BusinessProcessAutomation

The Challenge

LogiTrack, a mid-size logistics company, relied heavily on manual data entry to move order information between their CRM, warehouse management system, and invoicing platform. Staff spent roughly 15 hours each week copying data between systems, which was error-prone and created bottlenecks during peak periods. They needed a reliable automation layer that could connect their existing tools without requiring a full platform replacement.

The Approach

I mapped out every manual touchpoint in the order lifecycle and designed an event-driven automation pipeline using n8n as the orchestration engine, backed by a PostgreSQL database for state tracking and audit logging. Each workflow was containerised with Docker, making deployment and scaling straightforward.

The core pipeline listened for new orders via a REST API webhook, validated the payload, enriched it with customer data from the CRM, then routed it to the warehouse management system and queued an invoice. Every step wrote a status record to the database, so the operations team could trace any order through the entire chain from a simple dashboard.

// Webhook handler for incoming orders
import express from "express";
import { validateOrder, enrichWithCRM, routeToWarehouse } from "./pipeline";
import { db } from "./db";
 
const app = express();
 
app.post("/api/orders/inbound", async (req, res) => {
  const order = req.body;
 
  await db.insert("order_events", {
    orderId: order.id,
    status: "received",
    timestamp: new Date(),
  });
 
  const validated = await validateOrder(order);
  const enriched = await enrichWithCRM(validated);
  await routeToWarehouse(enriched);
 
  res.json({ status: "accepted", orderId: order.id });
});

Monitoring and Reliability

Reliability was critical because a missed order meant a missed delivery. I built a health-check system that pinged every connected service every 60 seconds and triggered Slack alerts if any endpoint became unreachable. A dead-letter queue caught any failed webhook deliveries and automatically retried them with exponential backoff, with a final escalation to email if retries were exhausted.

The Docker-based deployment made it simple to run the entire stack locally for testing and to spin up identical staging environments. I configured automated nightly backups for the PostgreSQL database and set up Grafana dashboards so the operations team could monitor throughput, error rates, and processing times at a glance.

Results and Impact

After going live, LogiTrack eliminated 15 hours of weekly manual processing almost immediately. Order processing speed tripled because data flowed between systems in seconds rather than waiting for a staff member to enter it. The system maintained 99.7 percent uptime over its first six months, and the operations team reported a dramatic reduction in data-entry errors. LogiTrack has since expanded the automation to cover returns processing and supplier communications.