put your facilities data to work: 5 steps for strengthening your case on campus

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Put Your Facilities Data to Work:5 Steps for Strengthening Your Case on Campus

December 19, 2017

© 2017 Sightlines, LLC. All Rights Reserved.2

Introduction & Agenda

Pete ZurawVP, Market Strategy & Development

Sightlines

What are the 5 steps for harnessing your facilities data to more effectively track performance?

How do you standardize data to ensure accuracy and create context?

How can you use this data to tell the facilities story and take action?

Today’s topics include:

© 2017 Sightlines, LLC. All Rights Reserved.3

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Enter questions here at any point during the webinar

Presentation slides and webinar recording

will be sent to each attendee following

today’s session

© 2017 Sightlines, LLC. All Rights Reserved.4

Leading provider of facilities

intelligence in higher

education helping to uncover

ways to use capital more

strategically and identify

opportunities to improve

operational effectiveness.

FACILITIES BENCHMARKING

& ANALYSIS

Take control of your

facilities and make the

case for change

without the guesswork

FACILITIES ASSESSMENT &

PLANNING

Plan and execute

capital investment

plans that are inclusive,

credible, flexible,

affordable and

sustainable

SPACE UTILIZATION

Ensure your space is

working up to its full

potential

SUSTAINABILITY SOLUTIONS

Measure and improve

environmental

stewardship

© 2017 Sightlines, LLC. All Rights Reserved.5

Sightlines by the NumbersRobust membership includes colleges, universities, consortiums, and state systems

Sightlines has advised state systems in:

• Alaska• California• Florida• Hawaii• Maine

• Massachusetts• Minnesota• Mississippi• Missouri• Nebraska

• New Hampshire• New Jersey• Pennsylvania• Texas• Washington

43States+DC

90%Memberretention

rate

360+ROPA

Members

450Colleges &

Universities

180New members

since 20135

Canadianprovinces

6

“In God we trust; all

others must bring data”

W. Edwards Deming

Data Can Serve as the Base for a Common Vocabulary

Asset Reinvestment

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

OperationalEffectiveness

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

Annual Stewardship

The annual investment needed to ensure buildings will properly perform and reach their useful life “Keep-Up Costs”.

Service

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

Asset Value Change Operations Success

© 2017 Sightlines, LLC. All Rights Reserved.7

© 2017 Sightlines, LLC. All Rights Reserved.8

5 Steps for Harnessing Your Facilities Data Strengthen your case and demonstrate value

Consistency

Accuracy

Normalization

Peer Group

Context

© 2017 Sightlines, LLC. All Rights Reserved.9

5 Steps for Harnessing Your Facilities Data Consistency

Consistency

Accuracy

Normalization

Peer Group

Context

© 2017 Sightlines, LLC. All Rights Reserved.10

Create consistency within the data

Focus on the right data:

1. Start with the end in mind

2. Create a finite list of priority data pieces

3. Think about the data you’ll need to tell your story

•Understand institutional language – Where do your data points fit?

•Exclude portions of data that are not directly related to building function

•Examples of excluded data:

Facilities Operating Budget

Security/Public Safety

Mailroom

Fleet Vehicles/Transportation

Insurance, Tax, Rent

Academic Equipment

Daily Service Staffing

Maintenance: excluded work (projects/sold-service/moves, etc.)

Custodial: non-cleaning duties (set-ups moves/special projects, etc.)

Grounds: non-landscaping duties (driver/mechanic/recycling, etc.)

Consistency

© 2017 Sightlines, LLC. All Rights Reserved.11

5 Steps for Harnessing Your Facilities Data Accuracy

Accuracy

Normalization

Peer Group

Context

-

10,000

20,000

30,000

40,000

50,000

60,000

70,000

2012 2013 2014 2015 2016

BTU

/GSF

Total Energy Consumption

Fossil Electric

© 2017 Sightlines, LLC. All Rights Reserved.12

Investigate and Validate Any Changes in the Numbers

$0

$100,000

$200,000

$300,000

$400,000

$500,000

FY12 FY13 FY14 FY15 FY16

Tota

l $

Utility Bills vs. Plant Logs

Electric Actuals Electric Logs

139 138

157

143

165

$-

$5

$10

$15

0

20

40

60

80

100

120

140

160

180

2011 2012 2013 2014 2015

$ in

Mil

lio

ns

To

tal F

TE

Physical Plant FTEs vs People Costs

FTE People Costs

© 2017 Sightlines, LLC. All Rights Reserved.13

Crosscheck Data Points from One Information Source with Others

Normalization

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5 Steps for Harnessing Your Facilities Data Normalization

Consistency

Accuracy

Peer Group

Context

© 2017 Sightlines, LLC. All Rights Reserved.15

Show truer comparisons with normalized data

$0

$2

$4

$6

$8

$10

$12

$/G

SF

Capital Investment - $/GSF

Recurring Capital One-Time Capital Average

$0

$10

$20

$30

$40

$50

$ in

Mill

ion

s

Capital Investment – Total $

Recurring Capital One-Time Capital Average

Determine the common denominator that makes sense for each metric

$0

$2

$4

$6

$8

$10

$12

$/G

SF

Capital Investment - $/GSF

Recurring Capital One-Time Capital Average

$0

$10

$20

$30

$40

$50

$ in

Mill

ion

s

Capital Investment – Total $

Recurring Capital One-Time Capital Average

© 2017 Sightlines, LLC. All Rights Reserved.16

Show truer comparisons with normalized data

Determine which factor most affects the metric:

• Size square footage, acreage, etc.

• Number of people full-time equivalents, headcounts, etc.

• Other dependent upon metric

Determine the common denominator that makes sense for each metric

Normalization

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5 Steps for Harnessing Your Facilities Data Peer Group

Consistency

Accuracy

Peer Group

Context

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Choose the “right” comparison group of peers

Who are you?

• Physical characteristics

• Location

• Region

• Financial capacity

• Current program

• Enrollment competitors

• Residential

Who do you want to be?

• Institutional mission

• Master plan

• Programmatic changes

• Future enrollment

© 2017 Sightlines, LLC. All Rights Reserved.19

Option 1: Same Peer Group for Every Benchmark

Comparative Considerations

Size, technical complexity, region, geographic location, and setting are all factors included in the

selection of peer institutions

3.1

4

0.0

1.0

2.0

3.0

4.0

5.0

Te

ch

Rati

ng

(1

-5)

Technical Complexity

50

5

0

50

100

150

200

250

300

350

400

450

500

550

FT

E/1

00

,00

0 G

SF

Density Factor

Research Intensive Peers

Carnegie Mellon University

Massachusetts Institute of Technology

Georgia Institute of Technology

Northwestern University

Purdue University

The Johns Hopkins University

The Pennsylvania State University

University of Florida

University of Georgia

University of Illinois – Urbana/Champaign

University of Minnesota – Twin Cities

© 2017 Sightlines, LLC. All Rights Reserved.20

Option 2: Customize Peers Groups for Particular Metrics

13

6

0

20

40

60

80

100

120

140

160

FT

E/1

00

,00

0 G

SF

Density Factor

Density Peers

Bowdoin College

Bryn Mawr College

Carleton College (MN)

Davidson College

Hamilton College

Mount Holyoke College

Pomona College

Smith College

Swarthmore College

Wesleyan University

Density factor peers are useful for custodial and operating budget metrics

Institution Location

Abilene Christian University Abilene, TX

Alcorn State University Alcorn, MS

Armstrong Atlantic State University Savannah, GA

Jackson State University Jackson, MS

Mississippi University for Women Columbus, MS

St. Edward’s University Austin, TX

Texas Christian University Fort Worth, TX

University of St. Thomas - Houston Houston, TX

Example: Density factor peers Example: Regional/energy peers

Regional peers are useful for energy cost and consumption metrics

Context

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5 Steps for Harnessing Your Facilities Data Context

Consistency

Accuracy

Normalization

Peer Group

0

5,000

10,000

15,000

20,000

25,000

30,000

35,000

40,000

45,000

50,000

GS

F/F

TE

Custodial Staffing

Peer Average

© 2017 Sightlines, LLC. All Rights Reserved.22

Data within context – quantitative & qualitative

0

10

20

30

40

FT

E/S

up

erv

iso

r

Custodial Supervision

Peer Average

$0

$1,000

$2,000

$3,000

$4,000

$5,000

$/F

TE

Custodial Materials

Peer Average Peers arranged in order of density factor

Do these charts show an overstaffed custodial department?

0

5,000

10,000

15,000

20,000

25,000

30,000

35,000

40,000

45,000

50,000

GS

F/F

TE

Custodial Staffing

Peer Average

© 2017 Sightlines, LLC. All Rights Reserved.23

Data within context – quantitative & qualitative

0

10

20

30

40

FT

E/S

up

erv

iso

r

Custodial Supervision

Peer Average

$0

$1,000

$2,000

$3,000

$4,000

$5,000

$/F

TE

Custodial Materials

Peer Average Peers arranged in order of density factor

Cleanliness Inspection Score

Demonstration Institution 4.8

Peer Average 4.1

Database Average 4.2

© 2017 Sightlines, LLC. All Rights Reserved.24

Data within context – “unrelated” metrics

$-

$0.2

$0.4

$0.6

$0.8

$1.0

$1.2

$1.4

0%

10%

20%

30%

40%

50%

60%

70%

80%

Pre-War MidCentury

PostModern

Complex

$/G

SF

% o

f C

am

pu

s

Work Order $ vs Building Age

% of Campus Space $ / GSF

0

5

10

15

20

25

30

0%

10%

20%

30%

40%

50%

60%

70%

80%

Pre-War MidCentury

PostModern

ComplexH

ou

rs/ 1,0

00G

SF

% o

f C

am

pu

s

Work Order Hours vs. Building Age

% of Campus Space Hours / 1,000 GSF

© 2017 Sightlines, LLC. All Rights Reserved.25

Data within context – selective metrics

$-

$0.2

$0.4

$0.6

$0.8

$1.0

$1.2

$1.4

$1.6

0%

5%

10%

15%

20%

25%

30%

35%

40%

45%

Over 50 25 to 50 10 to 25 Under 10

$/G

SF

% o

f C

am

pu

s

Work Order $ vs. Renovation Age

% of Campus Space $ / GSF

0

5

10

15

20

25

30

35

0%

5%

10%

15%

20%

25%

30%

35%

40%

45%

Over 50 25 to 50 10 to 25 Under 10

Ho

urs

/ 1,0

00G

SF

% o

f C

am

pu

s

Work Order Hours vs. Renovation Age

% of Campus Space Hours / 1,000 GSF

0

5

10

15

20

25

30

35

0%

5%

10%

15%

20%

25%

30%

35%

40%

Less than8k GSF

8k to 15kGSF

15k to 30kGSF

Over 30kGSF

Ho

urs

/ 1,0

00G

SF

% o

f C

am

pu

sWork Order Hours vs. Building Size

% of Campus Space Hours / 1,000 GSF

$-

$0.2

$0.4

$0.6

$0.8

$1.0

$1.2

$1.4

$1.6

$1.8

0%

5%

10%

15%

20%

25%

30%

35%

40%

Less than8k GSF

8k to 15kGSF

15k to 30kGSF

Over 30kGSF

$/G

SF

% o

f C

am

pu

s

Work Order $ vs. Building Size

% of Campus Space $ / GSF

© 2017 Sightlines, LLC. All Rights Reserved.26

Data within context – variable metrics

© 2017 Sightlines, LLC. All Rights Reserved.27

Data within context – pinpoint opportunities for improvement

$0

$20

$40

$60

$80

$100

$120$

/GSF

Capital Backlog vs PeersDemo School Peers

+49%

+19%

© 2017 Sightlines, LLC. All Rights Reserved.28

Data within context – pinpoint opportunities for improvement

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%%

of

Wo

rk O

rde

rs

Work Orders – Daily Service vs PM

Daily Service Planned Maintenance

Demo School Peers

© 2017 Sightlines, LLC. All Rights Reserved.29

How Do You Shift from Data to Knowledge to Action?

Strategize and Plan

Prioritize

Limit Jargon

Establish Clear Targets

© 2017 Sightlines, LLC. All Rights Reserved.30

What Can Benchmarking Data Do For You?

Build relationships across campus and with boards

Demonstrate that you are an effective campus steward

Generate support for success

Secure additional resources for ongoing maintenance or capital investments

31

Questions & Discussion

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What did you think?Please share your feedback by answering a few quick questions after the session to helps us get to know you better and improve our webinars for the future

33

Thank you for your time.

@sightlinesllc

Sightlines

Sightlines360