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    Intermediate
    E-commerce
    11 min read

    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.

    Who It's For

    E-commerce fulfillment centers, 3PLs, and retail warehouses managing large SKU counts who need faster, more accurate inventory verification

    Problem Solved

    Manual inventory counts take days, are error-prone (5-10% discrepancy rates), and require warehouse shutdowns that cost $10K+ per day in lost fulfillment

    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

    1

    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 views
    await 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, detail
    variant_grouping: true
    }
    });
    2

    Process Inventory Images

    Identify products in shelf/bin photos

    // Identify products in warehouse photos
    async function identifyInventory(imageUrl: string, location: string) {
    const detected = await client.extract({
    url: imageUrl,
    extractors: ['object-detection', 'barcode-detection']
    });
    // Match detected items against catalog
    const 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);
    }
    3

    Reconcile Against Expected

    Compare visual count to system inventory

    // Reconcile visual vs system inventory
    async 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 };
    }
    4

    Enable Continuous Monitoring

    Set up automated inventory verification

    // Automated inventory monitoring
    async function setupContinuousMonitoring(warehouseId: string) {
    // Configure camera feeds for monitoring
    await 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);
    }
    }
    });
    }

    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

    Ready to Implement This Use Case?

    Our team can help you get started with Visual Inventory Reconciliation for Warehouses in your organization.