1. Driver based rolling forecast model at MDACC: Hyperion Planning

2. Agenda 01 Presenter Background 02 Perficient Background
03 UTMDACC Background
04 Project Scope
05 Project Time line
06 Planning Solution
07 Reporting Solution
08 Challenges and Lessons learned

3. Presenters Background
Nandini Nehru
Sr. Solutions Architect at Perficient, Inc.
MBA, Rice University
10+ years of experience, proficient in Essbase, Planning, Financial Reporting
Oracle Hyperion Planning 11 Certified Implementation Specialist, Oracle Enterprise
Planning and Budgeting Cloud Service Certified Implementation Specialist
Janet Kloves
Associate Director, Financial Planning & Analysis
15+ years’ experience with Hyperion Planning and Essbase (technical and functional)
Provides end user support and training for Enterprise Operating Budget
Manages capital equipment budget process

4. Perficient Background
Founded in 1997
Public, NASDAQ: PRFT
2017 revenue $485 million
Major market locations:
Allentown, Atlanta, Ann Arbor, Boston, Charlotte, Chicago, Cincinnati, Columbus, Dallas, Denver, Detroit, Fairfax, Houston, Indianapolis, Lafayette, Milwaukee, Minneapolis, New York City, Northern California, Oxford (UK), Southern California, St. Louis, Toronto
Global delivery centers in China and India
Approx. 3,000 employees
Dedicated solution practices
Approx. 95% repeat business rate
Alliance partnerships with major technology vendors
Multiple vendor/industry technology and growth awards

5. University of Texas MD Anderson Cancer Center Background

6. UTMDACC Background
The mission of The University of Texas MD Anderson Cancer Center (UTMDACC) is to eliminate cancer in Texas, the nation, and the world through outstanding programs that integrate patient care, research and prevention, and through education for undergraduate and graduate students, trainees, professionals, employees and the public.
Created in 1941 as a component of The University of Texas System
Ranked number 1 in Cancer Care, America’s Best Hospitals for 13 out of the past 16 years
Over 20,000 employees
In Fiscal Year 2017:
More than 137,000 patients
More than 30,000 hospital admissions
More than 10,800 patients enrolled in 1,250+ clinical trials
Close to 7,100 trainees
Almost $5 billion in total revenue of that total only 4.1% was general revenue appropriated by State of Texas
For more information visit:
*In U.S.News & World Report’s annual “America’s Best Hospitals” survey

7. UTMDACC Financial Planning Cycle
Fiscal Year Sep – Aug
Based on Fund Accounting
Targets built based on Regents’ Budget
Annual Operating Budget is bottoms up
Forecast is done at a higher level

8. Traditional budgeting vs Driver-based forecasting
Based on assumptions – economic, market, etc. – most of which will be proven inaccurate
Significant time and effort for level of reliability in forecast
Outdated before finalized
Motivation and incentives based on achieving budget, resulting in efforts to minimize targets
Focus on meeting budget prevents optimization of performance based on external and internal factors
Enables entity to design models that focus on leading versus lagging indicators
Provides greater insight into what is truly driving the entity
Links strategy and performance
Enables management and planners to evaluate alternative scenarios based on driver fluctuations
Improves the predictive accuracy of forecasts over time and expedites speed of reaction to environment

9. Legacy Forecast Process
Excel Based
Decentralized
Manually maintained
No reporting model
No auditing, No versioning
No Security
At a much higher level
Restricted by excel data limitations

10. Rolling Forecast Project Scope
Business Objective
Objective of the RF app is to produce a driver based rolling forecast to help set Targets for the MDACC annual planning and budgeting cycle, initially and eventually replace the annual operating budget process.
High-level requirements
Forecast needs to be driver based
Forecast needs to be rolling 18 months
Forecast needs to be at a higher level
Forecast needs to be flexible

11. Project Background

12. Project Timeline 5 weeks 4 weeks 7 weeks 2 UATS 2 weeks each 2 weeks
Discover
5 weeks
Design
4 weeks
Develop
7 weeks
Test
2 UATS
2 weeks each
Deploy
2 weeks
Support
Continues
Project Plan
Req. Documents
Discovery meetings
Excel models
Solution Design Document
Prototype
Design Review
Application
Application Integrations
Reports
OBIEE Dashboards
UAT Test Scripts
Issue Log
UAT 1 – To test the calculations/Model
UAT 2 – To test the reports and reporting measures
Migration Document
Artifact List
Admin Guide
Enhancement Log

13. Planning Solution Delivered

14. Overall Process Flow

15. Forecast Application Dimensions
1 BSO Plan Type with 10 dimensions and 3 attribute dimensions
Default Dimensions (Planning Unit)
Measures (Accounts TYPE)
Entity (Country TYPE) Department Tree –
Attributes: Service, service line, Department Status
Version – Working, Calculated
Scenario – Actual, Rolling_Forecast
Period – Sep – Aug rolled up under Qtrs and yeartotal
Year – FY17,FY18…
Custom Dimensions
Account – P&L , No_Account
Jobcode
Procedure Categories
Fund Group

16. Reporting Application Dimensions (Re-used)
1 ASO Cube – with 10 Dimension and 2 Attribute Dimension
Dimensions
Account – P&L , No_Account
Entity – Department Tree –
Attributes: Service and service line
Version - Submit
Scenario – Actual, Budget,Rolling_Forecast
Period – Sep – Aug rolled up under Qtrs and yeartotal
Year – FY10,FY11…
Jobcode
Fund Type
Fund Group
Fund
Attribute Dimensions
Services
Service Line

17. Best Practices – Dimension Design
Add a “No Entity”, “No Fund Group”…member in the hierarchy
Add “Total Members” in the hierarchy
Consistent Naming convention between different applications/cubes
Dimension settings and Performance Settings – Dense Vs Sparse – don’t leave it for the end
Dense dimensions – Dynamic calc members, Sparse dimensions – Stored members
Driver members like ratios, percentage needed to be stored, but we also created dynamic members for reporting purposes
Attribute dimension on Sparse Dimension for reporting, calculations
Dynamic Time Series – If you plan to enable Y-T-D and P-T-D members make sure you rename “Year” and “Period” dimension because these are reserved words in the formula
Account
Fund Group
Job code
Procedure Category
Measures
Entity
Period
Year
Scenario
Version

18. Measures and Entity Dimension

19. Driver Based Model Overview
Each P&L category forecast using its own logic. Each Fund group and/or department (Entity) and Account combination can use a different driver.
Entity is a ragged- hierarchy and the relationships within the Entity hierarchy drive the calculations for other departments
Every month actuals are loaded on ad hoc basis and Forecast is recalculated based on prior 3 months of actuals
Final Output is the P&L Dollars, Quantity (Activity), FTEs and WRVUs
Revenue Dollars = Quantity * CPU
Personnel Expenses = FTE * AHR + Fringes
Non Personnel Expenses = Driver Ratio * FTE /Revenue
Revenue
Forecast
Personnel Expenses
Non Personnel Expenses

20. Non Personnel Expenses
Revenue
Revenue plan – Activity is calculated for every procedure category based on specific drivers per Department/Service Line/Service and Procedure Category
Downstream Revenue which is about 90% of the total patient revenue is driven by the relationship between Downstream depts. and Upstream/Midstream depts.
Other Revenue is calculated based on 3 months actual average.
Deduction is based on a deduction rate calculated on 3 months average
Enhancement: Downstream forecast being driven by the ordering department’s weighted growth rate
Upstream
Patient Revenue (~90%)
Midstream
Revenue
Other Revenue
Downstream (~90%)
Forecast
Personnel Expenses
Deductions
Non Personnel Expenses
Patient Revenue Dollars = Quantity(Activity) * CPU(Charge Per Unit)
CPU is average of 3 months actual, adjustments allowed
Quantity = Drivers based on department and procedure category

21. Non Personnel Expenses
Personnel Expenses for Jobcode categories – Faculty, Advanced Practice Provider (APPs), Admin., Educational and Classified.
Faculty FTE is an input. Other jobcode FTEs are calculated based on Faculty FTE. Eg. – APP FTE / Faculty FTE, CLA FTE / Faculty FTE
Logic for Personnel Expenses is standard for all depts., Fund groups –
AHR * FTE * = Salary dollars for each job code category
Fringe Rate * Salary dollars = Fringe benefits for each job code category
FTE
Revenue
Salary Dollars
Forecast
Personnel Expenses
Fringe Benefits
Non Personnel Expenses

22. Non Personnel Expenses
Depreciation
Depreciation is Forecasted as the last closed month value
Drivers for each of the other NPE account category varies by Fund group
For eg. Travel expenses is calculated by the driver “Travel per FTE”, “Medical and Drug Supplies” uses the driver “Medical and Drug Supplies per Hospital GPR” OR “Medical and Drug Supplies per FTE” based on Fund group
Some Fund groups are directly input by the planners at the UTMDACC level and the calculations split the dollars value across various departments based on last closed month value split.
Medical and drug supplies
Revenue
Facilities
Forecast
Personnel Expenses
Travel
Non Personnel Expenses
Purchased Services
Other Supplies
Fund group - A Chartfield that represents a source of funds as classified for governmental accounting – e.g. Gifts, grants, state appropriations

23. Best Practices – Calculations Design
Comments in the scripts
Divided the scripts – eg Revenue calcs, NPE calc which could again direct how the scheduled process runs vs ad hoc process
Scripts needed to run in order (we have about 12 Scripts in Monthly process and about 8 Scripts in nightly)
Data loads vs Data calculated. Load actual as is so that reconciling is easy. Or load data where forecast needs to be calculated, that would make writing calculations easier.
Reporting measures – dynamic calc formulas (Calculating the ratios at Parent levels was causing an additional calculation time of 20 minutes)
Create Blocks for new intersections using Datacopy – Use ClearData instead of Clearblock

24. Reporting Solution Delivered

25. Reports/ Dashboards
Utilized an ASO Reporting cube and Warehouse for Downstream-Upstream relations
Dimensionality is different – But there is the “Total members” and “No members” 
Used export/import via load rules
Security on ASO reporting cube using Essbase filters
Shortened our development time

26. Project Highlights
BUDASO – A Catch all final reporting application – Budget, actual and now the latest Rolling Forecast
Adjustment members – Dollars Adjustment, Quantity adj
Nightly vs Monthly jobs
Multiple Scenarios and Versions (“Calculated” vs “Working” Version)
Upstream/ Midstream/Downstream relationship maintained in a Warehouse table and BI dashboards on the table

27. Challenges and Next Steps

28. Challenges
User Involvement - Changing user mindset of forecast at a higher level, rather than the operational budget which is at lowest chart string level.
Standardization Vs Exceptions – Select most effective and important exceptions and try to standardize overall
Circular References and Inconsistencies in the data – Orders data coming from external system had a few circular references (about 2% of the data). If the impact is insignificant, we ignored.
About less than 5% of the data coming from external systems was pointing to unknown category. We ignored such cases.
Overall Project timing – Collided with the Budget season.
Organizational changes.

29. Next Steps Dynamic calculations via business rules
Enhancements to the forecast logic
Migrating to EPBCS
Initial migration is a “Lift and Shift”