Daily Scheduling in Real Time
Caire syncs nightly data, compares manual versus AI schedules, and recalculates routes in seconds whenever reality changes. The result is less travel, more service hours, and confident frontline teams.
Daily Scheduling – Real-Time Optimization
🎯 Overview: Daily Scheduling provides nightly automation that imports schedules from external schedule systems* (*Carefox, eCare Welfare, etc.), expands approved slingor into today’s baseline, and then optimizes around them with AI. Slinga-based visits are treated as locked by default, while explicitly movable or unassigned visits remain unlocked and available for optimization. The system enables real-time comparison between unplanned, manually planned, AI-optimized, and actual schedules (from Phoniro). Achieve up to 20% travel time reduction and 75-80%+ staff efficiency through scenario-based optimization while handling sick leave and cancellations seamlessly.
📋 Table of Contents
- Summary & Integration Maturity
- Integration Maturity Model
- Schedule Types & Scenarios
- Supply & Demand Rebalancing
- Constraint Validation System
- Complete 5-Phase Workflow
- Persona Journeys
- Efficiency Health Monitoring
- Financial Forecasting for Executives
- Resources & Scenarios Management
- Key Benefits & Success Metrics
🎨 Interactive UX Mockups
→ Click here to see the Interactive Scheduling Interface in action
Four interactive mockups show how Caire's scheduling interface works for professional, data-rich scheduling with AI optimization and real-time validation.
📸 Visual Screenshots
Caire's scheduling surfaces combine the professional calendar interface, metrics sidebars, and route navigation. Each screenshot is anonymized but captured from the production-grade experience.
Actuals metrics panel for executed schedules
Optimization insights for AI-generated schedules
Route map per caregiver shift
Optimization status while runs are active
Summary
Caire's scheduling system unifies pre-planning and daily optimization in a cohesive solution. The system handles ALL visits - both recurring movable visits (optimized long-term) and daily fixed visits (adjusted within small buffers). By combining both workflows, organizations achieve over 50% time savings in scheduling work, up to 20% travel reduction, and 75-80%+ staff utilization through intelligent scenario-based optimization.
Integration Maturity: Caire evolves from proof-of-value (comparing AI vs manual planning) to full closed-loop integration where all scheduling flows through Caire's AI engine. In the current state, we prove AI delivers better results. In the future state, AI becomes the production scheduling system with scenario-based strategies (Conservative, Balanced, Aggressive) for different operational situations.
Everything happens automatically through nightly synchronization with existing external schedule systems (Carefox, eCare Welfare, etc.), requiring zero manual data entry while delivering measurable improvements in efficiency, staff satisfaction, and financial performance.
Integration Maturity Model
Caire's integration evolves through two distinct phases:
Phase 1: Current State (Proof-of-Value)
Purpose: Prove AI scheduling delivers measurable improvements over manual planning
Architecture:
- Unplanned Schedule: Imported from external schedule system, unassigned visits
- Manual Planned: External system's human scheduling decisions (separate baseline)
- AI-Optimized: Caire's AI scheduling engine optimization
- Actuals: Phoniro field execution logs
Key Comparison: AI-Optimized vs Manual Planned shows concrete improvements:
- Travel time reduction (typically 5-20%)
- Better staff utilization (75-80%+ vs <75% baseline)
- Improved continuity of care
- More balanced workload distribution
Business Value: Build confidence in AI through side-by-side proof, demonstrate value to stakeholders, justify transition to full integration
Phase 2: Future State (Full Integration - Closed Loop)
Purpose: All scheduling flows through Caire's AI engine in production
Closed-Loop Architecture:
Unplanned Import] --> B[Caire
AI Optimize] B --> C[Export to
External System] C --> D[Re-import as
Planned Schedule] D --> E[Field Execution] E --> F[Phoniro
Actuals] F --> G[Plan vs Actual
Analysis] G -.->|Continuous
Improvement| B style B fill:#2563EB,color:#fff style G fill:#8B5CF6,color:#fff
Key Evolution:
- Manual Planned BECOMES AI-Optimized: Same schedule, exported and re-imported
- Comparison Shifts: From "AI vs Manual" to "Scenario Comparison"
- Scenarios Compared: Conservative vs Balanced vs Aggressive optimization strategies
- All Scheduling AI-Driven: Human role shifts from manual planning to scenario selection and approval
Business Value: Eliminate manual scheduling entirely, optimize continuously, scale without administrative burden, data-driven scenario decisions for different operational situations
Integration Evolution Summary
| Aspect | Phase 1 (Current) | Phase 2 (Future) |
|---|---|---|
| Manual Planning | Separate baseline from external system | Eliminated - All via Caire |
| Comparison Focus | AI vs Manual (proof of value) | Scenario vs Scenario (optimization strategy) |
| Export | Manual entry in external system | Automatic API export (closed loop) |
| Coordinator Role | Review AI vs Manual, approve best | Select scenario, approve, monitor execution |
| Value Proposition | Prove AI works better | Optimize continuously at scale |
🎨 Interactive UX Mockups
The Unified System
One System - Two Complementary Workflows
Caire handles scheduling as ONE unified system where:
- Pre-planning optimizes recurring visit patterns long-term (weeks/months)
- Daily optimization manages today's schedule within established parameters
- Movable visits integrate seamlessly with fixed visits in the same optimization
- Nightly synchronization keeps everything updated automatically
System Overview
New Services] --> B[Movable Visits
Flexible Time Windows] B --> C[AI Optimization
Finds Best Patterns] C --> D[Customer Approval] D --> E[Fixed Times] end subgraph Daily[DAILY OPERATIONS - Short-term Optimization] F[Import Schedule
Operations System] --> G[Fixed + Movable Visits] G --> H[AI Optimization
Within Approved Buffers] H --> I[Optimized Daily Schedule] end E --> G style PrePlanning fill:#E9D5FF,color:#000 style Daily fill:#D1FAE5,color:#000
The Four Schedule Types & Scenarios
Understanding Caire's schedule types is key to leveraging the platform's comparison and optimization capabilities.
Schedule Type 1: Unplanned (Oplanerad)
What It Is: Imported visits from external schedule system that need staff assignment
Source: Nightly import from external schedule system via API
Purpose: Starting point for both AI optimization and manual planning comparison
Example Data:
- Typical size: 300-500 daily visits
- Status: All visits unassigned, waiting for optimization
- Contains: Visit requirements, time windows, skill needs, customer locations
Who Uses This: Coordinators for pre-assignment of critical visits before running AI optimization (e.g., manually assign delegation-required visits to qualified staff)
Schedule Type 2: Manual Planned (Planerad)
What It Is: External schedule system's human scheduling decisions
Current State (Phase 1): Separate baseline created by manual planning in external system
Future State (Phase 2): Caire's AI-optimized schedule exported and re-imported (closed loop)
Purpose Evolution:
- Phase 1: Baseline for comparison to prove AI delivers better results than manual planning
- Phase 2: Production schedule from Caire, comparison shifts to scenario-based strategies
Example Data (Phase 1):
- Same 300-500 daily visits as Unplanned
- Status: Majority assigned, some unassigned
- Manual assignments by external system coordinators
- Serves as baseline for AI comparison
Transition Note: "Once export API integration is complete, this schedule type will represent Caire's optimized schedule that was exported to the external system and re-imported for execution. Comparison will shift from AI vs Manual to comparing different AI scenarios (Conservative vs Balanced vs Aggressive strategies)."
Schedule Type 3: AI-Optimized (Optimerad)
What It Is: Caire's AI scheduling engine optimization result
Source: AI scheduling engine processes unplanned visits with configured constraints and scenarios
Purpose: Demonstrate improved efficiency through intelligent route optimization, workload balancing, and continuity maximization
Example Improvements (AI vs Manual):
- Same 300-500 daily visits, majority assigned
- Travel Time: Reduced by 5-20% (intelligent route optimization)
- Staff Utilization: Increased by 3-5% (better workload distribution)
- Continuity: Improved by 5-10% (same caregiver assignments)
- Workload Balance: More even distribution across staff (reduced stress)
Scenario Integration:
AI-Optimized schedules can be run with different scenarios:
- Conservative: Minimize disruption, maintain existing patterns (70% continuity focus)
- Balanced: Optimize efficiency while preserving reasonable continuity (50/50 split)
- Aggressive: Maximum efficiency, travel time minimization (80% efficiency focus)
Who Uses This: Coordinators review and approve, Managers analyze performance, Executives evaluate business impact
Schedule Type 4: Actuals (Utförd - Phoniro)
What It Is: Reality - what actually happened in the field
Source: Phoniro check-in/check-out logs or similar time tracking system
Purpose: Compare planned vs actual execution, understand deviations, improve future planning
Example Data (Plan vs Actual):
- Planned: 300-500 visits scheduled
- Executed: ~85-95% of planned visits completed
- Cancelled: ~5-15% of visits not performed
- Extra: ~10-15% more visits added in field (NOT in original INPUT)
- Total logged: 300-500 visits accounted for
Critical Insight: Time and effort tracking tools (e.g., Phoniro) often contain MORE visits than external system INPUT due to offline additions, combined visits, and real-time adjustments.
Business Value: Phoniro represents municipality billing source of truth - actual time spent determines revenue. Understanding deviations helps improve planning accuracy and reveal operational patterns.
Who Uses This: Managers for performance analysis, Executives for financial reconciliation, Coordinators for continuous improvement insights
Scenario-Based Optimization Strategies
Overview of Scenarios
Caire offers three standard optimization scenarios. Continuity is measured as the number of unique caregivers per client over 14 days (Stockholm target: max 10, national average: 15-16):
- Conservative: Prioritizes continuity – aims for 6-8 unique caregivers per client over 14 days. Minimal disruption, familiar faces.
- Balanced: Optimal balance between continuity and efficiency – aims for 8-10 unique caregivers per client over 14 days. Recommended as default.
- Aggressive: Prioritizes efficiency – may accept up to 12-14 unique caregivers per client over 14 days if necessary to cover absences or reduce travel time.
Creating Custom Scenarios
Organizations can create custom scenarios with their own weights for continuity and efficiency. Use for seasonal variations, geographic areas, or temporary situations.
Client Exceptions
Some clients may require exceptions from the scenario:
- Fixed caregiver: Client must always see specific staff (e.g., dementia, complex medical needs)
- Minimum continuity: Client requires higher continuity than scenario default (e.g., palliative care)
- Strict time window: Client with tighter time windows than standard (e.g., medication timing)
Exceptions are used sparingly (5-10% of clients) and documented with medical/safety justification.
Supply & Demand Rebalancing
All scheduling is fundamentally about matching supply (available staff) with demand (required visits).
Caire's scenario-based optimization provides intelligent tools to handle mismatches in real-time.
Understanding Supply & Demand
Supply: Available staff capacity based on number of employees, skills, shift length, and availability. Can be adjusted through temporary staff, overtime, or shifting between areas.
Demand: Planned visits categorized as fixed (meals, medication), movable (cleaning, shopping), or variable (urgent needs, cancellations). Prioritized as mandatory, important, or optional.
Rebalancing Situations & Scenario Responses
Situation 1: Supply Shortage (High Demand, Limited Staff)
Scenario: More visits needed than capacity allows. Causes can include staff absences, urgent visits, or seasonal spikes.
Scenario Options:
- Conservative: Defer optional visits to next day. No extra cost but customers affected.
- Balanced: Hire temporary staff and defer fewer visits. Moderate cost, less customer impact.
- Aggressive: Overtime + temporary staff to schedule all visits. Higher cost but no customer impact. Profitable if revenue exceeds cost.
Caire Dashboard Shows:
- Side-by-side comparison of all three scenario results
- Financial impact analysis for each option
- Which specific visits would be deferred in each scenario
- Customer communication lists for deferrals
- One-click approval of chosen scenario
Situation 2: Supply Excess (Low Demand, Over-Staffed)
Scenario: Fewer visits than available capacity. Causes can include high cancellation day, post-holiday period, temporary overstaffing, or seasonal low period.
Key Insight: Municipal contracts allocate fixed hours per month that reset at month-end. Low-demand days are opportunities to recapture cancelled hours by extending or adding visits.
Scenario Options:
- Conservative: Maintain all staff, use time for longer visits to recapture cancelled hours. Full staff cost but higher revenue from recapture.
- Balanced: Release part-time staff and recapture cancelled hours through extended visits and extra visits. Balances cost savings with revenue.
- Aggressive: Maximize allocation hour recapture with proactive outreach. Extend visits, move flexible visits from tomorrow, and offer extra services. Maximizes utilization and revenue.
Caire Dashboard Shows:
- Capacity forecast that identifies low-demand days in advance
- Allocation Hours Widget: Clients with unused/cancelled hours (recapture opportunity)
- End-of-month alert for at-risk clients
- Financial impact of recaptured hours
Situation 3: Demand Spike (Morning Rush Hour)
Scenario: Uneven demand distribution within the same day with overcapacity at certain times and underutilization at others.
Scenario Actions:
- Conservative: Keep schedule as-is, accept overcapacity at peak and handle stress manually.
- Balanced: Move some flexible visits from overcapacity to underutilized time slots for better distribution.
- Aggressive: Rebalance entire period by moving multiple visits and smoothing the demand curve across the day.
Caire Dashboard Shows:
- Hour-by-hour capacity visualization
- Which visits can be moved with impact on travel time and continuity
- Drag-and-drop interface for manual rebalancing
Situation 4: Skill-Based Imbalance
Scenario: High demand for specialized skills (e.g., delegation certification) with limited supply of qualified staff.
Scenario Responses:
- Conservative: Cluster all specialist visits to qualified staff and accept higher workload for them. Ensures compliance.
- Balanced: Cluster specialist visits in morning, let specialist staff take regular visits in afternoon for balance. Consider overtime for more even distribution.
- Aggressive: Maximize specialist staff capacity and accept imbalance for compliance and efficiency. Flag need for training or hiring.
Caire Insights:
- Skill analysis and identification of imbalances
- Hiring and training recommendations
- Financial impact of skill imbalance
Situation 5: Geographic Imbalance
Scenario: Uneven demand across service areas with some areas overcapacity and others underutilized.
Scenario Responses:
- Conservative: Keep staff in assigned areas and accept imbalance. Reasoning: Staff familiar with their areas and customers.
- Balanced: Shift some staff between areas for the day, prioritize staff who live between areas for minimal travel cost.
- Aggressive: Shift multiple staff and optimize routes across both areas as a unified zone. Accept more staff travel for better balance.
Caire Tools:
- Heat map showing demand density by area
- Staff location overlay and travel impact calculator for geographic shifts
- Scenario comparison: Cost of imbalance vs cost of rebalancing
Supply/Demand Decision Matrix
| Situation | Conservative Response | Balanced Response | Aggressive Response |
|---|---|---|---|
| Supply Shortage | Defer optional visits | Temp staff + partial deferrals | Overtime + temp staff |
| Supply Excess | Maintain staff, light day | Release part-time staff | Proactive outreach for visits |
| Time Spike | Accept rush hour stress | Move 2-3 flexible visits | Rebalance entire period |
| Skill Shortage | Cluster to qualified staff | Balance with overtime | Maximize qualified utilization |
| Geographic Imbalance | Keep area assignments | Shift 2-3 staff between areas | Optimize across unified zone |
Constraint Validation System
Constraints are rules that schedules must follow. Caire's AI scheduling engine validates all constraints automatically, preventing errors before they happen.
What Are Constraints?
Constraints are scheduling rules that ensure:
- Quality: Right caregiver with right skills provides care
- Safety: No staff overwork, adequate travel time between visits
- Compliance: Labor laws, customer agreements, municipality requirements
- Efficiency: Optimal use of resources while meeting all requirements
Caire categorizes constraints into two types: Hard (cannot be broken) and Soft (should follow but can override with justification).
Hard Constraints (Must Follow)
Hard Constraint 1: Skills Matching
Rule: Staff must have required skills/certifications for the visit
Example: Medication administration requires delegation certification. Staff without certification cannot be assigned to such visits.
Other Skill Examples:
- Language requirements (Swedish, Arabic, English for communication)
- Specialized training (dementia care, diabetes management)
- Physical capabilities (lift assistance requires training)
- Gender requirements (e.g., shower assistance)
Business Impact: Prevents compliance violations, ensures quality care, protects organization from legal liability
Hard Constraint 2: No Overlaps
Rule: One staff member cannot be in two places at the same time
Example: A staff member cannot be assigned two visits that overlap in time. System prevents double-booking automatically.
Why This Matters: Physical impossibility - prevents double-booking and ensures realistic schedules
Soft Constraints (Should Follow)
Soft Constraint 1: Time Window Preferences
Rule: Visits should ideally be within preferred time windows, but can be moved if necessary
When to Override: Staff shortage, customer agrees to temporary change, within flexibility buffers
Soft Constraint 2: Continuity
Rule: Same caregiver should visit same customer regularly (builds relationship)
Soft Constraint 3: Customer Preferences
Rule: Respect customer preferences when possible (gender, language, specific caregiver)
Constraint Hierarchy & Priority
| Priority | Constraint Type | Examples | Can Override? |
|---|---|---|---|
| 1 (Highest) | Legal/Safety | Skills, Overlaps, Max hours | ❌ Never |
| 2 | Physical Reality | Travel time, Geographic distance | ⚠️ Rarely (with justification) |
| 3 | Quality/Efficiency | Continuity, Workload balance | ✓ Yes (scenario-dependent) |
| 4 (Lowest) | Preferences | Time windows, Caregiver choice | ✓ Yes (with customer communication) |
Scenario Impact on Constraint Priorities:
- Conservative: Respects all soft constraints strongly (rarely overrides preferences)
- Balanced: Balances hard constraints + efficiency soft constraints (override preferences when efficiency justifies)
- Aggressive: Strictly enforces hard constraints only (freely overrides soft constraints for maximum efficiency)
All scenarios ALWAYS respect hard constraints - safety and legality are never compromised.
Workflow 1: Pre-Planning Recurring Visits
Purpose: Optimize recurring visit patterns before they become part of the daily system
Process:
- Receive municipal decision: PDF upload or manual entry
- Define visit patterns: Service type, duration, frequency, time windows
- AI optimization: Find optimal times for all recurring visits
- Customer meeting: Present proposed times and gather feedback
- Finalize schedule: Approve and export to operations system
Key Concepts:
- Time Windows: Broad periods (e.g. "Morning 07:00-10:00") for flexible optimization
- Movable Visits: Visits that can be flexibly placed within the time window
- AI Proposals: System finds optimal fixed times based on staff, skills and routes
Pre-Planning Benefits
Time Savings:
- From 4-8 hours manual work → 1-2 hours with AI support
- Over 50% reduction in administrative scheduling work
- 70-80% of customers approve first proposal
Quality Improvements:
- 75-80%+ staff utilization (up from <75%)< /li>
- Up to 20% reduction in travel time
- Improved continuity in care relationships
- More balanced workload distribution
Complete 5-Phase Daily Workflow
Caire's daily scheduling operates through five distinct phases, from overnight automation to end-of-day analysis.
Each phase is designed to minimize manual work while maximizing scheduling quality and operational flexibility.
Phase 1: Nightly Automation
What Happens: System automatically imports visits and staff data from external schedule systems, identifies changes, and merges data without overwriting manual edits.
Steps:
- Import: Visit data, staff data, and updates for 60-day window
- Change Detection: Identifies new visits, cancellations, time changes, and staff changes
- Merging: Preserve manual edits, apply safe updates, flag conflicts
Phase 2: AI Optimization
What Happens: AI engine optimizes schedule according to three scenarios (Conservative, Balanced, Aggressive) with different weights for continuity and efficiency.
Process: Assigns mandatory visits first, clusters delegation visits, optimizes routes, balances workload, and maximizes continuity according to scenario. Generates metrics for each scenario.
Phase 3: Morning Review & Approval
What Happens: Coordinator reviews overnight optimization results, compares scenarios, handles unassigned visits, and approves schedule for execution.
Steps:
- Choose Scenario: Assess situation and select between Conservative, Balanced, or Aggressive
- Review Results: Check assigned visits and identify unassigned requiring action
- Compare: See improvements vs manual planning or compare scenarios
- Handle Unassigned: Contact temp agency, defer visits, or override with caution
- Approve: Complete approval and send to field staff
Phase 4: Real-Time Adjustments
What Happens: Handle unexpected events like sick leave or urgent needs during the day. System re-optimizes automatically and redistributes affected visits to available staff.
Process: Coordinator marks event, AI engine reallocates visits, coordinator reviews and approves changes. System sends updates to field staff automatically.
Phase 5: End-of-Day Analysis
What Happens: System imports actual execution from time tracking tools, compares with planned schedule, and generates insights for continuous improvement.
Analysis: Completion rate, cancellation reasons, pattern detection, and optimization recommendations. System automatically identifies at-risk customers, day-of-week patterns, and geographic trends.
Efficiency Health Monitoring
"Like iPhone Battery Health for Schedules"
Just as iPhone battery health degrades from 100% to 95% to 90% over time, schedule efficiency naturally degrades as organizations evolve. Caire monitors this degradation and triggers re-planning when needed.
The Degradation Concept
Why Schedules Degrade Over Time:
- New Customers Added: Filled into existing gaps without global re-optimization
- Staff Turnover: New employees join team, routes need re-optimization for new assignments
- Preference Drift: Temporary accommodations become permanent without review
- Stale Patterns: Movable visits not re-planned for 12+ months
Health Score and Thresholds
Caire calculates a health score based on travel time efficiency, staff utilization, continuity, and assignment rate. System displays status in four zones:
- Green (95-100%): Optimal schedule, no actions needed
- Yellow (90-94%): Acceptable but can be improved
- Orange (85-89%): Suboptimal, re-planning recommended
- Red (<85%):< /strong> Critical, immediate re-planning required
Financial Forecasting for Executives
Caire transforms schedule efficiency data into actionable financial intelligence for strategic decisions.
From Efficiency to Staffing Decisions
The Connection: Efficiency health + demand forecast → Staffing recommendation → Financial impact
Health 93%] --> B[Demand
Forecast] B --> C[Capacity
Analysis] C --> D[Staffing
Recommendation] D --> E[Financial
Impact] E --> F[Executive
Decision] style A fill:#FCD34D,color:#000 style E fill:#2563EB,color:#fff style F fill:#8B5CF6,color:#fff
Demand Projection Methodology
Three Data Sources Combined:
- Known Pipeline: Municipality decisions, confirmed customers, and ongoing requests
- Historical Growth Trends: Average growth, seasonal patterns, and trends
- Market Intelligence: Municipality budgets, demographic trends, and competitive situation
System combines these sources into conservative, balanced, and aggressive forecasts for future demand.
Capacity Analysis Framework
System calculates current capacity based on number of staff, working hours, and average visit duration. Capacity is compared with projected demand to identify need for hiring or efficiency improvements.
Buffer Levels: Tight buffer (5-10%) provides high utilization but moderate risk. Standard buffer (10-20%) is recommended for normal operations. Comfortable buffer (20-25%) provides low risk but lower utilization.
Staffing Recommendation Algorithm
System automatically generates staffing recommendations based on projected utilization:
- Utilization > 90%: Recommends hiring additional staff
- Utilization 85-90%: Consider hiring or efficiency improvements
- Utilization < 70%: Consider reducing temp/part-time staff
- Utilization 70-85%: Maintain current staffing
System shows alternatives with financial impact and recommends the most balanced option.
When to Re-Plan vs When to Hire
Re-Planning: Suitable when efficiency health has dropped but capacity is sufficient. Provides quick payback through reduced travel time and improved efficiency.
Hiring: Necessary when demand increases and capacity is insufficient, even after efficiency improvements. Combined solution (hiring + re-planning) provides best results.
Executive Dashboard
Executive dashboard shows projected demand, current capacity, and recommended staffing changes for upcoming 30, 60, and 90 days. System shows financial impact and enables approval of hiring plans directly from dashboard.
Resources & Scenarios Management Integration
Scenarios don't exist in isolation - they pull from and optimize your resource pools (staff and clients) based on real constraints and priorities.
How Resources Feed Scenarios
Employee Resources → Scenario Constraints
Skills Inventory:
- Delegation certification (hard constraint - all scenarios respect)
- Language capabilities (soft constraint - scenario-weighted)
- Specialized training (dementia, diabetes, etc.)
- Physical capabilities (lift assistance certification)
Availability Pools:
- Full-Time Staff: Primary resource (all scenarios prefer using FTE first)
- Part-Time Staff: Flexibility resource (Conservative maintains, Aggressive may reduce)
- Temporary Staff: On-demand resource (Conservative avoids, Aggressive leverages)
- Overtime Capacity: Premium resource (only Aggressive uses proactively)
Office-Based Routing:
- All staff routes start and end at office location (fixed constraint)
- First visit: Travel from office to first customer
- Last visit: Travel from last customer back to office
- Scenarios optimize middle visits differently (Aggressive minimizes total distance, Conservative maintains patterns)
Cost Structures:
- Regular hourly rate (standard pay)
- Overtime rate (typically 1.5x regular)
- Temp agency rate (typically 1.4x regular + agency fee)
- Scenarios use cost data to calculate financial impact of staffing decisions
Client Resources → Scenario Priorities
Visit Priority Levels:
- Mandatory: Always scheduled (all scenarios assign first)
- Important: Schedule if capacity allows (Conservative=always, Balanced=usually, Aggressive=if efficient)
- Optional: Fill remaining capacity (Conservative=maintain continuity, Aggressive=defer if shortage)
Flexibility Buffers:
- ±0 min: Time-critical (medication at exact time) - no scenarios can move
- ±15 min: Standard flexibility (most visits) - Balanced uses partially, Aggressive uses fully
- ±30 min: High flexibility (cleaning, walks) - Aggressive optimizes extensively
Customer Preferences:
- Caregiver continuity (same person each time) - Conservative prioritizes 100%, Balanced 50%, Aggressive 20%
- Gender preferences (shower assistance) - All scenarios respect unless impossible
- Language preferences - Conservative always matches, Aggressive may override for efficiency
Time Window Constraints:
- Hard windows (breakfast 07:00-10:00) - All scenarios respect
- Soft windows (flexible timing) - Scenarios use differently (Conservative narrow, Aggressive wide)
Scenario Library & Management
Pre-Configured Scenarios (Default):
| Scenario Name | Configuration | When to Use |
|---|---|---|
| Conservative | 70% continuity, 30% efficiency | Stable periods, post-disruption recovery |
| Balanced | 50% continuity, 50% efficiency | Standard daily operations (default) |
| Aggressive | 20% continuity, 80% efficiency | Staff shortage, cost reduction focus |
Custom Scenario Creation (Advanced):
- Continuity Focus: 90% continuity, 10% efficiency (maximum relationship stability)
- Travel Minimization: 10% continuity, 90% efficiency (absolute minimum travel)
- Geographic Optimization: Optimize by area first, then cross-area if needed
- Skill-Based Clustering: Cluster delegation visits optimally, then general visits
Scenario Performance Tracking:
- Historical performance: Which scenarios achieved expected metrics
- Accuracy rate: Projected vs actual results comparison
- Preferred scenarios: Which coordinators/managers use most often
- Situation matching: Automatic scenario suggestion based on detected situation
Supply/Demand Situational Scenario Types
Caire recognizes patterns and suggests scenario strategies:
Type 1: Understaffed Day Scenario
Trigger Detection:
- Demand > 110% of capacity
- Multiple staff sick reports
- Urgent visits added unexpectedly
Resource Actions:
- Activate temporary staff pool (Caire shows available temps with skills)
- Offer overtime to existing staff (shows who's below max hours)
- Identify optional visits for deferral (ranked by priority)
Demand Actions:
- Defer optional visits to next day (automatic customer notification)
- Combine visits where possible (suggest to field staff)
- Use full flexibility buffers (±30 min becomes actual range)
Type 2: Overstaffed Day Scenario
Trigger Detection:
- Demand < 75% of capacity
- High cancellation day detected
- Post-holiday low period
Resource Actions:
- Release part-time staff (Caire identifies who to release with minimal impact)
- Reduce temporary staff shifts
- Keep full-time staff (maintain team)
Demand Actions:
- Proactive customer outreach for flexible visit acceleration
- List of movable visits that could move earlier
- Same-day addition capabilities highlighted
Type 3: Skill Shortage Scenario
Trigger Detection:
- Delegation visits > 5 × delegation staff count
- Language-specific visits > speakers available
- Specialized skills needed beyond current capacity
Resource Actions:
- Prioritize delegation-certified staff for delegation visits
- Cluster delegation visits in morning (when qualified staff fresh)
- Long-term: Flag skill shortage for hiring/training
Demand Actions:
- Schedule all delegation visits optimally (hard constraint)
- Fill qualified staff remaining time with general visits
- Accept workload imbalance (qualified staff may work 9-10h, others 6-7h)
Type 4: Geographic Imbalance Scenario
Trigger Detection:
- Area A > 85% utilization AND Area B < 65% utilization
- Imbalance > 20 percentage points
- Persistent pattern over 3+ days
Resource Actions:
- Identify staff living between areas (minimal travel impact to shift)
- Calculate travel time for geographic reassignments
- Shift 2-4 staff from low to high demand area
Demand Actions:
- Move border visits to shared optimization pool
- Optimize routes across unified area (not separate areas)
- Balance without excessive cross-area travel
Integration with Operations Systems
Nightly Automation
Fully Automatic Synchronization (02:00 AM):
- Import: Automatically fetch new and updated schedules
- Smart change detection: Identify what's changed since last run
- Intelligent merging: Apply changes without overwriting existing data
- Pre-configured scenarios: Automatically run all configured optimization scenarios
- Morning results: Fresh KPIs and optimization insights ready for review
Time Savings: Zero manual work - everything happens automatically while you sleep
System Integration
| System | Import | Optimization | Export |
|---|---|---|---|
| Modern Operations Systems | ✅ Automatic nightly import (API) | ✅ AI optimization automatic | ⚠️ Manual export (API in development) |
| Legacy Operations Systems | 🔜 API integration planned | ✅ Can run manually | ⚠️ Manual export |
Persona Journeys - Day in the Life
Caire serves multiple roles within home care organizations, each with distinct needs and workflows.
These journeys show how different personas use Caire throughout their work day.
Journey 1: Coordinator Maria - Daily Optimization
Role: Coordinator (Samordnare)
Responsibility: Daily schedule management, visit assignment, staff coordination
Morning Workflow: Reviews overnight AI optimization, compares scenarios and selects appropriate scenario. Handles unassigned visits through temp agency or deferral. During sick leave, schedule is automatically re-optimized and coordinator approves reallocation.
Afternoon Monitoring: Monitors completion rate via live dashboard, handles cancellations, and prepares for next day.
Time Savings: Caire reduces scheduling time from hours to minutes, freeing time for customer relationships and quality work.
Journey 2: Manager Johan - Weekly KPI Review
Role: Operations Manager (Verksamhetschef)
Responsibility: Team performance, quality assurance, continuous improvement
Weekly Workflow: Reviews weekly summary with operational metrics and trends. Analyzes scenario performance to understand which strategies work best. Compares AI results vs manual planning to demonstrate value.
Time Savings: Schedule analysis reduced from hours to minutes per week, enabling data-driven decisions and clear team communication.
Journey 3: Executive Anna - Quarterly Financial Planning
Role: CEO/CFO
Responsibility: Strategic decisions, financial performance, growth planning
Quarterly Workflow: Reviews quarterly performance and efficiency health. Analyzes 90-day demand forecast based on known pipeline and historical trends. Performs capacity analysis to identify staffing needs.
Financial Analysis: Compares hiring alternatives with costs, revenue, and utilization. Makes data-driven staffing decisions based on forecasts and risk assessment.
Time Savings: Executive review reduced from hours to minutes per quarter, enabling proactive decisions and lower business risk.
Key Benefits & Success Metrics
Caire delivers measurable improvements across operational, financial, and quality dimensions.
Track these KPIs to measure improvements and continuous enhancement.
Key Benefits
Time Savings
- Over 50% reduction in manual scheduling work
- From hours to minutes: Daily schedule planning automated
- Nightly automation: Schedules ready every morning without manual work
Quality Improvements
- 75-80%+ staff utilization: Up from <75% baseline
- Up to 20% travel reduction: Intelligent route optimization
- Improved continuity: Same caregiver to same customer
- Balanced workload: Even distribution among staff
Business Value
- +1-2% improved margin: Better resource utilization
- Reduced staff turnover: Lower stress and better work environment
- Higher customer satisfaction: Predictability and continuity
- Scalability: Handle growth without increasing administrative burden
Operational Metrics
Travel Time Reduction
Definition: Total hours spent driving between customer locations per week
Target: 5-20% reduction vs manual baseline
Why This Matters:
- More time for service delivery (travel is non-billable)
- Reduced fuel costs and vehicle wear
- Lower staff fatigue (less driving stress)
- Environmental benefit (fewer km driven)
Staff Utilization
Definition: Percentage of shift time spent providing direct customer service
Target: 75-80% (up from <75% manual baseline)
Why This Matters:
- Higher revenue (more billable time)
- Better staff satisfaction (meaningful work vs driving)
- Can serve more customers without hiring
- Competitive advantage (efficient service delivery)
Continuity of Care
Definition: Number of unique caregivers per client over a 14-day period
Target: Maximum 10 caregivers per 14 days (lower is better)
Benchmark: Stockholm has formalized the target of maximum 10 unique caregivers per 14 days for clients with high care intensity. This is an industry standard supported by the National Board of Health and Welfare.
Scenario Impact:
- Conservative: 6-8 caregivers per 14 days (highest continuity)
- Balanced: 8-10 caregivers per 14 days (good balance)
- Aggressive: 10-12 caregivers per 14 days (acceptable during capacity shortage)
Why This Matters: Fewer caregivers provide better customer relationships, higher safety, and improved care quality.
Assignment Rate
Definition: Percentage of visits successfully assigned to qualified staff
Target: 98%+ (2% unassigned due to genuine constraints acceptable)
Actions for Unassigned Visits:
- Temp agency (short-term solution)
- Defer to next day (if non-critical)
- Hire/train staff (long-term solution)
Efficiency Health Score
Definition: Composite score measuring schedule degradation over time
Target: 90%+ (excellent to acceptable range)
Monitoring Frequency:
- 95%+: Monthly check
- 90-94%: Weekly check
- 85-89%: Daily reminder
- <85%: Critical alert (blocks actions)
Financial Metrics
Cost per Visit
Definition: Total staff cost divided by number of service visits delivered
Target: Reduce by 5-8% through efficiency gains
Higher staff utilization delivers more visits per staff cost, reducing cost per visit and improving margin.
Revenue per FTE
Definition: Total billable revenue generated per full-time employee
Target: Increase by 3-5% through utilization gains
Better staff utilization increases billable time per employee, raising revenue per FTE without increasing staff cost.
Margin Improvement
Definition: Net profit margin increase from efficiency gains
Target: +1-2% margin improvement
Higher revenue from better utilization with unchanged staff costs delivers direct margin improvement.
Adoption Metrics
Coordinator Time Savings
Definition: Reduction in manual scheduling hours per day
Target: 50%+ time reduction
Caire automates scheduling, route optimization, and crisis handling, reducing coordinator scheduling time from hours to minutes. Freed time can be used for customer relationships, quality improvements, and team development.
Scenario Adoption Rate
Definition: Percentage of days where coordinators actively select scenarios (vs defaulting)
Target: 80%+ active scenario selection (shows engagement)
Ideal Pattern:
- Thoughtful scenario selection based on day's situation
- Not always using Balanced (shows coordinator isn't engaged)
- Scenario chosen matches situation (Conservative after disruption, Aggressive during shortage)
Scenario Accuracy
Definition: Percentage of times AI predicted metrics match actual execution
Target: 90%+ accuracy (within ±5% of prediction)
Why This Matters: High accuracy builds trust in AI recommendations and enables data-driven decisions.
Quality Metrics
Customer Satisfaction (Indirect)
Definition: Customer feedback scores related to scheduling quality
Target: Maintain or improve vs manual baseline
Proxy Measurements:
- Continuity (number of caregivers per 14 days)
- Punctuality
- Same-day cancellations (initiated by organization)
- Complaint rate (schedule-related)
Net Result: AI scheduling improves customer satisfaction through better continuity and reliability
Staff Satisfaction
Definition: Field staff satisfaction with their daily schedules
Target: Maintain or improve vs manual baseline
Proxy Measurements:
- Workload balance
- Fair distribution of travel time
- Overtime requests
- Complaints about schedule changes
KPI Summary
| Category | Metric | Baseline | AI-Optimized | Improvement | Annual Value |
|---|---|---|---|---|---|
| Operational | Travel Time | Manual | 5-20% reduction | Reduced travel time | Financial value |
| Utilization | <75%< /td> | 75-80% | Higher utilization | Higher revenue | |
| Continuity | 12+ caregivers/14d | 8-10 caregivers/14d | Fewer caregivers | Quality ↑ | |
| Efficiency Health | Not applicable | 90%+ | New metric | Monitoring | |
| Financial | Cost per Visit | Manual | 5-8% reduction | Lower cost | Better margin |
| Revenue per FTE | Manual | 3-5% increase | Higher revenue | Better utilization | |
| Margin | Manual | +1-2% | Margin improvement | Higher profit | |
| Adoption | Coordinator Time | Hours/day | 50%+ reduction | Time savings | Freed time |
| Scenario Accuracy | Not applicable | 90%+ | New metric | Trust ↑ |
Caire delivers measurable improvements across operational, financial, and quality dimensions.