$2.1M Reduction in Annual Unplanned Downtime 

A discrete manufacturer was losing 14% of scheduled production hours to unplanned equipment failures. A predictive maintenance system, deployed in six weeks with zero operational disruption, brought that number to 8.7%. 

The Situation 

A PE-backed discrete manufacturer operating three production lines was experiencing unplanned downtime at 14% of scheduled production hours. The maintenance team was reactive by necessity: they responded to failures as they occurred rather than anticipating them. Equipment had sensors and a CMMS system that logged maintenance activity, but nobody was using the data to predict failures in advance. 

The PE operating partner had flagged the downtime cost in a quarterly ops review. The facility COO knew the number. The challenge was that previous attempts to address it through manual analysis had not produced a reliable early-warning system. The patterns were in the data. Nobody had a practical way to find them. 

The Approach 

The diagnostic took two weeks. The maintenance director, three shift supervisors, and the CMMS administrator were interviewed separately. Two years of work order history, equipment runtime logs, and sensor data from the three lines were extracted and reviewed. 

The data was not clean. Work order categories were inconsistent across the dataset, sensor data had gaps from calibration periods, and the CMMS had been maintained by two different teams with different logging conventions. None of that was unusual. The diagnostic team spent the second week normalizing the dataset and documenting a baseline: 14.2% unplanned downtime against scheduled production hours, at an annual cost of $3.1M calculated using the facility's own throughput records. 

The build took six weeks. A predictive model was trained on the normalized historical failure data, identifying failure precursors in equipment sensor readings and usage patterns across multiple machine types. The model was integrated directly into the maintenance dispatch workflow: each morning, maintenance supervisors received a ranked list of equipment showing elevated failure probability over the next 72 hours, along with the historical failure patterns driving each prediction. The team could act on it without changing any other part of their workflow. 

Training took one session. The interface was designed for the shift supervisor, not for a data analyst. Plain language. No statistical output. Predicted failure windows and recommended maintenance actions. 

The Outcome 

At 90 days post-deployment, unplanned downtime had dropped from 14.2% to 8.7% of scheduled production hours, a reduction of 38.7%. Against the documented baseline of $3.1M annual downtime cost, the annual value of that reduction was $1.2M in year one. 

The model continued improving. At six months, with additional failure data incorporated into the training set, the false-positive rate on failure predictions had dropped by 40%, reducing unnecessary maintenance interventions. The facility COO reported that maintenance scheduling confidence had improved measurably among supervisors who had been skeptical of the system at launch. 

The PE operating partner has since used the architecture as the template for deployments at two other manufacturing assets in the portfolio.