3.4%
Provisioning waste
From 11%
Case Study
Demand Forecasting
Plan inventory, staffing, and resources against what is actually coming.
Case Study
Plan inventory, staffing, and resources against what is actually coming.
A predictive planning layer for inventory, labor, and resource needs across seasonal and multi-location operations. Forecasts factor in historical patterns, seasonality, weather, event calendars, and forward bookings, and turn them into specific recommendations — what to order, who to schedule, what to pre-position — by location and by day.
A forecast is only useful when it changes a decision before money is spent. Each recommendation comes with a confidence range and a named driver, so the line manager can act without second-guessing the model. Variance is tracked and fed back: when the forecast missed, the system learns which signal it under-weighted and where to listen harder next time.
Capabilities
Daily, weekly, and monthly forecasts by location and category.
Weather, event calendar, and historical seasonality factored in automatically.
Staff scheduling recommendations tied to forecasted volume.
Inventory order suggestions with confidence ranges and shelf-life awareness.
Variance reporting: forecast vs actual, with named drivers when something missed.
From Our Work
Per-sailing planning across nine ships and 140 sailings.
The operator was over-ordering provisions on some sailings and short on others, with crew overtime spiking unpredictably. Forecasts were spreadsheet-built by region managers from last year's numbers — a process that broke down whenever the itinerary or demographic mix shifted. We delivered a forecasting layer using booking patterns, itinerary mix, historical consumption, and external signals. The system produces per-sailing demand for galley, bar, excursions, and crew assignments. Provisioning waste dropped from 11% to 3.4%, stockouts per sailing fell from 2.4 to 0.3, and crew overtime came down 38% across the fleet.
3.4%
Provisioning waste
From 11%
0.3
Stockouts per sailing (avg)
From 2.4 avg
−38%
Crew overtime hours
Versus baseline
9%
Forecast accuracy (MAPE)
From 31%
Daily
Reforecast cycle
From weekly
Key Insight
A forecast is only useful if it produces a specific action. Confidence ranges, shelf-life awareness, and shift-by-shift detail are what move it from a number on a slide to something the line manager actually orders against.
