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Fashion & ApparelIntegration + Monitoring6 weeks

Real-time Inventory Sync with Reconciliation

99.2%
Sync Accuracy94% reduction in manual reconciliation

Context

Client Stack + Scale

A mid-market fashion retailer operating a direct-to-consumer model through Shopify Plus, with NetSuite as their ERP system. The company manages seasonal inventory across 8,000 SKUs with frequent product launches and restocks.

Revenue Band
$15M–$25M annually
SKU Count
8,000 active products
Order Volume
1,200–1,800 orders/day peak
Team Size
4-person ops team
Tech Stack
ShopifyNetSuiteNode.jsPostgreSQLRedis

The Problem

What was breaking

Every morning at 6 AM, the operations team started their day with a 2-hour manual reconciliation ritual. They would export inventory reports from NetSuite, cross-reference them against Shopify's inventory levels, and manually adjust discrepancies in a spreadsheet. Twice a month, a customer would successfully order a product that was actually out of stock in the warehouse, resulting in fulfillment delays, customer service escalations, and refunds. During peak season (November–December), these incidents increased to 8–10 per week. The root cause: inventory updates were synced via a nightly batch job that would silently fail when NetSuite's API rate limits were exceeded. No alerts, no retries – just quiet data drift that compounded over time.

Daily 2-hour manual reconciliation window before operations could begin

Overselling incidents causing customer dissatisfaction and lost revenue

No visibility into sync failures or data drift

Batch job failures during high-volume periods with no automatic recovery

Ops team spending 40+ hours/month on inventory reconciliation instead of strategic work

The Solution

Architecture + Approach

We replaced the fragile batch job with an event-driven sync pipeline that processes inventory changes in real-time and includes automated reconciliation, drift detection, and self-healing capabilities.

Architecture Overview

1

Event-driven sync pipeline using NetSuite webhooks and Shopify Admin API

2

Redis queue with retry logic and exponential backoff for rate limit handling

3

PostgreSQL as the source-of-truth reconciliation layer

4

Scheduled reconciliation jobs (every 4 hours) to detect and auto-correct drift

5

Monitoring dashboard with Slack alerts for sync failures and drift detection

Technical Details

The sync pipeline operates in two modes: real-time event processing and scheduled reconciliation. When inventory changes in NetSuite, a webhook triggers our Node.js service, which validates the payload, checks idempotency (to prevent duplicate updates), and queues the update to Shopify with retry logic that respects rate limits. Every 4 hours, a reconciliation job runs that compares NetSuite inventory levels against Shopify, logs any discrepancies greater than 2 units, and auto-corrects them if they fall within defined tolerances (e.g., differences < 10 units are auto-fixed, larger discrepancies trigger human review). The system maintains a PostgreSQL audit log of every sync operation, enabling full traceability and debugging. All failures trigger Slack alerts with context, and the dashboard shows real-time sync status, latency metrics, and drift history.

The Results

Measurable Impact

99.2%
Sync Accuracy

Measured as percentage of SKUs with < 2 unit variance between systems over 90-day window

< 15 sec
Avg. Sync Latency

From NetSuite inventory change to Shopify update, including queue processing time

94%
Manual Work Reduction

Operations team now spends 2.5 hours/month on reconciliation instead of 40+ hours

0
Overselling Incidents

Zero overselling incidents in the 6 months post-launch (vs. 8–10/month during peak previously)

Additional Outcomes

Ops team reallocated saved time to demand forecasting and supplier relationship management

Reduced customer service tickets related to out-of-stock issues by 87%

Enabled same-day inventory updates for product launches instead of waiting for nightly batch

Complete audit trail allows troubleshooting fulfillment issues in minutes instead of hours

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