How It Works
How Seamore connects systems, signals, forecasts, scenarios, automations, and reports.
Process Steps
1
Data Collection
System connects to your data sources and imports historical information.
Technical Details:
- •Connections to ERP, WMS, TMS, POS, SQL, and CSV sources
- •Data validation and cleaning
- •Historical demand, inventory, supplier, and order analysis
- •Real-time sync setup
Performance:Live operating data ingestion
2
Feature Engineering
Raw data is transformed into features for machine learning models.
Technical Details:
- •Time series decomposition
- •Trend and seasonality extraction
- •External factor correlation
- •Outlier detection and handling
Performance:Variables attributed to price, promotions, competitors, and weather
3
Model Training
Multiple algorithms are trained and the best performing model is selected.
Technical Details:
- •LSTM, ARIMA, Prophet models
- •Cross-validation testing
- •Hyperparameter optimization
- •Performance evaluation (MAPE, RMSE)
Performance:50+ model configurations tested
4
Forecast Generation
Selected model generates predictions with confidence intervals.
Technical Details:
- •Multi-horizon forecasts (3-24 months)
- •Confidence interval calculation
- •Scenario modeling
- •Accuracy tracking
Performance:Optimistic, baseline, and conservative forecast bands
5
Inventory Optimization
Forecasts feed inventory policies, replenishment timing, and stockout risk views.
Technical Details:
- •Safety stock calculations
- •Replenishment schedule tracking
- •Lead time analysis
- •Stockout risk flagging
Performance:Replenishment and stockout risk tied to live operating data
6
Monitoring
System continuously monitors performance and updates models.
Technical Details:
- •Real-time performance tracking
- •Model drift detection
- •Automatic retraining triggers
- •Signal feed and automation triggers
Performance:Accuracy audited against actuals
Algorithms
LSTM Neural Networks
Deep learning for complex patterns and multiple variables.
Accuracy:Model-dependent
Best for:Complex seasonality, multiple variables, non-linear patterns
ARIMA/Prophet
Statistical models for trend and seasonal analysis.
Accuracy:Model-dependent
Best for:Clear trends, regular seasonality, interpretable results
Ensemble Methods
Combines multiple models for improved accuracy.
Accuracy:Model-dependent
Best for:Critical forecasts, diverse products, risk management
Technology Stack
Data Processing
Real-time data pipeline
Apache KafkaApache SparkRedis
Machine Learning
Model training and serving
TensorFlowPyTorchMLflow
Infrastructure
Scalable cloud deployment
KubernetesDockerAWS/GCP