Scheme claims paid before anyone checks the billing
Distributor health, claim checks against billing, scheme ROI and beat plans, with claims approved by amount and risk.
What goes wrong today
Primary sales look fine until a distributor stops ordering.
By the time the slowdown shows in the monthly review, the outlets have switched to a rival and the receivable is overdue. Reps visit the outlets they know best, and the ones slipping away go unvisited.
Trade schemes are the other leak. Claims arrive in bulk at month end, slabs and eligibility are checked by hand, and claimed quantities are rarely matched against what was actually billed. Stock pushed before a cut-off comes back as returns. Nobody can say which scheme lifted sales and which only pulled them forward.
Why existing approaches fall short
- ERP and DMS reports show what was billed, but they do not show whether a distributor is weakening or whether a claim matches the billing.
- Claims desks check slabs on spreadsheets, so at month end volume wins over scrutiny.
- Scheme reviews compare sales before and after, with no control group, so seasonal lift is credited to the scheme.
How FLX4 addresses it
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SalesI scores each distributor on billing regularity, payment timeliness, order frequency, secondary-sales ratio and visit compliance, and flags a sharp drop. Rule-based detectors look for claims that do not match billed quantities, return ratios that point to channel stuffing, outlets with orders and no visits, discounts below the price floor and order spikes before a cut-off.
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Claims are routed by amount and risk. Small, low-risk claims can clear under a limit you set, and the rest go to approval at one level or several. Scheme impact is measured against a control group you choose, and a budget optimizer proposes where trade spend should move next. Rep day plans rank outlets by priority and put the visits in order.
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SalesI reads distributor billing and secondary sales from DMS exports, so the scores start from data your distributors already keep.
The loop, step by step
- 1 · Predict
Score each distributor
A weighted health score and tier per distributor, with an at-risk flag when the score falls sharply.
- 2 · Predict
Check claims against billing
Claimed quantities are compared with billed transactions. Return ratios, stacked discounts and pre-cut-off spikes are flagged with an evidence score.
- 3 · Decide
Rank the actions
Visit, review, hold a claim or open an investigation. The rep's day plan puts at-risk outlets first.
- 4 · Approve
Approvers set by amount and risk
Claims above your limit go to one or more levels of approval in finance. An investigation opens automatically when the evidence is strong.
- 5 · Execute
Settle and visit
An approved claim produces a credit note and a payout record; posting to your ERP is configured per deployment. Visits are checked in against the outlet's location.
- 6 · Learn
Measure what worked
Visit outcomes are recorded against the recommendation, and each scheme's uplift is measured against a control group.
What it helps you work toward
Outcomes are measured against a baseline we agree with you in writing.
Claim leakage stopped before payout
Value of claims held, reduced or rejected after review, by scheme
Distributors kept
Distributors flagged at risk, and their health score one quarter later
Scheme spend that pays
Incremental sales per scheme against a control group
Receivables under control
DSO and overdue value by distributor
What powers this solution
SalesI
Catch the distributor that is slipping, and the claim that should not be paid, before the credit note goes out.
Explore SalesI →
PlanningNest
Order the right quantity before the festival, and know why the number is what it is.
Explore PlanningNest →
TransNest
See the late truck before your customer does, and pay only the freight your contract allows.
Explore TransNest →Integrations
Distributor billing and secondary sales arrive as DMS exports (e.g. Tally, Marg) in CSV or Excel. ERP pulls (e.g. SAP) and credit-note posting are configured per deployment. Marketplace and quick-commerce data arrive by file.
Bring us the decision behind this problem
In two weeks: the decision mapped, where money leaks, a baseline agreed in writing and a ninety-day plan with one target.
Read-only on day one. Your people approve every action. If the number does not move, you do not scale.