Module 8 – Predictive Analytics & Forecasting
The Predictive Analytics & Forecasting module uses machine learning trained on 6-9 months of historical data to automatically predict future stock requirements, order quantities, and cost fluctuations. It analyzes sales patterns, waste trends, seasonality, weather data, and public holidays to help reduce waste, prevent overstocking, and avoid missed sales opportunities.
View long-term trends and cost projections across all venues
Forecast order needs and menu performance for daily operations
Plan supplier orders and delivery schedules based on forecasts
Automatically predict future stock needs based on historical patterns
Determine optimal order quantities to reduce waste and prevent shortages
Anticipate cost changes and budget accordingly
Minimize waste from overstocking and prevent missed sales from understocking
AI-powered demand predictions with confidence scoring
Based on last 30 days
1 shortage, 2 demand spikes
How the system predicts future demand
Required: 6-9 months of historical data
Algorithms: Time series analysis + regression
Auto-retraining: Weekly model updates
How external factors affect demand forecasts
Expected demand increase based on historical patterns
Lower foot traffic expected on Saturday-Sunday
Nearby stadium event may increase evening traffic
MA = (Sum of Usage over Period) ÷ Number of Days
Example: 7-day MA for chicken
(50 + 48 + 52 + 49 + 51 + 53 + 47) ÷ 7 = 50 units/day
Trend = (Current Period Avg) ÷ (Historical Avg) × 100
Example: Summer produce usage
(45 units) ÷ (30 units baseline) × 100 = 150% seasonal factor
Confidence = 100 - (Std Deviation ÷ Mean × 100)
Example: Stable product
100 - (5 ÷ 50 × 100) = 90% confidence
6-9 months of transaction data for demand patterns
Historical waste data from Module 3 for accuracy
Integration with Module 2 for order generation
Delivery schedules and lead times from Module 1
External data sources for contextual predictions
Recipe costing data from Module 6 for value forecasts
Demand curves with 7, 14, 30-day views and confidence bands
Predicted vs current stock with recommended order quantities
Forecast confidence rating (0-100%) with historical performance
Public holidays and events with expected demand changes
Stock shortage warnings, cost spikes, overordering risks
Manual adjustment options for manager expertise input
Use line graphs with color-coded confidence bands. Show historical vs predicted with distinct styling
Allow managers to adjust predictions based on local knowledge. Track overrides to improve model
Display past forecast performance to build user trust. Include "Last 30 Days: 92% Accurate"
Use gentle animations for trend movement (up/down arrows, color transitions) without distraction
Seamless "Order Now" button that links directly to Module 2 with pre-filled quantities
Use visual indicators (high/medium/low) with color coding. Explain factors affecting confidence
Clearly mark upcoming events on timeline. Show expected impact with percentage changes
Group products by category (Meat, Produce, Dry) with drill-down to individual items
Simplify charts for mobile viewing. Swipe between timeframes. Tap products for details
Gather 6-9 months historical data
Train predictive model on patterns
Predict demand for selected period
Analyze trends and approve orders
Compare vs actual, retrain model