Replicating a Proven Workflow Across Three Portfolio Companies 

60% faster deployment on each successive implementation. Consistent, measurable outcomes across three separate operating companies. 

The Situation 

A PE operating partner overseeing three manufacturing portfolio companies in the same sub-sector needed to demonstrate AI-driven EBITDA improvement across the portfolio within a compressed timeline. Each company ran similar production operations. Each had the same core problem: unplanned downtime events were costing significantly more than the operations team could account for using existing maintenance schedules. 

The fund had previously evaluated a platform AI vendor whose implementation timeline was 14 months per company. At that pace, only one company would have a deployed system before exit preparation began. That approach was ruled out. 

The Approach 

We started with one company. Two weeks of diagnostic work: structured interviews with the maintenance director and three line supervisors, analysis of two years of CMMS work order data, and a quantification of downtime cost using actual production records. The diagnostic identified predictive maintenance scheduling as the highest-ROI workflow and documented a measurable baseline: 14% unplanned downtime against scheduled production hours, at a calculated cost of $1.8M annually. 

Design and build took six weeks. The model was trained on historical failure data from the CMMS and integrated directly into the existing maintenance dispatch workflow. No new systems were required. The team's daily routine did not change. The system surfaced predicted failure windows 72 hours in advance, allowing maintenance to schedule proactively rather than respond reactively. 

After 90 days, results were measured against the documented baseline. Unplanned downtime had dropped from 14% to 8.8% of scheduled production hours, a reduction of 37%. The annual value of that reduction was $1.1M on that facility alone. 

With the architecture documented and the results verified, the second company deployment took four weeks instead of six. The third took three. The data model required retraining on each facility's historical data, but the integration approach, the deployment sequence, and the change management process were identical. 

The Outcome 

All three companies had live systems within 13 weeks of the first engagement starting. Total timeline from first diagnostic to third deployment: four months. The portfolio-level impact, measured at 90 days post-deployment at each facility, was a reduction in aggregate unplanned downtime cost of approximately $2.7M annually across the three companies. 

The operating partner now has a documented playbook for deploying the same system at any new manufacturing acquisition in the portfolio. Each future deployment is expected to take 3-4 weeks.