₹1,68,622 revenue
The seasonal baseline estimates about 281 completed orders across the three outlets. Jubilee Hills leads revenue; Gachibowli leads orders.
Restaurant Forecasting · Live
One forecast that follows the restaurant operating chain.
Sales become item demand. Item demand becomes preparation. Recipes turn preparation into ingredients. Closing stock exposes the gap. Recorded waste tells the team whether the plan improved.
Revenue to ingredient planningOutlet-level recommendationsHuman overrides preserved
Restaurant Forecasting
One product
The modules are connected because the decisions are connected
The value comes from carrying the same outlet, item, date and unit definitions through the entire chain. Separate forecasts that use different grains create reconciliation work.
Worked example
Saturday, 1 August: what the sample data would put in front of the team
These values come from the synthetic TimesFM input workbook used in the prototype. They illustrate the decision flow, not commercial performance.
115 Chicken Dum Biryanis
Rounded outlet forecasts are 40 portions at Jubilee Hills, 38 at Gachibowli and 37 at Madhapur. The kitchen can then add an explicit service buffer.
Packaging boxes run short
Jubilee Hills shows roughly 517 boxes required over seven days against 12 recorded at close. The recommendation still needs pack size, delivery lead time and open purchase orders.
42.8 recorded portions
Chicken Dum Biryani had the highest recorded waste quantity in the latest week. That observation informs the buffer; it does not prove the forecast caused the waste.
Data contract
What the product needs at each stage
The product should not imply that a POS contains recipes, stock counts or actual waste. Those inputs often come from kitchen, inventory or procurement workflows.
| Decision | Minimum input | Better with | Output |
|---|---|---|---|
| Revenue | Completed orders, net revenue, outlet and timestamp | Channel, promotion and closure calendar | Revenue and order range by day/outlet |
| Menu demand | Order-line quantity and menu item | Daypart, availability and substitution events | Item forecast and preparation baseline |
| Ingredients | Recipe quantity and item forecast | Yield, wastage factor, pack size and lead time | Requirement, gap and purchase priority |
| Waste prevention | Preparation baseline and demand forecast | Actual leftovers, spoilage and plate-waste logs | Potential excess and measured outcome |
Product boundary
Claims Openfluid should not make
A narrower claim makes the product easier to trust and easier to evaluate during a pilot.
Forecasts do not guarantee revenue or savings
Accuracy depends on data quality, event coverage, the chosen horizon and whether operators use the result in time.
Potential excess is not actual waste
Spoilage, trim, cooking loss, plate waste and leftovers require direct measurement; sales history cannot reconstruct them reliably.
A requirement is not a procurement instruction
Vendor lead time, minimum order, open orders, pack size, shelf life and approval rules are needed before ordering.
FAQ
Questions operators usually ask.
Can Openfluid start with only POS data?
Yes for revenue, order and item-demand forecasting. Ingredient requirements and measured waste need recipes, stock and operational records.
Does every outlet need the same model?
No. Outlets can share a modelling framework while retaining their own demand level, weekly pattern, events and operating constraints.
How should a pilot be judged?
Use rolling historical backtests for accuracy, then track whether the approved plan improved stockouts, sell-through, excess or planning time during live operation.
Choose the first decision to improve.
Revenue is the easiest starting point. Preparation or inventory usually creates more operational value when the necessary data is available.