DuPont

DuPont's chemical manufacturing operations span dozens of global plants where equipment reliability is critical to maintaining continuous production and meeting strict safety standards. The company's maintenance teams relied on periodic, route-based inspections — technicians moving through facilities on fixed schedules to manually assess machine health. This approach left significant gaps between inspection cycles, during which developing faults went undetected. In an industry where unplanned downtime can cascade across interdependent chemical processes and create safety exposure, reactive maintenance carried real operational and financial cost. DuPont needed a scalable, data-driven approach that could surface equipment risk continuously, not just when an inspector happened to be present.

DuPont partnered with Augury to deploy its Machine Health Solution, transitioning from schedule-based inspections to continuous AI-powered predictive maintenance. Augury's platform uses Predictive ML models trained on vibration, temperature, and acoustic sensor data to detect early signs of mechanical degradation — often weeks before failure would occur. DuPont began with proof-of-concept deployments at select sites, using this controlled rollout to validate accuracy and measure ROI before committing to broader adoption. The solution was designed for low deployment friction: sensors attach to existing equipment without process interruption, and diagnostics are delivered through a centralized dashboard. With 100% prediction accuracy demonstrated at pilot sites, leadership had the evidence needed to justify enterprise-wide expansion across all DuPont business units.

DuPont achieved 7x ROI within less than one year at its proof-of-concept sites — a payback timeline unusually fast for enterprise-scale industrial technology. Key outcomes included:

• 100% machine failure prediction accuracy with zero missed faults during the pilot period
• Elimination of unplanned downtime events at monitored sites, replacing reactive repairs with planned interventions
• Accelerated decision-making: the speed and clarity of results gave leadership confidence to approve rollout across all business units

Beyond the numbers, the implementation shifted DuPont's maintenance culture from reactive to proactive, with technicians acting on AI-generated alerts rather than fixed inspection schedules.

• A controlled proof-of-concept with clear ROI measurement is the fastest path to securing budget for enterprise-wide AI rollout in large chemical manufacturers
• Prediction accuracy matters more than prediction volume — zero misses at pilot sites was the single metric that drove executive buy-in
• Low deployment friction (no process interruption, quick setup) removes the primary barrier to scaling AI maintenance tools across a global plant footprint
• Chemical manufacturers should define ROI metrics before pilot launch so results are unambiguous and comparable across sites
• Speed-to-value under 12 months is achievable with continuous monitoring ML — organizations should not accept multi-year payback timelines as a given