Case study 01 · Equipment analysis + website
O to go — around 30 convenience stores with a coffee counter, Baku
O to go runs roughly 30 convenience stores across Baku, each with counter coffee sold alongside the till. High footfall, short queues, no dedicated coffee staff — coffee is an attachment sale, and the margin on it has to survive the labour it costs.
- Client
- O to go · ~30 stores, Baku
- Machine chosen
- Franke A400
- Saving projected to December
- ≈ $300,000
- Volume modelled
- 250–300 cups/day per store
- Payback on the machine
- ≈ 13 months
01
The situation
When counter coffee was introduced across the stores, an entry-level Dr. Coffee bean-to-cup machine was installed specifically to avoid the cost of staffing a coffee counter. At real trading volume the cup was weak and inconsistent, the machine stalled under load, and customers noticed. The internal conclusion was that automation had failed, and the business began pricing traditional barista espresso machines and the staff required to operate them.
02
The trap: both obvious answers were wrong
The machine on the counter was rated for roughly 30 cups a day. The counters were pulling 250–300 — close to eight times its rated duty. That is not a failure of automation; it is a tiering error, a home-class machine placed on commercial duty. No equipment survives that. Meanwhile the barista line looked like the quality answer only because nobody had priced a barista's wages across three years.
- Duty rating — the cheapest machine is the one specified above your real volume, not below it.
- Labour — a loaded barista costs about AZN 700 a month per store. Across 30 stores that compounds every single month; a machine is paid for once.
- Downtime — a stalled machine at 8am is a lost sale plus a customer who buys coffee elsewhere tomorrow.
- Consistency — a super-automatic closes a control loop on brew pressure, water temperature, extraction time and dose. A human holds those variables loosely.
- The floor, not the ceiling — for a convenience brand the value is not the best cup possible, it is that every customer receives the same cup.
03
The analysis
The cost model was anchored to Azerbaijani operating data rather than imported assumptions — loaded barista cost, the current business electricity tariff, and the prevailing exchange rate — and run across three store formats. Below is the medium format: 300 cups a day. Figures marked client came from O to go's own payroll and trading data; est. figures are researched market estimates and were labelled as estimates to the client too.
| Line | Dr. Coffee incumbent |
Barista line machine + staff |
Franke A400 chosen |
|---|---|---|---|
| Rated duty | ~30 cups/day spec | Limited by staffing, not machine | Commercial, 300+ cups/day spec |
| Actual demand | 250–300 cups/day client | 250–300 cups/day client | 250–300 cups/day client |
| Dedicated staff | None — counter staff | 1 barista, ~AZN 700/mo loaded client | None — counter staff |
| Annual running cost | Stalling under load; unserviceable at this duty | ≈ $20,000/yr est. | ≈ $3,900/yr est. |
| Annual difference | — | + ≈ $16,100/yr, almost entirely labour | baseline |
| Labour cost per cup | — | materially higher | ≈ $0.14 on a cup sold at ≈ $2.20 |
04
The decision: what it is worth to the business
O to go selected the Franke A400. The argument that carried it was not taste, it was unit economics: the same cup, every time, at a labour cost of roughly 6% of its selling price, with no headcount added to any store.
“What we had experienced was not a failure of automation but a tiering error: a home-class machine placed on commercial duty. He argued against his own recommendation where the evidence required it. That is not always our experience with people selling us equipment.”
