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Copyright Tradeline Inc. 2014 Tradeline, Incorporated 115 Orinda Way Orinda, CA 94563 (925) 254-1744

New Facility Strategies and FM Staffing Models Emerge

Post the Great Recession of 2008

Steve Westfall, Ph.D., Founder & CEO, Tradeline, Inc.

March, 2014 – Revised November, 2015, with updated charts and commentary but with no changes in the original

data or findings. Exhibit 6 and associated commentary concerning the combined impact of space and population

served as drivers of FM headcount (pages 10 and 11) was revised February, 2016.

What are the new right staffing numbers?

Facility management organizations across the U.S. are adjusting their staffing models to

address major shifts that have occurred over the past 10 years in occupancies, facility

asset strategies, corporate financial objectives, and the retirement of experienced

technical personnel. Significantly, this is a decade of change that includes the Great

Recession of 2008. A wide variety of new facilities management staffing strategies are

being pursued to fit with a diversity of changing business scenarios. This report sets

out detailed and quantified findings from a new study of changes in facility strategies

and facilities management staffing models, and it demonstrates a graphical format for

evaluating and communicating the impact of these changes and performing a quick test

for the reasonableness of staffing solutions.

Five big ideas

The five big ideas gleaned from this study are:

There is a way to predict what FM headcounts should be for almost any mission

Population served has a greater impact on FM headcount than does space

Data on national trends and averages obscure the real story of change

Outsourcing is a significant factor but doesn’t account for the majority of change

Very lean (highly outsourced) staffing models are uncharted territory

The report

To quantitatively document the specifics of change, 13 facilities management

organizations which had participated between 2003 and 2008 in a 102‐firm study of FM

staffing in relation to Mission Complexity Scores were asked in the fall of 2013 to

update their previous data. Those 13 organizations are cumulatively responsible for

26.7 million square feet of space and facilities services that support 52,000 workers.

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Individually, they are responsible for either major parts of the facilities portfolios, or the

entire facilities portfolios for these companies:

Adobe Systems

Alliant Energy

Amgen

Armstrong World Industries

Dupont

Eastman Chemical

FreddieMac

Harley‐Davidson

InterAmerican Development Bank

Jack in the Box Corporate

Leidos Biomedical (Nat’l Cancer Inst)

Lexmark International

The RAND Corporation

The case profiles of facilities management change for each of these 13 corporate facilities

management groups are presented at the end of this report, and they demonstrate staffing

responses and solutions for widely diverse scenarios of change that include:

Redefinition of the mission, more and different space, change in outsourcing

Fixed space, declining occupancy, major outsource change

Declining space, lower occupancy, change in space types, more FM heads

Space reduction, change in space types, constant occupancy, total FM staff reduction

Growing occupancy, fixed space, fixed FM staffing

Declining occupancy, reduced space, increased outsourcing

Stable occupancy, less space, more vacant space, total FM staff reduction

Fixed facility portfolio, constant occupancy, leaner staffing

Rising space, declining occupancy, reduced FM staff

No changes in facilities or management model

Growing occupancy, constant space, fixed FM staffing

Stable portfolio, rising occupancy, major outsourcing initiative

Stable portfolio, lower occupancy, increased outsourcing

This report sets out:

The findings of the study …………………………………. pages 3 ‐ 16

Definitions of terms and metrics …………………..………pages 18 ‐ 20

Thirteen profiles of facilities management change……….pages 21 ‐ 34

Comments from study participants…………………..........pages 35 – 37

Appendix 1, Participants in the original 102‐firm study...page 38

It is important to acknowledge that this is a study of staffing‐model changes for 13 FM

organizations drawn from a sample of 102 FM organizations previously studied. That is what

it is. No representation is made here as to statistical signifance of the conclusions drawn.

Simply put, it is reported data on the changes for 13 diverse FM organizations, seen against a

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backdrop of a previous study of 102 such organizations, from which observations have been

made, findings summarized, and conclusions drawn.

By putting study findings, conclusions, and graphical exhibits at the beginning of this report,

the author recognizes that the reader may encounter terms, metrics, and concepts that may

require him or her to jump ahead to the Definitions of Terms and Metrics section starting on

page 18. Hopefully that will happen, as that will be a sign that the subject matter has

sufficiently captured the reader’s interest. To examine the 13 individual case profiles, one

needs to go to the end of the report.

The author is very grateful and extremely appreciative of the thoughtfulness, insight, and

considerable effort on the part of the knowledgeable representatives of these organizations

who made time beyond their normal work duties to contribute substantively to this study. In

the interest of promised anonymity, these participating organizations are identified in the

graphs and tables of this report by their Tradeline file numbers, the numerical order of which

does not match the order of the listing above. Their actual case files are found at the end of

this report.

FINDINGS OF THE STUDY

The main question this study sought to answer was, “How have the facility strategies and

facilities management (FM) staffing models graphically depicted in EXHIBIT 1 below (the

results of a previous 102‐firm staffing study) changed from pre‐2008 dates, and does the

modeling algorithm for computing Mission Complexity Scores in that original study reliably

predict those changes?” Among the organizations represented in the original 102‐firm study

(see Appendix 1 at the end of this report for the full listing) are the 13 organizations examined

here in this new study of changes in staffing and missions.

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EXHIBIT 1 – The original 102‐firm mission‐based staffing model study

How have the facility management missions and FM staffing models depicted in this plot changed from pre-2008 dates? The algorithm that produced this plot is a predictive tool for indicating what staffing models should be for different underlyling FM missions – how do those predictions stack up against what has actually occurred?

The dotted line in EXHIBIT 1 is the statistical regression line for staffing vs. Mission

Complexity Scores for a reference panel of 83 out of the 102 organizations in the original study.

Nineteen of the 102 organizations in that study were judged to be significantly over‐ or under‐

staffed outliers. The reliability of this reference line, referred to here as the statistically

determined mission‐appropriate staffing line, is what is being tested in this current study of

changing missions and staffing.

Major organizational changes correlate with Mission Complexity Scores

The 13 organizations examined in this study of change exhibit a wide spectrum of facilities

management missions ranging from moderate to large space portfolios with different mixes of

space types and occupancy profiles. This is seen in the 13 Profiles of Facilities Management

Change set out at the end of this report. Some of these 13 organizations manage purely office

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facilities while others manage various mixtures of office, lab, manufacturing, and other types

of space. These organizations have different policies on outsourcing and leasing, different

occupancy densities, different menus of services under the “facilities management” label,

different numbers and ages of buildings, and differences in several other mission variables.

Importantly, they exhibit different magnitudes and directions of change in all variables.

Two exhibits below, The Chart of Change and The Table of Change, demonstrate how

individual organizations have changed from pre‐2008 dates to 2013.

The Chart of Change, EXHIBIT 2 below, shows the before (pre‐2008) and after (2013)

headcount and mission scores for all 13 corporate FM organizations, and it illustrates two

things. First, it illustrates that for most organizations changes that have occurred at the

individual corporate level since pre‐2008 dates have been large. Second, it shows, with one

exception, that those staffing swings correlate closely with the statistically determined

Mission‐Staffing relationship that was developed in the orignial 102‐firm study, namely

FM heads / population served = K (a constant) x Mission Complexity

In other words, the Mission Complexity Score algorithm of the original 102‐firm study is

shown here to be an effective predictor of staffing changes when space, occupancy, and other

facility management variables change.

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EXHIBIT 2 – THE CHART OF CHANGE: Staffing model changes for 13 organizations from

pre‐2008 to 2013 (Numbers in boxes are study file numbers, see pages 21‐34 of this report)

With one exception (File 49), staffing model changes shown here cluster around and converge toward the relationship: FM Heads/1000 workers served = K(a constant) X Mission Complexity Score.

119

1387

5

102

0

10

20

30

40

50

60

70

0.00 0.50 1.00 1.50 2.00 2.50 3.00 3.50

MCS (Mission Complexity Score)

How FM staffing models have changed in "N" years (2003 - 2013)(Payrolled FM staff - excludes outsourced personnel)

"N" years ago2013

FM Heads per 1,000 workers served

The 102-firm mission-staffing regression: FM heads/1000 = 20 X MCS

108 118

71

86

49

46

5119

3

120

0

5

10

15

20

25

0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.90 1.00

MCS (Mission Complexity Score)

FM Heads per 1,000 workers served

The 102-firm mission-staffing regression: FM heads/1000 = 20 X MCS

Expanded View: MCS < 1.00

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The Table of Change, EXHIBIT 3 below, sets out the underlying data on Pre‐2008 to 2013

changes in facility and management strategies that are graphically depicted in EXHIBIT 2 .

EXHIBIT 3 – THE TABLE OF CHANGE: Underlying data for Exhibit 2 (Chart of Change)

Study file number 3 5 13 46 49 71 86 87 102 108 118 119 120Original reporting year 2004 2005 2004 2003 2006 2004 2005 2004 2006 2007 2008 2008 2008

Facility strategy issues2013 reported gross square feet (millions) 0.78 1.00 5.54 2.17 2.48 2.70 0.59 0.92 1.90 1.52 3.23 3.66 0.22

Previously reported gross square feet (millions) 0.51 1.00 6.25 3.03 2.50 3.00 0.65 0.95 1.59 1.52 3.20 3.72 0.22

2013 reported population served 2,122 950 8,078 4,000 5,554 3,022 2,200 1,018 2,500 2,543 12,250 7,519 520

Previously reported population served 1,500 1,350 9,800 4,000 4,610 6,600 2,204 1,092 3,000 2,542 7,500 6,736 670

2013 gross sq ft/population served 369 1053 686 543 446 894 269 899 761 596 264 486 423

Previous gross sq ft/population served 341 741 638 759 542 455 296 872 530 596 427 552 328

2013 weighting for Space Types* 0.94 2.23 3.61 1.10 0.85 0.72 0.87 3.47 3.53 0.98 0.96 1.95 1.31

Previous weighting for Space Types* 1.28 2.28 2.80 2.11 0.77 0.70 1.23 3.60 4.02 0.98 0.96 1.95 1.31

2013 reported % of leased space 26% 0% 1% 0% 5% 16% 32% 3% 32% 0% 13% 27% 0%

Previously reported % of leased space 4% 0% 6% 0% 5% 20% 31% 0% 7% 0% 26% 27% 0%

Management strategy changes2013 number of FM svcs offered (out of 25) 15 15 17 13 11 10 19 18 6 4 18 14 14

Previous number of FM svcs offered (out of 25) 12 16 17 11 10 11 19 16 6 4 18 14 13

2013 level of outsoucing (percent) 94% 98% 53% 95% 50% 91% 50% 71% 12% 93% 90% 90% 78%

Previous level of outsourcing (percent) 57% 61% 59% 80% 49% 82% 53% 67% 0% 92% 90% 60% 54%

2013 Mission Complexity Score 0.12 0.20 2.68 0.14 0.71 0.26 0.60 1.88 2.15 0.08 0.22 0.29 0.44

Previous Mission Complexity Score 0.63 2.49 2.10 0.71 0.74 0.38 0.69 1.81 2.68 0.08 0.22 1.21 0.71

2013 reported on-payroll heads/1000 served 2.4 5.3 54.6 3.8 9.4 6.0 14.4 39.5 44.8 2.0 3.3 5.2 8.7

Previously reported on-payroll heads/1000 served 12.0 60.7 42.8 14.8 12.6 8.0 17.5 45.8 55.3 1.6 5.2 26.7 10.4

2013 reported total on-payroll FM heads 5 5 441 15 52 18 31.6 40.2 112 5 40 39 4.5

Previously reported total on-payroll FM heads 18 82 419 59 58 53 38.6 50 166 4 39 180 7

2013 predicted on-payroll heads/1000 served 2.5 3.9 53.7 2.7 14.3 5.3 11.9 37.6 43.0 1.6 4.5 5.9 8.7

Previously predicted on-payroll heads/1000 served 12.5 49.7 42.0 14.2 14.7 7.7 13.9 36.3 53.5 1.6 4.5 24.2 14.2

2013 predicted total on-payroll heads 5.3 3.7 433.4 10.9 79.2 16.0 26.2 38.3 107.5 4.0 54.7 44.1 4.5

Previously predicted total on-payroll heads 18.8 67.1 411.5 56.7 67.8 50.6 30.6 39.6 160.6 4.0 33.5 162.8 9.5

2013 Lean Mission-Staffing Index** 0.95 1.34 1.02 1.37 0.66 1.12 1.21 1.05 1.04 1.24 0.73 0.88 0.99

Previous Lean Mission-Staffing Index** 0.96 1.22 1.02 1.04 0.86 1.05 1.26 1.26 1.03 1.00 1.16 1.11 0.73

2013 Lean Variance in Heads/1000 served*** -0.12 1.34 0.94 1.02 -4.90 0.66 2.44 1.91 1.79 0.38 -1.20 -0.68 -0.06

Previous Lean Variance in Heads/1000 served*** -0.54 11.01 0.76 0.58 -2.12 0.37 3.65 9.50 1.80 -0.01 0.73 2.56 -3.77

* Space Type weight examples: Office space = 1.00, Lab space = 4.80

** Lean Staffing Index = 1.00 for right staffing relative to the mission-staffing line

*** Lean Variance in Heads/1000= 0.00 for right staffing relative to the mission-staffing line

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Population served is a major driver of total FM labor

The findings of this study negate the notion that facilities management labor is driven purely

by square feet of space under management. In fact, what these data suggest is that in most

cases the change in the population served (the number of people who occupy space) has more

impact on change in total FM headcount than does change in the amount and type of space

under management. In other words, changes in population served is a better indicator of

changes in FM staffing than changes in square footage under management.

EXHIBIT 4 below shows pre‐2008‐to‐2013 percent changes in FM headcounts (vertical axis,

percent change in total FM headcount including on‐payroll plus outsourced headcount) for

each of the 13 study organizations plotted against corresponding percent changes in space

factors (horizontal axis, shown here as the combined change in the amount and types of space

– such as office, research, and manufacturing). R2 is the degree of correlation between changes

in FM headcount and changes in space factors. For a perfect (100%) correlation, R2 would be

1.00. As seen in EXHIBIT 4, the correlation between changes in FM headcount and changes in

space under management for the 13 cases in this study is only R2=.25, which is a very low level

of correlation (.00 being no correlation at all).

Here we see a very low correlation (small R2) in the relationship between changes in FM heads and changes in Space Under Management.

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EXHIBIT 5 shows pre‐2008‐to‐2013 changes in FM headcounts (vertical axis, percent change in

total FM headcount including on‐payroll plus outsourced headcount) for each of the 13 study

organizations plotted against corresponding changes in populations served (horizontal axis,

the percent changes in the numbers of people [workers] served). As shown in EXHIBIT 5, the

correlation between changes in FM headcount and changes in population served for the 13

cases in this study is .708, which is significantly higher than the correlation between FM

headcount and space under management.

The correlation between changes in FM heads and changes in Population Served is significantly greater than the correlation between changes in FM heads and changes in Space Under Management.

FM heads and FM service menus

One obvious explanation for a significant correlation between FM heads and population

served is that FM service menus, which can have as many as 25 services (the average number

per FM organization in this study being 14), have only a few services that are mainly space‐

driven. All the other services offered have mainly to do with the population served and have

little or no relationship to the amount of square footage under management or the types of

square footage under management (such as labs, offices, manufacturing). For example, moves

and relocations, environmental health and safety, IT connectivity, administrative services,

mail, reprographics, office supplies, trash, and shipping and receiving are some of the services

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commonly organized under Facilities Management that are driven by workforce numbers, not

square feet.

FM heads and porous FM service boundaries

Another contributor to the correlation between population served and headcount is that even

for services that are traditionally benchmarked by square footage, such as maintenance and

operations, there are for many organizations porous boundaries between strictly facilities

services and customer (or production, or program) support that entails performing services

directly related to the support of the business activities of building occupants – that is, services

for the populations being served. An example would be repairing or maintaining furniture

and equipment in labs or in manufacturing operations. Whereas accounting for such services

may be handled separately from FM accounts, nonetheless these service demands represent a

labor consuming activity that can add significantly to FM headcount.

FM heads and the “people‐exercising” of building assets

It is also recognized that even for such a space‐driven service as maintenance and operations,

population served (that is, building occupancy both in numbers and types of occupants),

affects FM headcount. In the words of one of this study’s participants, “What we see is that

the more people who are ‘exercising’ the building assets the more staff we need to deal with

that. [And] it’s not just the population served but it’s their ‘activity level.’“ [See Study

Participants Comments, Page 35.] Building occupants open and close doors, raise and lower

fume hood sashes, add heat to buildings that needs to be removed, and break things – all of

which makes supposedly space‐driven maintenance and operations significantly a people‐

driven service.

Conclusion on Population Served as the major FM labor driver

The findings set out here suggest that population served, even more than square footage,

should be a major component of FM organization planning, budgeting, and productivity

metrics, and that planning purely on the basis of “per square foot” metrics, without factoring

in population served, will lead to significant under‐ or over‐staffing. To test that conclusion, at

least for the 13 cases in this study, EXHIBIT 6 shows a very high R2=.875 correlation when the

percent change in the mission is calculated as a combination of changes in space and

population served using the very simple forumla below giving population served 1.3 times

the weight of changes in space.

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Where ΔHfm is the percent change in FM Headcount ΔSw is the percent change in the Space component which is the amount of space weighted according to mix of space type (office, lab, manufacturing, etc.) ΔP is the percent change in population served. The results of EXHIBIT 6 suggest that this formula could be a good predictor of FM staffing

levels when missions change, and certainly a better predictor than simply using square feet

alone.

When changes in population served is given 30% more weight (x1.3) than changes in the amount and types of space under management, the combined-change number (space + populations served) is a strong predictor of changes in FM headcount. Thus, the conclusion of this study is that changes in population served has a greater impact on FM staffing than do changes in the amount and types of space under management, and that population served needs to be considered along with space in predicting, evaluating, or benchmarking staffing levels.

ΔHfm = ΔSw + 1.3xΔP 2

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Changing workplace technologies and population‐driven service models

A discovery made from File number 49 [see page 26] is that office workplace technologies may

be reducing FM staffing for some purely population‐driven services. There we see that

significant headcount reduction has occurred (and is occurring) for Mail (“almost no mail

anymore”) and Reprographics (“new hires don’t use those services”). This suggests that

staffing models for population‐driven services for a technology‐enhanced workplace and

world characterized by electronic connectivity, phone messaging, email, electronic signatures

and file transfer, digital storage, desk‐top printers, etc. may need further study. See also

Participants’ Comments [Page 36] under Mail, Reprographics and other population‐driven

services.

Population AND space – drivers of total FM labor

That is not to say population served is the driving factor. It is saying that population, along

with space, is a driving and apparently dominant factor. In the words of one of the study

participants, “While we are saying here that population served drives staffing more so than

square footage, this is actually a classic multivariable equation [involving] population served

and square feet. We can tweak headcount for population‐driven services by reducing service

levels, but you can’t reduce service levels for air handling unit repairs.” Missions (Mission

Complexity Scores) must certainly reflect space as a factor in space‐driven services such as

engineering, maintenance, and custodial; and reflect population served for such population‐

driven services as moves and relocations, space management, IT connectivity, EH&S, copiers

and fax machines, office supplies, conference room reservations and management,

administrative services, mail, reprographics, shipping and receiving, etc.

Aggregated data – little change, a poor predictor of organizational change

In contrast to the large swings in staffing headcounts and Mission Complexity Scores for

individual organizations illustrated in EXHIBIT 2 (The Chart of Change), the aggregated data

of all 13 institutions shown in EXHIBIT 7 presents a picture of a surprizingly small amount of

change from pre‐2008 dates to 2013. As seen there, there is almost no change in the total

population served (number of workers supported), only a 5% reduction in the square footage

under management, a stable menu of FM services performed (13), and a relatively modest

change in the level of outsourcing from 65.3% to 74.3%. The conclusion here is that

aggregated data, such as national or industry‐sector data setting out means and medians, do

not reflect in any meaningful way what is actually going on at the individual organization

level.

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Aggregated staffing headcount tracks with Mission Complexity Score

As seen in EXHIBIT 7, the change in the aggregated 13‐organization headcount over this

period correlates closely with Tradeline’s 102‐firm mission‐based staffing standard of

FM heads/population served = K (a constant) x Mission Complexity

In other words, headcount for this portfolio of 13 FM organizations has changed collectively in

a predictable way with collective headcount moving in direct relationship to a corresponding

change in the collective mission as measured by the Mission Complexity Score.

EXHIBIT 7– Pre‐2008 to 2013 aggregated change: Missions and staffing models of 13 sample

institutions

More than a straight office portfolio

All of the 13 organizations in this report have office space as some part of their portfolio.

Seven have facilities management missions involving almost purely office space for which the

Space Type weight is 1.00. The Space Type number of 1.9 for the combined space portfolios of

all 13 organizations for 2013 and pre‐2008 dates shown in EXHIBIT 7 signifies that, taken as a

whole, this 13‐organization portfolio has a mix of office space and a considerable amount of

other more FM‐labor‐intensive spaces such as labs, cleanrooms, computer centers, vivaria, and

manufacturing space that have higher Space Type numbers.

2013 Change

Reported square feet (millions) 26.7 28.1 -5%

Reported population served (workers) 52,276 51,604 1%

Total gross square feet/total occupants 511 545 -6%

Average weighting for Space Types* 1.93 1.89 2%

Total reported percent of leased space 11.4% 11.6% -0.2%

Average number of services offered (out of 25) 13 13 0%

Reported level of outsourcing (percent of FM labor) 74.3% 65.3% 9.0%

Estimated number of outsourced personnel 2136 1979 157

Estimated total FTE heads (on-payroll + O-sourced) 2945 3153 -208

Mission Complexity Score 0.79 1.08 -27%

Total predicted on-payroll heads/1000 pop served 15.7 21.6 -27%

Total reported on-payroll heads/1000 pop served 15.5 22.7 -32%

Total number of predicted on-payroll FM heads 821 1115 -294

Total number of reported on-payroll FM heads 808 1174 -365

(Reported total heads) - (Predicted total heads) -13 59

Lean Mission-based FM Staffing Index** 0.98 1.05

Lean Variance in Heads / 1000 workers served*** -0.25 1.14



*** Lean Variance in Hds/1000= 0.00 for right staffing relative to the mission-staffing line

Prior: 2003-2008,

ave2005

Totals for 13 sample institutions

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Outsourcing is significant, but does not account for the majority of change

The increase in the level of outsourcing from 65.3% to 74.3%, which has the effect of reducing

the Mission Complexity Score from pre‐2008 dates to 2013, accounts for a significant part of

the reported reduction in on‐payroll staffing, but as shown below it is not the major

contributor. The effect of outsourcing is shown by using the numbers from the aggregated

change data shown in EXHIBIT 7 table and using the equation “Total Heads ‐ Outsourced

Heads = On‐payroll Heads” as follows:

From Exhibit 7:

Change in total FM heads (total all 13 cases) ‐208 ( 57%)

Less change (an increase) in outsourced heads ‐157 ( 43%)

Total reduction in on‐payroll heads (all 13 cases) ‐365 (100%)

The major contributor to on‐payroll headcount reduction (208 heads) comes from the

reduction in total FM heads and is the result of specific owner staffing adjustments made in

response to declining occupancy, the shedding of space with high Space Weights, increased

amounts of vacant space, and special leasing situations.

Rise in the level of outsourcing is mainly for office portfolios

The conclusion on outsourcing for this study group, both in the absolute levels of outsourcing

and changes levels of outsourcing, is that high levels of outsourcing and the biggest changes

in the levels of outsourcing involve space portfolios consisting mainly of office facilities. This

is illustrated below in EXHIBIT 8.

EXHIBIT 8 – Outsourcing, changes in outsourcing, and office space

Office spaces trending toward more outsourcing, but not high-tech space portfolios.

Study file number 3 5 13 46 49 71 86 87 102 108 118 119 120Original reporting year 2004 2005 2004 2003 2006 2004 2005 2004 2006 2007 2008 2008 2008

2013 level of outsoucing (percent) 94% 98% 53% 95% 50% 91% 50% 71% 12% 93% 90% 90% 78%

Previous level of outsourcing (percent) 57% 61% 59% 80% 49% 82% 53% 67% 0% 92% 90% 60% 54%

2013 weighting for Space Types* 0.94 2.23 3.61 1.10 0.85 0.72 0.87 3.47 3.53 0.98 0.96 1.95 1.31

Previous weighting for Space Types* 1.28 2.28 2.80 2.11 0.77 0.70 1.23 3.60 4.02 0.98 0.96 1.95 1.31

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In this exhibit, the number for Space Types is a number that reflects how much of what kind

of space is in the space portfolio mix. For example, office space is given a weight of 1.00, and

lab and manufacturing space is 5.0. Other types of space have other weights. Therefore a

space weight number of .94 or 1.31 would represent a very high percentage of office space. A

number of 3.5 would represent a high percentage of lab or manufacturing space. (See the

section of this report on Definitions of Terms and Metrics, page 18)

As indicated by the shaded cells of EXHIBIT 8, of the seven FM organizations reporting a 90%

or greater level of outsourcing (oval notations) five of the seven represent space portfolios

consisting almost entirely of space (office space weight = 1.0). Two of the seven highly

outsourced organizations have space weights on the order of 2.0 (2.23 and 1.95) which

correlates to approximately 75% office space (a mix of 75% “1.0” office space and 25% “4.8” lab

space). File number 120 at the end of this report shows a nearly‐all‐office portfolio (space

weight of 1.31) where the level of outsourcing increased significantly from 51% in 2008 to 77%

in 2013. There are two office‐only portfolios in the study (#49 and #86) that did not change

outsourcing models from pre‐2008 dates to 2013.

With respect to tech‐space (high space weights) there are three such portfolios in this study,

files 13, 87, and 102. They are shown in EXHIBIT 8 with their respective 2013 space weights of

3.61, 3.47, and 3.53. For each of these three tech‐space‐rich portfolios, there has been little

change in the level of outsoucing from pre‐2008 dates to 2013.

A common reason given for less outsourcing in facilities that contain large amounts of high‐

tech (high‐weight) spaces is that there is often a close working relationship between facilities

staff and the science or manufacturing groups they support where facilties workers are

regarded as important team members in the larger technical enterprise. In other words, in

those cases the high‐tech space itself is viewed as a critical tool of the technical program, and

the relationship between that tool manager (facilities management) and the technical program

leaders is not one to be relegated wholly to a contracted‐service type of status.

Very lean staffing models: murky numbers Seven of the 13 cases profiled in this study demonstrate 2013 staffing models for which the

level of outsourcing is at or above 90%. As shown in EXHIBIT 9 (below), these very lean

staffing models show a general correlation with the 102‐firm Mission‐based staffing

regression, but here the law of small numbers comes into play. Applying the band of

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confidence of +/‐ 1.0 head/1000 people served for low MCS numbers in the 102‐firm regression

(EXHIBIT 1), an organization that has reported a core team of 2.0 FM heads/1,000 people

served could be judged rightly staffed relative to the regression line if its headcount was as

many as 3.0 heads/1000 (2.0 + 1.0), or as few as 1.0 head/1000 (2.0 ‐ 1.0). If the organization in

question served a population of 3,000 people, that could mean a core staff as large as 9.0 total

heads (3h/1000 x 3[000]) or as small as 3.0 total heads (1h/1000 x 3[000]). Nine or three – that’s

a big spread of possible numbers!

EXHIBIT 9 – VERY LEAN STAFFING: Seven cases, uncharted territory for getting it right

Very lean staffing models: murky tasks Not only are the numbers murky for very lean staffing models, but so too are mission

definitions, job descriptions, and required skill sets. Very lean staffing seems to be uncharted,

ill‐defined territory where the management job to be done is reported to shift from a “do

team” to a “relationship management team” for which mission descriptions and staffing are

not well defined. In the words of one of the low‐MCS study participants, “I’m pretty sure I

have the right headcount, but I have discovered that I don’t have the people with the right sets

of skills.” For such uncharted and untried management territory, who is to say how many

heads are needed? One organization in the original 102‐firm study had outsourced all 10

services on its service menu 100% and still had a core team of 260 people! What do they do?

Another 100%‐outsourced organization from that study reported in 2002 a core team of 34 for

a mission of 8 million square feet and 31,000 workers served, and in 2009 a core team of 27 for

P a g e | 17


a mission of 12 million square feet and 55,000 workers served. What are the core‐team

missions statements, task definitions, and job descriptions that yield this kind of variability?

Very lean staffing models: the big error An all too common staffing error to be avoided in transitioning from a “do organization” to

that of a core team for a highly outsourced facilities management function appears to be that of

retaining on‐payroll staff (headcount) that duplicates work that the outsourcing contractors

are being paid to do. In spite of the issues of murky numbers and murky tasks raised above

for very lean staffing models, this core‐team staffing error will stand out glaringly when

headcount is plotted against Mission Complexity Score.

See the next page for Definitions of Terms and Metrics.

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DEFINITIONS OF TERMS AND METRICS

Facilities management heads (FM heads): What this study addresses is the number of

facilities management workers and staff members who are on an institution’s payroll. Here

the view is that on‐payroll headcount is the number that is subject to facility management

policies, processes, and control, and it is a number that can be readily and accurately obtained

from a corporation’s payroll department. Outsourced headcount is not included in the study

on the basis that outsourced services are purchased on a financial, competitive, open‐market

basis in which the number of outsourced workers is not under the management control of, nor

in many cases precisely known by, the owner‐institution.

1,000 workers served: This is the numerator in the metric, “FM heads/1000 workers

served.” It is the total population of the workforce that is served by facilities management

expressed in 1,000s. For example, a population of 1,500 workers served expressed in 1,000s

would be 1.50. While some facilities groups view their mission as managing square feet (using

“per square foot” metrics), the focus of this study is that of efficiently supporting a workforce

(using “per populaton served” metrics) where square feet, rather than being part of the

efficiency metric, are in fact quite scaleable and expendable variables in pursuit of efficient

management models for workforce support. Widely publicized post‐2008 corporate actions

involving large‐scale space shedding and consolidations suggest that this view has merit.

Also, it is the conclusion of this current study that population served is a major determiner of

FM staffing models. By using “1,000 workers served” as the denominator in the ratio of FM

heads/1000 workers served, large and small enterprises can be compared, and by counting

populations in terms of 1,000s instead of “per worker” we get numbers that are easy to

understand and work with – that is, 20 FM heads/1000 workers is easier to comprehend than

.02 FM heads/worker.

Facilities management services (and menu of services): This study covers a list of 25

services that are commonly found on the service menus of facilities management

organizations. The list of services is set out below. Some FM organizations may list only one

service (such as Maintenance), and some may have as many as 23, but none in the study offers

all 25 services. The average number of services offered in both the original 102‐firm study and

the 13 firms in this study is 13; however, rarely do two facilities management organizations

perform exactly the same menu of services under the label of “facilities management.” The

P a g e | 19


menu of services offered for a particular facilities management group is one of the several

mission variables to be factored in.

Real estate (acquisition, lease mgt, etc.) Engineering (not major capital projects) Planning & space management Moves & relocations Operations & maintenance IT connectivity IT systems operations Copiers & fax machines Environmental Health & Safety Mail Reprographics Office supplies Conference rooms reservations & mgt

Admin Services (if merged functionally with FM) Van pools & transportation services Travel services Fleet (vehicle) management Grounds Custodial services Food services Security Central tel & switchboard Trash handling/recycling Personal services (fitness ctr, child care, etc.) Shipping & receiving

Mission Complexity Score (MCS): This is a number that is calculated for each facility

management group based on a mathematical algorithm that assigns staffing values to mission

variables such as the menu of services performed, space types (offices, labs, vacant, etc.),

outsourcing and leasing policies, occupancy density, churn, shift and mobile workers, age and

number of buildings, and other mission variables. This algorithm was statistically developed

from data provided in Tradeline’s previous study of 102 facilities management organizations.

Space Type Weight: Ten classifications of space types are used in determining Mission

Complexity Scores. Space Type Weight applies only to FM servcies that are space‐related,

such as Planning & Space Management, Moves & Relocations, Engineering, Maintenance &

Operations, and Custodial. The ten classifications are:

Vacant space Warehouse/distribution Public Access (retail/atriums/lobby/food/auditorium) Office/administration Manufacturing (excluding office/admin) Research space/labs/high-tech) Computer rooms, computer centers Pilot plants, cleanrooms, biocontainment, vivaria Central utility plants Other

The 102‐firm mission‐staffing standard: This is the regression line for FM on‐payroll

staffing/1000 workers served vs. Mission Complexity Scores derived from the original

Tradeline study of the staffing models of 102 firms that is illustrated in EXHIBIT 1.

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Lean Mission‐Staffing Index: This is the ratio of the reported total number of facilities

management heads divided by the mission‐based predicted total number of facilities

management heads. If the reported actual number of heads is 27, and the Mission‐based

predicted total number is 30, the Lean Mission‐based Staffing Index will be 27/30, or .90,

signifying that the reported actual number of heads is 10% below what the 102‐firm mission‐

based staffing standard would predict for the organization’s Mission Complexity Score.

Lean Mission‐Staffing Variance (Heads/1000 workers served): This is the difference

between the reported FM heads per 1000 people served and the predicted FM heads per 1000

people served. It is a measure of how far from the 102‐firm mission‐based staffing line a

particular FM headcount/1000 is. An “on the line” staffing number would yield a Lean

Variance of 0.00.

Lean Staffing Test: Based on the range of data variability seen in the original 102‐firm

staffing study, staffing models are judged to pass the mission‐based staffing standard if either

of the following criteria are met: 1) The Lean Mission‐based Staffing Index lies between 1.15

(15% above the mission‐based standard) and .85 (15% below the mission‐based standard), or 2)

A Lean Mission‐Staffing Variance of between +1.5 heads/1000 people served and ‐1.5

heads/1000 people served.

See the next page for Thirteen Mission Staffing Profiles.

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THIRTEEN PROFILES OF FACILITIES MANAGEMENT CHANGE

Mission staffing profiles and data for each of the 13 study participants are presented below. In

the interest of promised anonomity, the participating organizations are identified by their

Tradeline file number and not in the order in which they are listed in the this report’s

introductory comments. Here we find organizations moving in various directions with respect

to the populations served (up and down), space potfolio under management (up and down),

occupancy density (up and down), types of space (less and more labor intensive), percentages

of leased space (up and down), and outsourcing (up and down) – and with different labor

headcount solutions (up and down). Along with data and graphs detailing changes in staffing

and missions, brief commentary is given highlighting the major areas of facility and

management strategy change.

See the next page for Mission Staffing Profiles.

P a g e | 22


File Number 3 – redefinition of the mission plus change in outsourcing

Facility Strategy Issues:

This is a case of redefinition of the facility mission by adding to the original mission an

additional region with additional square feet and more population served and removing

research space from the portfolio.

Portfolio size up 53%.

Occupancy up 42%.

Square feet per occupant up 8%.

Space Type weight is down significantly (2004 has some research space, 2013 does not).

Leased space up from 4% of the portfolio in 2004 to 26% 2013.

Management strategies:

Outsourcing, leasing, and FM headcount reduction: Here the management model change is

mainly outsourcing and more reliance on lease services. The level of outsourcing has gone

from 60% in 2004 to 96% in 2013. Three new population‐driven services were added to the

2013 service menu (Mail, Reprographics, and Fleet Management), and all of the 15 services on

the FM service menu in 2013 (excepting Engineering, Environmental Health & Safety, and

Centralized Switchboard) are essentially 100% outsourced. The staffing models for both 2013

and 2004 meet the Lean Management Staffing Test on both criteria: Lean Staffing Index = 1.00

+/‐ .15, and the Lean Variance < +/‐1.5 heads/1000 people served.

Period of change: 9 years 2013 2004 Change

Reported square feet (gross) 782,000 511,816 52.8%

Reported occupants, population served 2,122 1,500 41.5%

Gross square feet/occupant 369 341 8.0%

Weighting for mix of space types* 0.94 1.28 -26.5%

Percent of leased space 26% 4% 22.0%

Number of services offered (out of 25) 15 12 25.0%

Reported level of outsourcing (percent of FM labor) 94% 57% 37%

Estimated number of outsourced FTE personnel 59 24 35

Estimated total FTE heads (on-payroll + O-sourced) 64 42 22

FM Mission Complexity Score 0.12 0.63 -80%

Predicted on-payroll FM heads/1000 pop served 2.5 12.5 -80%

Reported on-payroll FM heads/1000 pop served 2.4 12.0 -80%

Predicted total number on-payroll FM heads 5.3 18.8 -14

Reported total number of on-payroll FM heads 5.0 18.0 -13

(Reported total heads) - (Predicted total heads) -0.3 -0.8


Lean Variance in Heads / 1000 workers served*** -0.12 -0.54




P a g e | 23


File Number 5 – fixed space, declining population, major outsource change


Space: Since 2005 there has been no indicated change in the space portfolio – the same gross

square footage and the same mix of space types (Space Weight = 2.22, 2.28). The space mix

includes 20% of highly technical space.

Occupancy: The population served has dropped by nearly 30%, with a 42% increase in the

gross square feet per occupant.


Outsourcing and FM headcount reduction: Here the main management model change is

outsourcing, which has gone from 61% in 2005 to 98% in 2013. Conference Room

Reservations was dropped from the service menu in the 2013 data, and all services are 100%

outsourced except for in‐house Engineering which is 0% outsourced. The reported FM

headcount of 5.0 FTE heads in 2013 is within 1.3 heads (Lean FM staffing quotient of 1.34) of

the predicted number of 3.7 FTE heads.


Reported square feet (gross) 1,000,000 1,000,000 0.0%

Reported occupants, population served 950 1,350 -29.6%




Number of services offered (out of 25) 15 16 -6.3%









(Reported total heads) - (Predicted total heads) 1.3 14.9


Lean Variance in Heads / 1000 workers served*** 1.34 11.01




P a g e | 24


File Number 13 – consolidation, higher MCS, and more FM heads


Space reduction: Since 2004 the space portfolio has been reduced by over 700,000 square feet

(‐11.4%) of mostly office space resulting in a higher percentage of research space in the 2013

portfolio resulting in a higher overall average space weight (up 29%).

Change in occupancy: The number of workers supported by this space portfolio is down but

with fewer office/admin workers, a higher percent of research personnel, and with 7.5% more

square feet per occupant.


This is the unique example in this study in which headcount has gone up per 1,000 people

served and in absolute numbers. But, the staffing model tracks with a rising Mission

Complexity Score as a result of the shift in the mix of space towards a higher percentage of

research space (more FM labor intensive) along with more space per occupant (consistent with

the more research‐focused portfolio). The staffing models for both 2013 and 2004 meet the

Lean Management Staffing Test on the criterion: Lean Staffing Index being in the range of 1.00

+/‐ .15.


Reported square feet (gross) 5,540,000 6,250,300 -11.4%

Reported occupants, population served 8,078 9,800 -17.6%


Weighting for mix of space types* 3.61 2.80 28.7%

Percent of leased space 1% 6% -5.0%


Reported level of outsourcing (percent of FM labor) 53% 59% -6%

Estimated number of outsourced FTE personnel 478 583 -105


FM Mission Complexity Score 2.68 2.10 28%

Predicted on-payroll FM heads/1000 pop served 53.7 42.0 28%

Reported on-payroll FM heads/1000 pop served 54.6 42.8 28%

Predicted total number on-payroll FM heads 433.4 411.5 22

Reported total number of on-payroll FM heads 441.0 419.0 22







P a g e | 25


File Number 46 – Shed space, change space type, constant population


Changed space portfolio, constant occupancy: As the result of the outsourcing of

manufacturing operations, manufacturing space was eliminated from the space portfolio

shifting the remaining portfolio to mainly office facilities. The result is less square feet (down

28%), reduced square footage per occupant (down 28%) and a lower Space Type Weight

(down 48%). There are no leases.


Headcount reduction of 44 heads [from 59 in 2003 to 15 in 2013 = 75% reduction) is consistent

with the 81% reduction in this organization’s Mission Complexity Score reflecting a higher

occupancy density (28% fewer square feet per occupant) and the shift to a mainly office

environment (Space Type Weight of 1.10 in 2013). The staffing models for both 2013 and 2003

meet the Lean Management Staffing Test on the criteria of Lean Variance < +/‐1.5 heads/1000

people served, but only the 2003 model meets the 1.00+/‐ .15 Lean Mission‐based FM Staffing

Test.




Gross square feet/occupant 543 759 -28.4%


















P a g e | 26


File Number 49 – growing occupancy, fixed space, reduced FM headcount


Growing occupancy, fixed space: This is essentially a fixed office‐environment space portfolio

where occupancy has increased by 20% with a resulting decrease of square feet per occupant.

Less vacant space in 2013 than in 2006 raises the Space Weight by 10%.


In spite of population growth and a near‐constant Mission Complexity Score (.74, .71), the FM

on‐payroll headcount has actually been reduced by 10% (6/58). Many services offered are not

outsourced and are population‐driven, such as Mail, Copiers & fax machines, Reprographics,

and Shipping and Receiving, and no increased staffing is indicated to service the population

growth. In fact, significant headcount reduction has occurred (and is occurring) for Mail

(“almost no mail anymore”) and Reprographics (“new hires don’t use those services”)

suggesting that Mission Scoring for services like these need to be adjusted downward to reflect

changes in office‐place technologies and work processes. Whereas the 2013 staffing model

does not meet the Lean Management Staffing Test (understaffed) on either criteria of Lean

Staffing Index = 1.00 +/‐ .15, or the Lean Variance < +/‐1.5 heads/1000 people served, this may

be due to overly high Mission Scores assigned to services like Mail and Reprographics given

that workplace processes have changed.
















(Reported total heads) - (Predicted total heads) -27.2 -9.8






P a g e | 27


File Number 71 – declining occupancy and space, increased outsourcing


A number of changes have occurred: 1) a large reduction in the population served (down by

54%), 2) a modest reduction in square footage under management (down by only 10%), and 3)

a modest increase in the level of outsourcing (up 10%). Specifically, workforce and occupancy

changes here consists of a major reduction of the office‐based workforce, the shedding of some

leased facilities, and substantially leaving intact the company’s warehousing and field

operations which are characterized by low‐occupancy, low‐maintenance space, and high

square footage per occupant. The combined staffing effect of multiple changes such as seen

here would be hard to forecast apart from a multi‐factor, mission‐based modeling algorithm.


The reported headcount reduction of 35 heads between 2004 and 2013 is consistent with the

Mission‐based‐staffing predicted reduction of 35 heads. Staffing models for 2013 and 2004

both meet the Lean Management Staffing Test on the criterion of Lean Variance < +/‐1.5

heads/1000 people served, and the criterion of the Lean Staffing Index = 1.00 +/‐ .15.







Number of services offered (out of 25) 10 11 -9.1%















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File Number 86 – stable occupancy, less space, more vacant space, fewer heads


Occupancy density and vacant space: From 2005 to 2013, leased space has remained a stable

portion of the space portfolio, and reported square footage under management has declined by

9.4% with essentially stable occupancy resulting in square feet per occupant down by 9.3%.

The big change over this period is a higher portion of the 2013 portfolio reported as vacant

space, which lowers the Space Weight (1.00 = office space) for 2013 significantly by 29% (from

1.23 to .87).


In 2005 this corporation carried excess staff that was allocated to a major office move initiative.

Following the move, staff numbers were cut, and this cut accounts for much of the 2013 shift

towards the 20X regression line. The 14% decline in Mission Complexity Score is less than the

29% drop in the Space Weight because 11 of the 19 FM services performed are population

driven (not space‐driven) and are not outsourced. Two important space‐driven services,

Engineering and Maintenance, have remained at a nearly constant 100% outsourced level. The

2013 reported on‐payroll FM headcount is 5.4 heads (+21%) above the Mission‐based predicted

headcount. While the company is moving closer to the Mission‐based Staffing regression line,

the staffing model for 2013 does not meet the Lean FM Staffing Index Test ( 1.00 +/‐ .15) nor the

Lean Variance test (< +/‐1.5 heads/1000 population served).


Reported square feet (gross) 591,000 652,500 -9.4%






Reported level of outsourcing (percent of FM labor) 50% 53% -3%














P a g e | 29


File Number 87 – fixed facility portfolio and occupancy, leaner staffing


Over the nine‐year period, there have been minor changes in the reported facility space

portfolio, occupancy, mix of space types, and leasing. The stated facility management strategy

is, “Let the buildings age, keep them maintained, but reduce FM staff.” The overall high Space

Type numbers of 3.60 and 3.47 for the years 2004 and 2013 reflect a significant continuing

amount of research space and pilot plant facilities.


The reported FM on‐payroll headcount per 1000 workers served in 2013 has moved closer to

the Mission‐Staffing Line and is now within 5% (Lean Staffing Index =1.05) of the 102‐firm

mission‐based FM staffing standard which meets the Tradeline Mission‐based Lean Staffing

Strategy Test of a Lean FM Staffing Index of 1.00 +/‐ .15.

The two services that were added to the 2013 facility management service menu were

food services (100% outsourced) and personal services (fitness and/or child care) (95%

outsourced).


Reported square feet (gross) 915,000 952,000 -3.9%




















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File Number 102 – rising space, declining occupancy, reduced FM staff


Changes on almost all facility fronts:

The space portfolio is up 19.5%; 25% more leased space (a new large building under a long‐

term lease commitment); occupancy is down; and vacant space is up. Occupancy plans for the

added leased space have not yet been realized, and the total number of workers supported on

the site has gone down 16.7% leading to a 43% increase in space per occupant. The Space Type

weight for the portfolio is down 12% as a result of the rise in the percent of vacant space.


The 33% decrease in the total reported number of on‐payroll FM heads (54/166 = 33%) is

consistent with a 20% decrease in Mission Complexity Score coupled with a 16.7% decrease in

the population served. Both the 2006 and 2013 staffing models meet the Tradeline Mission‐

based Lean Staffing Strategy Test with Lean Mission‐based FM Staffing Indexes of 1.03 and

1.04 respectively. The old and new staffing models are both close to meeting the Lean

Variance of +/‐ 1.5 heads per 1,000 workers served

Whereas Custodial Services are performed as a 100% on‐payroll service, Custodial has

been excluded from the menu of services offered along with associated custodial headcounts

because of complexities in accounting for custodial access rules concerning certain types of

space for which custodial workers do not have access.






















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File Number 108 – no changes in facilities or management model


This is a no‐change scenario. In the six year period between 2007 and 2013 there is no reported

change in this portfolio of purely office facilities.


Likewise, there is virtually no reported change in the facilities management model. Over the

six‐year period it has remained a highly outsourced FM operation (92‐93%) with a reported

near‐constant number of on‐payroll heads. The staffing models for both 2013 and 2007 the

test of Lean Variance < +/‐1.5 heads/1000 people served. The added single FM head in 2013

puts the 2013 Lean Mission‐based FM Staffing Index at 1.24 which is above the Lean Staffing

Index test of 1.00 +/‐ .15. The 2007 staffing of 4.0 FTEs was the predicted number using the

MCS regression.













Reported on-payroll FM heads/1000 pop served 2.0 1.6 25%





Lean Variance in Heads / 1000 workers served*** 0.38 -0.01




P a g e | 32


File Number 118 – Population growth, constant space, fixed FM staffing


The major change here is a 63% growth of the population served in a fixed facility footprint.

An estimated 170,000 sq. ft. of space that is subleased to other corporate entities, and is not

supported by the owner’s FM services (essentially triple‐net with no services to the lessee), has

been taken out of what was originally reported as a 3.4‐million‐sf 2013 space portfolio.


The facility management model from 2008 to 2013 has continued to be a highly outsourced

function at a level of outsourcing equal to 90%. Almost all services on the 18‐function service

menu are related to the population of workers served and are 100%, or near 100%, outsourced.

While there is a reported 63% increase in the population served, on‐payroll FM heads have

remained essentially constant (39 in 2008 and 40 in 2013) with the headcount to serve an

additional 4,750 occupants coming from an estimated 176 additional outsourced service

contractor heads. Both 2013 and 2008 FM staffing models meet the Lean Variance test of +/‐

1.5 heads/1000 workers served. The 2013 model, however, appears to be as many as 15 heads

understaffed based on the statistical MCS vs. Staffing regression which shows as a FM Staffing

Index number of .73 (27% understaffed).
















(Reported total heads) - (Predicted total heads) -14.7 5.5






P a g e | 33


File Number 119 – stable portfolio, rising occupancy, outsourcing initiative


With a virtually unchanged space portfolio (negligible changes in square footage, mix of space

types, and leasing), and a nearly 12% reported increase in population served, space per

occupant has decreased by nearly 12% (from 552 sq. ft./worker served in 2008 down to 486 sq.

ft./worker served in 2013).


The big change here is a major outsourcing initiative in which Operations and Maintenance is

reported to have gone from 5% outsourced in 2008 to 100% outsourced in 2013 with the result

that the overall level of outsourcing (across the whole menu of 14 services) went from 60% in

2008 to 90% in 2013. In 2008 the reported total number of on‐payroll FM heads was 17.2 above

what was predicted by the Mission‐based staffing analysis. The reported 2013 on‐payroll

staffing headcount of 39 represents a 78% reduction (141/180) from 2008 and is consistent with

the 76% reduction in Mission Complexity Score (the outsourcing effect). The staffing model

for 2013 meets both the Lean Mission‐based FM Staffing Index test of 1.00 +/‐ .15, and the Lean

Variance in heads/1000 workers served of < +/‐1.5.
















(Reported total heads) - (Predicted total heads) -5.1 17.2






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File Number 120 – stable portfolio, lower occupancy, increased outsourcing


Since 2008 there have been no changes in square footage or the mix of space types (the space

type weight is unchanged at 1.3). A 22% decline in population served has resulted in lower

occupancy density (+28.8% square feet per occupant).


The major management change over the five year period is a change in the menu of services

offered and a significant increase in the level of outsourcing. The 2013 service menu has 14

services as compared to 13 in 2008, but some services have been eliminated and some new

services added. In 2008, the staffing model was judged to be 2.5 heads below (understaffed)

relative to the 102‐firm mission‐based staffing regression line. The reported staffing model of

2013 is exactly equal to the predicted number according to the mission‐based staffing

standard. Whereas the 2008 staffing model failed both the FM Staffing Index and Variance in

Heads/1000 people served tests (significantly understaffed), the staffing model for 2013 meets

the Lean Management Staffing Test on both the criteria of Lean Staffing Index = 1.00 +/‐ .15,

and a Lean Variance < +/‐1.5 heads/1000 people served.


Reported square feet (gross) 220,000 220,000 0.0%

Reported occupants, population served 520 670 -22.4%













(Reported total heads) - (Predicted total heads) 0.0 -2.5






P a g e | 35


STUDY PARTICIPANTS’ COMMENTS ON THE DRAFT REPORT

(Boxed numbers refer to the commentator’s profile number)

On populations served:

The biggest finding is that square footage is not the defining character of the

organization. This says that population served drives staffing more so than square

footage, but not alone. A classic multivariable equation – population served and square feet.

We can tweak headcount for population‐driven services by reducing service levels, but you

can’t reduce service levels for air handling unit repairs.

In my own experience here with new bosses and new senior leadership teams for the

past 5 years, emphasis has always been on “what’s being done” (mission) and for “how

many people” (workers‐served) and rarely, if ever, on “how much space.” I’ve actually been

measuring costs per worker served on a monthly basis for my corporate campus for a couple

years now.

I would wholeheartedly agree that population served has a greater impact than square

footage. What we see is the more people who are “exercising” the building assets the

more staff we need to deal with that. I would expand on this though (in addition to the

comments on population‐driven services). It’s not just the population served but it’s their

“activity level” to which is probably related to their spending budgets (hard to tease out of

clients though).

On the idea of an N‐year picture:

This is awesome stuff. The comparisons over 10 years lends tons of credibility to this

entire concept.

On the 13‐organization aggregated data:

Could the aggregated data reflect a statistically significant reduction or simplification

of the mission and a corresponding “leaning” of the FM headcount? Is there an overall

trend here? [Author: That is a conclusion I would make from this study – that FM headcounts

have gone down, but that missions have been simplified (mission complexity reduced) proportionally. That is seen in the aggregated data and in the individual case files as well.]

The conclusion that aggregated data are of little value is a big deal. But on the small

sample size issue one could argue that what we are seeing here is that this particular

conclusion is applicable for this 13‐company sample. [Author: I agree that this is a small

sample, but I believe that this illustrates in solid graphical terms the simple mathematical truth

102

3

87

3

87

102

P a g e | 36


that the average of any N change-events will always yield a lesser indicated magnitude of change than the variances of N events observed individually. The point here is that the “average” doesn’t tell one anything about any one event.]

I agree with the conclusion of the aggregated staffing headcount tracking with MCS.

There is value in this nomograph (Exhibit 7) with this outcome.

On Mail, Reprographics, and other population‐driven services:

Our headcount for these services has gone down by over 30% since 2005 due to

changes in our company business model, work practices, and communication

technologies. Thirty percent of our total headcount served at the moment are contract workers

who donʹt use these services. Reprographics volumes in 2005 for b&w and color was about 15

million and this year 9 million. Incoming mail is almost nothing these days – mail staff way

down since 2005, and we are looking at digitizing incoming mail which should reduce

headcount even further.

On outsourcing:

In almost all of our service areas, my group is the low‐cost provider. However, I’m

being pushed towards outsourcing even though we benchmark as being very

competitive if not one of the best. This is being driven from above not by service or cost but by

a simple drive to reduce full‐time, on‐payroll headcount.

The reasons for outsourcing may be to reduce overall headcount, or simply that

outsourcing is the current management wave of what large companies are going

through right now – for us it was a combination of both.

On non‐FM heads:

There are people in our group who perform services that have nothing to do with

facilities management – and I am taking them out of the FM headcount. One such

service is event management, and our staffing for that is actually significant. Another service

person on our FM staff is an AV technician (now excluded from the MCS headcount).

On the small study sample of 13:

Some may argue the statistics of validating a model based upon 100 firms with only 13

firms. What are the chances the other 87 firms regressed to the same line over this

102

49

49

5

86

87

P a g e | 37


time? Is the regressed line still valid? Would all 100 firms regress to the same line today?

Technology could shift the line.

I donʹt have a problem with the small data set of 13. I like that the mix covers a broad

spectrum in size and staffing as well as ranges in MCS.

On very lean staffing models (murky territory) and costs:

The discussion on very lean staffing models is fascinating, and it made me ask myself if

the form we completed had costs included. So I went back and checked and said, ah

ha, nope. I think we’re missing an important item here. We can’t tell what is going on with

the operations when high levels of outsourcing are in play because of a few factors: the costs

expended (as I noted, but also as you noted the mission statements); task definitions; and job

descriptions that may or may not overlap. I think it may be time to collect operational budget

data. I suppose people hold that close and that’s why you don’t have it, but it sure would

make this work better. [Author: Agreed. Cost is certainly the bottom-line test. However, I

would argue that FM costing formulas are particular to particular institutions. From my discussions with many FM organizations, wading through the morass of different accounting and financial reporting policies, practices, and nuances, not to mention labor cost implications of different regional locations and labor (union) work rules, to arrive at comparative cost reporting that people understand, really trust, and find useful is a huge undertaking, and one in my view that is unnecessary in the pursuit of good management solutions. The key words here are “understand, trust, and useful” – especially the word “useful.” I have heard many stories of organizations spending extensive time and effort to get such comparative data only to declare at the end, “Yes, but that doesn’t relate to us because we’re different.” And I would stay that that is a true statement. In contrast, the objective of the Mission Complexity Scoring approach is much simpler. It is to do a quick management test on that component of FM labor that is subject to management decisions and control – namely, on-payroll heads. Headcount is an easy-to-get, understandable metric that is a major bottom-line FM cost component. To check (do the quick test) on what is happening on the vendor side of a very lean owner staffing model, (the part that’s been contracted out for someone else to manage) one simply has the vendor fill out the data form for the vendor’s mission and headcount to see where the vendor stands on heads versus mission.]

102

102

P a g e | 38


APPENDIX 1 – Participating organizations in the 102‐firm staffing study

AAMC Adobe Systems AIM Investments Alaska Court System Alliant Energy Amgen Corporate Armstrong BASF- RTP Bayer Pharmaceuticals BC Cancer Berlex Laboratories BlueCrossShieldMA Boehringer Ingelheim Bristol-Myers Squibb Canadian Border Svcs Cargill Chiron Corixa Corning Inc Covidien DaimlerChrysler Dell Dow Corning Dupont Eastman Chemical FAA – Dept of Transportation Fed Reserve Bank Boston Fed Reserve Bank Chicago Ford R&D Hdqtrs Fred Hutchinson Cancer Ctr FreddieMac Gannett GE Corp (2 groups) GE Healthcare Gen Dynamics C4 Sys General Motors Hdqtrs GlaxoSmithKline (4 sites) Harley-Davidson (6 sites) Hillsborough Cty, FL Honeywell HTI - Hutchinson IBM (3 sites) IndyMac Bank Intel (3 sites) InterAmerican Dev Bnk

Int'l Truck & Engine Jack in the Box Jackson National Life Insurance Johnson & Johnson (2 sites) L-3 EDD Lawrence Livermore Nat’l Lab Lexmark International Lincoln Fin Group Luxottica Retail Microsoft Puget Sound Millennium Pharmaceuticals MTVNetworks NASA Goddard National Cancer Institute - SAIC Navy Fed Credit Union Nortel Networks Northrop Grumman Onyx Software Pfizer (3 sites) Philips Semiconductors Pioneer – a Dupont Co. Polk County Florida RAND Ricketts Lab – Univ of Chicago Roche Palo Alto Royal Mail – United Kingdom SAFECO Sandia Nat'l Labs Sears Sempra Energy Siemens - ICN State of WA, Dept of Ecology Texas Guaranteed Student Loan Corp Thrivent Financial Time Warner Cable U.S. Dept of Commerce Univera Unum - Chattanooga USAA Viacom Vision Services Plan Walter Reed AIR World Bank Wyeth BioPharma

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