Web App · 2024 · TODO: client name (or keep anonymous)
Petrol Pump Chain Management
CompletedSales, stock, and reconciliation for a fuel station chain
Problem
Shift sales, nozzle readings, and tank stock lived in paper registers. Day-end reconciliation took hours, and variances between meter readings and actual stock surfaced too late to explain.
Solution
A web app the chain runs every shift on: nozzle opening/closing readings, tank dips, credit-customer ledgers, and staff accounts — with daily dashboards that flag variances the same day they happen.
Outcome
- TODO: time saved on day-close per station
- TODO: variance/shrinkage reduction after rollout
- Every shift closes against the meters, so discrepancies are caught while the shift staff is still on site
Background
A fuel station chain ran its operations the way most do: paper registers per pump, a day-end tally, and a manager reconciling meter readings against cash and stock long after the shift that produced them had gone home. Honest mistakes and real losses looked identical by then.
What I built
A web application shaped around how a station actually runs — the shift.
The shift is the unit of truth
Staff open a shift by recording each nozzle's meter reading; they close it the same way. Litres sold is the difference, and expected cash follows from the day's price. Cash counted, credit issued, and expenses are entered against the same shift, so every rupee has to land somewhere.
Tank reconciliation
Deliveries and daily dip readings track physical stock per tank. The app compares stock movement against metered sales and flags variance beyond tolerance — the difference between "we have a leak", "a meter is drifting", and "nothing to see" becomes a report instead of an argument.
Credit customers
Fleet and commercial customers buy on credit. Each has a ledger — sales accrue against it, settlements clear it, and the dashboard shows the chain's total exposure at a glance.
Outcome
The chain closes each station daily with a variance report instead of a paper tally.
TODO: add real figures — day-close time before/after, variance trend after rollout.
Running a similar operation?
I build systems like this end to end — from the data model to the day-to-day screens your staff actually use.
Get in touch