1. City of Surrey Fire Service: Reducing Uncertainty for Annual Staffing Costs

2. Background City of Surrey, British Columbia, Canada. Surrey

3. Background Len Garis Fire Chief, 2001-Present Assistant Chief, 1998-2001 Surrey, BC, Canada Pitt Meadows, BC, Canada Fire Chief/Director of Protective Services, 1992-1998 Volunteer/Contract Fire Chief, 1984-1992 Justice Institute of BC Program Coordinator, 1988-1992

4. Background Frederick G. Culbert, P. Eng. 37 years of experience in the international development, trade, transportation and energy sectors in Canada, the United States, Asia and South America M. Sc. (Stanford), Engineering-Economic Planning Focus on the implementation and development of economic and financial planning models to evaluate public and private sector business cases

5. Motivators and Approach

6. Problem Statement 1. Inability to predict annual operating costs 2. Inability to dynamically measure: new regulations and policies new staffing techniques modifications to existing practices Ineffective management and allocation of resources Resulting in:

7. Drivers Unpredictable staffing levels cause difficulties in planning.

8. Drivers Under or over-staffing is common.

9. Methodology Modeling must be systematic and inclusive Model Development Process and Inputs Information flow Dataflow  Source information input  Integration  Analysis, forecasting  Planning, modeling   Strategy Decision-making

10. Model Types Model development is iterative, where results must be validated. 1. Deterministic approach enables situational analysis. 2. Probabilistic approach enables projected analysis. Two approaches to test the model:

11. Inputs, Outputs and Process The model calculates the probability of achieving consistent four-person staffing under various scenarios. MODELING CONSTRAINTS Standards Policies Budget MODIFY until objectives met RESULTS FULL TEAM

12. Case Studies

13. Simulation 1 Input Parameters (simplified) : Incremental Staff Added Personnel Required by Shift Staff Absence Adjustment Staffing Offset by Shift 0 57 No No

14. Simulation 1 - Results

15. Simulation 1 - Observations Teams A and B most inefficient (average C $94,000 annually) Team D most efficient (average C $49,000 annually) Variable performance direct result of historically low absences Average annual cost C $300,000

16. Simulation 2 Input Parameters (simplified) : Incremental Staff Added Personnel Required by Shift Staff Absence Adjustment Staffing Offset by Shift 0 57 Yes Yes

17. Simulation 2 - Results

18. Simulation 2 - Observations Spread between teams decreasing Variance between individual team performance decreasing Average annual cost C $266,000

19. Simulation 3 Input Parameters (simplified) : Incremental Staff Added Personnel Required by Shift Staff Absence Adjustment Staffing Offset by Shift 0 57 + 2 = 59 Yes Yes

20. Simulation 3 - Results

21. Simulation 3 Enormous shift in average annual cost (from C $266,000 to C $516,000 annually) Addition of 2 staff not cost-effective Currently under-utilized resources in shifts

22. Changes Modeled Just-in-time Staff Replacement - no gapping Fill long-term absences with temporary staff Staffing pools for WCB and special leave Intershift transfers for dynamic scheduling 56-day cycle Add FR truck unit when shift is overstaffed Balance scheduled absences based on historic patterns Stabilization of scheduled absences, using “City Days”

23. Conclusion Projected callback cost at straight time = C $30/hr Where: Minimum = C $172,630 Average = C $250,103 Maximum = C $276,063 Budget = C $425,000 Assumes: Suggested techniques applied Absence conditions stable No extreme events occur

24. Contingency Plans Reduce on-duty staff to 55 prior to budget running out to maintain 4-person staffing Simulation indicates that if average absence/shift reduced by 0.6, then 55 on-duty staff (with addition of FR unit), can be realized within budget.