The challenge
A manufacturer serving construction, mining and agriculture ran three plants, 1,200+ B2B clients and $350M+ in revenue on fragmented legacy systems. Reactive maintenance caused around 12 hours of unplanned downtime a month and $1.8M in annual losses. Manual order tracking created delays and errors, supplier performance was inconsistent, and siloed systems slowed every cross-plant decision.
Our approach
We delivered a centralised ERP without disrupting live manufacturing. The hardest parts were integrating IoT data from several equipment makers into one predictive model, migrating five legacy systems cleanly, and bringing plant teams along through the change. We rolled out in stages, plant by plant, so operations never stopped.
What we built
- A predictive maintenance module using IoT sensors to flag failures 10 to 14 days ahead
- A centralised order management system with automated scheduling, live tracking and dispatch alerts
- A supplier control panel with performance scoring, delivery tracking and automated purchase orders
- Multi-plant integration for unified visibility and decisions across all three facilities
Maintenance went from firefighting to planned work, and the savings showed up on the bottom line within two quarters.
The results
Within six months, unplanned downtime fell 72% and order-fulfilment accuracy rose from 84% to 98%. Supplier delays dropped 65%, decision-making sped up 40%, and predictive insights cut annual maintenance costs by $1.2M. The ERP architecture now scales to five or more plants.
Technologies: IoT · Predictive analytics · Cloud ERP · Multi-plant integration. Client identified on request under NDA. Figures are this engagement’s measured outcomes.