Visual Inventory Reconciliation for Warehouses
For e-commerce warehouses with 50K+ SKUs. Automate inventory verification using visual AI. 95% accuracy, 80% reduction in count time.
E-commerce fulfillment centers, 3PLs, and retail warehouses managing large SKU counts who need faster, more accurate inventory verification
Manual inventory counts take days, are error-prone (5-10% discrepancy rates), and require warehouse shutdowns that cost $10K+ per day in lost fulfillment
Ready to implement?
Why Mixpeek
95% identification accuracy, 80% reduction in count time, and continuous inventory monitoring without operational disruption
Overview
Inventory accuracy is critical for e-commerce profitability. This use case shows how Mixpeek enables visual inventory verification that is faster, more accurate, and less disruptive than traditional counts.
Challenges This Solves
Count Duration
Full physical counts take 2-5 days for large warehouses
Impact: $10K-50K per day in lost fulfillment capacity
Human Error
Manual counts have 5-10% error rates
Impact: Overselling, stockouts, customer dissatisfaction
SKU Proliferation
Thousands of similar products hard to distinguish
Impact: Wrong items picked, returns increase 15-20%
Cycle Count Burden
Continuous cycle counting requires dedicated staff
Impact: Labor costs, staff pulled from fulfillment
Implementation Steps
Mixpeek analyzes images from warehouse cameras and handheld devices to identify products, count quantities, and flag discrepancies against expected inventory
Build Product Visual Catalog
Index all products with visual fingerprints
import { Mixpeek } from 'mixpeek';const client = new Mixpeek({ apiKey: process.env.MIXPEEK_API_KEY });// Index product catalog with multiple viewsawait client.buckets.connect({collection_id: 'product-catalog',bucket_uri: 's3://catalog/product-images/',extractors: ['image-embedding', // Visual similarity'barcode-detection', // UPC/EAN codes'text-extraction', // Product labels'color-analysis', // Color variants'dimension-estimation' // Size estimation],settings: {sku_field: 'sku',images_per_product: 4, // Front, back, side, detailvariant_grouping: true}});
Process Inventory Images
Identify products in shelf/bin photos
// Identify products in warehouse photosasync function identifyInventory(imageUrl: string, location: string) {const detected = await client.extract({url: imageUrl,extractors: ['object-detection', 'barcode-detection']});// Match detected items against catalogconst identified = await Promise.all(detected.objects.map(async (obj) => {const match = await client.retrieve({collection_id: 'product-catalog',query: {type: 'image',embedding: obj.embedding},limit: 1});return {bounding_box: obj.bbox,sku: match[0]?.sku,confidence: match[0]?.score,quantity: obj.count || 1,location: location};}));return identified.filter(i => i.confidence > 0.85);}
Reconcile Against Expected
Compare visual count to system inventory
// Reconcile visual vs system inventoryasync function reconcileInventory(visualCount: InventoryItem[],systemCount: InventoryItem[],location: string) {const discrepancies = [];for (const item of systemCount) {const visualItem = visualCount.find(v => v.sku === item.sku);const visualQty = visualItem?.quantity || 0;if (Math.abs(visualQty - item.quantity) > 0) {discrepancies.push({sku: item.sku,location: location,system_qty: item.quantity,visual_qty: visualQty,variance: visualQty - item.quantity,action_required: Math.abs(visualQty - item.quantity) > item.threshold});}}// Items seen but not in system (potential putaway errors)const unexpected = visualCount.filter(v => !systemCount.find(s => s.sku === v.sku));return { discrepancies, unexpected };}
Enable Continuous Monitoring
Set up automated inventory verification
// Automated inventory monitoringasync function setupContinuousMonitoring(warehouseId: string) {// Configure camera feeds for monitoringawait client.streams.connect({stream_id: `warehouse-${warehouseId}`,sources: [{ camera_id: 'aisle-1', interval: '1h' },{ camera_id: 'aisle-2', interval: '1h' },// ... more cameras],on_frame: async (frame) => {const inventory = await identifyInventory(frame.url, frame.location);const system = await getSystemInventory(frame.location);const { discrepancies } = await reconcileInventory(inventory, system, frame.location);if (discrepancies.some(d => d.action_required)) {await alertInventoryTeam(discrepancies);}}});}
Feature Extractors Used
Retriever Stages Used
Expected Outcomes
95% product identification accuracy
Identification Accuracy
80% reduction in physical count duration
Count Time
From 90% to 98%+ inventory accuracy
Inventory Accuracy
40% reduction in inventory shrink
Shrink Reduction
$200K+ annual savings for large warehouses
Labor Savings
Frequently Asked Questions
Related Resources
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Ready to Implement This Use Case?
Our team can help you get started with Visual Inventory Reconciliation for Warehouses in your organization.
