visualizing the predicted aircraft downtime.

Deploying MRO What-if Analysis in the Cloud

Deploying What-if Analysis solutions in the cloud brings scalability to MRO operations. Using cloud services like AWS Lambda or Azure Functions, Decision Tree models can be deployed as serverless functions that accept input scenarios and return maintenance predictions in real time.

Here’s an example of setting up an AWS Lambda function for What-if Analysis in MRO:

import json
import joblib
import pandas as pd
from sklearn.tree import DecisionTreeRegressor

# Load the model (assuming it's been trained and saved)
def load_model():
    try:
        model = joblib.load('/tmp/decision_tree_model.pkl')
    except FileNotFoundError:
        raise Exception("Model file not found. Ensure the path is correct.")
    return model

def lambda_handler(event, context):
    # Parse input scenario data
    try:
        input_data = json.loads(event['body'])
    except (KeyError, json.JSONDecodeError) as e:
        return {
            'statusCode': 400,
            'body': json.dumps({'error': 'Invalid input format', 'details': str(e)})
        }

    # Load pre-trained Decision Tree model
    try:
        model = load_model()
    except Exception as e:
        return {
            'statusCode': 500,
            'body': json.dumps({'error': 'Failed to load model', 'details': str(e)})
        }

    # Convert input to DataFrame
    new_data = pd.DataFrame([input_data])

    # Predict outcome using the Decision Tree model
    try:
        prediction = model.predict(new_data)
        return {
            'statusCode': 200,
            'body': json.dumps({'predicted_downtime': prediction[0]})
        }
    except Exception as e:
        return {
            'statusCode': 500,
            'body': json.dumps({'error': 'Prediction failed', 'details': str(e)})
        }

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Conclusion

What-if Analysis combined with Decision Trees provides MRO teams in the aviation industry with the tools needed to explore different maintenance scenarios, optimize technician schedules, and make data-driven decisions. By understanding the impact of different choices, airlines and MRO providers can ensure that aircraft are maintained efficiently, minimizing downtime and keeping operations running smoothly.

At ePlaneAI, we specialize in providing tailored solutions to help you navigate the complexities of MRO operations with confidence. Whether you need a custom-built data dashboard or integration with your existing cloud-based solutions, we are here to help. If you're interested in exploring how What-if Analysis can transform your MRO operations, let's connect and take aviation maintenance to new heights.