The losses were always there.

A plant reporting an alarming 16.46% OEE wasn't underperforming, it was being measured wrong. Reclassifying mislogged 'Power Off' time and correcting quality inputs lifted the true figure to 44.70% and exposed the real bottleneck.

Seven months of monitoring took a ceramics plant from reactive maintenance to operational stability: average OEE from 37.2% to 65.6%, one line from 33% to 77%, and R76.8m of annualised recovered opportunity.

A 120-day transformation on a beverage filling line: micro-stops down 20%, an air-supply loss cut 97%, and a live shop-floor display that turned data into a culture of accountability, lifting availability through the festive peak.

A 440% surge in mechanical downtime on one cutting unit told the data a bearing or blade was nearing end of life, months before a catastrophic failure. The diagnosis pointed to 15+ hours a month of recoverable capacity.

A mill logged 3,000 downtime events a month with almost no root-cause value. Re-linking every event to a specific asset cut recorded events to 445, surfaced a single component causing 20% of downtime, and turned reactive maintenance data-driven.

A three-shift bag maker replaced manual tracking with live production and downtime data, then scaled to meet a 30% demand surge without missing a delivery.

Three identical lines, one running at half the OEE of its sisters. The data traced it to a single failure code costing 61 hours a month, and a path to double the output.

No single breakdown, yet OEE sat at 26.5%. The losses were 218,000 micro-stops lumped into one uncategorised bucket, until a change in data resolution made them visible.

A before-and-after on one line: planned downtime cut from 33% to 8% and output up 11%, while monitoring surfaced the emerging technical and quality issues to watch next.

Two packing lines fed by one tank. The data showed the filler starving while the boxing machine ran on, pinpointing a supply-synchronisation problem worth 30+ hours a month.