Factory, dealers, and the MD's desk in one view
A PVC and CPVC pipe manufacturer sells through a statewide dealer network run by 10+ salesmen, and kept its numbers in Tally exports and month-end spreadsheets. FireAI built a live layer on the same Tally data: SKU-level margin with landed cost, a dashboard per salesman with dealer-wise aging, raw material price tracking, and a CFO cash flow view.
- 10+
- salesman dashboards
- ₹23.63 Cr
- FY2025 revenue, fully visible
- 6
- manufactured lines + traded goods
- SKU-level
- margin with landed cost
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A PVC and CPVC pipe manufacturer with ₹23.63 crore FY2025 revenue and a dealer network run by 10+ salesmen used FireAI to build a live analytics layer on its Tally data: SKU-level margin with landed cost, a dedicated dashboard per salesman with dealer-wise receivables aging, raw material price trend tracking, and a CFO view with a live cash conversion cycle.
The factory had the data. The MD had assumptions.
The company had everything a growing manufacturer needs to make good decisions: years of sales history, a full raw material purchase trail, salesman-level transaction records, and dealer-wise outstanding balances. None of it talked to each other. Every month the commercial team pulled Tally exports and tried to build a picture of the business, and by the time the picture was ready, the month it described was over.
The deeper problem was structural. Manufactured pipes and traded fittings carry different cost structures, but the two were reported together, so a blended margin figure masked which part of the portfolio was actually profitable. Items with a ₹0.00 purchase price flowed into margin reports because cost data had not been mapped, quietly inflating the apparent margin on those lines.
- No live SKU-level margin after raw material cost, GST, and freight
- No way to compare 10+ salesmen on sales, outstanding balances, and order growth
- Dealer concentration risk was known in principle but never shown as a number
- Raw material purchases were reactive, with no price trend tracking across the year
- Matching dealer outstanding to the responsible salesman took manual cross-referencing of Tally reports
- Manufactured and traded goods margins were blended, hiding true profitability
- No forward view of cash: working capital in receivables and inventory was reconstructed by hand
Tally exports and month-end spreadsheets
Tally was the system of record, and everything meaningful had to be extracted manually, reformatted in Excel, and reconciled by someone who knew where the numbers lived.
Salesman reports, consolidated by hand
Each salesman's performance came from individual Tally ledger reports, merged into one sheet manually. Target versus achievement lived in a separate file that was not linked to actual transactions.
One blended margin figure
Monthly raw material cost was divided across the whole product mix, ignoring freight, yield loss, and the different cost structures of manufactured versus traded goods, so significant variation across product lines stayed hidden.
Receivables without owners or age
Dealer-wise outstanding existed in Tally's party ledger, but tying it to the responsible salesman and computing aging buckets was a manual cross-referencing exercise, so balances sat unattributed.
A live intelligence layer across every dimension of the business
The build was structured around the four audiences that most needed clarity: the MD, the CFO, the sales team, and procurement. Each got a dedicated view on the same underlying Tally data.
FireAI connected to the company's Tally data and built a multi-tab dashboard covering sales, purchase, margin, receivables, salesman performance, and CFO-level cash flow. Nothing in the existing Tally setup was changed or replaced.
Sales and dealer dependency
Month-wise and quarter-wise gross sales tracked against prior year, with dealer dependency and order growth analysis that surfaces concentration risk and flags which dealer relationships are growing and which have gone quiet. Target versus achievement per salesman sits in the same view, tied to actual Tally transactions.
A dashboard per salesman
Every salesman gets a dedicated view of total sales, outstanding receivables, dealer-wise performance, and SKU-level contribution, all on the same template, so one review session gives the MD a complete picture across every territory.
Receivables aging by salesman and dealer
Outstanding balances rank by salesman, and the aging table beneath shows every dealer in that territory with average days to collect and amounts bucketed from 0-30 through 91-120 days, with no manual cross-referencing.
SKU-level margin with landed cost
For each SKU, the margin dashboard shows purchase price ex-GST, purchase GST, freight component, total landed cost, and GST-inclusive selling price. Manufactured and traded goods are tracked with separate cost structures, and items with missing cost data are flagged instead of flowing through as misleadingly high-margin lines.
Raw material purchase tracking
Month-on-month purchase amounts against prior year, with SKU-level quantity and average purchase price for every raw material, from PVC Resin to Calcium Carbonate, so procurement sees price trends and volume swings instead of discovering them at month end.
A CFO view with a live cash cycle
Debtor days, inventory days, creditor days, and the cash conversion cycle, alongside month-by-month cash movement, operating cash flow, capex, interest, and salary outflows, plus inventory value by category with month-on-month changes.
Why they chose FireAI
- The dashboards read directly from Tally, so no data re-entry, no parallel system, and no change to the finance team's workflow
- Manufactured and traded goods are tracked separately from day one, the single most important gap in the prior approach
- Salesman-wise drill-downs with dealer-level aging made a previously invisible layer of the business visible
- SKU-level margin with landed cost components gave the MD a real basis for pricing conversations for the first time
What became visible for the first time
Reported as what the dashboards surfaced; quantified business impact is being confirmed with the client's management.
The MD can now see in one session which salesmen are hitting target, which dealers are overdue, and which product lines earn real margin, a review that used to take most of a day and still produced an incomplete picture. SKU-level margin analysis immediately surfaced items in the CPVC and plumbing fitting categories with missing or zero purchase cost in Tally, which had been inflating their apparent margins. Dealer concentration became a number rather than a feeling, with the top two dealers' share of current year revenue quantified for the first time. Target versus achievement moved out of a separate Excel file and into a live view tied to actual transactions, and raw material tracking flagged a 47.69% month-on-month rise in purchase value in May 2026, followed by a 24.35% fall in June, as it happened rather than at month end.
How the rollout went
Three phases, live within the agreed windowThe company came to FireAI with years of transaction history in Tally and no analytics layer on top. The implementation required no change to Tally, no new data entry process, and no disruption to the finance team's workflow, and the finance team continues to work in Tally as before.
- 1
Data mapping and structure definition
Product categories, raw material SKUs, salesman codes, and the dealer master were mapped into the analytics layer. The manufactured/traded split, the core structural requirement, was defined and locked before any dashboard was built.
- 2
Dashboard build on one data layer
Sales, Purchase, Profit Margin, Outstanding Receivables, and CFO dashboards were built on the same underlying data, and the salesman dashboard was templated and replicated across all 10+ salesmen, so new salesmen onboard without a rebuild.
- 3
Validation and handover
Outputs were cross-checked against Tally source data by the client's finance team before go-live, SKUs with missing cost data were flagged for correction in Tally, and the MD and CFO were walked through their views in a single session.
Key takeaways
- A manufacturer running Tally already has every number it needs; the gap is a structured layer the MD can act on
- Separating manufactured and traded goods is a precondition for margin analysis, not a nice-to-have
- Salesman-level visibility with dealer-wise aging changes the conversation in every weekly review
- SKU-level margin is only as good as the cost data behind it, and surfacing the gaps in Tally is as valuable as the dashboard itself
- Tracking average raw material purchase price month by month is the earliest warning signal a manufacturer can have
Who should consider FireAI?
Manufacturers on Tally with a field sales team and a dealer network, where month-end reporting takes more than two days and margin, salesman performance, and raw material cost live in files that do not talk to each other.
Frequently asked questions
See margin, salesmen, and cash in one live view
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