decision support system

Decision Support System

Dr. William ScrogginsPresident

Dr. Irene MalmgrenVice President of Instruction

Bob HughesDirector, Enterprise Applications Systems

Daniel LamoreeSr. Systems Analyst/Programmer

RP Group Conference 2014April 10th, 2014

The Role of Executive Leadership

President’s CabinetDecision Support System given high priorityRegular updates to Cabinet

Instruction TeamDemonstration of application to DeansGather Feedback

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

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

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 programmerLocated in IT Build trust with other IT staff Improved access to data

Learning Objectives

1. How Mt. SAC calculates FTES Targets

2. How Mt. SAC decides sections to add or cut

3. How Mt. SAC Deans develop prospective schedules

Mt. SAC Story: Lost FTES

Scheduling 2014-2015 Overview

Top-Down ApproachGet Annual FTES TargetDistribute 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 . . .

Annual Targeting

ExampleFunded FTES for Prior Year = 29371.99Growth = 3.5%Unfunded FTES for Prior Year = 400 ((29371.99 * 1.035) – 400) = 30000CR: 27000 (90%), 2400 ENHC_NC (8%), 600 NC (2%)10% Summer; 42% Fall; 8% Winter; 40% SpringOf 10% Summer: 36.22% HSS; . . .

Annual Targeting

Annual Targeting

Annual Targeting

Annual Targeting

Annual Targeting

Just one catch . . .

Moving Target

Minimizing Spring UncertaintyKnowns

Sections Scheduled for SpringScheduled Hours per Section for SpringHistorical Fill Rate for Spring

UnknownsFuture Contact Hours (Fill Rate for WSCH/DSCH or

PACH)

Mt. SAC DecisionProjection

Projection: Weighted Averages4 3 2 1

-25%

-20%

-15%

-10%

-5%

0%

5%

10%

15%

20%

25%

1st Most Recent2nd Most Recent3rd Most Recent4th Most Recent

Does the projection work?

Spring 2012

Acct Potential Projected Actual Error # Error %

W 8432.9192 8312.95 8381.988 69.038 0.824%

IW 289.5956 311.02 266.019 45.001 16.916%

ID 117.6122 110.82 97.9406 12.8794 13.150%

D 374.2581 395.12 340.651 54.469 15.990%

LD 33.2027 25.87 28.3751 2.5051 8.829%

LW 256.2772 236.96 224.4053 12.5547 5.595%

TOTAL 9503.865 9392.74 9339.379 53.361 0.571%

Does the projection work?

Fall 2012

Acct Potential Projected Actual Error # Error %

W 8980.0658 8939.189004.8445 65.6645 0.729%

IW 294.9029 323.12 268.3027 54.8173 20.431%

ID 146.9111 148.73 123.4596 25.2704 20.469%

D 393.9151 385.57 337.1664 48.4036 14.356%

LD 29.3333 27.49 29.0263 1.5363 5.293%

LW 234.8028 208.51 228.1898 19.6798 8.624%

TOTAL 10079.931 10032.69990.9893 41.6107 0.416%

Does the projection work?

Spring 2013

Acct Potential Projected Actual Error # Error %

W 9147.3702 8924.058839.0069 85.0431 0.962%

IW 320.2368 337.38 290.7746 46.6054 16.028%

ID 141.3104 131.81 116.7168 15.0932 12.931%

D 426.6003 445.74 359.189 86.551 24.096%

LD 27.8053 23.8 26.2312 2.4312 9.268%

LW 271.2639 245.29 231.6928 13.5972 5.869%

TOTAL 10334.5869 10108.079863.6113 244.4587 2.478%

Does the projection work?

Fall 2013

Acct Potential Projected Actual Error # Error %

W 9444.0141 9523.719263.0738 260.6362 2.814%

IW 350.3353 418.18 314.465 103.715 32.981%

ID 189.386 215.32 162.3545 52.9655 32.623%

D 369.7928 397.66 317.5203 80.1397 25.239%

LW 93.0252 124.5 83.0736 41.4264 49.867%

TOTAL 10446.5534 10679.3710140.487 538.8828 5.314%

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

APEX

HighlightsOracle’s primary tool for developing Web

applications using SQL and PL/SQLOnly requires web browser to developNo cost option of the Oracle Database

ReportsWhat sections should we add?

Demand 90%+ Fill Waitlists

Registration Acceleration

What sections should we cut?Lagging SectionsRegistration Acceleration

What else?Room UsageExcluded CRNs

Demand 90%+ Fill

Response?

Waitlists

Registration Acceleration

Lagging Courses

Lagging Courses

Room Usage

Excluded CRNs From 320

Sandbox

Sandbox

Sandbox

Sandbox

Sandbox

Sandbox

Sandbox

Sandbox

Sandbox

Sandbox

Sandbox

Sandbox

Sandbox

Sandbox

Sandbox

Sandbox

Sandbox

Sandbox

Future

Building Predictive Model

Room/Space Efficiency

Reporting Off Sandboxes

Task/Directive Assignment