FMCG companies do not lose capital because they lack ERP, BI, S&OP, DMS, sales, finance or inventory systems. They lose capital because each system sees only part of the movement. Quantos sits above this stack and closes the decision loop — turning SKU velocity, distributor inventory, primary-secondary mismatch, trade-spend drift, promo ROI decay, demand variance and working-capital pressure into forward risk, named ownership, measured outcome and learning.
The structural problem in FMCG is not data availability. It is fragmented accountability. Sales, distributor stock, secondary movement, inventory ageing, schemes, production planning, margin and cashflow are reviewed in different systems and different meetings. By the time the operator connects the pattern, the decision window has already narrowed.
ERP records billing, inventory, receivables, payables, claims and cost. It does not ask whether today’s SKU movement becomes dead stock, margin leakage or trapped cash next month.
Dashboards show sales decline, ageing stock and region performance. They do not assign an owner, verify action, score outcome or improve the next cycle.
Forecasts, schemes, production plans and seasonal assumptions are discussed. But the system rarely scores whether the plan worked and carries that learning forward.
Visits, orders, claims and follow-ups are captured. The stack does not always connect them to distributor overload, weak secondary movement or credit pressure.
Receivables, credit limits, claims, discounts and margin are tracked. But their connection to SKU velocity and channel stock is usually manual and late.
Production, depot, dispatch and transfer decisions are made with lagging signals. Quantos converts movement into action before write-off becomes inevitable.
Quantos turns FMCG operating data into deterministic risk signals across SKU velocity, distributor inventory, trade spend, demand forecasting, secondary sales, working capital, margin leakage, channel mix and supply-chain response. Same input, same output, same rupee exposure — auditable across future cycles.
Quantos compares SKU movement against demand, channel behaviour, seasonality and forecast band. When velocity breaks the band, the gap becomes dead-stock probability, exposure value and action deadline.
The system identifies when too much inventory is sitting with too few distributors, especially where secondary sales are declining. The operator separates real demand from pushed primary billing.
Quantos surfaces regions where primary orders look healthy but secondary movement, retailer pull or channel velocity is weakening — the earliest sign of future returns, discounts or blocked working capital.
Quantos tracks whether schemes improved incremental volume, market movement and margin. If spend continues without lift, the action is scored and the next decision is adjusted.
Trade spend may remain inside approved budgets and still damage margin. Quantos compares spend movement against net sales, elasticity, distributor response and actual outcome.
A stockout in one region and dead stock in another are often one structural mismatch. Quantos correlates supply, demand, depot inventory, distributor load, forecast variance and dispatch timing.
Quantos computes when stock is still recoverable, when discounting is viable, when transfer is possible and when write-off becomes unavoidable.
Receivables, distributor credit, inventory ageing, vendor payment pressure and sales quality are evaluated together to show which region, channel or SKU family is about to trap cash.
Quantos monitors whether its own predictions are weakening. When accuracy decays because market behaviour changed, it raises a risk against the forecast itself.
Modern trade, quick commerce, general trade, e-commerce, institutional sales and distributor-led retail have different margin and cash signatures. Quantos shows when growth masks deterioration.
A launch may look successful while killing an existing SKU. Quantos separates organic decline, cannibalisation, scheme-driven movement and channel substitution.
Discounts, claims, freight decisions, stock transfers, replacement schemes and credit extensions are scored against outcome so the system learns which decisions protected margin.
Quantos consumes signals from ERP, BI, S&OP plans, DMS, CRM, distributor files, inventory ledgers, production plans, finance systems and manual operating inputs. It does not replace those systems. It closes the loop they leave open: forecast → risk → action → outcome → score → learning.
Quantos reads sales, inventory, finance, promotion, supply-chain and planning data. The goal is not more data. The goal is usable truth that can be acted on.
A slow SKU, overloaded distributor, weak secondary sale or poor promo result becomes a named risk with rupee exposure, deadline, severity and source trail.
Every risk is routed to one accountable owner: CFO, sales head, supply-chain head, category manager, regional sales manager, planning lead or operations lead.
Quantos watches whether the action reduced stock, improved secondary sales, protected margin, released working capital or simply moved the problem elsewhere.
The system records whether the decision worked against the original risk. That score becomes institutional memory, not a discussion lost inside a review meeting.
The next forecast, risk score, owner recommendation and action priority are influenced by what actually happened. Every decision improves the next one.
FMCG execution is multi-owner. Quantos translates the same forward risk into the decision language of the Sales Head, CFO, Supply Chain Lead and Category Team — without forcing them into another reporting ritual.
Quantos compares primary billing, distributor inventory, secondary sales, credit ageing and scheme dependency. It flags regions where sales look healthy on billing but weak in actual consumption.
Quantos connects slow inventory, receivables pressure, distributor concentration, margin erosion and payables movement into one capital exposure view.
Quantos compares demand forecast, depot stock, distributor stock, production plan, shelf-life risk and current velocity so operators get an action window before ageing reports arrive.
Quantos links trade spend, promotional lift, post-promo decay, channel movement, SKU cannibalisation and margin effect. It tells the category team whether the action worked.
Signal → owner → action → outcome → learning. The objective is not reporting risk. The objective is reducing exposure before it becomes dead stock, trade-spend waste, margin leakage or cash pressure.
The monthly review showed stock coverage inside an acceptable range. Quantos read the same data differently: velocity breached its forecast band for three weeks, distributor concentration was rising and secondary sales were weakening. The operator got a recoverable decision window, not another dashboard.
Trade spend continued because the scheme had history and internal acceptance. Quantos linked rising scheme cost with declining incremental lift, weak repeat movement and poor post-promo retention. The risk became annualised trade-spend waste instead of a marketing opinion.
Receivables moved slightly, inventory ageing moved slightly, and one distributor cluster became slightly more concentrated. None crossed a traditional threshold alone. Quantos combined the signals into a working-capital pressure event before month-end surprise.
The planning system continued with the previous demand assumption. Quantos detected forecast-confidence decay across affected SKU families and raised a risk against the forecast itself. The issue was a structural shift in pack-size preference that needed planning recalibration.
Quantos does not require a perfect data warehouse before value starts. It can begin with the operational files and systems most FMCG companies already maintain, then strengthen the loop as data maturity improves.
Primary sales, secondary sales, order history, region, channel, distributor, outlet, SKU, category, pack size, billing and return data.
Factory stock, depot stock, distributor inventory, ageing, shelf life, batch movement, replenishment cycles and liquidation records.
Receivables, payables, credit limits, claims, discounts, margin, freight cost, vendor exposure, working-capital movement and cash-pressure signals.
Trade schemes, campaign period, spend, uplift, retailer participation, post-promo drop, distributor response and category elasticity.
Production plan, dispatch plan, capacity, raw material coverage, service levels, lead time, stock transfer, depot constraints and fulfilment delays.
Demand forecast, S&OP plans, seasonal assumptions, launch calendar, category targets, price changes and historical forecast accuracy.
Quantos will show where your FMCG software stack stops — and how closed-loop intelligence surfaces the risk, assigns the owner, measures the action and learns from the outcome.
