ManuFuture at Purdue is a first-of-its-kind applied research ecosystem. Factories running legacy systems — whether large or small — lack real-time visibility into their own operations. It's not that real-time visibility is impossible. It's too expensive and too fragmented for legacy industry.
We've built three proven building blocks to change that:
1. Low-cost, open platform technology — the OAK sensor kit ($35), open-access, no subscriptions, privacy-preserving. Makes real-time visibility affordable for any factory.
2. Field-tested knowledge — 70+ factory projects with 30 manufacturers since 2018. We observe what works and what doesn't inside real manufacturing operations, capture those learnings, and make them available to others.
3. Peer learning network — manufacturers learn from each other by doing. Not webinars — real projects with real results.
The research network spans 14 faculty across Purdue, MIT, and Harvard, with 32 peer-reviewed publications. Our AI capabilities include anomaly detection, predictive maintenance, machine learning on sensor data, data pipeline architecture, and real-time dashboards — all deployed on real factory floors, including legacy machines dating to the 1980s. Kirby Risk, Steel Technologies, and others are aligning strategic investments with this work. Peer learning has been rated 4.9/5 across seven industry workshops.
1. Steel Technologies (TMF) — Visibility at a Fraction of Market Cost
Through the ManuFuture network, Steel Technologies implemented operational visibility systems at a fraction of market cost, served as a test bed for advanced AI researchers deploying solutions on low-cost technology, and learned from peers as they optimized their business.
2. Root Cause Analysis for Complex Manufacturing Processes
We combine causal and statistical analytics, physics-based simulation (CFD), and new sensor deployment to help manufacturers connect cause and effect in processes where cycle times and environmental variability make defect tracing difficult. Three Purdue teams work iteratively: statistical correlation narrows the focus, simulation reveals interior physics, and new sensors fill measurement gaps. Each capability sharpens the others. This approach is currently deployed with an Indiana manufacturer on a complex thermal process.
3. Kirby Risk — From Exposure to Adoption
Our engagement with Kirby Risk illustrates the catalytic effect of university partnership. After exposure to ManuFuture's monitoring capabilities and data analytics approaches, Kirby Risk independently adopted Machine Metrics for commercial monitoring and implemented Kanban process innovations — outcomes directly traceable to the visibility our collaboration provided into what was possible. Kirby Risk doubled its productivity and saw a 97% drop in customer past-dues.