Six years of Excel reports, now one live layer
A Dubai-based electronics distributor ran five profit centers across two ERP systems, assembling month-end reports by hand from dozens of files. Gross profit could not be compared across entities, and intercompany sales inflated group revenue. FireAI unified the entire data estate into one live intelligence layer, without replacing a single existing system.
- 5
- entities on one dashboard
- 12,000+
- SKUs tracked with margin
- 6 years
- of history unified
- 4-step
- GP cost logic automated
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A Dubai-based electronics distributor used FireAI to replace six years of hand-assembled Excel reports with one live intelligence layer spanning five profit centers and two legal entities. Group gross profit is now calculated on a standardised cost base, intercompany sales are stripped from external performance, and inventory KPIs across 12,000+ SKUs compute automatically.
They had data. What they did not have was visibility.
The group runs five profit centers across two legal entities and two ERP systems, distributing more than 12,000 SKUs through walk-in sales, B2B projects, direct container shipments, export invoices, and online marketplaces. Every entity generated its own transaction records independently.
Month-end reporting meant assembling dozens of files by hand: pulling item-level sales for each entity, reconciling returns, mapping cost prices, and rebuilding a comparative view in Excel. By the time the report was ready, the month it described was already history, and management was deciding on data that was weeks old, incomplete, and inconsistent across the entities it was meant to represent.
- Gross profit could not be calculated consistently across entities; each used its own cost price, so margins were incomparable
- Intercompany sales were mixed with external sales, systematically overstating group revenue
- Customer analysis was flat: no view of which accounts were growing, churning, or taking deep discounts at thin margins
- Inventory sat in a separate system, with no live stock value, turnover, or dead-stock alerts
- Multi-year sales trends required cleaning seven years of annual files and rebuilding a pivot table from scratch each time
What they tried before
The workflow was built entirely on manual exports and Excel, and the picture had to be rebuilt by hand every month.
Manual exports into Excel
Each month, entity-level sales-invoice and sales-return reports were exported from the ERP, formatted, and analysed separately, with cost prices taken as-is from each showroom's own records rather than mapped back to a common purchase cost.
Monthly stock-valuation snapshots
Inventory lived in standalone files, one per month per year going back to 2019. Turnover, days on hand, and dead-stock analysis were never computed because joining files across periods was too time-consuming to do regularly.
A CRM disconnected from sales
The deal pipeline was maintained in Zoho but never connected to sales data, so deal owners could not compare pipeline against actual revenue or historical customer behaviour.
One analytics layer over the entire data estate, nothing replaced
FireAI unified eight years of transaction history, monthly stock-valuation snapshots, a group-wide customer master, an L1/L2/L3 product taxonomy, the CRM pipeline, and the after-sales system, with filter logic built around the group's real entity structure.
Rather than replace any existing system, FireAI read from all of them and consolidated five profit centers into a single analytics layer. The build was organised around three design principles specific to the group's structure.
A master dashboard that respects the entity structure
Every transaction is classified as internal, external, or second-entity-specific, so each number on the dashboard respects the legal and operational boundaries of the group instead of blurring them together.
Four-step gross-profit cost mapping
For every transaction the system finds the HQ cost price for the same SKU and period; failing that, the HQ stock-valuation WAC; failing that, a make-based adjustment of the shop cost. Zero-cost service items are excluded. The result is two GP figures per transaction, location-level and group-level, side by side.
Entity dashboards with full history
Each of the five profit centers gets its own dashboard: a revenue and margin trend from 2020 to today, a multi-year customer heatmap, brand and category analysis on the L1/L2/L3 taxonomy, returns analysis with threshold alerts, an intercompany flag layer, and a day-wise sales table.
Inventory KPIs computed automatically
Six years of monthly stock-valuation files are unified into one trend view, with inventory turnover, days on hand, GMROI, dead-stock value, and sell-through rate calculated automatically instead of never.
Why they chose FireAI
- It unified the entire data estate without replacing or modifying a single existing system
- Filter logic and cost mapping were built around the group's actual entity structure, not a generic template
- The full analytical logic was specified and approved upfront, so the numbers were trustworthy from day one
A consolidated intelligence layer that did not exist in any form before
Reported as capability and operational impact; exact financial figures kept internal.
Group gross profit is now calculated on a standardised cost base for the first time, enabling true entity-to-entity comparison. Intercompany revenue is stripped from external metrics automatically, so management sees real market performance instead of inflated group numbers. The all-years customer heatmap immediately surfaced accounts that had stopped buying, accounts growing consistently, and one-time buyers never retained, while a discount-versus-margin view exposed customers taking heavy discounts at thin margins, prompting renegotiation of specific accounts.
For the first time, we can see exactly what each entity is contributing to group profit, not just group revenue. The difference between the two was something we suspected but could never quantify until FireAI made it visible.
How the rollout went
Live within days of an approved specThe implementation began with a structured data audit across every source file, and no existing system was replaced or modified. The full analytical logic was documented and approved before any code was written.
- 1
Structured data audit first
Sales reports, returns, stock-valuation snapshots, the customer master, product taxonomy, inventory history, CRM deals, and service job cards were audited before development began.
- 2
The wireframe was the product
Every KPI, formula, data source, chart, filter, and insight was documented in a multi-tab Excel wireframe that served as the complete specification, so the client approved the full logic before a single query was written.
- 3
Cost logic validated against real data
The four-step GP cost mapping was validated against actual transactions before build, with dual GP fields, a cost lookup table, and a monthly WAC table specified in full.
- 4
Delivered in a single release
All five entity dashboards, the master dashboard, the inventory tab, and the deals and after-sales tab shipped together.
Key takeaways
- Multi-entity businesses cannot rely on entity-level reporting alone; without a consolidated layer that respects intercompany flows and standardises cost, decisions rest on numbers that overstate revenue and misrepresent profit
- Gross profit means different things at location level and group level; both views are valid, and a platform that shows only one gives half the picture
- Six to eight years of transaction history, once unified and queryable, becomes trend, cohort, and churn analysis at the same time
- A dashboard is only as good as its logic; the cost mapping, intercompany flag, and segmentation rules are what make the numbers trustworthy
Who should consider FireAI?
Distribution or trading businesses operating across multiple entities, channels, or geographies, where management depends on manually assembled reports and has no single consistent view of revenue, margin, and inventory.
Frequently asked questions
Replace your manual reports with live intelligence
If your revenue, margin, and inventory live in separate ERPs and stock files that do not talk, FireAI can unify them into one live intelligence layer, without replacing your existing systems. Book a demo on your own data.
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