AI-POWERED ABSENCE INTELLIGENCE, NOT JUST SICK DAY COUNTING
Counting sick days tells you nothing. The Bradford Factor weights frequent short absences higher than occasional long ones - because they cause more disruption. Our system calculates scores automatically from time clock data, detects patterns across 7 absence types using AI, models the true financial impact including cascade costs, and predicts what happens next.
Real-time monitoring • Live updates
92%
Compliance Rate
Zone A
Production Floor
100%
Zone B
Warehouse
75%
Zone C
Loading Dock
45%
The Bradford Factor uses the formula S² × D (spells squared times days lost) to weight frequent short absences higher than occasional long ones. Our system is schedule-aware - it uses actual working patterns, not calendar days. Five configurable threshold tiers trigger appropriate actions automatically. AI analyses patterns across seven absence types, comparing employees to peers with similar schedules. Financial impact modelling includes cascade costs when cover arrangements create chain reactions.
See how absence intelligence works
Absences are detected from time clock no-shows and scheduled shifts. Working days lost calculated from actual schedules, not calendar assumptions.
Bradford Factor scores calculated in real-time using the S²×D formula. Employees automatically classified into 5 configurable threshold tiers.
AI detects patterns across 7 types: post-rest-day, pre-rest-day, peak period, shift type, position, bank holiday, and seasonal. Compared against peers.
Threshold crossings trigger appropriate actions: wellbeing checks, return-to-work interviews, or formal reviews. Track every action to completion.
Just counting sick days taken
No pattern visibility across absences
Unknown true cost of absence
Inconsistent management response
Reactive - problems discovered too late
Weighted scoring reveals disruptive patterns
AI detects 7 pattern types automatically
Cascade cost analysis shows real financial impact
Threshold-based actions ensure consistent response
Predictive scoring shows what happens next
Real results from real businesses
AI analyses absences across 7 pattern types - post-rest-day, bank holiday proximity, seasonal, shift-type, and more. Compare against peers with similar schedules to spot statistical anomalies.
Financial impact goes beyond lost wages. Cascade analysis tracks the chain reaction: when A is absent, B covers, C covers B's shift - every cost in the chain is calculated.
Five configurable tiers ensure every employee is treated consistently. Thresholds trigger appropriate actions - from wellbeing checks to formal reviews - removing subjectivity from absence management.
'What if one more spell?' forecasting shows managers the projected impact before absence occurs. Understand scoring trajectories and intervene early.
Everything you need for absence management
S²×D formula calculated from actual working patterns, not calendar days. Real-time per-employee scoring with 52-week rolling period and 12-point historical snapshots.
Configurable tiers from Low to Critical. Each tier defines a score range and recommended action. Automatic HR action triggers when thresholds are crossed.
7 pattern types: post-rest-day, pre-rest-day, peak period, shift type, position/location, bank holiday proximity, and seasonal. Peer comparison with confidence scoring.
Direct cost (productivity lost), cover cost (overtime/agency), and cascade analysis tracking the full chain of cover arrangements and their costs.
Auto-detect absences from time clock no-shows. Create absence spells from missed shift blocks. Calculate working days lost from actual schedules.
Wellbeing checks before disciplinary (policy-configurable). Return-to-work interviews post-absence. HR action tracking from open to completion with outcome recording.
See how businesses like yours manage absence
High-turnover environments with seasonal variation and shift-based patterns
Pattern visibility, consistent absence management
Shift-based workforce where absences cause immediate production impact
Real-time cost visibility, reduced absence rates
Staff shortages amplified by absences requiring agency cover
Cascade cost tracking, early intervention
Part-time workforce with weekend and bank holiday absence patterns
Pattern detection, fair treatment
Operational disruption from absence requiring immediate replacement
Predictive scoring, financial impact clarity
Term-time patterns and absence impact on student continuity
Seasonal analysis, consistent policy application
See the impact from day one
Average reduction in short-term absences through early intervention
AI detection across 7 absence pattern types
Threshold-based actions ensure consistent, fair treatment
Financial impact including cascade costing calculated instantly
The Bradford Factor uses the formula S²×D where S is the number of separate absence spells and D is the total working days lost. It weights frequent short absences higher than occasional long ones because they cause more operational disruption.
Absences are detected automatically from time clock no-shows and missed scheduled shifts. Working days lost are calculated from actual schedules rather than calendar assumptions, giving more accurate scores.
The system includes an exclusion mechanism. Absences related to disability, maternity, workplace injury, or other protected reasons can be excluded from Bradford Factor calculations with full reason tracking and audit trail.
The system analyses absences across 7 pattern types including post-rest-day, bank holiday proximity, seasonal trends, and shift-type correlations. Employees are compared against peers with similar schedules, and patterns are flagged with confidence scores based on statistical significance.
Yes. All 5 tiers are fully configurable - score ranges, recommended actions, and trigger behaviours. You can also configure whether wellbeing checks are required before any disciplinary action.
AI-powered pattern detection, Bradford Factor scoring, and financial impact analysis.
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