Darigold

Darigold, a leading Northwest dairy cooperative, operates multiple processing facilities where unplanned equipment failures carry outsized consequences — a single line stoppage can ripple across perishable product schedules and erode already-thin margins. Like much of the food and beverage sector, the company faced a convergence of rising operational costs, persistent labor shortages, and global economic uncertainty. Its maintenance organization was still largely reactive: faults in fans, pumps, and other critical rotating equipment went undetected until failure, triggering emergency repairs, contractor scrambles, and costly production delays. Without visibility into asset health between scheduled PMs, the team had no reliable way to act before a breakdown occurred.

Darigold partnered with Augury to deploy Machine Health — a continuous vibration monitoring platform combining IoT sensors with AI-powered diagnostics. The team began with a structured pilot, using criticality ranking to identify the 34 assets where a failure would immediately halt production. Augury sensors were mounted on these machines to stream real-time vibration data, which the platform's AI analyzes against failure signatures for bearings, shafts, fans, and other rotating components. Crucially, the system provides component-level specificity: rather than flagging a general anomaly, it identifies which bearing race or shaft section is degrading. Alerts are routed to the maintenance team with enough lead time to create work orders, source long-lead parts, and schedule contractors — shifting the workflow from emergency response to planned, precision repairs.

Within six months of deployment, Darigold's results validated the business case decisively. The program delivered 105 hours of production downtime avoided and nearly $1 million in cost avoidance — all from monitoring just 34 assets. One early detection stands out: Augury sensors flagged a bearing anomaly on a critical fan before any symptoms were visible. The team validated the data over two days and executed a planned repair, avoiding what could have been a 12+ hour production shutdown. Beyond the financials, the qualitative shift was significant. Maintenance technicians no longer fielded emergency calls in the middle of the night, reducing burnout and improving team retention. Leadership approved expansion of the program to additional facilities.

• Criticality-first deployment accelerates ROI: Starting with the 34 assets most likely to halt production maximized impact and built internal confidence quickly.
• Component-level diagnostics eliminate guesswork: Knowing which specific bearing race is failing — not just that a machine is degrading — enables targeted, cost-efficient repairs.
• A validation window builds trust: Darigold's practice of confirming sensor alerts over 1–2 days before acting reduced false-positive anxiety and helped the team adopt the technology.
• Human impact matters as much as cost savings: Reducing off-hours emergency calls improved maintenance team wellbeing — a retention factor worth quantifying alongside financial metrics.
• Pilot scope determines expansion speed: A tightly scoped, well-instrumented pilot produced results fast enough to justify facility-wide rollout within the same fiscal year.