Logistics automation platform that cut fulfillment time by 62%

Overview

A fast‑growing third‑party logistics (3PL) provider operating nine regional warehouses was struggling to keep pace with rising order volume and tighter same‑day shipping SLAs. The business had scaled customer acquisition rapidly, but operations were still anchored to paper‑based pick lists, manual barcode scanning, and siloed inventory reporting. Each facility ran a slightly different workflow, which made training inconsistent and caused order exceptions to spike during peak hours. Warehouse managers were spending as much time reconciling discrepancies as they were coordinating fulfillment.

Webskyne was brought in to modernize fulfillment operations end‑to‑end. The objective was clear: cut order‑to‑ship time, improve accuracy, and reduce the labor burden without disrupting warehouse throughput. We delivered a mobile‑first logistics platform that unified scanning, picking, packing, and shipping across facilities and connected all events to a centralized analytics layer. The result: a 62% reduction in fulfillment time, a 99.2% pick accuracy rate, and a 38% decrease in labor hours per order within 16 weeks of rollout.

Challenge

Despite strong demand, the client’s operational stack could not keep up. Their WMS provided basic inventory counts but lacked real‑time event tracking. Pickers relied on printed lists that were updated in batches, causing frequent mis‑picks when inventory levels shifted during the day. Manual exception handling added delays, especially for high‑velocity SKUs. These problems were compounded by the fact that each warehouse used different processes and tools, leading to variable performance across sites.

Key pain points included:

• Long order‑to‑ship times (average 5.2 hours during peak).
• High exception rates from manual workflows and inconsistent inventory updates.
• Limited visibility for operations and customer support teams.
• Training overhead due to non‑standard processes across warehouses.
• Unreliable scanners and printers that required frequent re‑pairing and manual resets.

Goals

The engagement focused on measurable outcomes with a four‑month delivery horizon:

• Reduce order‑to‑ship time by at least 40%.
• Improve pick accuracy to 99%+.
• Lower labor hours per order by 25%+ without sacrificing service levels.
• Introduce real‑time operational visibility for warehouse managers and client support.
• Deploy a standardized workflow across all facilities with minimal downtime.

Approach

We executed the project in two phases. Phase one delivered the core workflow engine and mobile picking app, while phase two layered on advanced analytics and exception automation. We used a modular architecture to ensure compatibility with the existing WMS and shipping carriers, allowing us to modernize operations without requiring a full system replacement.

Our approach emphasized:

• Mobile‑first workflows with offline support for low‑signal zones.
• Event‑driven telemetry to create a real‑time source of truth.
• Standardized task orchestration to reduce training time and operational drift.
• Incremental rollout to avoid facility downtime.

Solution Architecture

The new platform combined a Flutter mobile app for warehouse operators with a backend orchestration service and an analytics pipeline. The app provided role‑based tasks (pick, pack, ship, cycle count) and guided users through each step with barcode validation and guardrails. The backend coordinated tasks, synchronized inventory, and handled exceptions with automated routing rules.

Key components included:

• Task Orchestrator: A Node.js service that assigns pick paths and prioritizes urgent orders based on SLA windows.
• Event Streaming: Kafka captured all scan, pick, and pack events to power real‑time dashboards.
• Inventory Sync: Bi‑directional integration with the legacy WMS to keep counts aligned across systems.
• Device Management: A BLE manager that handles scanners/printers with automatic reconnection logic.
• Analytics Layer: Aggregations in a PostgreSQL warehouse with D3.js dashboards for operations KPIs.

Implementation

1) Workflow mapping and standardization

We conducted on‑site discovery across three warehouses to document current workflows and identify variances. From this, we produced a single standardized process map and built a task template system that could be adjusted per warehouse but kept the core flow consistent.

2) Mobile picking app with offline‑first reliability

The Flutter app was designed for rugged devices and intermittent connectivity. A local datastore cached active tasks and allowed scanning even when Wi‑Fi dropped. Once connectivity was restored, events were synchronized and reconciled to avoid double picks.

3) Device pairing and scan buffer

Barcode scanners and thermal printers often lost pairing under heavy usage. We implemented a connection manager that automatically re‑paired devices and a scan buffer that handled rapid‑fire input to prevent missed reads.

4) Event‑driven telemetry

Every scan and task completion emitted structured events into Kafka. This created a live stream of operational data and allowed near‑real‑time visibility into order status, queue backlogs, and bottlenecks.

5) Exception automation

We built rules for common exceptions (short picks, damaged goods, wrong bin location) so they could be routed automatically without manager intervention. This reduced stall time and kept pickers moving.

6) Analytics and KPI dashboards

Warehouse managers gained access to live dashboards showing pick rate, SLA compliance, backlog volume, and top exception causes. Customer support teams could see order status across all facilities without logging into the WMS.

Results

Within 16 weeks of rollout, the client observed measurable improvements across their operations:

• Order‑to‑ship time: 5.2 hours → 2.0 hours (−62%)
• Pick accuracy: 97.1% → 99.2%
• Labor hours per order: −38%
• Exception resolution time: −71%
• Customer escalations: −54%

Peak season performance improved dramatically. The business processed 1.8× average daily volume without hiring surges, and the standardized workflow reduced new‑hire training time from 10 days to 6 days.

Lessons Learned

Operational change needs tight feedback loops. The most successful improvements came from weekly feedback sessions with floor supervisors. We used their insights to refine task prompts and exception handling rules.

Offline reliability is non‑negotiable. Even small dead zones caused cascading delays in the old system. The offline‑first approach ensured scanning and task progress continued uninterrupted.

Standardization drives scalability. By unifying workflows and metrics, leadership could finally compare facility performance apples‑to‑apples and make targeted improvements.

Conclusion

Webskyne delivered a modern logistics platform that transformed a manual, fragmented fulfillment process into a unified, data‑driven operation. The client now has a scalable foundation that supports growth, reduces operational risk, and improves customer satisfaction. The new platform continues to evolve with predictive labor planning and AI‑driven demand forecasting modules currently in pilot.

Looking to modernize warehouse operations? We help logistics teams build real‑time, automation‑ready systems that unlock speed and accuracy. Let’s map your next phase.