Decision Support System

1. RP Group Conference 2014 April 10th, 2014 Decision Support System Dr. William Scroggins President Dr. Irene Malmgren Vice President of Instruction Bob Hughes Director, Enterprise Applications Systems Daniel Lamoree Sr. Systems Analyst/Programmer

2. The Role of Executive Leadership • President’s Cabinet • Decision Support System given high priority • Regular updates to Cabinet • Instruction Team • Demonstration of application to Deans • Gather Feedback

3. IT and Research Collaboration • Information Technology staff goal – protect the data; restrict access • Researchers goal – more data leads to better decision-making • When these units are in different divisions, the need for cooperation and collaboration is critical

4. The approach at Mt. SAC • Data Users Group (DUG) meeting – every 2 weeks • Researchers Data Warehouse (RDW) instance of the Banner Database • Report Designer access to Argos DEV • SQL training of Researchers by IT Staff

5. Leveraging Talent • Director of RIE recognized a team member with a unique aptitude for programming • Director of EAS (Enterprise Resource Applications) responded to a need for a Decision Support System • Best solution – temporary reassignment of a researcher to IT as a programmer • Located in IT • Build trust with other IT staff • Improved access to data

6. Learning Objectives • How Mt. SAC calculates FTES Targets • How Mt. SAC decides sections to add or cut • How Mt. SAC Deans develop prospective schedules

7. Mt. SAC Story: Lost FTES

8. Scheduling 2014-2015 Overview • Top-Down Approach • Get Annual FTES Target • Distribute Annual Target between CR, ENHC_NC, NC • Grow only Credit? Distribute as before? • Distribute CR, ENHC_NC, NC among Terms • Grow Summer (yes, please)? Fall? Winter? Spring? • Distribute FTES among Divisions . . .

9. Annual Targeting • Example • Funded FTES for Prior Year = 29371.99 • Growth = 3.5% • Unfunded FTES for Prior Year = 400 • ((29371.99 * 1.035) – 400) = 30000 • CR: 27000 (90%), 2400 ENHC_NC (8%), 600 NC (2%) • 10% Summer; 42% Fall; 8% Winter; 40% Spring • Of 10% Summer: 36.22% HSS; . . .

10. Annual Targeting

11. Annual Targeting

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13. Annual Targeting

14. Annual Targeting • Just one catch . . .

15. Moving Target

16. Minimizing Spring Uncertainty • Knowns • Sections Scheduled for Spring • Scheduled Hours per Section for Spring • Historical Fill Rate for Spring • Unknowns • Future Contact Hours (Fill Rate for WSCH/DSCH or PACH) • Mt. SAC Decision • Projection

17. Projection: Weighted Averages

18. Does the projection work?

19. Does the projection work?

20. Does the projection work?

21. Does the projection work?

22. What happened? • No variance in pervious years; easy to project when fill rates hover around 100% (after drops and adds) • What now? • More robust model, an actual predictive model • Will that help given downward trends? Always 1 year or term behind? • Maintain Agility • Reporting via Argos and APEX • Sandboxing via APEX

23. APEX • Highlights • Oracle’s primary tool for developing Web applications using SQL and PL/SQL • Only requires web browser to develop • No cost option of the Oracle Database

24. Reports • What sections should we add? • Demand • 90%+ Fill • Waitlists • Registration Acceleration • What sections should we cut? • Lagging Sections • Registration Acceleration • What else? • Room Usage • Excluded CRNs

25. Demand 90%+ Fill

26. Response?

27. Waitlists

28. Registration Acceleration

29. Lagging Courses

30. Lagging Courses

31. Room Usage

32. Excluded CRNs From 320

33. Sandbox

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