AI-Powered Distribution Analytics for CPG: Void Detection & OSA Intelligence

AI-powered distribution analytics measures where your products are actually available for purchase versus where they should be—and automates the detection, prioritization, and tracking that previously required manual data reconciliation. It encompasses tracking distribution breadth (how many stores carry the product), distribution depth (how many SKUs per store), on-shelf availability (whether authorized products are actually sellable), and execution compliance (whether merchandising and promotional plans are implemented correctly).

Distribution matters because it's the prerequisite for sales. The best brand marketing, optimal pricing, and most compelling promotions are worthless if the product isn't on shelf when shoppers are ready to buy. CPG companies typically lose 3-5% of potential sales to distribution gaps—authorized products that aren't actually available.

AI changes distribution analytics by detecting voids within days instead of weeks, distinguishing true gaps from normal sales variability, and prioritizing recovery efforts by estimated lost sales opportunity.

ACV (All Commodity Volume) distribution measures the percentage of total market sales volume represented by stores carrying your product. If stores representing 80% of category sales carry your product, you have 80% ACV distribution.

ACV weights distribution toward high-volume stores—a product in 100 stores representing 80% of market volume has higher ACV than a product in 200 stores representing 60% of market volume. This weighting matters because a distribution gap in a high-ACV store costs more in lost sales than a gap in a low-ACV store.

CPG teams prioritize ACV over numeric distribution (simple store counts) because ACV better predicts sales impact. Gaining 5 points of ACV distribution typically translates to 5%+ sales growth, assuming product velocity holds constant across new stores.

TDP (Total Distribution Points) measures distribution breadth and depth simultaneously by summing ACV across all SKUs. If you have 5 SKUs each at 80% ACV distribution, your TDP is 400. TDP captures portfolio-level distribution: adding a new SKU increases TDP even if it goes to the same stores as existing products.

TDP growth indicates either expanding store coverage (more stores carrying existing SKUs) or deepening assortment (more SKUs per store). Declining TDP signals distribution erosion—either losing store authorizations or SKU rationalizations reducing your shelf presence.

CPG commercial teams track TDP alongside ACV to understand both the breadth of distribution and the depth of assortment. A brand might maintain 90% ACV while TDP declines if retailers reduce the number of SKUs they carry—same store coverage, smaller presence per store.

Distribution is the foundation of sales performance—you can't sell what isn't available. The connection works at multiple levels.

At the macro level, distribution gains directly translate to sales opportunity. Gaining 5 points of ACV distribution typically generates 5%+ sales growth, assuming velocity holds.

At the store level, distribution gaps (voids, out-of-stocks) represent immediate lost sales. If a product averages $200/week in selling stores but isn't available in 500 stores, that's $100K/week in lost sales.

At the execution level, promotional effectiveness depends on distribution. A promotion can't drive incremental sales in stores where the product isn't available or isn't properly displayed.

AI-powered distribution analytics quantifies these connections—showing not just where distribution is, but what it's worth in dollars.

Distribution gaps have multiple root causes. Authorization gaps occur when products aren't listed at the retailer or store level—the product isn't supposed to be there. Execution gaps occur when authorized products fail to reach the shelf—due to ordering failures, receiving errors, or shelving breakdowns.

Supply gaps occur when inventory isn't available to ship—production constraints, allocation decisions, or supply chain disruptions. Demand gaps occur when products are delisted due to poor performance or retailer decisions.

Out-of-stocks specifically result from demand exceeding supply at the store level—either because replenishment didn't keep pace with sales or because inventory was allocated incorrectly.

Understanding root causes matters because the fix differs. Authorization gaps require retailer negotiation. Execution gaps require field team intervention. Supply gaps require supply chain coordination. Effective distribution analytics identifies not just where gaps exist but why.

Voids and out-of-stocks both result in empty shelves but have different causes, durations, and solutions.

A void is a store where your product has authorized distribution but isn't generating any sales—often because the product never reached the shelf, was locally discontinued without updating authorization status, or has a data registration issue. Voids persist indefinitely until actively investigated and fixed. They represent structural distribution failures.

An out-of-stock (OOS) is a temporary availability gap where a normally-selling product runs out of inventory—due to demand exceeding supply, replenishment delays, or inventory misallocation. OOS typically self-corrects when the next replenishment arrives, though extended OOS can become voids if stores stop reordering.

The distinction matters for action: voids require field investigation and intervention to identify root cause and restore distribution; OOS requires supply chain coordination to improve replenishment, forecasting, or inventory allocation. AI-powered distribution analytics distinguishes between the two by analyzing sales patterns—a store with zero sales for 4+ weeks is likely a void; a store with normal sales that dropped to zero mid-week is likely OOS.

Retail execution encompasses all activities required to make products available and visible to shoppers in stores: ensuring products reach shelves from backrooms, displays are set correctly, prices are accurate, promotional signage is placed, and merchandising plans are implemented as designed. Perfect retail execution means authorized products are on shelf, in the right location, at the right price, with planned merchandising support.

Retail execution matters because the best marketing, pricing, and promotion strategies fail if products aren't available or visible when shoppers arrive. CPG companies lose 3-5% of potential sales to execution gaps—products sitting in backrooms instead of on shelves, displays not built, promotions not implemented correctly.

AI-powered distribution analytics monitors execution continuously by detecting anomalies in expected sales patterns. If a promotion should generate 30% lift but certain stores show no lift by Day 3, those stores likely have execution failures—displays not set, product not on shelf, or signage missing. Flagging these gaps in real-time enables mid-promotion intervention rather than post-mortem analysis.

Distribution and promotion effectiveness are directly linked—promotions can only drive sales where products are available. A promotion might generate 30% lift in stores with good availability but 0% lift in stores with out-of-stocks.

This connection creates a compounding problem. High-performing promotions accelerate out-of-stocks because demand spikes exceed inventory. The promotion that should have driven $1M in incremental sales actually delivers $600K because 40% of stores ran out during the promotional period.

AI-powered analytics connects distribution monitoring to promotional calendars—flagging elevated OOS risk during promoted periods and tracking execution compliance (displays set, features run) alongside availability.

A void is a store where your product has authorized distribution but isn't generating sales—either because it never reached the shelf, went out of stock and wasn't replenished, or was discontinued locally without updating authorization status.

Voids matter because they represent immediate, recoverable revenue. Unlike distribution gains that require retailer negotiation, voids are stores where you already have the right to sell. Fix the void, recover the sales.

The challenge is detection. Identifying voids requires comparing authorization data (what should be selling) to actual sales data (what is selling), across thousands of stores, continuously. Manual analysis can't keep up. By the time someone notices a void, weeks of sales have been lost.

Traditional void detection relies on periodic reports—someone pulls authorization files, compares to sales data, builds a list, and distributes it to field teams. This happens monthly at best, often quarterly. Voids can persist for weeks before anyone knows.

AI-powered void detection automates and accelerates this process. As sales data arrives (typically weekly for syndicated, daily for some retailer feeds), AI compares selling stores to authorized stores, identifies gaps, and flags new voids immediately.

Beyond speed, AI improves accuracy by accounting for expected patterns. A store with zero sales last week isn't necessarily a void—it might be a low-volume store with lumpy sales patterns. AI learns normal variability and distinguishes true voids from noise.

The result: voids detected in days instead of weeks, with fewer false positives.

AI prioritizes voids by estimating lost sales opportunity for each gap. Not all voids are equal—a void in a high-volume store for a high-velocity SKU represents far more lost sales than a void in a low-volume store for a slow mover.

For each void, the model estimates what sales would be based on store volume, product velocity in comparable stores, and historical performance. A void estimated at $5,000/week gets attention before a void estimated at $50/week.

Effective prioritization also considers recoverability. Some voids are quick fixes (product in backroom, just needs shelving). Others are structural (store doesn't want to carry it). Prioritization should factor in likelihood of successful resolution alongside opportunity size.

The output is ranked action lists that direct field team effort toward the highest-value recovery opportunities—not just void counts that treat all gaps equally.

AI predicts and prevents out-of-stocks by connecting demand signals to supply chain actions before shelves go empty. Out-of-stocks occur when demand exceeds available inventory at the store level—due to replenishment failures, forecast errors, supply constraints, or shelf execution failures.

AI-powered analytics helps by: predicting demand spikes (especially around promotions) based on historical lift patterns, identifying stores with elevated OOS risk based on current inventory levels and velocity trends, and alerting supply chain and field teams before stockouts occur rather than after.

For promotional periods specifically, prevention means adjusting inventory allocation based on expected lift, monitoring early sell-through to catch faster-than-expected demand, and enabling mid-promotion adjustments when OOS risk emerges.

The shift from reactive to predictive: traditional OOS reporting tells you what went wrong last week; AI-powered analytics tells you what's about to go wrong this week.

On-shelf availability (OSA) measures whether products authorized and in-stock are actually available for shoppers to purchase. OSA differs from distribution (which measures store coverage) and inventory (which measures stock position). A product can have 95% ACV distribution and inventory in every store but still have 85% OSA if backroom stock isn't making it to shelves.

Traditional OSA measurement approaches include: POS-based proxies (stores with zero sales this week assumed unavailable), inventory-based calculations (stores with zero inventory assumed unavailable), and physical audits (actual shelf checks by field teams or third parties).

AI improves OSA measurement by learning expected velocity patterns and detecting anomalies that likely indicate out-of-stocks rather than natural variability. A store that normally sells 20 units/week but dropped to zero mid-week—while inventory shows stock on hand—likely has an availability problem. AI flags these patterns without requiring physical audits at every store.

Phantom inventory occurs when retailer systems show stock on hand but the product isn't actually available for purchase. Causes include: backroom inventory that never reached the shelf, receiving errors where inventory was logged but not actually received, shrink (theft, damage, spoilage), and system errors in inventory tracking.

Phantom inventory is particularly problematic because it creates false confidence. Retailer systems show the product is available, so no replenishment is triggered. Meanwhile, the shelf is empty and sales are lost.

AI detects likely phantom inventory by correlating inventory data with sales velocity. A store showing 50 units on hand but zero sales for two weeks—when comparable stores sell 20 units/week—likely has phantom inventory. These flags trigger field verification and inventory correction, focusing audit resources on stores where phantom inventory is most likely rather than spreading them thin across the entire network.

Traditional compliance tracking relies on post-event audit data—by which point the promotional window has closed and the lost sales are unrecoverable. AI-powered real-time detection uses POS velocity patterns to catch failures while they're still fixable.

If a promotion should generate 35% lift based on historical performance, and some stores show that lift while others show baseline or below-baseline sales by Day 3, the flat stores likely have execution failures. Flagging these stores within the first 3-4 days of a 14-day promotion enables mid-promotion field intervention—getting displays set while the promotion still has 10+ days to run.

The distinction matters: post-event compliance audits explain why a promotion underperformed; real-time AI detection catches failures while they're still fixable. A display set on Day 4 recovers 10 days of promotional lift. A display confirmed missing on Day 15 just explains what went wrong.

Distribution directly affects demand—you can't sell what isn't available. AI connects distribution signals to demand planning by adjusting forecasts for known distribution changes, estimating "true demand" that accounts for availability constraints, and improving forecast accuracy by explaining historical variance.

For distribution changes: AI adjusts forecasts when stores gain distribution (new volume coming online), lose distribution (volume disappearing), or have gaps (voids, out-of-stocks) that suppress sales below true demand.

For true demand estimation: AI calculates what would have sold if products were available everywhere, providing a cleaner demand signal for supply planning than actual sales (which are constrained by availability).

For new product launches, distribution build directly drives the forecast. Knowing that ACV will reach 60% in month one, 80% in month two, and 90% in month three enables realistic demand projections rather than assuming full distribution from day one.

New product distribution tracking monitors the gap between authorization (where products should be selling) and actual sales (where products are selling). The goal is identifying stores where authorized products haven't reached the shelf—and accelerating time-to-sale.

Track three metrics: authorization-to-sale velocity (how quickly authorized stores start selling—target under 3 weeks for 90%+ of stores), distribution build curve (cumulative ACV over time compared to plan), and void rate (percentage of authorized stores not yet selling at any point in time).

AI-powered distribution analytics accelerates launch tracking by comparing authorized stores to selling stores as data arrives—flagging execution gaps within the first week rather than waiting for monthly distribution reports. For a product authorized in 5,000 stores, knowing within Week 1 that 800 stores haven't started selling enables immediate field intervention rather than discovering the gap at Month 1 review.

The analysis also identifies where products get stuck: ordering (retailer systems didn't trigger orders), receiving (product shipped but not logged in), shelving (received but still in backroom), or data (product selling but not registering in POS). Each failure point routes to the appropriate team for resolution.

Lost sales from distribution gaps equals the sales that would have occurred if products were available. The calculation estimates expected sales for each gap based on comparable store performance.

For voids: Identify stores with similar volume profiles that are selling the product, calculate their average weekly sales, and apply that velocity to voided stores. If comparable stores sell $500/week and you have 200 voids, estimated lost sales is $100,000/week or $5.2M annually.

For out-of-stocks: Calculate the sales rate before the stockout, multiply by the duration of unavailability. A product selling $200/week that was OOS for 2 weeks lost approximately $400 in that store.

For the full network: Sum void opportunity + OOS losses + phantom inventory impact across all stores. Most CPG companies discover $10M+ in annual lost sales when they first quantify distribution gaps comprehensively.

AI-powered analytics improves these estimates by accounting for store-specific factors (traffic, demographics, competitive presence) rather than simple averages. Tellius scores every void by estimated opportunity, enabling prioritized recovery that focuses on the highest-value gaps first.

Initial void detection typically becomes available within 3-4 weeks of project kickoff—faster than full platform deployment because void detection requires fewer dependencies.

Once syndicated distribution data and authorization files are connected (usually the first integration milestone), basic void comparison can run immediately. The subsequent weeks add sophistication: opportunity scoring requires historical velocity data to estimate lost sales; field team workflow integration requires connecting to your execution systems; and predictive OOS capabilities require inventory feed integration.

Teams often run parallel tracks—using early void lists while the full platform continues deployment. The first void list won't have sophisticated opportunity ranking, but it will identify the gaps. Ranking improves as more data connects.

Expect: basic void detection by Week 3-4, opportunity-prioritized lists by Week 6-7, and full platform capabilities including predictive alerts by Week 8-10.

Distribution analytics connects to and complements existing systems rather than replacing them. Syndicated platforms (Nielsen Answers, Circana Unify) remain sources for distribution metrics. Retailer portals (Walmart Luminate, Kroger 84.51°) provide store-level data. Internal systems hold authorization files and shipment records.

Integration works through data flows: distribution analytics pulls from these sources to create unified visibility. Outputs can feed downstream systems—void lists pushed to field team mobile apps, OOS alerts sent to supply chain systems, compliance flags routed to retail execution platforms.

The goal is connecting fragmented data into actionable intelligence, not replacing operational systems. Teams continue using existing tools for their core functions while gaining the unified visibility they previously lacked.

Void value depends on product velocity and store volume, but typical ranges are $200-$2,000 per void per week. A high-velocity SKU in a high-volume store might represent $1,500/week in lost sales, while a slow mover in a small store might be $50/week.

Most CPG companies discover 2-5% of their authorized store-item combinations are voided at any given time. For a brand with 50,000 authorized store-item combinations (500 stores × 100 SKUs, or similar), that's 1,000-2,500 voids. At $500/week average opportunity (conservative estimate), resolving just 30% of voids recovers $7-20M annually.

The discovery is often shocking—teams assume distribution is "handled" until they see the actual void counts. The first void report typically surfaces 2-3x more gaps than anyone expected. This is the "cost of not knowing" that justifies distribution analytics investment.

Void lists without action generate no value—and field teams are already overwhelmed with priorities. Three elements drive adoption.

Prioritization by opportunity: Ranking voids by estimated lost sales ($5K/week void gets attention, $50/week void doesn't) shows you're not creating busywork. Field teams have limited time; prove you're directing it toward high-value activities.

Integration with existing workflows: Void alerts pushed to the systems field teams already use (mobile apps, CRM, territory planning tools) get acted on. Separate portals requiring new logins get ignored. Meet teams where they work.

Visible attribution: When a void gets resolved, track the recovery and credit the person who fixed it. Nothing drives adoption like demonstrating that void resolution directly contributed to someone's sales performance. Some teams gamify recovery rates across territories.

Start with one enthusiastic regional manager who will champion adoption before rolling out broadly. Their success stories sell the program better than any business case.

Effective distribution analytics delivers void lists where field teams actually work—in stores, not at desks. Mobile-optimized interfaces show prioritized void lists filtered by territory, ranked by opportunity size, with store addresses and navigation integration.

During store visits, field managers can mark voids as resolved, add notes about root causes (backroom inventory found, product discontinued, display issue), and see real-time updates as resolution status changes. Photos can be attached to document shelf conditions.

The critical design principle: if accessing void information requires returning to the office or logging into a desktop portal, adoption drops dramatically. Teams with mobile-native void lists see 3-4x higher resolution rates than teams using desktop-only reports. Push notifications for high-priority new voids enable same-day response instead of waiting for the next scheduled report review.

Void detection inherently involves a tradeoff: catch more real voids but flag more false positives, or reduce false positives but miss some real voids. Acceptable false positive rates depend on field team capacity and credibility requirements.

If your team can investigate 100 voids per week and you're generating 150 flags, a 30% false positive rate means 50 wasted investigations—frustrating but potentially tolerable if the true positives recover significant value. If capacity is 100 but you're generating 500 flags, even 10% false positives means 50 wasted investigations with 400 voids unaddressed.

Measure false positive rate by tracking field verification results: what percentage of flagged voids turn out to be valid gaps versus data artifacts, stores correctly not carrying the item (local discontinuation, seasonal authorization), or explained non-selling situations?

Most teams target 10-15% false positive rates. Higher rates erode field team trust in the void list. Lower rates may indicate overly conservative detection that's missing real voids. Tune detection sensitivity based on capacity, credibility feedback, and verification results over time.

The best AI platform for distribution analytics needs five capabilities most tools lack.

First, multi-source data integration: seamless connection to syndicated distribution data (Nielsen, Circana), retailer feeds (Walmart Luminate, Kroger 84.51°), authorization files, and internal shipment data—with automated store and product matching.

Second, intelligent void detection: AI that distinguishes true voids from normal sales variability, accounting for store volume patterns, seasonal effects, and expected velocity ranges.

Third, opportunity prioritization: every void scored by estimated lost sales so teams focus on high-value recovery, not just void counts.

Fourth, predictive alerts: OOS risk flagged before stockouts occur, especially during promotional periods when demand spikes.

Fifth, conversational access: sales and supply chain users asking questions in plain English and getting immediate distribution intelligence.

Tellius is purpose-built for CPG distribution analytics and delivers all five capabilities.

The best AI platform for void detection combines speed, accuracy, and prioritization. Detection speed means identifying voids within days of occurrence rather than weeks. Accuracy means distinguishing true voids from stores with naturally lumpy sales patterns. Prioritization means ranking voids by estimated lost sales opportunity.

Tellius excels at all three. Automated void detection runs as syndicated and retailer data arrives, flagging gaps immediately. Machine learning accounts for expected sales variability, reducing false positives. Opportunity scoring estimates weekly lost sales for each void based on store volume and product velocity.

The output is prioritized action lists that field teams can execute against—not just void counts that require additional analysis to become actionable. Integration with field team workflows means void lists flow directly to mobile apps for store visits.

The best AI platform for OSA tracking combines multiple data signals to infer availability without requiring physical audits at every store. POS velocity patterns, inventory positions, promotional execution, and historical OOS patterns all contribute to AI-based availability estimates.

Tellius integrates these signals to flag likely out-of-stocks: stores where sales velocity dropped unexpectedly, stores with declining inventory trends, stores where promotional lift isn't materializing. This AI-inferred OSA is more scalable than physical audits while still actionable.

For high-priority situations—major promotions, key accounts, new product launches—Tellius OSA alerts can trigger targeted physical verification, focusing audit resources where they matter most rather than spreading them thin across all stores.

Walmart Retail Link provides Walmart-specific distribution and inventory data—store-level sales, inventory positions, and out-of-stock metrics for products sold at Walmart. It's essential for Walmart business management but limited to one retailer.

Tellius integrates Retail Link data alongside Nielsen/Circana syndicated data, Kroger 84.51°, and other retailer feeds to provide cross-retailer distribution visibility. This matters because distribution gaps at one retailer can't be seen in isolation—a void at Walmart might be offset by strong performance at Target, or might indicate a broader execution problem affecting multiple accounts.

Additionally, Tellius adds capabilities Retail Link lacks: opportunity prioritization (ranking voids by estimated lost sales across your entire network), predictive OOS alerts (flagging risk before stockouts based on velocity trends), and conversational access (asking questions in plain English instead of navigating reports).

Teams use Retail Link for Walmart-specific operational data while using Tellius for strategic distribution intelligence across their entire retail footprint.

Retail execution platforms like Repsly, Trax, and GoSpotCheck focus on field team workflow: scheduling store visits, capturing shelf photos, recording audit data, and managing merchandising compliance. They're designed for in-store work and generate valuable execution data.

Tellius focuses on distribution intelligence: detecting voids across thousands of stores using sales data, prioritizing gaps by opportunity size, and predicting OOS risk before it occurs. It's designed for analytical decision-making rather than field workflow management.

The two are complementary: Tellius identifies which stores need attention and prioritizes by impact; retail execution platforms manage the field team's response. Some organizations integrate both—Tellius void alerts trigger tasks in Repsly or Trax, and field verification data flows back to improve Tellius detection accuracy.

The combination provides both strategic visibility (what's wrong across the network) and operational execution (how to fix it store by store).

Excel-based distribution tracking is common but has three fundamental limitations.

First, data integration: Reconciling syndicated distribution data, retailer feeds, and authorization files requires manual work every reporting cycle. What takes hours in Excel happens automatically in Tellius as data arrives.

Second, detection accuracy: Excel comparisons flag every store with zero sales, including low-volume stores with naturally lumpy patterns. AI-powered detection learns expected variability and distinguishes true voids from noise, reducing false positives by 50%+.

Third, prioritization: Excel can sort voids by store volume, but estimating lost sales opportunity for each void requires modeling that's impractical in spreadsheets. Tellius ranks every void by estimated weekly sales impact so teams focus on high-value recovery.

Excel works for small-scale tracking (a few hundred stores, monthly reviews). Beyond that scale—or when faster detection and smarter prioritization matter—purpose-built analytics outperforms spreadsheets significantly.

Request a void detection pilot using your actual authorization file and syndicated distribution data. A credible vendor will accept 30-60 day access to your data and return a prioritized void list with opportunity estimates.

Validate the results by:

(1) Spot-checking 20-30 flagged voids—are they real gaps or false positives? Send field teams to verify. If 25% or more are false positives (stores correctly not selling due to local discontinuation, data errors, or intentional store-level exclusions), the detection logic needs tuning.

(2) Comparing void counts to your existing process—do they find gaps you missed? If the vendor's list substantially matches your current void tracking, they're not adding detection capability.

(3) Evaluating opportunity estimates—do their velocity assumptions align with your actual performance data? Inflated opportunity estimates look good in demos but create credibility problems with field teams.

A vendor confident in their detection capabilities will accept this challenge. One that insists on contract signing before demonstrating with your data may be hiding capability gaps.

Start with a priority brand or category: Choose one where you have good data coverage across syndicated and key retailer sources, and where distribution gaps are a known concern. Proving value in one area builds momentum.

Assess data readiness: Confirm you can access syndicated distribution data (Nielsen or Circana), at least 2-3 major retailer feeds, and internal authorization files. Gaps in authorization data are common blockers—you can't identify voids without knowing what should be distributed.

Define success metrics: What does "working" look like? Typical metrics include number of voids identified, estimated lost sales recovered, and time-to-detection improvement.

Identify pilot users: Select field sales managers or key account leads who will act on void lists. Their engagement turns insights into recovered sales.

Plan for workflow integration: Void detection is only valuable if someone acts on it. Define how alerts reach field teams and how resolution gets tracked.

Nielsen provides syndicated distribution metrics (ACV, TDP, numeric distribution) as part of their retail measurement service. These metrics are essential inputs but have limitations for operational distribution management.

Nielsen data updates weekly or monthly at market/retailer level—not store level. It shows aggregate distribution trends but doesn't identify specific voided stores or prioritize by opportunity. And it doesn't connect to authorization files to distinguish "not distributed" from "should be distributed but isn't selling."

Tellius ingests Nielsen data as one input alongside retailer-specific data, authorization files, and internal sources. It transforms aggregate distribution metrics into store-level void detection, opportunity prioritization, and predictive OOS alerts.

Teams use Nielsen for market-level distribution tracking and trend analysis while using Tellius for operational void recovery and execution management.

Sales and Key Account Managers use distribution analytics to identify opportunities and gaps by retailer, prepare for business reviews, and track execution compliance. Their questions include "Where are we losing distribution at Walmart?" and "Are our new items getting authorized stores on shelf?"

Field Sales teams use it to prioritize store visits, focus on high-value voids, and verify that promotional displays are set. They need store-level action lists ranked by opportunity.

Demand Planning uses distribution data to improve forecasts—knowing where products will (and won't) be available affects demand projections.

Supply Chain uses distribution signals to optimize inventory allocation—positioning stock where sell-through is happening and avoiding overstock where distribution is weak.