Manufacturing clients deploying AI flight risk scoring typically see 25-40% reductions in unexpected turnover within the first 12 months, translating directly to fewer unplanned absences and shorter ramp times for replacement hires. For a mid-sized plant with 200 production floor employees, preventing 8-12 unplanned departures annually saves $480,000 - $720,000 in replacement costs alone (recruiting, training, lost productivity). Beyond headcount stability, OEE typically improves 3-7% as institutional knowledge retention reduces operator error and quality incidents; scrap rates and defect PPM decline as experienced inspectors and technicians remain in role. Shift supervisors flagged for burnout and reassigned before departure prevent cascading quality escapes that would otherwise cost $50,000 - $200,000 per incident.
ROI compounds over the 12-month deployment cycle as the model's accuracy improves and your HR team internalizes predictive retention workflows. By month 6, most clients report 60-70% accuracy in flight risk identification; by month 12, accuracy often exceeds 80% as the model learns your plant's unique operational-to-turnover correlations. The system also surfaces structural insights - revealing, for example, that CNC operators on third shift have 3x the turnover rate of day shift, or that quality inspectors assigned to your lowest-yield line leave within 14 months - enabling strategic staffing or equipment decisions that prevent future attrition. A typical mid-sized manufacturer recoups deployment costs within 8-10 months and realizes $300,000 - $500,000 in net retention savings by month 12.