A multi-location restaurant chain needed BI that served every function — not just the owner. We built a single Power BI solution delivering tailored dashboards to five distinct audiences: executive, sales, churn, production, and raw materials — all from one governed data model.
This chain had grown from two outlets to eight in three years. Their reporting hadn't grown with them. Each function was flying blind.
The only executive report was a WhatsApp message from each location manager with the day's revenue. No trend data, no comparisons, no context. The owner could tell if yesterday was good or bad — but not why, or which location was driving it.
Menu item profitability was completely invisible. High-revenue dishes might have the worst margins. Promotions were run on gut feel, with no ability to measure whether they'd worked. The sales manager couldn't tell which day part or which location had the most opportunity.
The chain had a loyalty programme in place, but nobody was analysing it. They had no idea what percentage of customers returned, how long it took them to come back, or which locations had the highest churn. Repeat visits were celebrated anecdotally — never measured.
Each kitchen manager estimated prep quantities from experience. Over-production led to wastage. Under-production meant the kitchen running out of popular items mid-service — resulting in poor guest experience and lost revenue. No system existed to compare forecast demand to actual output.
Purchasing decisions were made by each location independently, based on what they'd ordered last time. No chain-level view of supplier pricing, consumption rates, or wastage percentages. Food cost was known approximately at month-end — never in real time, never by item.
Each location operated as an island. There was no way to compare performance across outlets, identify which location had best practices worth replicating, or spot underperformers early enough to intervene. The chain was managed as eight separate businesses — not as a chain.
The key architectural decision was to build a single, governed data model and layer audience-specific views on top — rather than five separate reports with five different versions of the same metrics.
A CEO doesn't need item-level wastage. A kitchen manager doesn't need the loyalty programme churn rate. Good multi-audience BI means each person only sees what they need — and sees it in the language of their job.
Restaurants were the starting point. The same data architecture — and deep HORECA domain knowledge — applies across the entire hospitality, foodservice, and accommodation sector.
Before this project, the owner's revenue figure sometimes differed from the sales manager's. Different people were pulling different cuts of data. One governed Power BI model means every audience is working from the same numbers — just filtered to their view.
Row-level security means a location manager only sees their location's data. The owner sees everything. The purchasing team sees chain-level raw materials but not individual customer records. Access is governed by Power BI, not by trust.
The data model was designed to accommodate new locations without rebuilding the report. When the chain opens location 9 and 10, they are automatically included in every dashboard that uses location as a dimension — no BI work required.
The chain used a popular Indian POS system. Our Power BI connector works with Petpooja, POSist, LimeTray, Revel, Square, and others. We normalise the data model regardless of source — the dashboards look and function identically whatever POS the chain uses.