Bespoke System Live

Forecasting & Demand Planning

Eighteen months of your numbers, projected forward. The orders, shifts and cash buffers it implies land where the team already works.

Industry
Restaurants, distributors, SaaS, multi-site retail
Best for
Operators whose forecasts live in a spreadsheet and never quite arrive at the people who need them
How we work

We sit with your business. We find the operational problem costing you the most. We build the system that fixes it.

The Problem

Somebody pulls eighteen months of history into a spreadsheet every Friday afternoon. They draw a line forward, guess Saturday's staffing, and email the supplier an order they don't trust. Sunday night, they're still wondering. By Tuesday the numbers are stale, the rota is wrong, and the kitchen over-prepped desserts nobody is ordering. Every week, the same money walks out the back door.

What We Built

A forecasting engine trained on the operator's own history. Weather, local events, promo cadence and trend are first-class inputs the team can move. Forecasts redraw live. The confidence band widens visibly when the model is unsure. Recommendations — flour orders, Saturday rotas, hiring defers — push to the supplier portal, the rota tool, and the FP&A model. Same engine, repointed: restaurant covers today, parts reorder points tomorrow, SaaS cash collections the day after.

What Changed

Friday afternoon went away. The forecast was already on the screens that mattered by Monday morning. Forecast accuracy moved from a 20%+ trailing average to single digits. The operator stopped finding out about Wednesday's over-prep on Thursday.

Example deployment

One example — restaurant covers, parts demand, SaaS cash. Yours would be shaped around your numbers, your drivers, and the systems your team already opens.

Live demo
Forecasting — Restaurant staffing
Demand · 18-month history + 8-week forecast
Covers / week
Wk +8 forecast 978 ·+5.9%
6057579101,0621,215 Now Q1 ’24Q2 ’24Q3 ’24Q4 ’24Q1 ’25Q2 ’25NowFcst
History Forecast (median) Confidence band Naive baseline (12-wk avg)
Holdout accuracy · last 12 weeks
System forecast vs naive 12-week trailing average — measured on the same window.
System
4.5%
MAPE
Naive
4.8%
MAPE
Delta
−0.3pp
Better
Recommendations
Drop them where the team already works
Order 200kg flour Tuesday
Sized to the forecast peak — covers Wed–Sat prep without surplus.
Destination
Supplier portal
Schedule 15 staff Saturday lunch
Peak Saturday demand at one FOH per ~55 covers / shift.
Destination
Rota tool
Reduce dessert prep 10% Wednesday
Midweek dessert pull lags the weekly average — cut to avoid waste.
Destination
Kitchen brief

Want one built for your business? The first conversation is free.

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How it fits the three pillars

One forecast, three jobs.

Automation

Recommendations push to the systems the team already opens — the supplier portal, the rota tool, the PO drafts, the FP&A model. The forecast doesn't live in a tab; it lands in the inboxes that drive the work.

Audit Trails

Not the primary focus for this system.

Anomaly Detection

The model flags the weeks it is least sure about — usually around events, promo launches, or unfamiliar weather. A "Highest uncertainty" chip surfaces the week and the driver behind it, so the operator sees the risk before it costs them.

Analytics

Forecasts compose from the operator's real history plus the drivers they actually know about — weather, events, promos, trend. Every number is sized by the model, not by gut feel. The holdout accuracy sits next to the chart, so the operator can see what the model is worth.

Next Step

Want one built for your business?

The first conversation is free. And useful either way.