Strategic Checkup Central Michigan University

Presented by:Sarah Petsis, MBASenior Consultant

Ad Astra Information Systems

CMU Strategic Checkup Goals

• Alignment to CMU’s strategic plan:• Focused on student success and student retention

• Visibility into academic space utilization and management opportunities (Fall 2012 data used)

• Address current, reported scheduling challenges:• Limited Classroom availability in primetime

• Understand course offerings inefficiencies directly impacting budgets and capacity

• Understand course offerings warning signals that potentially impact student access to required courses and graduation

• Solution framework to leverage data from the SIS in future terms

Overview

Typical Strategic Issues

• Academic schedules are vitally important– Means of allocating faculty and space – Means of providing students with a path to completion

• Academic schedules are created in a decentralized process that is difficult to measure or manage

• Strategic opportunities to efficiently and effectively allocate academic resources are rarely realized

Typical Schedule Building Process

1. Course offerings are based on a historical schedule, typically a roll-forward of a “like” term

2. Departments refine offerings in silos (distinct processes and decision makers, limited collaboration and decision-support tools)

3. Student Information System is updated

4. Room assignments are made/refined

5. “Final” schedule is posted (changes still occur after registration or even after classes start)

The goal is commonly completion v. improvement

Course Offering ComplexityWhat is the impact of…

Students from other departments who need our courses?

Curriculum changes?The incoming freshman class?

Increasing retention rates?

Changes in my department’s headcount?Changing course eligibility requirements?

Improving graduation rates?

Changing classroom availability and capacity to add sections at certain times?

Changing transfer student enrollment?

Faculty load and capacity?

Scheduling for Student Success

Noel Levitz 2011 National Student Satisfaction & Priorities Report

• Identified key challenges for institutions

• Student Response: “Ability to get the courses I need with few conflicts” was the top challenge for 4-Year Public institutions

• Institution Response: “Ability to get the courses I need with few conflicts” was not ranked in top 25 for 4-Year Public institutions

Strategic Checkup Approach

1. Drill down from high-level metrics to related, and more granular and manageable, success drivers

2. Benchmark existing efficiencies of granular success drivers

3. Integrate relevant institutional goals and priorities (enrollment growth, cost savings, student outcomes, etc.)

4. Identify, quantify and prioritize opportunities

5. Select strategies that address opportunities and fit institution's culture

6. Implement and continually refine policy supporting strategies

Course Offerings + Capacity = High Impact Change

Course Offerings Analysis

Course Offering Analysis ConceptsGeneral Terms and Concepts

• Seats – Seats offered in the term being analyzed

• Blended Demand – Average of trend of historical course enrollment from like terms (Fall 2008, Fall 2009, Fall 2010, Fall 2011 and Fall 2012) and enrollments from last like term (Fall 2012)

• Enrollment Ratio – Course-specific fill rate calculated as average enrollments divided by average enrollment caps (last like term)

• Balanced Course Ratio – Courses wherein Enrollment Ratios are between 70% and 95% (last like term)

• Overloaded Course Ratio – Courses wherein Enrollment Ratios are over 95% (last like term)

Course Offering Analysis Concepts Analysis Term Disconnects

• Statistical Excess Seats – Seats offered in excess of Blended Demand• Statistical Additional Seats Needed – Blended Demand in excess of Seats• Reduction Candidates – Potentially superfluous sections of courses that can

be removed• Elimination Candidates – Courses that can potentially be removed from a

schedule entirely• Addition Candidates – Potentially needed sections of courses that can be

added to a schedule

Candidate Example

Course Seats Demand

Enrollment Ratio

Sections

Sections

Needed

Reduction AAA 400 300 145 48% 6 3Elimination AAA 401 50 8 16% 1 0

Addition AAA 100 500 585 101% 10 12

Course Offering Analysis – Fall 2012Undergraduate Only

Measurement Percent

Number Courses Percentile

Enrollment Ratio 87.17%Avg. Enroll / Avg. Enroll

Cap. 27.51 / 31.59

89th Percenti

le

Balanced Course Ratio 35.80% 435 of

1,21571st

Percentile

Overloaded Course Ratio 27.33% 332 of

1,21544th

Percentile

Average Enrollment / Average Enrollment Capacity

CMU Mean4-year Public Institutions

MeanMin Max

27.51 / 31.59

22.60 / 29.70

25.16 / 32.23

13.26 / 17.99

41.35 / 52.87

Course Offering Analysis – Fall 2013 Undergraduate Only

Measurement Percent Number Courses

Percentile

Statistical Excess Seats 18.65% 16,359 of

87,703 seats 87472nd

Percentile

Statistical Additional Seats Needed 16.61% 14,566 of

87,703 seats 71362nd

Percentile

Additional Seats Needed (Fall 2013 courses only)

5.85% 5,135 of 87,703 seats 249

39th Percenti

le

Reduction Candidates 4.86% 135 of 2,779 sections

97th Percenti

le

Elimination Candidates 3.20% 89 of 2,779

sections75th

Percentile

Addition Candidates 17.81% 495 of 2,779 sections

68th Percenti

le

Addition Candidates (Fall 2013 Only) 2.84% 79 of 2,779

sections69th

Percentile

Course Offering Analysis – By Level

LevelBaselin

e Sectio

ns

Enrollment Ratio

Enrollment

Enrollment Cap

Sections per

Course

000 Level 36 79.19% 23.36 29.47 1.89100 Level 1,096 83.66% 31.63 37.85 3.81200 Level 618 88.33% 28.12 31.87 2.52300 Level 729 91.89% 25.51 27.76 1.63400 Level 334 92.75% 17.66 19.07 1.55Undergrad 2,813 87.17% 27.51 31.59 2.32

500 Level 261 83.18% 18.41 22.16 1.34600 Level 197 76.61% 13.19 17.21 1.08700 Level 94 70.58% 8.63 12.22 1.31800 Level 24 95.45% 5.25 5.50 1.60Graduate 576 79.82% 14.48 18.15 1.24Totals 3,389 86.40% 25.29 29.30 2.02

Course Offering Analysis – By Level

LevelFall

2013 Section

s

Addition Candidat

es

Addition Candidates,

Fall 2013 Only

Reduction

Candidates

Elimination

Candidates

000 Level 36 0 0 5 1100 Level 1,083 124 40 66 11200 Level 613 91 22 32 9300 Level 698 197 17 6 37400 Level 312 83 0 26 31Undergrad 2,779 495 79 135 89

500 Level 246 136 14 1 18600 Level 159 96 5 0 54700 Level 76 21 0 1 20800 Level 15 20 14 0 1Graduate 533 273 33 2 93Totals 3,312 768 112 137 182

Course Offering AnalysisBy Sections per Course

Undergraduate Only• Findings vary by Sections per Course:

Sections per

CourseCourse

sAverage Enrollme

ntEnrollment Ratio

Balanced Course Ratio

Overloaded Course

Ratio1 767 24.34 79.21% 28.55% 25.81%2 200 27.37 82.29% 47.00% 29.00%

3 to 5 161 32.07 90.38% 52.17% 27.33%6 to 10 57 27.42 98.83% 40.35% 36.84%11 + 30 27.23 89.60% 50.00% 36.67%

Totals 1,215 27.51 87.17% 35.80% 27.33%

Course Offerings by Enrollment Ratio Tier Undergraduate Only

Enrollment Ratio Courses % of

TotalAverage

EnrollmentAverage

Enrollment Cap

0 Seats 109 8.97% 18.27 0.001-19% 49 4.03% 1.68 17.4120-49% 137 11.28% 12.32 35.8550-69% 153 12.59% 23.84 39.0870-95%

(Balanced) 435 35.80% 30.01 34.84

> 95% (Overloaded) 332 27.33% 32.55 32.35

Course Offering Opportunities

• Improved graduation rates from additional seats offered in “gateway” addition candidates (focus on required courses)

• Reduction of inefficiency/expense from reduction and elimination candidates (224 total candidates; 8.06% of all sections)

• Increased scheduling flexibility and capacity

• Reallocation of faculty, moving from reduction candidates to addition candidates

• Identification of unused faculty capacity (limited opportunity, CMU has already exceeded 85% enrollment ratio goal)

Course Offering Analysis Dashboards

70%75%80%85%90%

Enrollment Ratio

CMUMean Goal

0%10%20%30%40%50%

Balanced Course Ratio

CMUMean Goal

0%

10%

20%

30%

Addition Candidates

CMUMean Goal

0%

4%

8%

12%

Reduction Candidates

CMUMean Goal

Capacity Analysis

Average Utilization does not reflect capacity or inform space management

Space Bottleneck Concept

Room Type Campus “A”Primetime Util.

Campus “B” Primetime Util.

Classrooms (2) 50% 50%Science Lab (1) 50% 10%

Tech Auditorium (1) 50% 90%

Average Util. 50% 50%

Capacity Management Process

1. Identification of enrollment capacity for analysis term (Fall 2012):a. 80% primetime utilization and/orb. 95% effective utilization of any of the most dominant

primetime meeting patterns

2. Analysis of strategies to maximize quality/capacity

3. Selection of scheduling strategies/policies

4. Scheduling policy refinement/enforcement (ongoing)

5. Strategic renovation/new construction planning

Capacity Management Findings 1

• Average utilization of all instructional rooms:• 65-hour standard week (8am – 10pm M-R; 8am – 5pm F) – 37.44%• 32-hour prime week (9:00 am – 5:00 pm M-R) – 53.74%

• Utilization is significantly higher in certain room types:

• Classroom utilization during the standard week is Moderately High (69th Percentile)

• Classroom Prime Ratio (percentage of all usage in primetime) is High (13th Percentile)* – 69.71% – Mean is 58.13%

* Even spread would be 49% (32 of 65 hours)

Classroom (CR & DS) (163)

Other Rooms (196)

65-hour Standard Week 51.80% 25.49%

32-hour Prime Week 73.35% 37.43%

Capacity Management Findings 2• CR - Classroom utilization varies by size category during the 32-

hour primetime: SEATS ROOMS PRIME ROOM

HRS.PRIME

UTILIZATIONPRIME RATIO

16 – 25 16 250.53 48.93% 71.39%26 – 50 56 1,445.57 80.67% 72.33%51 – 100 44 1,172.60 83.28% 71.23%100+ 11 286.10 81.28% 80.61%Total 127 3,154.80 77.63% 72.51%• DS - Department Scheduled Classroom utilization varies by size

category during the 32-hour primetime: SEATS ROOMS PRIME ROOM

HRS.PRIME

UTILIZATIONPRIME RATIO

1 – 15 2 0.00 0.00% 0.00%16 – 25 6 53.80 28.02% 56.83%26 – 50 20 434.23 67.85% 58.07%51 – 100 7 155.27 69.32% 61.91%100+ 1 27.60 86.25% 66.88%Total 36 670.90 58.24% 58.72%

Capacity Management Findings 3• Classroom (CR & DS) utilization varies by region:

• Top Ten Classroom (CR & DS) regions by highest primetime utilization:

REGION ROOMSOVERALL

UTILIZATION

PRIME UTILIZATIO

NPRIME RATIO

CR - Academic Advising and Assistance 1 58.31% 98.33% 83.03%CR - Earth and Atmospheric Sciences 1 60.51% 98.33% 80.00%CR - Finance and Law 2 74.69% 93.70% 61.76%CR - Management 2 74.67% 92.08% 60.71%CR - Accounting, School of 2 68.31% 91.35% 65.84%DS - 1 - Hum and Soc & Behav Sciences 1 51.18% 89.06% 85.67%CR - Mathematics 11 56.90% 88.42% 76.50%CR - Chemistry 3 52.10% 86.08% 81.33%CR - Broadcasting and Cinematic Arts, School of/Psychology

1 67.69% 85.83% 62.42%

CR - Computer Science 3 53.20% 84.90% 78.57%

Capacity Management Findings 3• Classroom (CR & DS) utilization varies by region:

• Bottom Ten Classroom (CR & DS) regions by lowest primetime utilization:

REGION ROOMSOVERALL

UTILIZATION

PRIME UTILIZATIO

NPRIME RATIO

DS - 0 - Non-Departmental 2 12.13% 0.00% 0.00%CR - MSA Program 1 12.26% 24.06% 96.65%DS - 3 - Education & Human Services 16 40.65% 46.70% 56.56%CR - Engineering and Technology, School of 2 31.10% 48.75% 77.16%CR - Biology 2 36.10% 58.33% 79.55%CR - Business Information Systems 2 50.82% 60.68% 58.78%DS - 5 - Communication & Fine Arts 1 42.36% 61.35% 71.31%CR - Human Environmental Studies 4 41.09% 61.38% 73.54%CR - Psychology 5 39.77% 62.90% 77.85%CR - College of Business 2 42.51% 64.06% 74.19%

Capacity Management Findings 4• Seat fill ratios in Classrooms (CR & DS) vary by capacity:

• Classroom Seat Fill (Enroll) ratio comparison: 27th Percentile• Classroom Seat Fill (Cap) ratio comparison: 18th Percentile

• CR - Classroom seat fill ratios:SEATS ROOM

S CAPACITY ENROLL FILL (ENROLL)

ENROLL CAP

FILL (CAP)

16 – 25 16 21.27 13.57 63.78% 18.38 86.42%26 – 50 56 44.18 26.38 59.72% 31.55 71.43%51 – 100 44 65.49 37.60 57.41% 45.15 68.94%100+ 11 202.03 109.22 54.06% 137.37 68.00%Total 127 63.31 36.37 57.45% 44.29 69.96%• DS - Department Scheduled Room seat fill ratios:SEATS ROOM

S CAPACITY ENROLL FILL (ENROLL)

ENROLL CAP

FILL (CAP)

1 – 15 2 12.00 7.00 58.33% 7.00 58.33%16 – 25 6 24.02 13.67 56.91% 19.41 80.78%26 – 50 20 41.24 25.40 61.59% 28.23 68.47%51 – 100 7 68.71 41.67 60.64% 46.11 67.11%100+ 1 200.00 107.87 53.93% 117.93 58.97%Total 36 51.37 30.85 60.05% 34.52 67.19%

Capacity Management Findings 5

MEETING PATTERN

TOTAL USAGE

TOTAL UTILIZATIO

NOFF-GRID

USAGEOFF-GRID

UTILIZATION

MWF 9:00 - 9:50 285 58.28% 111 22.70% MWF 10:00 - 10:50 369 75.46% 141 28.83% MWF 11:00 - 11:50 348 71.17% 147 30.06% MWF 12:00 - 12:50 333 68.10% 177 36.20% MWF 1:00 - 1:50 255 52.15% 138 28.22% MW 2:00 - 3:15 447 91.41% 93 19.02% TR 9:30 - 10:45 441 90.18% 96 19.63% TR 11:00 - 12:15 423 86.50% 102 20.86% TR 12:30 - 1:45 435 88.96% 81 16.56% TR 2:00 - 3:15 438 89.57% 75 15.34%Total 3,774 77.18% 1,161 23.74%

• On-grid Primetime Meeting Pattern usage in Classrooms (CR & DS rooms) varies by Pattern: *Denominator = 489 total weekly hours

Capacity Management Findings 5, continued (summary)

• There is Moderately Low off-grid meeting pattern usage in Classrooms (CR & DS rooms) during primetime meeting patterns:

• 30.76% (69th percentile) of usage for all Classrooms (CR & DS rooms)

• There is a Moderately Low off-grid “waste factor”

• 441 hours or 11.69% of all Classroom (CR & DS) capacity is wasted (71st Percentile)

Capacity Management Opportunities• Evenly utilize all CR & DS Classrooms at 60% across the 65-

hour standard week• Result: Balance scheduling across all Classrooms (15.83% capacity

increase, or 3,246 students)• Graph category label: “Optimize Rooms”

• Limit Off-Grid scheduling waste in CR & DS Classrooms from 11.69% to 5%• Result: Eliminate waste inherent in off-grid scheduling (6.69%

capacity increase, or 1,372 students)• Graph category label: “Meeting Patterns”

• Reduce primetime scheduling in CR & DS Classrooms to 60%• Result: Move activities outside of primetime to reduce prime ratios

of 69.71% (13.93% capacity increase, or 2,856 students)• Graph category label: “Prime Ratio”

Capacity Management Dashboards

30%

35%

40%

45%

50%

55%Space Utilization

CMUMeanGoal

0%10%20%30%40%50%60%70%80%

Prime Ratio

CMUMeanGoal

2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 20,000 20,500 21,000 21,500 22,000 22,500 23,000 23,500 24,000

Enrollment Capacity

EnrollmentExisting CapacityOptimize RoomsMeeting PatternsPrime Ratio

CMU Strategy Options to Evaluate

Course Offering Efficiency Strategies:• Evaluate Elimination Candidates for degree requirement impact• Select Reduction and Elimination Candidates (224 total candidates;

8.06% of all sections) to remove from Fall 2013 schedule

Course Offering Student Access Strategies:• Evaluate Addition Candidates for degree requirement impact• Consider implementation of Platinum Analytics (uncover key Addition

Candidates to improve student completion rates)

Capacity Bottlenecks Strategies:• Optimize Rooms (15.83% potential capacity)• Meeting Patterns (6.69% potential capacity)• Prime Ratio (13.93% potential capacity)

• Develop a schedule review team and process– Senior leadership and academic department representation– Focused on leveraging and sharing schedule analysis

• Develop data-driven scheduling policies– Course offering efficiency and effectiveness– Meeting pattern and room assignment efficiency

• Integrate other academic planning processes (curriculum planning, academic space planning, student success initiatives, etc.)

CMU Potential Next Steps

Which Category Describes Your Institution?• Category 1:

• Motivated to aggressively improve student outcomes and address inefficiencies and capacity issues

• Mobilized to implement change (a schedule review team is in place that includes senior administrative and academic representation)

• Category 2: • Interested in understanding how to improve student outcomes and

address inefficiencies and capacity issues• Considering needed process changes (candidates exist for a schedule

review team, including senior administrative and academic representation)

• Category 3: • Discussing and considering a need to improve student outcomes, address

inefficiencies and capacity• Not ready to discuss needed process changes (no candidates identified for

a schedule review team)

Questions?

Sarah PetsisSenior Consultantspetsis@aais.com