Documentation Index
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Overview
Demand Prediction uses machine learning to analyze your historical sales data and forecast future vehicle demand by segment. This powerful feature helps you make data-driven decisions about inventory management, pricing, and purchasing strategies.How It Works
SGIVU’s demand prediction system analyzes:- Historical sales contracts and trends
- Vehicle characteristics (type, make, model, year, price range)
- Seasonal patterns and market conditions
- Customer preferences and buying patterns
Getting Predictions
Requesting a Prediction
Navigate to Demand Prediction
Access the Demand Prediction section from your dashboard or analytics menu.
Select Vehicle Segment
Choose the vehicle characteristics you want to predict demand for:
- Vehicle type (car or motorcycle)
- Make and model
- Year range
- Price range
- Other relevant features
Generate Prediction
Click to generate the prediction. The system sends your parameters to the ML service and returns the forecasted demand.
Understanding Prediction Results
Demand Estimate
The system provides an estimated number of units expected to sell in your specified segment and timeframe. This is based on historical patterns and current trends.
Confidence Level
Each prediction includes a confidence score indicating how reliable the forecast is based on data quality and historical consistency.
Historical Context
View historical sales data for the same segment to understand trends and validate predictions against past performance.
Prediction with Historical Trends
For more detailed analysis, use the “Prediction with History” feature:View Historical Data
The system retrieves historical sales data for your selected segment, showing past performance over time.
Compare Trends
Visualize the historical trend alongside the future prediction to identify patterns and validate the forecast.
Use Cases
Inventory Optimization
Stock Planning
Predict which vehicle segments will be in high demand to guide your purchasing decisions.
Avoid Overstocking
Identify slow-moving segments to avoid tying up capital in low-demand inventory.
Seasonal Preparation
Anticipate seasonal demand changes and adjust inventory accordingly.
Budget Allocation
Allocate purchasing budget to high-demand segments for better ROI.
Pricing Strategy
Dynamic Pricing
Use demand predictions to inform pricing decisions. High predicted demand may support premium pricing, while low demand segments may need competitive pricing.
Sales Planning
Sales Targets
Set realistic sales targets based on predicted demand for different vehicle segments.
Marketing Focus
Target marketing efforts toward high-demand segments predicted by the system.
Model Training
The prediction system continuously improves through model retraining:Automatic Training
Continuous Learning
The ML model is periodically retrained with your latest sales data, ensuring predictions remain accurate as your business evolves.
Manual Retraining
Request Retraining
Access the model management section and request a manual retrain if you have significant new data or market changes.
Specify Data Range
Optionally specify a date range for training data. By default, the system uses all available historical data.
Training Process
The system retrains the model using your latest contract data. This process may take several minutes depending on data volume.
Model Validation
The new model is validated against test data to ensure improved accuracy before being deployed.
Model Information
Viewing Model Details
Model Metadata
Access information about the current prediction model, including:
- Training date and version
- Data volume used for training
- Model performance metrics
- Feature importance
Best Practices
Regular prediction reviews
Regular prediction reviews
Generate predictions regularly (monthly or quarterly) to stay ahead of market trends and adjust inventory proactively.
Compare with actual results
Compare with actual results
Track prediction accuracy by comparing forecasts with actual sales. This helps you understand model reliability and refine your strategies.
Use multiple segments
Use multiple segments
Generate predictions for various vehicle segments to get a comprehensive view of your entire inventory needs.
Consider external factors
Consider external factors
Remember that predictions are based on historical data. Consider external factors like economic changes, new regulations, or market disruptions.
Combine with market research
Combine with market research
Use demand predictions alongside traditional market research and industry insights for the most informed decisions.
Data Requirements
Minimum Data
Historical Sales Data
The prediction system requires sufficient historical sales contracts to generate accurate forecasts. More data generally leads to more reliable predictions.
Data Quality
Complete Contracts
Ensure contracts have complete vehicle and sale information for better predictions.
Accurate Dates
Correct transaction dates are crucial for identifying trends and patterns.
Consistent Categories
Use consistent vehicle categorization (type, make, model) for meaningful segment analysis.
Regular Updates
Keep contract data current by regularly entering new transactions.
Integration with Other Features
Purchase & Sales Contracts
Training Data Source
The prediction system uses your contract history as training data. More complete contract records lead to better predictions.
Vehicle Management
Inventory Planning
Use predictions to inform vehicle purchasing decisions and inventory composition in Vehicle Management.
Technical Details
Machine Learning Technology
Machine Learning Technology
SGIVU uses advanced machine learning algorithms including scikit-learn and optionally XGBoost for demand forecasting. The models are trained on your specific data for customized predictions.
Feature Engineering
Feature Engineering
The system automatically extracts relevant features from your contract data including temporal patterns, vehicle characteristics, and transaction trends.
Model Versioning
Model Versioning
Each trained model is versioned and stored, allowing you to track model improvements over time and roll back if needed.
Prediction API
Prediction API
The prediction service exposes REST APIs that can be integrated with other systems or custom applications.
Troubleshooting
No predictions available
No predictions available
This usually means no model has been trained yet. Ensure you have sufficient historical sales data and trigger a model training.
Low confidence predictions
Low confidence predictions
Low confidence may indicate insufficient historical data for the specific segment. Try broader segment definitions or accumulate more sales history.
Predictions seem inaccurate
Predictions seem inaccurate
Compare predictions with actual results. If consistently inaccurate, retrain the model with more recent data or review your contract data quality.
Model training fails
Model training fails
Verify you have sufficient and valid historical data. Check system logs or contact your administrator if training errors persist.
Prediction request times out
Prediction request times out
Very complex predictions may take time to process. Simplify your segment criteria or try again during off-peak hours.
Future Enhancements
The demand prediction system is continuously evolving. Future enhancements may include:- External market data integration
- Competitor pricing analysis
- Economic indicator incorporation
- Multi-location demand forecasting
- Automated inventory recommendations
- Real-time prediction updates