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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
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
© 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
© 2017 Sightlines, LLC. All Rights Reserved.14
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
© 2017 Sightlines, LLC. All Rights Reserved.17
5 Steps for Harnessing Your Facilities Data Peer Group
Consistency
Accuracy
Peer Group
Context
© 2017 Sightlines, LLC. All Rights Reserved.18
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
© 2017 Sightlines, LLC. All Rights Reserved.21
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
© 2017 Sightlines, LLC. All Rights Reserved.32
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