Smarter Rentals with AI Forecasting
SulitSuot leverages Machine Learning to predict attire demand, prevent stockouts, and ensure every student gets the cultural wear they need — right when they need it.
93.2%
Forecast Accuracy
4,800+
Data Points Analyzed
6 Months
Prediction Horizon
91.5%
Model Confidence
How Machine Learning Powers SulitSuot
Machine Learning (ML) is a branch of Artificial Intelligence that enables systems to learn from data and improve predictions over time — without being explicitly programmed. At SulitSuot, we apply ML to demand forecasting: predicting how many students will need specific attire items during upcoming school events and cultural celebrations.
Our model is trained on historical booking records, school event calendars, and seasonal patterns. It continuously refines its predictions as new data comes in, helping us maintain optimal inventory and serve every student reliably.
Last Model Update
December 2024
ML Models We Use
We combine three complementary models to deliver robust, reliable demand forecasts.
LSTM Neural Network
Accuracy: 93.2%Long Short-Term Memory networks capture complex seasonal patterns in rental booking data, making it ideal for multi-step demand forecasting across cultural event cycles.
Key Advantages
- Handles long-term dependencies
- Adapts to seasonal spikes
- High accuracy on time-series
Demand Forecast Dashboard
Real-time visualization of actual vs. predicted rental demand, plus 6-month forward projections.
Rental Demand Forecast
ML-powered prediction using historical booking patterns
What the Model Tells Us
Category Demand Breakdown
ML-analyzed rental distribution across attire categories
Seasonal Pattern Detection
ML model identifies November–December as peak rental season with 94% accuracy, driven by school cultural events and graduation ceremonies.
Trend Forecasting
Year-over-year analysis shows a consistent 15–20% growth in Cultural category rentals, correlating with increased school cultural programs.
Stock Shortage Alert
Predictive model flags Kalinga Gown and Ifugao Attire as high-risk items for stockouts in Q4 2024 based on booking velocity.
Dynamic Pricing Suggestion
Based on demand elasticity analysis, the model recommends a 10% price adjustment during peak months to optimize revenue without reducing bookings.
High-Demand Item Predictions
Items forecasted to have highest rental demand next season
Kalinga Ceremonial Gown
CulturalIfugao Traditional Attire
CulturalFormal Barong Set
FormalBeaded Headpiece
AccessoriesOur ML Pipeline
From raw booking data to actionable inventory decisions — here's how our forecasting pipeline operates end-to-end.
Data Collection
Booking records, event calendars, and seasonal patterns are gathered continuously.
Data Preprocessing
Raw data is cleaned, normalized, and transformed into time-series features.
Model Training
LSTM, Random Forest, and ARIMA models are trained and validated on historical data.
Forecasting
Models generate 6-month demand predictions with confidence intervals.
Action Alerts
Admins receive stock alerts and pricing suggestions based on forecast outputs.
Technology in Service of Sustainability
Our ML forecasting system directly supports SulitSuot's commitment to SDG 12 (Responsible Consumption) by reducing over-procurement and waste, and SDG 4 (Quality Education) by ensuring every student has access to cultural attire when they need it most.
SDG 12
Reduces over-stocking & textile waste
SDG 4
Ensures attire availability for all students
Innovation
AI-driven decisions for a smarter platform