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Virginia State University Wagner College Washburn University Wellesley College Wesleyan University West Chester University of Pennsylvania West Virginia Health Sciences Center West Virginia University Western Connecticut State University Western Oregon University Westfield State University Wheaton College (MA) Whitworth University Widener University Williams College Williston Northampton School Worcester State University Xavier University Yeshiva University Youngstown State University Fact, Fiction, or Crap How is Your Facilities Data?

Is Your Facilities Data Fact, Fiction, or Crap? - Creating Facilities Intelligence for Data Driven Decision Making on Campus

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In this session, Abilene Christian University, University of Nebraska at Kearney, and New Mexico State University will share with you the steps they have taken to harness vast amounts of facilities and financial data to create facilities intelligence. Additionally, they will share how they have used this knowledge to provide strategic decision making support not only within their respective facilities organizations but also with senior administration and across the broader campus community. In a time of limited resources and competing demands, the value of validated data has never been greater. Through a process of independent third party validation, benchmarking, and analysis they have been able to position their organizations for success. The creation of a common vocabulary allows information to be communicated effectively from the boiler room to the board room, thus helping their institutions understand both the impact of historic decisions and what the impact of future decisions may be on campus facilities. Much like institutions analyze the ROI of their endowments, this data-driven, fact-based analysis allows campuses to understand the interrelation of annual stewardship, asset reinvestment, operating effectiveness, and customer service; and how decisions in one of these areas can either positively or negatively impact other areas.

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Page 1: Is Your Facilities Data Fact, Fiction, or Crap? - Creating Facilities Intelligence for Data Driven Decision Making on Campus

Virginia State UniversityWagner College

Washburn UniversityWellesley College

Wesleyan UniversityWest Chester University of

PennsylvaniaWest Virginia Health Sciences Center

West Virginia University Western Connecticut State University

Western Oregon UniversityWestfield State University

Wheaton College (MA)Whitworth University

Widener UniversityWilliams College

Williston Northampton SchoolWorcester State University

Xavier UniversityYeshiva University

Youngstown State University

Fact, Fiction, or CrapHow is Your Facilities Data?

Page 2: Is Your Facilities Data Fact, Fiction, or Crap? - Creating Facilities Intelligence for Data Driven Decision Making on Campus

About Sightlines

Page 3: Is Your Facilities Data Fact, Fiction, or Crap? - Creating Facilities Intelligence for Data Driven Decision Making on Campus

Who Does Sightlines Provide Value To?

400 + College and University

campuses, in 44 states rely on Sightlines to improve their

facilities management.

Contextthrough

benchmarking

Common facilities

vocabulary

Consistent analytical

methodology

Page 4: Is Your Facilities Data Fact, Fiction, or Crap? - Creating Facilities Intelligence for Data Driven Decision Making on Campus

Facilities Database45,000 buildings

1.2 Billion square feet50,000+ workers

177M MMBTUs of energy

Finance Database$17B in annual

operating and capital

Capital Renewal Database

Lifecycle data on 5000 buildings

250M GSF

Carbon DatabaseAccess to 600+ campus carbon

programs

Based on Industry-Leading DatabasesChanging the way higher education looks at facilities

Page 5: Is Your Facilities Data Fact, Fiction, or Crap? - Creating Facilities Intelligence for Data Driven Decision Making on Campus

• Sightlines collects and assembles data on campus to quantify, verify, and qualify facility performance.

Measure

• Through the benchmarking process, institutions have the capability to create custom comparisons that help them understand context and performance.

Benchmark

• Sightlines synthesizes an institution's verified data to develop strategic directions for change.Analyze

• Sightlines continues to support each campus through the member website, educational webinars, and ongoing consultation with staff.

Membership

The Sightlines process

Page 6: Is Your Facilities Data Fact, Fiction, or Crap? - Creating Facilities Intelligence for Data Driven Decision Making on Campus

Changing the Conversation O

pera

tions

Suc

cess

ROPA Radar Chart

Annual Stewardship

Ass

et

Rei

nves

tmen

t

Operating

Effectiveness

Service

Target

Actual

Optimal

The annual investment needed to ensure buildings will properly perform and reach their useful life

“Keep-Up Costs”

AnnualStewardship

The accumulated backlog of repair /modernization needs and the definition of resource capacity to correct them “Catch-Up Costs”

Asset Reinvestment

The effectiveness of the facilities operating budget, staffing, supervision, and energy management

OperationalEffectiveness

The measure of service process, the maintenance quality of space and systems, and the customers opinion of service delivery

Service

Ass

et v

alue

cha

nge

Page 7: Is Your Facilities Data Fact, Fiction, or Crap? - Creating Facilities Intelligence for Data Driven Decision Making on Campus

An integration of formerly unrelated partsTracking change over time to identify strengths & opportunities

1. Stewardship falls2. Failures increase3. Customer satisfaction 

decreases4. Increases operational 

demand5. Capital investment driven by 

customers. Space wins over systems.

6. Backlog increases

1. Increase Stewardship2. Limit failures3. Increased customer 

satisfaction4. Decrease operational 

demand5. Increased PM6. Reduce backlog

Page 8: Is Your Facilities Data Fact, Fiction, or Crap? - Creating Facilities Intelligence for Data Driven Decision Making on Campus

Corey RuffExecutive Director, Facilities

& Campus Management(325) 674-2665

[email protected]

Page 9: Is Your Facilities Data Fact, Fiction, or Crap? - Creating Facilities Intelligence for Data Driven Decision Making on Campus

Campus profile

> Type: Private, comprehensive university> Founded: 1906> Located in: Abilene, TX> Enrollment: 4,600 undergrad/800 grad> Size: 1,966,315 GSF> Mission: To educate students for Christian

service and leadership throughout the world> 21st-Century Vision: To become the premier

university for the education of Christ-centered, global leaders

Page 10: Is Your Facilities Data Fact, Fiction, or Crap? - Creating Facilities Intelligence for Data Driven Decision Making on Campus

Challenges

> Change in Leadership> New President – 2010

> New Senior Leadership Team

> More data driven

> Historical Data Spotty

> Master Planning Process

> Deferred Maintenance/Backlog/Capital Renewal/ Broken Stuff/Honey Do List

> New Facilities Leadership

Page 11: Is Your Facilities Data Fact, Fiction, or Crap? - Creating Facilities Intelligence for Data Driven Decision Making on Campus

Tech rating impacts:• Energy Consumption• Maintenance Staffing• Replacement Values• Stewardship Targets• Operational Demand

Determining Peers

Density Factor Impacts:• Churn on Campus• Drives Custodial• Maintenance Demand

Private, small town or near mid size city, 1-2.5M GSF

Page 12: Is Your Facilities Data Fact, Fiction, or Crap? - Creating Facilities Intelligence for Data Driven Decision Making on Campus

12% 13%

25% 26%

45% 45%

18% 16%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

ACU FY12 Peers

% o

f Tot

al C

ampu

s G

SF

Campus Age by Category

Under 10 10-25 25-50 Over 50

Campus age profile – Impacts investment profile63% of space is considered in high risk for life cycle failures

Buildings Under 10Little work. “Honeymoon” period.

Low Risk

Buildings 10 to 25Short life-cycle needs; primarily space renewal.

Medium Risk

Buildings 25 to 50Major envelope and mechanical life cycles come due.

Higher Risk

Buildings over 50Life cycles of major building components are past due.

Failures are possible.Highest risk

High Risk High Risk

Page 13: Is Your Facilities Data Fact, Fiction, or Crap? - Creating Facilities Intelligence for Data Driven Decision Making on Campus

How much should we spend?ACU spending well under target

$13.4

$5.2$3.9

$1.1

$5.8

$2.9

$0.1

$0$1$2$3$4$5$6$7$8$9

$10$11$12$13$14$15

3% Replacement Value Life Cycle Need(Equilibrium)

Functional Obsolescence(Target)

FY12 Actual Spending

$ in

Mill

ions

Envelope/Mechanical Space/Program

FY12 Stewardship TargetsReplacement Value = $448M

Life Cycle need is discounted to account for programmatic shifts and the churn of space

Industry Standard Sightlines RecommendationsSightlines Recommendations ACU Actual Spent

Page 14: Is Your Facilities Data Fact, Fiction, or Crap? - Creating Facilities Intelligence for Data Driven Decision Making on Campus

Gap Widening = Backlog is increasing

$0.0

$2.0

$4.0

$6.0

$8.0

$10.0

$12.0

2008 2009 2010 2011 2012

Mill

ions

Annual Stewardship Investment vs. Target

Env/Mech Space/Program

$2.3M$3.7M $4.6M

$4.9M $5.7M

Total 5 year deferral = $21.2M

Backlog grew40% to $147.6M

between FY08 and FY13

Page 15: Is Your Facilities Data Fact, Fiction, or Crap? - Creating Facilities Intelligence for Data Driven Decision Making on Campus

Capital Spending vs. Peers

$3.38Peers

5Y avg.

$1.25ACU

5Y avg.

Both ACU & peers are spending less on total projects

-70%

-44%

Page 16: Is Your Facilities Data Fact, Fiction, or Crap? - Creating Facilities Intelligence for Data Driven Decision Making on Campus

Capital Spending by Package = Adding credibility

34%

13%27%

22%

4%

ACU Spending FY12

FY12 $/GSF Investment$0.82

11%

24%

12%

45%

8%

Peers 5Y Spending Average

Average $/GSF Investment$3.42

31%

24%10%

33%

2%

ACU 5Y Spending Average

Average $/GSF Investment$1.25

74%

Page 17: Is Your Facilities Data Fact, Fiction, or Crap? - Creating Facilities Intelligence for Data Driven Decision Making on Campus

Facilities Operating Budget = Comparing ResourcesDaily service lags, planned maintenance is on par with peers

Page 18: Is Your Facilities Data Fact, Fiction, or Crap? - Creating Facilities Intelligence for Data Driven Decision Making on Campus

Off the Chart Maintenance CoverageFacilities staff delivers superior results with fewer resources

Peer: $0.18

DB: $0.21

Institutions ordered by technical complexity

ACU: 211,158

ACU: $0.19

Peer: 105,072

DB: 90,040

Peer: 16.5

DB: 11.2

ACU: 20.2

Inspection Score 1-5

ACU: 3.9/5.0

Campus Peers: 3.8/5.0

Distribution of Coverage

DB: 90,040 Small private institutions: 90,209 ACU: 211,158

Page 19: Is Your Facilities Data Fact, Fiction, or Crap? - Creating Facilities Intelligence for Data Driven Decision Making on Campus

Intuition to FactData helped support our intuitions

> Having a 3rd party collect, quantify, verify, and qualify data validated the feeling and added credibility to the facilities staff

> We learned what data is important to measure & monitor

> Data presented to the senior budget staff

> Increased credibility in facilities.

> And…. $7 Million additional in Capital Renewal and funding for FCA

Page 20: Is Your Facilities Data Fact, Fiction, or Crap? - Creating Facilities Intelligence for Data Driven Decision Making on Campus

Lee McQueenDirector of Facilities

Management & Planning(308) 865-1700

[email protected]

Page 21: Is Your Facilities Data Fact, Fiction, or Crap? - Creating Facilities Intelligence for Data Driven Decision Making on Campus

Campus profile

> Type: Public, Residential, Comprehensive University

> Founded: 1905> Located in: Kearney, NE> Enrollment: 5,442 undergrad/1658 grad> Size: 1,966,315 GSF> Vision: The University of Nebraska at

Kearney will achieve national distinction for a high quality, multidimensional learning environment, engagement with community and public interests, and preparation of students to lead responsible and productive lives in a democratic, multicultural society.

Key to such improvement will be: clear focus on mission imperatives, fidelity to historic core values, and continuous and rigorous self-appraisal or assessment of outcomes.

Page 22: Is Your Facilities Data Fact, Fiction, or Crap? - Creating Facilities Intelligence for Data Driven Decision Making on Campus

Yesterday….. Questions being asked

Issues• Lots of data, limited information.• Non-validated data• How do we use it?• Are we on track?• Is our data fact or fiction

Desires• Needed clean data• Validation to separate fact from

fiction.• Tools to change the discussion on

campus.

Page 23: Is Your Facilities Data Fact, Fiction, or Crap? - Creating Facilities Intelligence for Data Driven Decision Making on Campus

Physical Profile

Page 24: Is Your Facilities Data Fact, Fiction, or Crap? - Creating Facilities Intelligence for Data Driven Decision Making on Campus

Age profile for UNKUNK’s age profile is well distributed; Revenue Bond space is younger on average

16%

27% 26%

31%

19%

23%

32%

26%

0%

5%

10%

15%

20%

25%

30%

35%

0 - 10 10-25 25-50 50+

% o

f GSF

Renovation Age of UNK’s Campus

UNK - State Aided UNK - Revenue Bond

17%27%

25%

18%

29%

38%

29%17%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

UNK ‐ Composite Peer Average

Campus Age Profile vs. Peers

Under 10 Years 10 to 25 Years25 to 50 Years Over 50 Years

1.1M GSF 874K GSFSize:Renovation Age: 37.1 years old 34.9 years old

Page 25: Is Your Facilities Data Fact, Fiction, or Crap? - Creating Facilities Intelligence for Data Driven Decision Making on Campus

Age profile for UNKUNK’s age profile is well distributed; Revenue Bond space is younger on average

65%

Buildings Under 10Little work .“Honeymoon” period.

Low Risk

Buildings 10 to 25Lower cost space renewal updates and

initial signs of program pressures Medium Risk

Buildings 25 to 50Life Cycles are coming due in envelope and mechanical

systems. Functional obsolescence prevalent.Higher Risk

Buildings over 50Life cycles of major building components are past due. Failures

are possible. Core modernization cycles are missed.Highest risk

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

UNK ‐ Composite Peer Average

Campus Age Profile vs. Peers

Under 10 Years 10 to 25 Years25 to 50 Years Over 50 Years

High Risk

Moderate Risk

Low Risk

Page 26: Is Your Facilities Data Fact, Fiction, or Crap? - Creating Facilities Intelligence for Data Driven Decision Making on Campus

0%

1%

2%

3%

4%

5%

6%

7%

8%

$0

$5

$10

$15

$20

$25

$30

$35

$40

$45

$50

$55

$60

$/GSF

<10 Years 10 – 25 Years 25-50 Years

% of C

ampus G

SFCampus’ detailed age distributionYoung space on campus will soon be aging into a more demanding age range

Average Life Cycle Costs by Age of Space (Renovation Age)

* Life cycle costs based on the average tech 3 academic space.17% 25% 29%

Amortization

State AidedRevenue Bond

Over 50 Years29%

Page 27: Is Your Facilities Data Fact, Fiction, or Crap? - Creating Facilities Intelligence for Data Driven Decision Making on Campus

Capital Investments

Page 28: Is Your Facilities Data Fact, Fiction, or Crap? - Creating Facilities Intelligence for Data Driven Decision Making on Campus

Total capital spendingTotal FY12 spending was $1.9M

$0.0

$5.0

$10.0

$15.0

$20.0

$25.0

$30.0

$35.0

FY2007 FY2008 FY2009 FY2010 FY2011 FY2012

Millions

Total Capital Spending

State Aided Revenue Bond Non‐Facilities/New Space

$10,580,036 $17,821,668 $29,899,331 $6,695,813 $3,349,940 $1,913,315

Avg: $11.7M

Page 29: Is Your Facilities Data Fact, Fiction, or Crap? - Creating Facilities Intelligence for Data Driven Decision Making on Campus

Annual stewardship investment vs. targetState Aided performance vs. Revenue Bond

$0.0

$1.0

$2.0

$3.0

$4.0

$5.0

$6.0

$7.0

$8.0

$9.0

$10.0

2007 2008 2009 2010 2011 2012

Mill

ions

Sustaining NAV

2007 2008 2009 2010 2011 2012

Space/Program

Sustaining NAV Target Need

Equilibrium Need

Annual Stewardship Funding vs. Target

State Aided Revenue Bond

Envelope/Mechanical

Page 30: Is Your Facilities Data Fact, Fiction, or Crap? - Creating Facilities Intelligence for Data Driven Decision Making on Campus

Total project spending by funding sourceState Aided performance vs. Revenue Bond

$0.0

$1.0

$2.0

$3.0

$4.0

$5.0

$6.0

$7.0

$8.0

$9.0

$10.0

2007 2008 2009 2010 2011 2012

Mill

ions

Sustaining NAV

2007 2008 2009 2010 2011 2012

Sustaining NAV Target Need

Equilibrium Need

Total Funding vs. Annual Stewardship TargetState Aided Revenue Bond

Annual Stewardship Asset Reinvestment

Page 31: Is Your Facilities Data Fact, Fiction, or Crap? - Creating Facilities Intelligence for Data Driven Decision Making on Campus

0

20

40

60

80

100

120

140

160

07 08 09 10 11 12

Total funding vs. target compared to peersPeers have funded consistently more than UNK over the last six years

Recurring capital Planned maintenance One-time capital

% o

f Tar

get

Peer Average

Avg: 133%

UNK – Composite

0

20

40

60

80

100

120

140

160

07 08 09 10 11 12

Avg: 53%

Decreasing Net Asset Value

Sustaining or Increasing Net Asset Value

Page 32: Is Your Facilities Data Fact, Fiction, or Crap? - Creating Facilities Intelligence for Data Driven Decision Making on Campus

$0.00

$0.20

$0.40

$0.60

$0.80

$1.00

$1.20

$1.40

$1.60

$1.80

$2.00

Envelope Systems Infrastructure Space Code

$/G

SF$/GSF by type of project

FY07-FY12 Avg.

UNK - Composite Peer Average

Project spending by type Revenue bond has invested a lot into safety, while state aided is more balanced

Page 33: Is Your Facilities Data Fact, Fiction, or Crap? - Creating Facilities Intelligence for Data Driven Decision Making on Campus

Asset Reinvestment BacklogFailing to reach target spending recently has caused backlog to grow rapidly

-

20.00

40.00

60.00

80.00

100.00

120.00

Peers UNK - State Aided UNK - Revenue Bond

$/G

SF

Total Backlog ($/GSF)

07 070712 1212

Page 34: Is Your Facilities Data Fact, Fiction, or Crap? - Creating Facilities Intelligence for Data Driven Decision Making on Campus

Operations Overview

Page 35: Is Your Facilities Data Fact, Fiction, or Crap? - Creating Facilities Intelligence for Data Driven Decision Making on Campus

Facilities operating budget compared to peers

Page 36: Is Your Facilities Data Fact, Fiction, or Crap? - Creating Facilities Intelligence for Data Driven Decision Making on Campus

Energy consumption remains above peer averageLower unit costs than peers avoided over $550,000 in energy costs in FY12

Stationary FuelElectricity

Page 37: Is Your Facilities Data Fact, Fiction, or Crap? - Creating Facilities Intelligence for Data Driven Decision Making on Campus

Maintenance operationsSpace is harder to maintain at a high level due to low capital investment

Institutions ordered by technical complexity

Peer: 78,864 DB: 86,312 Peer: 14.2 DB: 11.9

Peer: $0.16 DB: $0.17

Inspection Scores:

0 1 2 3 4 5

General Repair

Envelope 3.4

3.7

3.9

4.0

UNK Peers

Page 38: Is Your Facilities Data Fact, Fiction, or Crap? - Creating Facilities Intelligence for Data Driven Decision Making on Campus

Custodial operationsCustodians have less personnel but more materials resources to keep campus clean

Peer: 34,088 DB: 36,339 Peer: 20 DB: 22.1

Peer: $0.11 DB: $0.11

Institutions ordered by density factor

Inspection Scores:

0 1 2 3 4 5

Cleanliness 3.9 4.4

UNK Peers

Page 39: Is Your Facilities Data Fact, Fiction, or Crap? - Creating Facilities Intelligence for Data Driven Decision Making on Campus

Grounds operationsGrounds department is utilizing less resources than peers

Peer: 25.5DB: 21.2 Peer: 12.3DB: 7.8

Peer: $311DB: $588Institutions ordered by grounds intensity

Inspection Scores:

0 1 2 3 4 5

Grounds 3.7 4.0

UNK Peers

Page 40: Is Your Facilities Data Fact, Fiction, or Crap? - Creating Facilities Intelligence for Data Driven Decision Making on Campus

UNK Actions Taken and Future Strategy

• Reassigned custodial staff to bridge supervision gap• Determined maintenance staffing was appropriate,

leveraging work order system to drive more effective completion and communication

• Defining expectations.• Leveraging our strengths- outsourcing our

weaknesses. • Focusing on being really good at less things.• Providing VP with tools to increase administrative

engagement and to make the case for additional funding

Page 41: Is Your Facilities Data Fact, Fiction, or Crap? - Creating Facilities Intelligence for Data Driven Decision Making on Campus

Glen HauboldAssistant Vice President, Facilities

(575) [email protected]

Page 42: Is Your Facilities Data Fact, Fiction, or Crap? - Creating Facilities Intelligence for Data Driven Decision Making on Campus

New Mexico State University

42

> Type: Public, Land Grant, Extensive Doctoral/Research University

> Founded: 1888> Located in: Las Cruces, NM> Enrollment: 17,651> Size: 5,619,456 GSF> One of only two universities in the nation

to reach the platinum (highest) level of service to NASA’s Space Alliance Technology Outreach Program.

Page 43: Is Your Facilities Data Fact, Fiction, or Crap? - Creating Facilities Intelligence for Data Driven Decision Making on Campus

Good Old Days?

Page 44: Is Your Facilities Data Fact, Fiction, or Crap? - Creating Facilities Intelligence for Data Driven Decision Making on Campus

Physical Profile

Page 45: Is Your Facilities Data Fact, Fiction, or Crap? - Creating Facilities Intelligence for Data Driven Decision Making on Campus

Campus Age ProfileComposite campus: 4.6M GSF

8%

14%

44%

34%

14%

34%

40%

12%

0%

5%

10%

15%

20%

25%

30%

35%

40%

45%

50%

< 10 10 to 25 25 to 50 Over 50

% o

f Tot

al G

SF

NMSU – CompositeConstruction vs. Renovation Age

Construction Age Renovation Age

65%

Buildings Under 10Little work .“Honeymoon” period.

Low Risk

Buildings 10 to 25Lower cost space renewal updates and

initial signs of program pressures Medium Risk

Buildings 25 to 50Life Cycles are coming due in envelope and mechanical

systems. Functional obsolescence prevalent.Higher Risk

Buildings over 50Life cycles of major building components are past due. Failures

are possible. Core modernization cycles are missed.Highest risk

13 yearsTaken off NMSU’s age

due to major renovations

Page 46: Is Your Facilities Data Fact, Fiction, or Crap? - Creating Facilities Intelligence for Data Driven Decision Making on Campus

Lifecycle Cost by Age of Space9% approaching period of increased maintenance need, 4% nearing end of lifecycle

$0.00

$10.00

$20.00

$30.00

$40.00

$50.00

$60.00

$70.00

0

50,000

100,000

150,000

200,000

250,000

300,000

350,000

400,000

450,000

500,000

0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40 42 44

$/GSFG

SF

Average Life Cycle Costs by Age of Space (Renovation Age)

14% 34% 40% 12%Under 10 Over 5025 to 5010 to 25

4% of space will reach end of

lifecycle within five years9% of space will

age into a higher risk category

within five years

Amortization

Page 47: Is Your Facilities Data Fact, Fiction, or Crap? - Creating Facilities Intelligence for Data Driven Decision Making on Campus

Building IntensityComposite Campus Building Intensity is 147

Sutherland Village: 200 buildings Tom Fort Village: 100 buildings

Each building = 712 GSF

Institutions ordered by density.

I&G Housing

More small buildings on campus generally means:

- More components (e.g. systems, roofs) resulting in additional capital needs- More sites to service increases operational demand

Page 48: Is Your Facilities Data Fact, Fiction, or Crap? - Creating Facilities Intelligence for Data Driven Decision Making on Campus

Capital Investments

Page 49: Is Your Facilities Data Fact, Fiction, or Crap? - Creating Facilities Intelligence for Data Driven Decision Making on Campus

$0.0

$5.0

$10.0

$15.0

$20.0

$25.0

$30.0

$35.0

2005 2006 2007 2008 2009 2010 2011 2012

Mill

ions

Annual Stewardship Investment vs. Target

Env/Mech Space/Program

Annual Stewardship - CompositeDeferring an average of $7.9M/year to the backlog

Average deferral: $7.9M/year

Deferred since FY05: $63.5M

Page 50: Is Your Facilities Data Fact, Fiction, or Crap? - Creating Facilities Intelligence for Data Driven Decision Making on Campus

$0.0

$5.0

$10.0

$15.0

$20.0

$25.0

$30.0

$35.0

2005 2006 2007 2008 2009 2010 2011 2012

Mill

ions

Total Capital InvestmentOne-time spending has helped to sustain NAV in I&G space

50

2005 2006 2007 2008 2009 2010 2011 2012

Total Capital InvestmentI&G Housing

Page 51: Is Your Facilities Data Fact, Fiction, or Crap? - Creating Facilities Intelligence for Data Driven Decision Making on Campus

Capital Spending vs. PeersProject spending by GSF is less than peers

$4.28Peers

8Y avg.

$2.92NMSU8Y avg.

NMSU spent $1.36/gsf less than peers. Based on composite campus size, this translates into $6.2M

less spent than peers.

Page 52: Is Your Facilities Data Fact, Fiction, or Crap? - Creating Facilities Intelligence for Data Driven Decision Making on Campus

Project Mix vs. PeersNMSU focused on space, less on envelope & mechanical needs

8%

24%

30%

31%

7%

I&G Project MixFY08-12

30%

13%

6%

50%

1%

Housing Project MixFY08-12

$0.00

$0.20

$0.40

$0.60

$0.80

$1.00

$1.20

$1.40

Envelope Systems Infrastructure Space Code

$/G

SF

$/GSF by Project Type5-Year Average (FY08-12)

NMSU 5Y avg. Peers 5Y avg.

Page 53: Is Your Facilities Data Fact, Fiction, or Crap? - Creating Facilities Intelligence for Data Driven Decision Making on Campus

Operations Overview

Page 54: Is Your Facilities Data Fact, Fiction, or Crap? - Creating Facilities Intelligence for Data Driven Decision Making on Campus

Facilities Operating Budget5Y Daily Service average is $1.87/gsf, below FY12 peer average

$0.00

$2.00

$4.00

$6.00

$8.00

$10.00

$12.00

A B C D NMSUComposite

F G H I J

Facilities Operating Budget

Daily Service Planned Maintenance Utilities

Page 55: Is Your Facilities Data Fact, Fiction, or Crap? - Creating Facilities Intelligence for Data Driven Decision Making on Campus

Regionally AdjustedRegionally adjusted, NMSU spends closer to peers

$0.00

$2.00

$4.00

$6.00

$8.00

$10.00

$12.00

A B C D NMSUComposite

F G H I J

Facilities Operating Budget – Regionally Adjusted

Daily Service Planned Maintenance Utilities

NMSU original: $3.44NMSU, regionally adjusted: $3.54

Original peer average: $6.10Adjusted peer average: $5.46

Page 56: Is Your Facilities Data Fact, Fiction, or Crap? - Creating Facilities Intelligence for Data Driven Decision Making on Campus

Daily Service – Regionally AdjustedResources available to NMSU F&S are still scarce vs. peers

$3.37Peers FY12

$1.92NMSU FY12

$-

$1.00

$2.00

$3.00

$4.00

$5.00

$6.00Daily Service – Regionally Adjusted

Given NMSU’s composite campus of 4.6M GSF, daily service budget is smaller by $6.7 million.

Page 57: Is Your Facilities Data Fact, Fiction, or Crap? - Creating Facilities Intelligence for Data Driven Decision Making on Campus

Maintenance StaffingStaffing levels in line with peer average, higher materials per GSF used in FY12

Institutions ordered by ascending tech rating.

0 1 2 3 4 5

General Repair

Envelope

Inspection Scores:

NMSU Peers Database

Page 58: Is Your Facilities Data Fact, Fiction, or Crap? - Creating Facilities Intelligence for Data Driven Decision Making on Campus

Custodial StaffingLower supervision level, overall lower cleanliness score

Institutions ordered by ascending density.

Inspection Scores:

0 1 2 3 4 5

Cleanliness

NMSU Peers Database

Page 59: Is Your Facilities Data Fact, Fiction, or Crap? - Creating Facilities Intelligence for Data Driven Decision Making on Campus

Grounds StaffingSignificantly fewer materials available to staff, lower inspection scores

Institutions ordered by ascending grounds intensity.

Inspection Scores:

0 1 2 3 4 5

Grounds

NMSU Peers Database

Page 60: Is Your Facilities Data Fact, Fiction, or Crap? - Creating Facilities Intelligence for Data Driven Decision Making on Campus

Energy ConsumptionReduction in energy use saves NMSU $646k vs. FY11

Saving $646k by consuming less fuel in FY12 vs. FY11

Institutions ordered by ascending tech rating. NMSU composite: 6th of 10.

Page 61: Is Your Facilities Data Fact, Fiction, or Crap? - Creating Facilities Intelligence for Data Driven Decision Making on Campus

Future…

Page 62: Is Your Facilities Data Fact, Fiction, or Crap? - Creating Facilities Intelligence for Data Driven Decision Making on Campus

Closing Remarks

Page 63: Is Your Facilities Data Fact, Fiction, or Crap? - Creating Facilities Intelligence for Data Driven Decision Making on Campus

ROI = The Multiplier Effect of Reinvested SavingsMeasuring the impact of proactive costs vs. reactive

$1 per GSF Invested in Stewardship** …$3 per GSF in Capital Backlog Need

Is equal to

…$2.73 per GSF in Annual Operating Costs*

Is equal to

$1 per GSF Invested in Planned Maintenance

Another investment impact is....

* Analysis developed by Marq Ozanne, Ph.D. of OZANNE Customer Analytics Group** Analysis developed by analyzing the Sightlines facility database of project costs

Page 64: Is Your Facilities Data Fact, Fiction, or Crap? - Creating Facilities Intelligence for Data Driven Decision Making on Campus

If you remember, remember this…

• Independent Third Party Verification

• Consistent & Credible Collection Methodology

• Tie Investments to Outcomes

• Track Annual Performance Metrics

• Common Vocabulary

• Single Platform

• Improve Strategic Decision Making

Annual Stewardship

Ass

et

Rei

nves

tmen

t Operating

Effectiveness

Service

…Credibility & Perspective to Drive

Transformational Change

Page 65: Is Your Facilities Data Fact, Fiction, or Crap? - Creating Facilities Intelligence for Data Driven Decision Making on Campus

National Trends

Page 66: Is Your Facilities Data Fact, Fiction, or Crap? - Creating Facilities Intelligence for Data Driven Decision Making on Campus

66

Page 67: Is Your Facilities Data Fact, Fiction, or Crap? - Creating Facilities Intelligence for Data Driven Decision Making on Campus
Page 68: Is Your Facilities Data Fact, Fiction, or Crap? - Creating Facilities Intelligence for Data Driven Decision Making on Campus

#1 Space Getting OlderBoth groups have increasing percentage of over 50 year old space

46% 45% 44% 44% 43% 42%34% 34% 33% 32% 31% 29%

14% 15% 15% 16% 17% 18%23% 23% 23% 24% 24% 27%

0%

10%

20%

30%

40%

50%

60%

70%

2007 2008 2009 2010 2011 2012 2007 2008 2009 2010 2011 2012

(%) Square Footage over 25 years old(Renovation Age)

25 to 50 Years of Age Over 50 Years of Age

Public Private

Page 69: Is Your Facilities Data Fact, Fiction, or Crap? - Creating Facilities Intelligence for Data Driven Decision Making on Campus

#2 Privates recovered from recession faster than publicsBoth private and public campuses commit more annual funding

$1.0 $1.2 $1.3 $1.1 $1.3 $1.4 $1.5 $1.5 $1.5 $1.5 $1.8 $2.0

$3.0

$4.0 $4.2

$3.6 $3.7 $3.5 $3.5

$4.0 $4.0

$2.8 $3.0

$3.2

$-

$1.0

$2.0

$3.0

$4.0

$5.0

$6.0

2007 2008 2009 2010 2011 2012 2007 2008 2009 2010 2011 2012

Capital Investment into Existing Space

Annual Capital One-Time Capital

PrivatePublic

Page 70: Is Your Facilities Data Fact, Fiction, or Crap? - Creating Facilities Intelligence for Data Driven Decision Making on Campus

#3 Where Capital Dollars are SpentBoth Public and Private Investing More Into Core Building Systems and Components

15%

30%

15%

31%

10%

2007 Public13%

26%

13%

41%

7%2007 Private

Total Project Spending

16%

32%

16%

28%

8%

2012 Public

16%

25%

16%

37%

7%

2012 Private

Page 71: Is Your Facilities Data Fact, Fiction, or Crap? - Creating Facilities Intelligence for Data Driven Decision Making on Campus

#4 Publics have high backlog, but slower growthPrivate institutions backlog is less, but growing at a faster rate

$84 $85 $86 $87 $91 $94

$66 $68 $68 $68 $74 $77

0%

2%

4%

6%

8%

10%

12%

14%

16%

18%

20%

$40

$50

$60

$70

$80

$90

$100

$110

$120

2007 2008 2009 2010 2011 2012 2007 2008 2009 2010 2011 2012

$/G

SF

Backlog $/GSF

Public Private

Page 72: Is Your Facilities Data Fact, Fiction, or Crap? - Creating Facilities Intelligence for Data Driven Decision Making on Campus

#4b Deferred Maintenance Backlog and Inspection ScoresCorrelation between Building Age, Backlog and Campus Appearance

3.0

3.2

3.4

3.6

3.8

4.0

4.2

0-25 26-39 40+

Campus Appearance

Average of General RepairImpressionAverage of Exterior

$0

$20

$40

$60

$80

$100

$120

0-25 26-39 40+

$/G

SF

Deferred Maintenance Backlog

Average of Backlog Lump Sum GSFAverage of Backlog Maint/Repair/GSF

(Scale of 1-5)

Backlog of need grows exponentially in older spaces

General repair and exterior appearance decline as

buildings age and backlog of need increases

Page 73: Is Your Facilities Data Fact, Fiction, or Crap? - Creating Facilities Intelligence for Data Driven Decision Making on Campus

#5 Public and Private Institutions Have Flat Growth

$4.15 $4.35 $4.45 $4.22 $4.33 $4.41 $4.24 $4.41 $4.44 $4.44 $4.50 $4.51

$-

$0.50

$1.00

$1.50

$2.00

$2.50

$3.00

$3.50

$4.00

$4.50

$5.00

2007 2008 2009 2010 2011 2012 2007 2008 2009 2010 2011 2012

$/G

SF

Daily Service

Public Private

Daily Service includes: people, costs and expenses for Maintenance, Custodial, Grounds, & Administration

Page 74: Is Your Facilities Data Fact, Fiction, or Crap? - Creating Facilities Intelligence for Data Driven Decision Making on Campus

#5 Increasing Maintenance Coverage

50,000

55,000

60,000

65,000

70,000

75,000

80,000

85,000

90,000

95,000

2007 2008 2009 2010 2011 2012 2007 2008 2009 2010 2011 2012

GSF

/FTE

Maintenance Coverage

Public Private

Page 75: Is Your Facilities Data Fact, Fiction, or Crap? - Creating Facilities Intelligence for Data Driven Decision Making on Campus

#5 Increasing Custodial Coverage

20,000

22,000

24,000

26,000

28,000

30,000

32,000

34,000

36,000

38,000

40,000

2007 2008 2009 2010 2011 2012 2007 2008 2009 2010 2011 2012

GSF

/FTE

Custodial CoveragePublic Private

Page 76: Is Your Facilities Data Fact, Fiction, or Crap? - Creating Facilities Intelligence for Data Driven Decision Making on Campus

76

Page 77: Is Your Facilities Data Fact, Fiction, or Crap? - Creating Facilities Intelligence for Data Driven Decision Making on Campus

Questions & Comments

Thomas Huberty(203)682-4981

[email protected]

Glen Haubold(575) 646-2101

[email protected]

Corey Ruff(325) 674-2665

[email protected]

Lee McQueen(308) 865-1700

[email protected]