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 Zuraw
VP, 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:

Join the Conversation
Enter questions
here at any point
during the
webinar
Presentation slides
and webinar recording
will be sent to each
attendee following
today’s session

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

Sightlines by the Numbers
Robust 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
43
States+DC
90%
Member
retention
rate
360+
ROPA
Members
450
Colleges &
Universities
180
New members
since 2013
5
Canadian
provinces

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”
Operational
Effectiveness
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

5 Steps for Harnessing Your Facilities Data
Strengthen your case and demonstrate value
Consistency
Accuracy
Normalization
Peer Group
Context

Consistency
Consistency
Accuracy
Normalization
Peer Group
Context

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
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
Investigate and Validate Any Changes in the Numbers
$0
$100,000
$200,000
$300,000
$400,000
$500,000
FY12 FY13 FY14 FY15 FY16
Total$
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
$inMillions
TotalFTE
Physical Plant FTEs vs People Costs
FTE People Costs
Crosscheck Data Points from One Information Source with Others

Normalization
Normalization
Consistency
Accuracy
Peer Group
Context

Show truer comparisons with normalized data
$0
$2
$4
$6
$8
$10
$12
$/GSF
Capital Investment - $/GSF
Recurring Capital One-Time Capital Average
$0
$10
$20
$30
$40
$50
$inMillions
Capital Investment – Total $
Determine the common denominator that makes sense for each metric

$0
$2
$4
$6
$8
$10
$12
$/GSF
Capital Investment - $/GSF
$0
$10
$20
$30
$40
$50
$inMillions
Capital Investment – Total $
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
Peer Group
Consistency
Accuracy
Peer Group
Context

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

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
3.14
0.0
1.0
2.0
3.0
4.0
5.0
TechRating(1-5)
Technical Complexity
505
0
50
100
150
200
250
300
350
400
450
500
550
FTE/100,000GSF
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

Option 2: Customize Peers Groups for Particular Metrics
136
0
20
40
60
80
100
120
140
160
FTE/100,000GSF
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
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
GSF/FTE
Custodial Staffing
Peer Average
Data within context – quantitative & qualitative
0
10
20
30
40
FTE/Supervisor
Custodial Supervision
Peer Average
$0
$1,000
$2,000
$3,000
$4,000
$5,000
$/FTE
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
GSF/FTE
Custodial Staffing
Peer Average
Data within context – quantitative & qualitative
0
10
20
30
40
FTE/Supervisor
Custodial Supervision
Peer Average
$0
$1,000
$2,000
$3,000
$4,000
$5,000
$/FTE
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

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 Mid
Century
Post
Modern
Complex
$/GSF
%ofCampus
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 Mid
Century
Post
Modern
Complex Hours/1,000GSF
%ofCampus
Work Order Hours vs. Building Age
% of Campus Space Hours / 1,000 GSF

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
$/GSF
%ofCampus
Work Order $ vs. Renovation Age
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
Hours/1,000GSF
%ofCampus
Work Order Hours vs. Renovation Age

0
5
10
15
20
25
30
35
0%
5%
10%
15%
20%
25%
30%
35%
40%
Less than
8k GSF
8k to 15k
GSF
15k to 30k
GSF
Over 30k
GSF
Hours/1,000GSF
%ofCampus
Work Order Hours vs. Building Size
$-
$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 than
8k GSF
8k to 15k
GSF
15k to 30k
GSF
Over 30k
GSF
$/GSF
%ofCampus
Work Order $ vs. Building Size
Data within context – variable metrics

Data within context – pinpoint opportunities for improvement
$0
$20
$40
$60
$80
$100
$120$/GSF
Capital Backlog vs Peers
Demo School Peers
+49%
+19%

Data within context – pinpoint opportunities for improvement
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%%ofWorkOrders
Work Orders – Daily Service vs PM
Daily Service Planned Maintenance
Demo School Peers

How Do You Shift from Data to Knowledge to Action?
Strategize and Plan
Prioritize
Limit Jargon
Establish Clear Targets

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

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

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

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