1. Data Driven Decision Making, What to Measure? 1

2. HCPS Vision Data Driven Decision Making, What to Measure? 2 To become the nation's leader in developing successful students.

3. Reaching the Vision: The Way We Work Data Driven Decision Making, What to Measure? 3  Be the best at what we do through training and practice.  Continue to improve all areas big and small.  Use data to plan and monitor progress.  Shape the path through clear and detailed procedures.  Manage like a business with a focus on efficiency, quality and customer service.  Maintain urgency to ensure children benefit today.

4. Key Components to Data Driven Decision Making Data Driven Decision Making, What to Measure? 4 Components  Transportation 2019  SWOT Analysis  Environmental Scan  Strategic Plans  Scorecards

5. The Future – Transportation 2019 Component Number One….. Data Driven Decision Making, What to Measure? 5  Identify what HCPS Transportation Services looks like in 5 years

6. Transportation Operations wants to focus on efficiency and safety in the services it provides to our customer groups. Electronic tracking of students for both FEFP and student location that will allow parents to know when the student gets on and off the bus every day. Technology will allow schools to monitor on-time arrival real time to manage its staff in monitoring their bus loops and students. Routing will be integrated with real-time GPS that will set route times accurately and minimize ride times for students by allocating the correct resources by both time frame and geographic locations. Field trips will become more customer friendly and allow complete online reservations, confirmation and automatic billing system to minimize paperwork. While HCPS does not control the amount of FEFP allocated by the State, it can maximize its FEFP through enhanced Average Bus Occupancy (ABO) tracking and reducing cost per student through controls of expenditures. Balance of service levels will be optimized with expenditure control to minimize any service impacts to its current and future customers. Transportation Operations Data Driven Decision Making, What to Measure? 6

7. In five years, Fleet will transform our current operation into a paperless shop. For the customer, we will have an online vehicle repair request system that will alleviate current wait times for shop personnel. Accurate cost per mile data will identify what make and model of equipment will be the most cost efficient to purchase for the school system. Warranty recovery will be expanded beyond vehicles and parts to cover all equipment within Transportation Services, including pc’s and other office and support equipment. Five years will find HCPS with a fleet that is sized to ensure preventive maintenance and inspection schedules are followed with no interruption to the daily service need of equipment. Transportation Fleet Data Driven Decision Making, What to Measure? 7

8. Customer service will move from a goal in five years to a development of culture. A Transportation Institute, modeled after Disney, will immerse employees in a customer focused and driven culture. Customer contact will move from multiple contacts to a customer service center approach that will facilitate a fast response and ownership of the issue. Response time to phone inquiries will be reduced to be more responsive to customers. Surveys to schools, departments, work force and parents/students will identify satisfaction levels with follow-through for any corrective action necessary. Customer Service Data Driven Decision Making, What to Measure? 8

9. Staff availability will be successfully manage to minimize any impact of service. Vacancy rates will be reduced through active recruiting process that will be moved from annually to a continual process. There will be established transportation curriculum for all employees. Training will include all facets of transportation management in order for everyone to understand the workings of transportation. Understanding the complexities will help them understand and foster a more positive relationship and open up management for a larger resource of ideas for improvement. Work Force Data Driven Decision Making, What to Measure? 9

10. National and State – Top District for efficiency and innovation in the nation. Local – Waiting list to join our department. Other department heads asking our management team to give workshops on employee training, morale and customer service. Overall in Five Years Data Driven Decision Making, What to Measure? 10

11. Component Number Two….. Data Driven Decision Making, What to Measure? 11 SWOT Analysis

12. Strengths Routing Department has identified program costs Scorecards by Routing gives ownership for Average Bus Occupancy Scorecards by Area Managers help manage assigned employees ABO is above State Average Runs per Bus is in the top quartile of the nation On-Time Arrival Rate is in the top quartile of the nation Routes per day is managed to provide budget consistency Weaknesses Cost per mile is at the edge of the upper quartile in the nation Cost per Student Average Daily Ride Time for students is higher than state average Transportation Operations Data Driven Decision Making, What to Measure? 12

13. Opportunities Expansion of surveys to identify customer needs Expanded use of technology to improve service Capitalize on ride time to enhance curriculum, accelerated reader program Maximize service with efficiency Threats Expansion of current state and federal programs ABO reduced due to board implemented programs and policies Equipment reliability issues Program placement School construction for traffic issues Transportation Operations Data Driven Decision Making, What to Measure? 13

14. Component Number Three….. Data Driven Decision Making, What to Measure? 14 Environmental Scan

15. FAPT* Operations - Survey FAPT Fleet - Survey CGCS** – Survey CGCS – Top Quartile * Florida Association for Pupil Transportation **Council of Great City Schools Agencies Compared Data Driven Decision Making, What to Measure? 15

16. FAPT Operations Survey Data Driven Decision Making, What to Measure? 16 District Average Bus Occupancy Average Runs per Day On-time arrival rate Average Daily Ride Time/Mins Average Driver Vacancy Rate Average Daily Driver Absenteeis m Rate Average Daily Monitor Absenteeis m Rate Average Daily Mechanic Absenteeis m Rate Miles between Preventable Crashes Annual Miles Parent Survey Collect Data on Customer Calls Orange75.13.0498.6%85.14.8%5.8% 11.07%218,49916,500,000NoYes Hernando79490.0%570%5%3%1%N/A3,193,892No Okeechobee77.92.7798.0%900%8%<1 500,0001,158,140No Calhoun50290.0%45N/A2% N/A 143,000NoN/A Gulf652N/A120<11%0%N/A117,130.85234,262No Volusia105.125.4399.0%382%5.4%6.9%N/A227,3394,319,452Yes Clay81.813N/A300%15%10% 452,7044,074,343YesNo Okaloosa602.595.0%N/A10%20%5%1%N/A3,000,000Yes Osceola75N/A 62,613.935,790,481NoN/A St. Johns105.542.8N/A 5.6%6%1%N/A3,769,999No Gadsden60490.0%450%10%1% N/A No Brevard652.7N/A455%8%N/A2%N/A6,600,000YesNo Flagler1064N/A<1205%6%5% 200,0001,600,000No Palm Beach90.5N/A 12,550,000NoN/A

17. FAPT Fleet Survey Data Driven Decision Making, What to Measure? 17 DistrictBuses Owned Buses OperatedSpare Ratio Out of Service Percentage Mechanic/bu s Ratio to One Alternative Fuels - Bus Alternative Fuels - White Fleet Purchase/Lea se White Fleet Centralized Fleet Program White Fleet Replacement Program Cell-based GPS Orange113490620%4.7%22BioDieselNoPurchaseNo Hernando16611531%5.0%21Propane PurchaseNoYesNo Okeechobee754935%5.0%18.75No N/AYesNo Calhoun412344%5.0%13.7No PurchaseYesNo Gulf302130%5.0%10No PurchaseN/AYesNo Volusia30022824%8.0%17BioDieselNoPurchaseNoYes Clay27018432%7.5%27No PurchaseYes No Okaloosa25019024%5.0%13No N/ANo Osceola39033015%1.0%30No N/aYesNo St. Johns22116326%5.2%27.6No PurchaseYes No Gadsden857512%2.0%14.2No PurchaseNoYes Brevard55440826%8.0%25No PurchaseYes No Flagler1278930%6.0%26No PurchaseYes Palm Beach78963120%11.0%20NoHybridPurchaseYes

18. CGCS Survey Data Driven Decision Making, What to Measure? 18 District Centralized white-fleet program White- Fleet Replaceme nt Plan Purchase/L ease Cell-based GPSVendor Average Daily Driver Absenteeis m Rate Average Daily Monitor Absenteeis m Rate Average Daily Mechanic Absenteeis m Rate Average Driver Vacancy Rate Parent Surveys Method of Delivery Call Abandonm ent Rate Average Customer Hold time OrangeNo PurchaseNo5.8% 11.07%4.8%No21.81%:56:01 Richmond, VAYesNoN/ANoN/A5% 0%3%No Toledo, OHYesNoPurchaseYesZonar6%7%5%10%No3%No Nashville, TNYes PurchaseNo10.5%9%<210%No25.94%1:38 Birmingha m, ALNo 10%2%0%2%No Anchorage, AlaskaN/A PurchaseYesZonar8%5%7.5%0%N/A Norfolk, VANo 9% 5%7%No Boston, MANo YesZonar7.98%N/A0.35%0%No10%:90

19. CGCS – Upper Quartile Data Driven Decision Making, What to Measure? 19 Cost per StudentCost per MileOn-Time Arrival RateDaily Buses as % of Total BusesFleet In-Service DistrictMeasureDistrictMeasureDistrictMeasureDistrictMeasureDistrictMeasure Orange $ 855.78Orange $ 3.39Orange98.6%Orange80.97%Orange95.35% 14 $ 465.6041 $ 1.84899.99%26100%3 41 $ 522.8816 $ 2.17299.94%43100%3599.28% 23 $ 547.2849 $ 3.004899.94%2397.52%3997.48% 7 $ 560.7444 $ 3.05696.97%697.39% 3 $ 565.9514 $ 3.152894.91%997.02% 44 $ 611.928 $ 3.165493.76%6296.88% 55 $ 618.4655 $ 3.176691.83%2596.00% 18 $ 629.0539 $ 3.21491.51%7195.40% 56 $ 662.8653 $ 3.391490.95%2695.00% 3090.91% 4590.91% 3490.83%

20. Strategy Maps Component Number Four….. Data Driven Decision Making, What to Measure? 20

21. Data Driven Decision Making, What to Measure? 21 Department Name | Owner District GoalStrategic ObjectivePlanned OutcomeLong-Term Target Timeline Efficient Operations Decrease cost per student over the next 5 years without impacting service level. Continual implementation of best practices. Even though the District has one of the lowest costs per student in the country, new strategies will need to be implemented to meet future service level requirements. Long term goal is to show a decrease over current levels after the increase of inflation is accounted for. 10% in 5 years $5.5 million dollars Target Goals per year 2014-15 Goal2015-16 Goal2016-17 Goal2017-18 Goal2018-19 Goal  Decrease Cost per Student yielding a cost avoidance of 2% ($1.1 Million)  Return rate of 30% on service surveys to customers to identify satisfaction levels.  Decrease Cost per Student yielding a cost avoidance of 2% ($1.1 Million)  Return rate of 30% on service surveys to customers to identify satisfaction levels.  Decrease Cost per Student yielding a cost avoidance of 2% ($1.1 Million)  Return rate of 30% on service surveys to customers to identify satisfaction levels.  Decrease Cost per Student yielding a cost avoidance of 2% ($1.1 Million)  Return rate of 30% on service surveys to customers to identify satisfaction levels.  Decrease Cost per Student yielding a cost avoidance of 2% ($1.1 Million)  Return rate of 30% on service surveys to customers to identify satisfaction levels.

22. Data Driven Decision Making, What to Measure? 22 Strategic Details Department Data Management Environmental Scan Supporting Function or ProgramIndividual Goals for Function/ProgramData SourceMeasure Comparative / Competitive Source Competitive / Comparative Benchmark Average Bus Occupancy Increase ABO to maximize FEFP Funding per Student Trapeze/FTE Counts/Stop by Stop Counts 75.1 FAPT106 Hi / 72.92 Avg. Runs Per Bus Maximize bus use to reduce costs associated with adding additional buses – Maintain high level Trapeze/Monthly Scorecard 3.04 (6.08 based on CGCS Measurement guidelines) CGCS6.0 Hi / 3.95 Avg On-Time Arrival Rate 100% of all buses arrive to school on timeGPS/Monthly Scorecard98.8%CGCS100% Average Daily Ride Time Total AM plus PM ride time Trapeze/Monthly Scorecard 85.1FAPT45 low / 83.31 Avg. Routes Per Day Minimize number of routes used to support current service level requirements Trapeze/Monthly Scorecard 906OCPS893 – previous year Cost per Mile Reduce Cost per Mile with operational efficiencies SAP/Monthly Scorecard$3.39CGCS $1.84 Low / $3.39 upper qrtr Cost per Student Reduce cost per student with operational efficiencies SAP/Monthly Scorecard$855.78 CGCS 465.60 Low / $662.86 upper qrtr

23. Data Driven Decision Making, What to Measure? 23 Strategic Details Department Data Management Environmental Scan Supporting Function or ProgramIndividual Goals for Function/ProgramData SourceMeasure Comparative / Competitive Source Competitive / Comparative Benchmark Average Bus Occupancy Increase ABO to maximize FEFP Funding per Student Trapeze/FTE Counts/Stop by Stop Counts 75.1 FAPT106 Hi / 72.92 Avg. Runs Per Bus Maximize bus use to reduce costs associated with adding additional buses – Maintain high level Trapeze/Monthly Scorecard 3.04 (6.08 based on CGCS Measurement guidelines) CGCS6.0 Hi / 3.95 Avg On-Time Arrival Rate 100% of all buses arrive to school on timeGPS/Monthly Scorecard98.8%CGCS100% Average Daily Ride Time Total AM plus PM ride time Trapeze/Monthly Scorecard 85.1FAPT45 low / 83.31 Avg. Routes Per Day Minimize number of routes used to support current service level requirements Trapeze/Monthly Scorecard 906OCPS893 – previous year Cost per Mile Reduce Cost per Mile with operational efficiencies SAP/Monthly Scorecard$3.39CGCS $1.84 Low / $3.39 upper qrtr Cost per Student Reduce cost per student with operational efficiencies SAP/Monthly Scorecard$855.78 CGCS 465.60 Low / $662.86 upper qrtr

24. Scorecards Component Number Five….. Data Driven Decision Making, What to Measure? 24

25. Data Driven Decision Making, What to Measure? 25

26. Data Driven Decision Making, What to Measure? 26

27. Data Driven Decision Making, What to Measure? 27 Shop Manager Scorecard FY 11-12 Baseline FY 12-13 Target 1st Quarter 2nd Quarter3rd Quarter4th QuarterYTD8/31/12 Efficient Operations: to minimize the district's expenditures through efficient maintenance of buses. Road Call Rate - ALL 0.81%0.69% 0.73%1.12% HMG 0.90%0.66% 0.85%0.91% LNG 0.48%0.46% 0.51%0.64% PHG 1.06%0.92% 1.04%1.71% Road Call # - ALL 424386 128351 HMG 11489 32310 LNG 9598 31311 PHG 215199 64730

28. Metric Definition Data Driven Decision Making, What to Measure? 28 1 Measure Average Bus Occupancy (ABO) 2 DefinitionNumber of riders on a daily basis divided by the total number of route buses. 3 ImportanceThis is a basic measurement of the cost efficiency of student transportation services. Maximizing seat utilization reduces the number of buses needed. This data provides a baseline comparison across districts that will inevitably lead to further analysis based on a district’s placement. 4 InfluenceFactors that Influence this Measure:  Effectiveness of the routing plan  Ability to use each bus for more than one run each morning and each afternoon  Bell schedule  Type of programs served (special needs)  Level of inclusion  Strategic procurement of buses leveraging seating capacity  State guidelines on maximum ride time  Federal mandated programs such as McKinney-Vento

29. Metric Definition Data Driven Decision Making, What to Measure? 29 5 Division YTD Formula Auto sum, max,(select range of quarterly cells) - enter 6 Periodic Calculation n1/N1 7 n1 Total number of students riding on buses n2 n3 n4 8 N1 Total number of route buses N2 N3 N4 9 Data Source Edulog 10 Department Data Collection Frequency 6 times per year 11 Division Reporting Frequency Quarterly 12 Data SupplierIT, Transportation Routing 13 Data CycleData is updated daily from IT to Edulog.

30. Pulling it all Together Data Driven Decision Making, What to Measure? 30 Data does not change how we are empathetic to a students need. How we measure and manage it changes how quickly and correctly we can adjust our service to meet their needs.