Machine Data Integration & Materials Tracking Platform

An export-focused garment manufacturer operating at significant scale needed to improve traceability and control across raw-material handling and fabric processing. Manual recording, slow internal logistics, and opaque machine output were constraining throughput and creating avoidable operational risk.

The challenge

The client’s materials handling process relied heavily on notebooks, manual weighing and labour-intensive separation activities. Accuracy depended on human input, making variance hard to detect and reconciliation time-consuming. Material movement between machines, containers and warehouses lacked real-time visibility, driving delays and bottlenecks.

Although the factory had already invested in machinery and sensors, the machine output was encoded and not directly usable in operational systems. The business required a dependable way to translate machine signals into structured, validated data that could support planning, inventory control and quality assurance.

The solution

We designed and delivered a bespoke platform to capture, decode and govern machine-generated data, then integrate it into the client’s existing ERP environment. A dedicated ingestion and decoding service translated encoded sensor output into structured records, which were validated and mapped to business rules for materials classification, allocation and movement.

The solution was implemented as a modular microservices architecture with queue-based processing for resilience and scale. This approach allowed separate services to handle ingestion, decoding, validation and tracking while providing a single operational view across production and warehousing.

Delivery was phased over approximately eight months, with calibration and accuracy tuning completed in the live environment. Operational acceptance focused on repeatable data quality, clear exception handling and predictable performance during peak material flows.

The results

The platform delivered a step-change in visibility and efficiency across materials tracking and internal logistics:

• Labour requirements in the targeted materials-handling area reduced from several thousand workers to a few hundred, enabling redeployment to higher-value activities.
• Truck turnaround improved from two to three days of waiting to same-day processing measured in hours.
• Material accuracy increased materially once sensor calibration stabilised, reducing inconsistencies inherent in manual recording.
• Management gained real-time insight into material usage, warehouse allocation and production inputs, improving planning, throughput and cost control.
• A scalable foundation was established for future capabilities such as alerts, mobile notifications, expanded inventory management and deeper supplier/buyer integration.

Next phase

Following successful machine data integration and materials tracking, the roadmap focuses on extending the platform into broader inventory and operational management: real-time stock across warehouses, automated notifications and mobile access for operational teams, and integrated address books for supplier and buyer workflows.