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LABOR SUPPLY / DEMAND ANALYSES OPTIMIZE, OPTIMIZE, OPTIMIZE AND OPTIMIZE. OPTIMIZING LABOR SUPPLY / DEMAND REQUIRES The right METRICS

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LABOR SUPPLY / DEMAND ANALYSES

OPTIMIZE, OPTIMIZE, OPTIMIZE AND OPTIMIZE


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  • OPTIMIZING LABOR SUPPLY / DEMAND REQUIRES

  • The right METRICS

  • A data rich system with the major focus on Automated employee shift scheduling with automated labor supply / demand analysis and forecasting. The major focus of the data collection is on the reason why employees are not deployable and the average usage (real-time and projected)

  • The right quantifier of labor demand (providing quantitative data) so that labor demand can be compared to labor supply

  • Automated labor supply / demand projections so that management can intervene before funds are expended


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An Example

1. Performance Guideline:

Average Annual Deployable Days

2. Performance Goal:

220.47 Average Annual Deployable Days (364 – 143.63 not deployable days)

3. Performance Measure:

Real-time time and attendance data to project end of fiscal year average not deployable and deployable days.

Example Projection = 210 deployable days or 4 less than budgeted.

4. Performance Evaluation & Action:

Strategy to decrease average not deployable days to 137



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Automated Employee Shift Schedules SCHEDULING

Essential Shift Scheduling

Functions

  • Automated Labor Supply / Demand Analysis and Forecasting

  • Master and Daily Schedules

  • Automated Budget-Based Daily Rosters

  • Deployable and not deployable days accounting

  • Correctly calculating the number of employees that MUST be scheduled off duty for leaves that can be planned



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  • THE SIX MOST IMPORTANT SCHEDULING

  • METRICS

  • Average annual deployable / not deployable days

  • Employee off duty limits

  • Average number of vacant positions and the number of days from vacancy to fill for each vacancy

  • Unplanned time off

  • Post / Position flexibility factors

  • Overtime Hours (compensatory time and reductions in programs and services) to maintain coverage

1

2

3

4

5

6


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EXAMPLES SCHEDULING

OPTIMIZE, OPTIMIZE, OPTIMIZE


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EXAMPLE 1 SCHEDULING

DEPLOYABLE DAYS

Explanation:

The goal is to get 227.69 deployable days from each employee.

The 227.69 deployable days value is based on all average deployable and not entitlements.

"Actual" and "Projected“ values are based on real-time data and reflect projections 12-month out.


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EXAMPLE 2 SCHEDULING

OFF DUTY LIMITS

Explanation:

The goal is to schedule an average of 7.6 people off every day for absences that can be planned (vacation, military leave, etc).


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EXAMPLE 3 SCHEDULING

AVERAGE VACANCIES

Explanation:

The goal is to average 4 open positions (about 7 terminations per month) with no more than 3 weeks to hire a replacement employee.

"Actual" represents the number of weekly roster positions without an employee assigned.


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EXAMPLE 4 SCHEDULING

UNPLANNED TIME OFF

(NOT DEPLOYABLE TIME)

Explanation:

Available relief positions can cover the goal of about 66 days of "call ins" each month.

Call ins" exceeding the goal will be covered through either flexing other posts or using compensatory time / overtime. The other alternative is to reduce programs / services.


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EXAMPLE 5 SCHEDULING

FLEXIBILITY

Explanation:

Flexibility means moving an employee from one assignment to another. Then the initial assignment is closed for the shift.

In this example, the goal is 1,054 hours of flexibility per month except February (28 days in month)


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EXAMPLE 6 SCHEDULING

Overtime Hours compensatory time and reductions in programs and services to maintain coverage

Explanation:

If overtime usage were an option to provide coverage then the Actual overtime hours should approximate the hours needed for proper coverage.

In this case the overtime goal is 926 hours per month.


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PERFORMACE CHARTS SCHEDULING


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The Right Data and Real-Time Projections Reporting SCHEDULING

Explanation: The goal is to get 228 deployable days from each employee position and is based on all absence conditions expected. "Actual" and "Projected“ reflect current absence conditions.

  • If the budget supports 228 average annual employee deployable days then the projected and annualized bars need to be at or very near the yellow goal line.

  • When the actual bars are below the goal line than staff are less deployable that required.

  • Typically, overtime, and post flexibility is used to cover deployable day shortages.

  • The burgundy bar projects the deployable days average that is projected based on planned leave planned leave data.

This can be a huge “budget-buster”


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The Right Data and Real-Time Projections Reporting SCHEDULING

Explanation: The goal is to average 4 open positions (about 7 terminations per month with no more than 3 weeks to hire a replacement). "Actual" represents the number of weekly rosters positions with no people assigned.

  • The length of the position vacancy to fill cycle can dramatically drive overtime costs upwards

  • The vacancy to fill timeline is referred to as the “Vacancy Factor.”

  • Each “Vacancy Factor” day can have the same effect on staffing and closed programs and services (also overtime and compensatory time) as does an unplanned sick leave.

  • Salary savings provide some labor cost recovery.

  • When closed posts and reduced services ( overtime too) occur to frequently operations suffer.


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The Right Data and Real-Time Projections Reporting SCHEDULING

Explanation: The goal is to schedule an average of 7.6 people off every day for absences that are plannable (military leave, 10 days of PTO per person, in-service training, and emergency response training)

  • Avoid staff “fat” and staff “lean” days.

  • Too few staff on planned leave is as bad as too many.


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The Right Data and Real-Time Projections Reporting SCHEDULING

Explanation: There are available relief positions to cover approximately 66 days of "call ins" each month. "Call ins" exceeding the goal must be covered through either flexing other posts or paying for overtime. With PTO, "projected" unplanned leave is expected to be higher.

  • Unplanned leave, in excess of the days and dollars budgeted, is potentially a huge “ “budget-buster.”

  • Projecting end of fiscal year averages, by not deployable resason, enables proactive planning and strategy development to manage to the averages.


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The Right Data and Real-Time Projections Reporting SCHEDULING

Explanation: Certain non-priority posts may be pulled or shut down occasionally to cover mandatory posts to avoid overtime on days of high absences. The capacity for flexing these posts is equivalent to approximately 1,000 hours per month.

  • Post flexibility policies and procedures help assure that some reductions in programs, services, compensatory time and overtime can be minimized.

  • Historical data helps establish flexibility policies and procedures.


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The Right Data and Real-Time Projections Reporting SCHEDULING

Explanation: Actual overtime hours should approximate that needed for proper post coverage. Needed overtime reflects hours available (standard work hours less absences) less work demand (schedule work, plus support for other services, less flexibility hours).

  • Notice that during April, May, and June there was no overtime needed.

  • However, there was a substantial amount of overtime used.

  • During January and June less overtime was used than required.


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MANAGEMENT REVIEW SCHEDULING


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Management Review SCHEDULING

  • Implement a management review strategy.

  • Conduct monthly management review sessions.

  • Record action items developed during the session.

  • Assign responsibility for follow-up

  • Follow-up

  • Report to upper management


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MEASURES SCHEDULING


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EMPLOYEE DEPLOYABLE DAYS SCHEDULING

  • In this example, the budgeted average annual deployable days is 214.118 days

  • Because the Staff Demand is seriously greater than the supply the only way to manage without reduced programs and services, compensatory time and overtime is to figure out how to get 242.29 deployable days per employee.

  • Otherwise some serious staffing plan re-design is required


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OFF DUTY LIMITS – ALL SHIFTS SCHEDULING

  • Use the values in green (budgeted columns) as the values WHEN LEAVE PLANNING AND APPROVING LEAVES

  • The total off duty limit days is 51.4. This value needs to be broken down into a value for each not deployable reason.

  • The red scheduled values are displayed simply to show the impact that vacancies have on the off duty limits.


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For more information please contact Hatrak Associates SCHEDULING

mailto:[email protected]?subject=Requesting information concerning performance metrics

PHONE: 702.869.8900


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