Auto Scheduling

Summary

This article explains how to use the auto scheduling features in the Shifts platform to automatically generate optimized shift assignments based on availability, preferences, skills, and other factors.

Overview

Auto scheduling uses an intelligent recommendation engine to assign the most suitable employees to shifts based on multiple configurable factors. This feature saves managers time while creating more balanced and fair schedules that respect employee preferences and rest requirements.

How the Matching Algorithm Works

The auto scheduling system uses a sophisticated multi-factor scoring algorithm that calculates a compatibility percentage (0-100%) for each potential employee assignment. Understanding how these percentages are calculated helps you optimize your scheduling settings and interpret the recommendations.

Core Scoring Components

The algorithm combines four main weighted factors to create the base compatibility score:

1. Availability Score (Default Weight: 30%)

This measures how well an employee’s declared availability matches the shift time:

• Full Availability: 100% score - Employee is completely available for the entire shift
• Partial Availability: 75% score - Employee covers at least 80% of the shift duration
• No Availability: 0% score - Employee has no availability during the shift time

2. Preference Score (Default Weight: 25%)

This considers the employee’s stated preferences for shift types and willingness to work:

• Preference Rank: Based on 1-10 scale preference settings (up to 70% of this component)
• Extra Shifts Bonus: Additional 30% if employee is willing to take extra shifts
• No Preferences: 0% score if no preferences are configured

3. Fairness Score (Default Weight: 25%)

This promotes equitable distribution of shifts among employees:

• Workload Distribution: Employees with fewer recent shifts get higher scores (up to 50% of component)
• Recency Factor: Longer time since last shift increases score (up to 50% of component)
• Shift Capacity: Employees who can’t take more shifts get reduced score (20% minimum)

4. Role Score (Default Weight: 20%)

This ensures proper role coverage based on shift requirements:

• Required Role Match: 100% score if employee’s role is specifically needed for the shift
• General Role: 50% score if employee has any role but no specific requirement exists
• No Role: 0% score if employee has no defined role

Performance Multipliers

After calculating the base weighted score, the system applies performance-based multipliers:

MangoScore Performance Multiplier (Range: 0.8 - 1.2)

MangoScore is your organization’s performance scoring system that significantly impacts matching percentages. It acts as a reliability filter that can boost or penalize employees based on their proven track record.

How MangoScore is Calculated:

MangoScore evaluates three main performance components:

1. Attendance Rate (Default weight: 50%)
   • Based on shifts completed vs. shifts assigned in last 30 days
   • 100% = perfect attendance, 0% = consistent no-shows
2. Cancellation Rate (Default weight: 30%)
   • Based on late cancellations and no-shows
   • Lower cancellation rate = higher MangoScore
3. Manager Rating (Default weight: 20%)
   • Average of 1-5 star ratings from managers after shifts
   • Normalized to 0.0-1.0 scale (5 stars = 1.0)

MangoScore Tiers and Multiplier Impact:

• Top Tier (90%+ MangoScore): 1.08-1.2× multiplier (8-20% boost)
• Reliable (75-89% MangoScore): 1.0-1.08× multiplier (0-8% boost)
• Watchlist (Below 75% MangoScore): 0.8-1.0× multiplier (0-20% penalty)
• Unranked (Less than 5 shifts in 30 days): 1.0× neutral multiplier

Real-World MangoScore Impact Examples:

Example 1: High Performer Boost

• Base weighted score: 60%
• Employee with 95% MangoScore (Top Tier):
  • Performance multiplier: 1.18×
  • Final match: 60% × 1.18 = 71% (jumps from “acceptable” to “quality match”)

Example 2: Average Performer

• Base weighted score: 60%
• Employee with 80% MangoScore (Reliable):
  • Performance multiplier: 1.04×
  • Final match: 60% × 1.04 = 62% (modest improvement)

Example 3: Poor Performer Penalty

• Base weighted score: 60%
• Employee with 65% MangoScore (Watchlist):
  • Performance multiplier: 0.86×
  • Final match: 60% × 0.86 = 52% (significant penalty)

MangoScore Requirements:

• Minimum activity: 5 shifts in the last 30 days to receive a MangoScore
• Automatic updates: Recalculated when shift performance records are updated
• Manager input: Real-time updates when managers rate employees after shifts
• Business customization: Weights can be adjusted in MangoScore settings

Skill Compatibility Factor (Range: 0.0 - 1.0)

• Perfect skill match: No reduction (1.0× multiplier)
• Partial skill match: Proportional reduction based on percentage of requirements met
• Missing critical skills: Complete elimination (0.0× multiplier)
• No skill requirements: No impact (1.0× multiplier)

Penalties and Restrictions

The algorithm applies additional penalties for scheduling violations:

Rest Period Violations

• 50% penalty (0.5× multiplier) if assigning would violate minimum rest period between shifts
• Only applied when rest period enforcement is enabled in business settings

Hour Limit Violations

• 30% penalty (0.7× multiplier) if assignment would exceed:
  • Maximum daily hours
  • Maximum weekly hours
• Only applied when hour limit enforcement is enabled

Workload Priority Adjustment

Based on business workload balance priority settings:

• Low Priority: 90% base score + 10% workload score
• Medium Priority: 70% base score + 30% workload score (default)
• High Priority: 50% base score + 50% workload score

Final Score Calculation

The complete calculation follows this formula:

1. Calculate base weighted score (0-1 scale):
   base_score = (availability_score × 0.30) + 
                (preference_score × 0.25) + 
                (fairness_score × 0.25) + 
                (role_score × 0.20)

2. Apply MangoScore performance multiplier:
   adjusted_score = base_score × mango_score_multiplier

3. Apply skill compatibility:
   skill_adjusted_score = adjusted_score × skill_compatibility_factor

4. Apply workload priority blending:
   priority_adjusted_score = apply_workload_priority(skill_adjusted_score, workload_score)

5. Apply penalties:
   if (rest_period_violation):
     priority_adjusted_score × 0.5
   if (hour_limit_violation):
     priority_adjusted_score × 0.7

6. Convert to percentage:
   match_percentage = (final_score × 100), rounded to nearest integer

Quality Match Thresholds

The system categorizes matches based on final scores:

• Quality Matches: 70%+ score - Considered good assignments
• Acceptable Matches: 40-69% score - Workable but not ideal
• Poor Matches: Below 40% score - Generally not recommended

When you see “Found 0 quality matches (70%+ score)” it means no employees scored above the 70% threshold for that particular shift.

Understanding Low Match Percentages

If you’re seeing low match percentages (like 46-48%), this typically indicates:

• Limited availability for the specific shift times
• No strong preferences set for these shift types
• Recent shift assignments affecting fairness distribution
• Adequate but imperfect skill matches
• Minor scheduling constraint penalties
• Lower MangoScore impacting the performance multiplier

MangoScore’s Role in Low Scores:
For employees with 46-48% base matches:

• High MangoScore (90%+): Could boost to 54-58%
• Average MangoScore (75-89%): Might reach 48-52%
• Low MangoScore (below 75%): Could drop to 37-42%

Setting Up Auto Scheduling

Configuring Recommendation Weights

You can customize the importance of each scoring factor to match your operational priorities:

1. Navigate to Settings > Scheduling > Recommendation Weights
2. Adjust the weight sliders for each factor:
   • Availability Weight: How strongly to prioritize employee availability (default: 30%)
   • Preference Weight: How strongly to consider employee preferences (default: 25%)
   • Fairness Weight: Importance of even distribution of shifts (default: 25%)
   • Role Weight: Priority for role-specific requirements (default: 20%)
3. Ensure weights total exactly 100%
4. Click Save Changes to apply your customized weights

Weight Adjustment Tips:

• Increase Availability Weight (to 40-50%) if having fully available staff is critical
• Increase Fairness Weight (to 35-40%) if equitable shift distribution is a priority
• Increase Preference Weight (to 35-40%) to boost employee satisfaction
• Increase Role Weight (to 30-35%) when specific role coverage is essential

Configuring MangoScore Settings

MangoScore settings directly impact the performance multiplier in matching:

1. Navigate to Settings > Workforce Intelligence > MangoScore Settings
2. Configure component weights:
   • Attendance Weight: How much attendance impacts the score (default: 50%)
   • Cancellation Weight: Impact of late cancellations and no-shows (default: 30%)
   • Manager Rating Weight: Influence of manager feedback (default: 20%)
3. Set tier thresholds:
   • Top Tier Threshold: Minimum score for top tier (default: 90%)
   • Reliable Threshold: Minimum score for reliable tier (default: 75%)
4. Customize tier names if desired
5. Click Save Settings to apply changes

MangoScore Optimization Tips:

• Higher attendance weight if reliability is your top priority
• Higher manager rating weight if quality of work is crucial
• Lower tier thresholds if you want more employees to receive performance boosts
• Higher tier thresholds for more selective performance advantages

Configuring Rest Period Settings

1. Navigate to Settings > Scheduling > Rest Period Settings
2. Configure the following settings:
   • Minimum Rest Hours: Required hours between shifts (default: 8)
   • Maximum Weekly Hours: Limit on weekly work hours (default: 40)
   • Maximum Daily Hours: Limit on daily work hours (default: 12)
   • Enforcement Level: Choose whether to enforce strictly, warn only, or ignore
   • Workload Balance Priority: How much to prioritize fair workload distribution
3. Click Save Settings to apply changes

Setting Up Shift Requirements

1. Navigate to Scheduling > Shift Requirements
2. Click Add New Requirement to create role-specific staffing requirements
3. Specify:
   • Number of employees needed for each role
   • Minimum total staff requirements
   • Required skills and proficiency levels
   • Critical vs. preferred skills
   • Location requirements
4. Save your requirements to apply them to the auto scheduling process

Using Auto Scheduling

Generate a Complete Schedule

1. Navigate to Scheduling > Auto Schedule
2. Select the date range for schedule generation
3. Choose the locations to include in the schedule
4. Configure additional options:
   • Respect Existing Assignments: Keep or replace existing assignments
   • Prioritize Fairness: Toggle enhanced fairness distribution
   • Use Forecast Data: Incorporate demand forecasts
5. Click Generate Schedule to run the auto scheduling algorithm
6. Review the generated schedule before publishing

Get Recommendations for Individual Shifts

1. From the scheduling calendar, select a shift that needs staffing
2. Click Get Recommendations or Auto Assign
3. Review the list of recommended employees, sorted by compatibility score
4. For each recommendation, you can see:
   • Overall compatibility percentage (the calculated match score)
   • Availability status (full, partial, or unavailable)
   • Skills match information
   • Recent shift workload
   • Rest period compliance status
   • Performance indicators (MangoScore tier and trend)
5. Select the employee you wish to assign and click Assign

Interpreting Match Scores

When reviewing recommendations, use these guidelines:

• 80-100%: Excellent matches - ideal assignments
• 70-79%: Good matches - suitable for assignment
• 60-69%: Fair matches - acceptable with minor trade-offs
• 40-59%: Poor matches - consider only if necessary
• Below 40%: Very poor matches - avoid unless no alternatives

MangoScore Influence Indicators:

• Green performance badges: High MangoScore providing score boost
• Yellow performance badges: Average MangoScore with minimal impact
• Red performance badges: Low MangoScore causing score reduction
• Gray performance badges: Unranked (insufficient shift history)

Advanced Features

Rule Templates

Create reusable scheduling rule templates to standardize scheduling across locations:

1. Navigate to Settings > Scheduling > Rule Templates
2. Create templates with specific requirements for different shifts
3. Apply templates to locations or departments

Forecast-Based Scheduling

Integrate demand forecasts to adjust staffing levels automatically:

1. Ensure forecasts are enabled and configured
2. When using auto scheduling, select the Use Forecast Data option
3. The system will adjust staffing levels based on predicted demand

Bulk Operations

Perform auto scheduling for multiple locations at once:

1. Navigate to Scheduling > Bulk Operations
2. Select Auto Schedule as the operation type
3. Choose multiple locations and date ranges
4. Launch the operation and monitor progress

Optimizing Match Quality

Improving Low Match Scores

If you’re consistently seeing low match percentages:

1. Update Employee Data:
   • Ensure availability schedules are current
   • Verify skill certifications and proficiency levels
   • Confirm role assignments are accurate
2. Adjust Shift Requirements:
   • Review if skill requirements are too restrictive
   • Consider making some skills “preferred” rather than “required”
   • Verify role requirements match available staff
3. Fine-tune Weights:
   • Increase availability weight if employees have limited availability
   • Reduce fairness weight temporarily if workload distribution is causing issues
   • Adjust preference weight based on how well employee preferences are documented
4. Review Business Settings:
   • Check if rest period or hour limit settings are too restrictive
   • Adjust workload balance priority if needed
   • Consider relaxing enforcement levels during transition periods
5. Improve MangoScore Performance:
   • Train managers to provide timely and accurate performance ratings
   • Encourage attendance through recognition programs for high MangoScore employees
   • Address performance issues with employees in Watchlist tier
   • Set clear expectations about attendance and reliability standards

Enhancing Employee Satisfaction

To improve preference scores and employee satisfaction:

1. Encourage Preference Setting:
   • Train employees to set shift preferences in their profiles
   • Regularly remind staff to update their availability
   • Provide incentives for maintaining current preference data
2. Balance Fairness and Preferences:
   • Adjust fairness vs. preference weights based on feedback
   • Monitor workload distribution to ensure equity
   • Consider rotating less desirable shifts fairly
3. Leverage MangoScore Benefits:
   • Communicate the value of maintaining high MangoScore
   • Provide early access to premium shifts for Top Tier employees
   • Offer performance feedback to help employees improve their scores
   • Recognize high performers publicly to encourage others

MangoScore-Specific Optimization

To maximize the positive impact of MangoScore on matching:

1. Ensure Adequate Data:
   • Verify employees have at least 5 shifts in 30 days for scoring
   • Encourage managers to rate employees consistently after shifts
   • Review attendance tracking accuracy
2. Address Low Performers:
   • Identify Watchlist employees and provide targeted support
   • Create improvement plans for employees with low MangoScore
   • Consider additional training for employees with attendance issues
3. Optimize Score Components:
   • Adjust component weights based on your priorities (attendance vs. ratings)
   • Set appropriate tier thresholds for your organization’s standards
   • Monitor score distribution to ensure fair representation across tiers

Best Practices

1. Start Small: Begin with a single department or location to fine-tune settings
2. Review Before Publishing: Always review auto-generated schedules before finalizing
3. Adjust Weights Gradually: Make small adjustments to recommendation weights to see effects
4. Keep Requirements Updated: Regularly update shift requirements as needs change
5. Ensure Complete Data: Make sure employee availability, skills, and preferences are up to date
6. Monitor Match Quality: Track average match percentages and adjust settings to improve them
7. Respect Exceptions: Use manual assignments for special cases that the algorithm can’t handle
8. Regular Calibration: Periodically review and adjust weights based on operational feedback
9. Maintain MangoScore Health: Regularly review MangoScore distribution and address performance issues
10. Communicate Transparently: Help employees understand how MangoScore affects their shift opportunities

Troubleshooting

Low-Quality Recommendations (Below 70% Average)

• Check Employee Data: Ensure availability, skills, and preferences are complete and current
• Review Shift Requirements: Verify requirements aren’t overly restrictive
• Adjust Weights: Consider rebalancing recommendation weights
• Examine Penalties: Check if rest period or hour limit settings are too strict
• Analyze MangoScore Impact: Review if low MangoScore employees are being penalized excessively

Unbalanced Schedules

• Increase Fairness Weight: Boost fairness component to 35-40%
• Review Manual Assignments: Check for manual assignments disrupting balance
• Check Availability Patterns: Look for employees with limited availability creating bottlenecks
• Adjust Workload Priority: Increase workload balance priority in rest period settings

Missing Skill Coverage

• Verify Skill Configuration: Ensure skill requirements are correctly set up
• Check Employee Skills: Confirm employee skills are properly recorded
• Review Proficiency Requirements: Consider if minimum proficiency levels are too high
• Mark Skills as Preferred: Change critical skills to preferred if appropriate

Consistently Low Preference Scores

• Employee Training: Help employees set up their shift preferences
• Preference Weight: Increase preference weight if employee satisfaction is priority
• Availability Overlap: Ensure employee availability aligns with shift needs
• Incentive Programs: Consider incentives for employees to work less preferred shifts

MangoScore-Related Issues

Low MangoScore Distribution

• Review scoring criteria: Ensure MangoScore components align with business priorities
• Adjust tier thresholds: Consider if thresholds are too high for your organization
• Improve data quality: Ensure attendance tracking and manager ratings are accurate
• Provide performance support: Help struggling employees improve their scores

MangoScore Not Updating

• Check minimum shift requirement: Ensure employees have at least 5 shifts in 30 days
• Verify manager rating process: Confirm managers are rating employees after shifts
• Review calculation frequency: MangoScore updates automatically with new performance data
• Check business settings: Ensure MangoScore is enabled and configured properly

Excessive Performance Penalties

• Review multiplier ranges: Consider if 0.8-1.2 range is appropriate for your business
• Adjust component weights: Reduce impact of components causing excessive penalties
• Provide improvement opportunities: Help low-scoring employees develop better performance
• Consider grace periods: Temporarily adjust scoring for employees working on improvement

Related Resources

• Managing Shift Requirements
• Rest Period Settings
• Employee Skills Management
• User Availability Management
• Managing Availability Conflicts
• Recommendation Weights Configuration
• MangoScore Performance Management
• Performance Metrics and Ratings