Restorative Early Warning System (REWS™) – Predictive Analytics for Conflict Risk

REWS™ is the only predictive analytics engine in the sector dedicated to conflict risk. Unlike generic dashboards or lagging indicators, REWS™ systematically analyses real-time data across five domains—case management, workforce wellbeing, employee voice, equity and inclusion, and cultural learning—anchored in the CLIPA pillars: Compassion, Leadership, Inclusivity, Psychological Safety, and Accountability.

REWS™ employs advanced machine learning and statistical models to detect early warning signals—behavioural drift, psychological safety decline, discretionary effort drop, case mix anomalies, leadership micro-signals, union relationship drift, equity pressure points, policy fatigue, trust erosion, operational spikes, governance lag, and a composite dispute index. Data is drawn from HRIS, surveys, case logs, and wellbeing metrics, and is triangulated for reliability. Predictive thresholds trigger CLIPA-aligned interventions: Level 1 (coaching), Level 2 (restorative reviews and mediation), Level 3 (diagnostic review and senior oversight).

Outputs include RAG-rated dashboards—Conflict Probability Index, Restorative Readiness Score, Equity Exposure Index, and Resolution Velocity—reviewed quarterly by a Restorative Oversight Board. All protocols are aligned with ACAS, EHRC, and tribunal standards, providing audit-ready, tribunal-defensible evidence.