First operational checklist to systematically map gaps between distributor inventory and POS availability. 12 steps + KPIs to connect technical diagnosis to actual sell-through results.
"We have 15,000 units in distributor stock, but the product isn't reaching the POS." This phrase echoes in trade marketing meeting rooms across global B2B operations every Monday. The ERP report shows full inventory, the distributor confirms availability, but field teams report constant stockouts at points of sale. The result: sell-out well below potential, with millions sitting in stock that doesn't convert to sales. The problem isn't just logistical — it's systemic. Most companies treat the stock-POS gap as a point anomaly, when it's actually a symptom of structural disconnection between three layers: distributor stock policy, logistics execution, and POS performance. This checklist connects technical diagnosis of each layer to real sell-out impact, transforming analysis into measurable corrective action.
Diagnosis only generates results if you have the right data and the right people involved. The "cegueira do sell-out" (sell-out blindness) is killing forecasting accuracy and credibility for both industry and distribution. Without real POS data, there's no way to validate whether the problem lies in stock policy, execution, or demand.
Distributor stock reports: Current position per SKU + turnover history for the last 90 days + current minimum stock policy. Without these three elements, you're flying blind. Sell-in history: 3-6 months of your company's sales to the distributor, segmented by category and SKU. This allows crossing inbound vs. outbound flows and identifying where the process bottlenecks. Sell-out data (when available): POS information on actual sales to end consumers. If unavailable, diagnosis still works but with less precision in real demand validation. Anchor SKU list per category: Products representing the largest share of volume or margin. Diagnosis should prioritize these items — anchor stockouts kill entire category performance.
Can't be just "report for the drawer." Distributor commercial representative: Essential to explain internal policies, operational constraints, and feasibility of proposed changes. Field team for POS audit: Person who will physically visit points of sale for actual counting. Without on-site validation, diagnosis becomes theoretical exercise. Data/BI analyst: To cross-reference information from different systems and calculate KPIs. Manual spreadsheets work to a point, but are limited for robust diagnosis.
Spreadsheet for POS audit — can be simple, but must capture SKU, physical quantity, and date/time of count. Mobile app for field data collection speeds up process and reduces input errors. Dashboard for turnover and stockout analysis — even if it's an Excel panel, as long as it automates main calculations. Complete diagnosis takes about a week if you have prerequisites organized. If you need to collect basic data first, add more time to the timeline.
The indirect channel has historically played an irreplaceable role in Brazilian distribution strategy, but requires industry empowerment to optimize performance. This checklist structures that empowerment through technical diagnosis.
[ ] Step 1 - Baseline mapping: Collect detailed report of distributor's current stock per SKU, including turnover for the last 90 days and current minimum stock policy. The first step reveals whether the problem is in inbound (inadequate policy) or outbound (execution failure). Distributors frequently use generic minimum stock rules that don't consider seasonality or specific cycles of each category. High-turnover SKUs can't have the same policy as slow-moving products. [ ] Step 2 - Discrepancy analysis: Compare recorded sell-in vs. actual entry into distributor stock — significant discrepancies require investigation. Systematic discrepancies indicate system problems or receiving process issues. If what you sold isn't arriving integrally in distributor stock, the rest of the diagnosis may generate wrong conclusions. [ ] Step 3 - Turnover validation: Calculate turnover per SKU at distributor vs. real POS demand — low turnover with high demand signals a problem. This is where many diagnoses fail: assuming low turnover = low demand. Actually, low turnover with proven demand indicates inadequate stock policy or POS replenishment problem. [ ] Step 4 - Seasonality mapping: Validate if distributor stock policy considers seasonal variations from the last 12 months per category. Distributors who don't adjust stock for seasonality end up with excess in low periods and stockouts in high periods. Sell-out impact is devastating because seasonal stockouts are the most expensive — you lose sales precisely at peak demand moments.
[ ] Step 5 - POS audit: Conduct physical count in representative sample comparing real availability vs. distributor report. Physical audit is non-negotiable. Systems lie, people forget to update, products get poorly positioned in stores. Cross-referencing distributor reports with physical counts at POS can reveal discrepancies that impact sell-out performance. [ ] Step 6 - Replenishment timing: Measure time between confirmed sell-in and effective POS availability — long gaps indicate operational bottleneck. For high-turnover products, each delay day costs sales. If distributor confirms receipt on Tuesday but product only reaches POS on Friday, you have days of lost sell-out. [ ] Step 7 - Stockout analysis: Identify anchor SKUs with prolonged stockout periods at POS, even with available distributor stock. Stockouts in anchor items protect revenue and require specific measurement for distributors. Prolonged periods indicate systemic failure, not normal variation. [ ] Step 8 - Logistics validation: Confirm if distributor delivery schedule to POS is being met as commercially agreed. Unmet schedules become predictable stockouts. If the agreement is delivery on specific days, but in practice it happens irregularly, your stock policy is calculated wrong.
[ ] Step 9 - Loss audit: Identify products with loss/damage at distributor that impact final POS availability. Invisible losses are silent sell-out killers. The system shows stock, but in practice part is damaged, expired, or lost. Without controlling losses, you can't calibrate demand forecasting. [ ] Step 10 - ABC Curve analysis: Verify if distributor correctly prioritizes Class A SKUs in replenishment and if stock reflects commercial importance. Stock control in distributors requires specific techniques like ABC Curve, turnover, minimum stock, stockout analysis, inventory, losses and traceability. Many distributors treat all SKUs equally in replenishment, killing performance of the most important items. [ ] Step 11 - Sell-out cross-check: When available, compare real sell-out data vs. projections based on distributor stock. Final cross-check validates if your demand hypotheses are correct. If stock theoretically supports certain sell-out volume but actual sales were lower, the problem may be at POS (exposure, price) and not in stock. [ ] Step 12 - Gap report: Consolidate all identified discrepancies with quantified sell-out impact and action plan per critical gap. Final report must connect each technical gap to lost sales impact. Gap without quantified impact doesn't generate action. Action plan must have responsible party, deadline, and validation metric for each correction.
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Calculate the ROIDiagnosis is technical, but errors are human. Pressure for quick results leads to shortcuts that invalidate the entire process.
The most common error: manager requests distributor report, crosses with internal data, and draws conclusions. Managing B2B stock becomes a logistics nightmare when volume grows, requiring essential specific functionalities. Why it happens: Physical audit is work, costs time and field resources. It's easier to "trust the numbers." Real impact: Significant discrepancies between reported and actual stock go unnoticed, generating corrective actions in the wrong direction. Solution: Always combine data analysis with on-site audit in significant sample. Audit doesn't need to be total, but must be statistically valid.
Each category has its own cycle — cleaning products turn differently from personal hygiene products. Using generic rules to diagnose stockouts generates false positives. Why it happens: Operational simplification. It's simpler to apply single rule than segment by category. Real impact: False positives in gaps that are just natural business variation, wasting time on non-existent problems. Solution: Segment analysis by category with specific cycles and seasonalities. High-turnover products can't use the same ruler as slow-moving products.
Many diagnoses assume distributors will adjust stock policy according to industry demand. In practice, distributors have limited capital and their own turnover and profitability policies.
Why it happens: Lack of prior alignment with distributor about operational and financial constraints. Real impact: Incorrect conclusions about POS unavailability causes, generating unfeasible proposals. Solution: Validate if stockouts stem from inadequate policy or operational failure. If it's policy, solution may be commercial (better conditions) or logistics (flow change), not operational.
Diagnosis may reveal two problem types: poorly predicted demand (forecasting) or poorly executed flow (operations). Solutions are completely different. Why it happens: Pressure to find single culprit, when it may actually be factor combination. Real impact: Incorrectly directed corrective actions — attacking forecasting when problem is logistics, or vice versa. Solution: Distinguish gaps by origin: forecasting (wrong prediction), purchasing (inadequate policy), warehousing (losses/damages), or distribution (logistics failure). Each origin has specific solution.
Diagnosis without validation becomes academic exercise. These KPIs confirm you're on the right track.
Calculation: (Physical POS stock / Distributor reported stock) × 100 Target: Above acceptable threshold in audited sample Low rate indicates inadequate control system or failed update process. Without minimum accuracy, any optimization becomes shooting in the dark.
Calculation: % of Class A SKUs with prolonged stockout periods Target: Below low percentage of anchor SKUs Anchor stockouts are more serious than common stockouts because they kill entire category sales. Essential to keep this indicator controlled.
Calculation: Average hours between sell-in and POS availability Target: Within acceptable timeframe for high-turnover products For fast-moving products, each delay day represents significant sales loss. Must define sufficient time for distributor to process and deliver without compromising sell-out.
Calculation: (Potential sell-out - Real sell-out) / Potential sell-out × 100 Target: Consistent gap reduction after improvement implementation This is the master KPI that validates whether all diagnosis effort generated commercial results. Gap that doesn't reduce indicates you identified technical problems but didn't translate them into effective action.
Calculation: % of SKUs with turnover in adequate range at distributor Target: Majority of SKUs in optimized turnover range Very low turnover indicates excess stock (idle capital). Very high turnover indicates constant stockout risk. Must find balance between availability and capital efficiency.
Diagnosis maps problems, but only generates results if it becomes executable action plan. The biggest mistake is stopping at final report without connecting findings to specific operational changes. At Evous, the Knowledge to Action (K2A) framework transforms diagnoses like this into practical training for field teams. There's no point identifying that "replenishment timing is off ideal" if the team executing replenishment doesn't know how to optimize the process. The GTDI pillars — Gestão, Transformação, Distribuição, Insights — structure the connection between technical diagnosis and operational competence: Gestão: Organize diagnosis findings into specific playbooks per gap type Transformação: Convert technical procedures into practical field training Distribuição: Scale knowledge across all chain points (industry → distributor → POS) Insights: Monitor sell-out KPIs to validate if training generated real improvement Want to structure a stock-POS gap diagnosis that connects technical analysis to real sell-out improvement? Schedule a 15-minute demonstration to see how the K2A framework transforms diagnoses into measurable field results.
Co-founder and Product Manager at Evous. Writes about how product and the GTDI method connect knowledge to action at the commercial front line.
In 15 min we'll show how to prepare your sales team to act with the right knowledge and measure the impact in pipeline.
