Key performance indicators for facility performance...

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Key performance indicators for facility performanceassessment: simulation of core indicatorsSarel Lavya, John A. Garciab, Phil Scintoc & Manish K. Dixita

a Department of Construction Science, Texas A&M University, College Station, TX77843-3137, USAb Alpha Facilities Solutions, San Antonio, TX, USAc Scinto Statistical Services, Cleveland, OH, USAPublished online: 08 Dec 2014.

To cite this article: Sarel Lavy, John A. Garcia, Phil Scinto & Manish K. Dixit (2014) Key performance indicators for facilityperformance assessment: simulation of core indicators, Construction Management and Economics, 32:12, 1183-1204, DOI:10.1080/01446193.2014.970208

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Key performance indicators for facility performance assessment:simulation of core indicators

SAREL LAVYa*, JOHN A. GARCIAb, PHIL SCINTOc and MANISH K. DIXITa

aDepartment of Construction Science, Texas A&M University, College Station, TX 77843-3137, USAbAlpha Facilities Solutions, San Antonio, TX, USAcScinto Statistical Services, Cleveland, OH, USA

Received 5 November 2013; accepted 22 September 2014

Assessing a facility’s performance is important for measuring its contribution towards organizational goals.

Among many approaches to performance assessment is the holistic key performance indicator (KPI) approach.

However, there are numerous KPIs available, and the chosen KPI needs to be relevant to facility goals and must

be calculated, analysed and evaluated to allow for the future state of the facility to be acceptable at the lowest

cost. The value of several key descriptive analytics in facility performance assessment may be enhanced through

the use of simulation. Simulation transforms the descriptive analytics into predictive and prescriptive analytics by

allowing for the robust assessment of plans and future outcomes through the creation of multiple scenarios, in

this case, for an education facility. The simulation approach quantifies the interrelationship and interdependence

of KPIs, and is potentially effective in analysing how maintenance expenditures can be optimized to maintain a

desired level of Condition Index as demonstrated by several simulation scenarios.

Keywords: Educational facilities, facility management, key performance indicators, maintenance, simulation.

Introduction

The context of this study

Evaluating a facility’s performance is critical for track-

ing its contribution towards accomplishing organiza-

tional goals and for making future decisions regarding

facilities management (Douglas, 1996; Amaratunga

et al., 2000; Barrett and Baldry, 2003; Cable and Davis,

2004). A facility supports core business activities by

providing a conducive and productive work environ-

ment (Douglas, 1996). A range of measurement

approaches have been proposed in the literature, such

as benchmarking, balanced scorecard, critical success

factor, and key performance indicator (KPI) method

(Lavy et al., 2010), with a preference for the KPI

method being emphasized (Lavy et al., 2010). How-

ever, an extensive list of KPIs exists that includes indi-

cators that are either not quantifiable, not measurable,

or provide redundant measurements (Neely et al.,

1997; Shohet, 2006). A need to weed out unnecessary

and repetitive indicators and to derive a concise list of

core, quantifiable, and measurable indicators has been

highlighted by numerous scholars (Slater et al., 1997;

Hinks and McNay, 1999; Augenbroe and Park, 2005;

Gumbus, 2005; Shohet, 2006).

Facility managers face increasing pressure to prior-

itize the direction of limited resources to address main-

tenance and capital renewal needs. The consequences

of failing to prioritize the direction of sustainment and

capital appropriately may include unscheduled facility

maintenance, or system availability, and additional

expense to repair or replace failed components or sys-

tems due to short/emergency notice procurements.

Equipped with KPI simulation (modelling) tools, facil-

ity managers can better prioritize maintenance and cap-

ital renewal actions by developing scenarios to examine

the interrelationships between funding levels, funding

timing, business rules and critical KPIs. This enables

them to make more informed decisions, and offers

another way to communicate funding priority require-

ments to other stakeholders in order to build enterprise

awareness and support for requirements.

*Author for correspondence. E-mail: [email protected]

© 2014 Taylor & Francis

Construction Management and Economics, 2014Vol. 32, No. 12, 1183–1204, http://dx.doi.org/10.1080/01446193.2014.970208

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mailto:[email protected]

http://dx.doi.org/10.1080/01446193.2014.970208

An organizational plan is divided into three levels.

The highest level contains organizational goals, which

focus the attention of different stakeholders and constit-

uents toward achieving a set of goals that are believed to

advance the organization. In the case of education facil-

ities, organizational goals may include raising student

scores, decreasing student absenteeism, reducing tea-

cher turnover, eliminating lost time in class, and reduc-

ing student accidents to the lowest possible level, among

others. The second level, which supports these organi-

zational goals, is the facility performance assessment.

This level involves setting targets for aspects that affect

the facility’s operation, such as personnel (e.g. recruit-

ing, training, retaining), maintenance issues (e.g. fre-

quency, quality), environmental issues (e.g. noise, air

quality, aesthetics), space (e.g. amount of space, quality

of space, privacy), among other factors. If an organiza-

tion is unsuccessful in assessing its progress towards

reaching these targets at the facility performance assess-

ment level, how does it know that it is still on track to

achieve its organizational goals? These targets can, of

course, be refined and modified with time, as per

changes that may occur in the business environment

as well as changes that may occur in the overall organi-

zational goals (level one). Level three is where key per-

formance indicators may be found. At this level, the

organization develops metrics (performance indicators)

to help it measure (past), analyse trends (present), and

predict (future) how successful it is and what action is

to be taken in order to successfully achieve the targets

set at the second level: facility performance assessment.

In other words, the KPIs (in level three) support reach-

ing the targets set in facility performance assessment

(level two), which in turn, support reaching the organi-

zational goals (level one). This is the typical hierarchy of

an organizational plan.

The scope of this paper is limited to the third level

of the organizational plan hierarchy, i.e., the level of

development of key performance indicators in order

to set facility performance assessment targets. This

study focuses on educational facilities due to the fact

that, according to various studies, such as the American

Federation of Teachers (2006), Young et al. (2003),

Cash and Twiford (2009), and Collins and Parson

(2010), there is strong evidence that the condition of

an education facility (level three in the organizational

plan hierarchy) affects the performance of not only

the students, but also its teachers (level one in the orga-

nizational plan hierarchy).

Research goal and objectives

To make performance measurement more meaningful,

its analysis should also focus on predicting future

impacts and on prescribing measures to improve facility

performance. In Lavy et al. (2014a, 2014b), core KPIs

and the variables that affect them were identified, listed

and discussed, and equations to calculate them were

derived. The goal of this study is to propose a platform

to simulate dynamic, complex facility performance out-

puts (metrics), utilizing realistic data inputs. The data

inputs are also dynamic, complex and subject to special

cause (war, natural disasters, etc.) and common cause

(natural market fluctuations) variability. Input vari-

ables, which themselves may be metrics and KPIs,

include facility factors such as cost of construction,

renewal rate, maintenance costs and rates, and covari-

ates such as cost of capital, interest rates, etc. As real

data is not readily available for all input variables due

to the complex nature of this topic, the following are

the specific objectives for this study:

(1) Simulate the identified core KPI outputs for

facility performance assessment using hypo-

thetical input data.

(2) Demonstrate how simulation allows for the

study of correlations and relationships between

and among KPIs and input variables within a

system facility and for the entire facility.

(3) Highlight the sensitivity of outputs and out-

comes to input variable sensitivity.

(4) Demonstrate that, due to variability and future

uncertainty, simulation is a valuable tool for

generating possible scenarios and making deci-

sions based upon forecasts and logic.

Literature review

Facility performance assessment

Measuring a facility’s performance includes reviewing

past and current conditions to compare its performance

within similar and across different organizations. The

process of performance assessment evaluates the contri-

bution of a facility to achieving organizational goals,

and therefore is vital to establishing future facility

management strategies (Kincaid, 1994; Lebas, 1995;

Amaratunga and Baldry, 2000; Barrett and Baldry,

2003; Cable and Davis, 2004). Decisions relating to a

facility’s extension, acquisition, and strategic changes

dependon reviewing the facility’s performance (Douglas,

1996; Amaratunga and Baldry, 2000). Syakima et al.

(2011) explained that the term ‘facilities’ includes the ser-

vices required to support core business activities of an

organization, whereas the term ‘performance’ relates to

efficiency and effectiveness. Hence, facility performance

means assessing a facility’s efficiency and effectiveness in

providing services for organizational business activities.

The performance measurement helps in understanding

the impacts of management decisions on success and

1184 Lavy et al.

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failure of the facility portfolio and in suggesting possible

improvements (Cable and Davis, 2004). According to

Atkin and Brooks (2000), in a performance assessment,

it is important to identify issues crucial to organizational

success. Such issues can be tied to organizational

goals and objectives and eventually to performance

measures.

Among the most common approaches for

performance evaluation are benchmarking, balanced

scorecard, post-occupancy evaluation (POE), key per-

formance indicators (KPIs) and critical success factors

(CSFs). According to Ho et al. (2000), the performance

metrics that include KPIs can be used for making com-

parisons within and across organizations. Hitchcock

(2002) and O’Sullivan et al. (2004) stated that perfor-

mance metrics can define performance objectives clearly

and quantifiably. Assessing a facility’s performance

using a set of KPIs provides the user with an opportunity

to select the indicators of choice (Lavy et al., 2010).

Performance assessment of school facilities

According to Syakima et al. (2011), the assessment of

performance must focus on physical condition (poor,

good or excellent), functional appropriateness (ability

of facilities to support required functions) and issues

relating to user satisfaction and productivity. In the

case of school facilities, the quality of the built environ-

ment affects the performance of students and teachers

significantly. Research has shown that the condition

of school facilities is strongly and positively correlated

with student achievement (American Federation of

Teachers, 2006; Cash and Twiford, 2009; Collins

and Parson, 2010). If socioeconomic factors are con-

trolled, a significant difference (5–17 percentile points)

between academic performance of students from poor

and standard school conditions is suggested by studies

such as American Federation of Teachers (2006),

Young et al. (2003) and Earthman (2002). The condi-

tion of school facilities also has an impact on teacher

empowerment, which is strongly related to student aca-

demic achievements (Collins and Parson, 2010).

The factors responsible for poor school conditions

can be categorized as physical, spatial and environmental

(Young et al., 2003; Lippman, 2010). The physical con-

dition of a school includes factors such as interior and

exterior conditions, for example age of the building and

its components, structural damage, leakage, cracks,

and expired building components. Spatial factors

include aspects that affect the adequacy of space to fulfil

the required function. Despite having sufficient floor

area, a school facility could have inadequate space due

to the lack of proper space management. Three types

of spaces exist in a school environment: instructional

(e.g. classrooms), supplemental (e.g. laboratories) and

support spaces (e.g. gymnasium and auditorium), with

space standards for each. The National Clearinghouse

for Educational Facilities provides space standards for

school settings at various levels such as elementary, mid-

dle and high school. The space standards are provided in

gross square foot area per student for different annual

enrolment categories. Space standards for various

instructional, supplemental and support school spaces

are also provided by state agencies such as Rhode Island

Department of Education or RIDE (2007) and counties

such as Clark County School District in Nevada (Fife,

2006). The assessment of spacemanagement can be per-

formed by comparing existing floor areas with those

specified by the standards. The quality of school spaces

is governed by issues such as cleanliness, indoor air qual-

ity, lighting, thermal comfort and noise. Young et al.

(2003) covered these issues under the environmental

category.

Although numerous sets of KPIs have been derived

in the literature to cover most aspects of a facility’s per-

formance, the sets are extensive and, in some cases,

include KPIs that are not relevant, measurable, or

quantifiable (Shohet, 2006; Lavy et al., 2010). For

instance, Hinks and McNay (1999) derived a list of

172 KPIs categorized under eight categories: business

benefits, equipment, space, environment, change,

maintenance/services, consultancy, and general.

According to literature (Slater et al., 1997; Ho et al.,

2000), a succinct list of core KPIs that are quantifiable

on the basis of readily available information is still lack-

ing. The existing extensive lists must be filtered through

a set of criteria to develop a concise list of those indica-

tors that express one or more aspects of performance

assessment effectively (Slater et al., 1997; Ho et al.,

2000). Hinks and McNay (1999) and Slater et al.

(1997) suggested establishing concise lists of no more

than 4–6 and 7–12 KPIs, respectively. Literature also

recommends identifying the core KPIs that not only

cover financial aspects but also focus on aspects such

as business, organization goals, and other non-financial

qualitative aspects (user satisfaction and productivity).

The core KPIs must also be measurable and quantifi-

able on the basis of readily available information in

the industry in order to make genuine comparisons

(Tsang, 1998; Tsang et al., 1999; Ho et al., 2000;

Chan et al., 2001; Shohet, 2003; Cable and Davis,

2004; Augenbroe and Park, 2005; Gumbus, 2005).

Performance measurement approaches using

KPIs

According to Ahluwalia (2008), a building houses a

range of components (up to 170 different types of

KPIs for facility performance assessment 1185

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components) that have differing maintenance require-

ments, making the building’s maintenance a complex

exercise. Moreover, each component has a different

service life, making replacement and its schedule com-

plicated. One conventional focus of facility mainte-

nance is on reducing maintenance expenditures while

providing a healthy, comfortable and safe workplace

for occupants (De Groote, 1995; Horner et al., 1997;

Tsang et al., 1999; Office of the Legislative Auditor,

2000; Kutucuoglu et al., 2001; Shohet and Lavy,

2004). It is also vital to determine how often a facility

is replacing its components that are expired or nearing

the end of their service life (Association of Higher Edu-

cation Facilities Officers et al., 2003; Kinnaman,

2007).The Facility Condition Index (FCI) is an indica-

tor often used for the condition assessment of a facility,

demonstrating the impact of deferred maintenance as a

ratio of deferred maintenance to total current replace-

ment value (Vanier, 2000; Briselden and Cain, 2001;

Teicholz and Edgar, 2001). The condition assessment

at each building system level and at overall building

level is more accurate, as it demonstrates specific

defects and their severity (Ahluwalia, 2008). Ahluwalia

(2008) defined Condition Index (CI) as a value

between 0 and 100, derived mathematical expressions

and conducted a case study.

In order to evaluate the maintenance performance

of a facility, a Maintenance Efficiency Indicator

(MEI) was proposed by Lavy and Shohet (2004). An

MEI is defined as the ratio of maintenance expenditure

to a facility’s CI. The amount spent on deferred main-

tenance was calculated as a ratio of actual to targeted

deferred maintenance by Lavy et al. (2014b), who also

defined a Replacement Efficiency Indicator (REI) to be

the ratio of the sum of capital renewals to the total cost

of expired systems in a given year. The purpose of MEI

and REI is to provide metrics to assess the maintenance

and replacement of a facility.

Apart from the facility’s maintenance, replacement,

and condition, it is important to evaluate the suitability

of a facility to house a particular function. The func-

tional suitability depends on the provision of sufficient

space in the facility and effective space management

(Douglas, 1993/94; Schroeder et al., 1995; Douglas,

1996). Other indicators, such as indoor air quality,

are responsible for health, comfort and safety of

building occupants and hence could affect a facility’s

overall performance (Fowler et al., 2005; Prakash,

2005; Mozaffarian, 2008).

A facility’s performance over its life cycle can also be

assessed using various computer and web-based tools

currently available. Tools such as BLAST, ECOTECT,

TRNSYS, HOT 2000, Energy Plus, DOE 2.1 (US

DOE), RIUSKA, MOIST 3.0, CONDENSE, Airpak

and hyglRC can be used to simulate the economic and

environmental performance of a built facility (Crawley

et al., 2005). Although a facility’s performance can be

simulated using computer programs, it still needs vali-

dation (Hammad et al., 2005). Moreover, often com-

plex inputs are required for running the simulation

collection, which can be either time-consuming or

expensive (Bazjanac, 2001; Hamza and Horne, 2007).

Among studies focused on KPIs are Shohet (2003),

who developed and validated four KPIs for healthcare

facilities by performing six case studies; Ahluwalia

(2008) derived an approach to assess the condition of

building components on the basis of maintenance data

and validated the approach using a case study approach;

and Enoma and Allen (2007) developed a set of KPIs

for assessing the performance of airport facilities using

a case study method to validate the KPIs.

Data analytics for facility management

KPIs have become a part of a growing area of study

called analytics, a field that is more prescriptive than

descriptive in nature (Cooper, 2012; Neiger et al.,

2012). By prescriptive, we mean that decisions regard-

ing how to improve the performance of a facility are

made based on data analysis. These decisions are

actionable rather than a mere description or reporting.

In the context of post-compulsory education, Cooper

(2012) defined the field of analytics as ‘the process of

developing actionable insights through problem defini-

tion and the application of statistical models and anal-

ysis against existing and/or simulated future data’. In

simpler terms, analytics is the process of generating

and obtaining an optimal, realistic decision based on

existing or simulated data.

Elias (2011) discussed a five-step process of aca-

demic analytics that involves data capturing, reporting,

predicting, acting, and refining. The gathered and

refined data is then analysed from three perspectives

(Evans, 2012). The first perspective is descriptive, which

answers the questions of what has happened in the past

and what is currently happening (Fitz-enz, 2009;

Evans, 2012). According to Fitz-enz (2009), descrip-

tive analytics help identify and analyse the relationships

and differences of various groups of a dataset. The

second perspective is predictive, which predicts what

will happen next based on the analysis of past data

(Evans, 2012). Prescriptive is the third level, also the

most advanced one, which utilizes the process of opti-

mization to identify the best solution. In other words,

it prescribes how to achieve the best outcome consider-

ing the effects and variability (Fitz-enz, 2009; Evans,

2012). This is the recommendation phase where deci-

sion and support tools are coupled with expert opinions

to create tactical and strategic guidance for the organi-

zation. The process of data analysis may be performed

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using actual data or simulated data that is based on

reasonable assumptions (Cooper, 2012). In summary,

the process of analytics can be effectively used with

simulated data to analyse the relationships and impacts

of KPIs.

Simulations in facility management

Simulation as a descriptive or prescriptive tool has been

used in maintenance and operations management

(Montazer et al., 2003). In a study of steel rolling mills,

Bala Krishnan (1992) divided an entire multistage

machine into sub-systems and simulated maintenance

policy options. According to his findings, opportunistic

maintenance is far better than other maintenance poli-

cies. Chen et al. (2003) developed and used a Markov

Regenerative Process (MRGP) model to simulate con-

dition-based maintenance with generally distributed

inspection intervals as opposed to the exponential dis-

tribution adopted by previous studies. In an effort to

integrate a maintenance evaluation tool into a build-

ing’s design and construction phase, Dessouky and

Bayer (2002) developed a simulation model using

maintenance type, maintenance quality attributes, and

task hours to estimate excess labour hours. By optimiz-

ing labour hours, they were able to allocate a mainte-

nance cost to a building’s construction and design

phase. Montazer et al. (2003) noted how simulation

has become not only a descriptive but also a predictive

tool for making decisions about future strategies in

operations management.

Another area in which simulation has been exten-

sively used as a descriptive, predictive, and prescriptive

tool is the assessment of a facility’s energy performance.

Zhu (2006) conducted an energy simulation study of a

complex facility in the southeast region of the United

States to help facility managers optimize the facility’s

energy performance. In this study, eQuest was used

to simulate the energy performance in order to devise

strategies to save energy use in the facility’s operations

(prescriptive role). According to Augenbroe (2002),

simulation modelling that was once used mainly during

the design and construction phase is now being applied

extensively to post-construction phases such as

commissioning and facility management. In fact,

simulation has become an integral part of the whole

building design, engineering, and operations process

(Augenbroe, 2002).

Research methods

A simulation approach was applied in this study to ana-

lyse the impacts of key input variables on facility KPI

estimates and robustness, as well as relationships

between and among input variables and KPIs. The

KPIs calculated from simulating outputs from the input

variables include CI, MEI, and REI. The main variable

considered for optimization is the Net Present Value of

Dollars Spent. A complete list of the variables and

abbreviations used in this paper may be found in the

Appendix. The simulation process is presented in a

high level flowchart in Figure 1 (life cycle simulation

of a single system). The flowchart illustrates the process

of converting input variables in KPIs and the Net

Present Value of Dollars Spent throughout the process.

The following sections describe the simulation sce-

narios and the main assumptions made to perform

them.

Simulation scenarios

This study contains two parts. In Part 1, we simulate

and examine input and output variable relationships

for a single system within a facility (note that the single

system may be the facility itself). In Part 2, we simulate

and examine input and output variable relationships for

multiple systems within a facility. The difference

between Part 1 and Part 2 is an added level of complex-

ity. One could imagine adding sub-systems within sys-

tems which would add yet another level of complexity.

Downsides to adding levels of complexity include the

need to gather additional data, adding more time-

consuming and complex simulations, and the introduc-

tion of bias. Bias is introduced because the finer the

details introduced, the higher the likelihood that other,

more subtle but just as important, fine details would be

missed. On the positive side, adding levels may reduce

the variability in outputs, as well as better map out the

impact and relationships between and among major

KPIs, such as Condition Index (CI), Maintenance Effi-

ciency Index (MEI), and Replacement Efficiency Index

(REI). Ultimately, recognizing the mutual impacts of

these KPIs may lead to a better understanding of orga-

nizational goals, such as students’ test scores and

absenteeism rates. The following paragraphs describe

Part 1 and Part 2 in more detail.

Part 1: This included an analysis of the relationships

between CI and its variables, such as Plant Replace-

ment Value (PRV) and/or System Replacement Value

(SRV), Deferred Maintenance (DFM), and Dollars

Spent on Maintenance (DSM) at a facility level. In this

part, four scenarios were analysed. The service life of

the educational facility was assumed as 50 years. The

main goal at this stage was to identify maintenance

budget and equipment replacement strategies to main-

tain a desirable CI while optimizing the total spending

on maintenance.


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Part 2: In this part, the KPIs were calculated and

analysed for their relationships and dependence at indi-

vidual system levels. The main goal at this stage was to

study the impacts of KPIs at individual system levels for

the overall condition of the facility. In this scenario, five

building systems were considered in the calculation of

the Facility Condition Index: Roof, HVAC, Plumbing,

Electrical, and Other (the remaining major systems in a

typical building, combined into one system).

Values for the input data used in the simulations for

Parts 1 and 2 are listed in Tables 1 and 2, respectively;

four scenarios were developed for each part. While

numerous input variables are possible, the maintenance

budget and the years until replacement are the only

variables changed over the four scenarios. The other

difference to note between the two parts is that built-

in variability is shown in the plots of KPIs in Part 2,

but not in Part 1. All costs in the simulation scenarios

are given in thousands of dollars ($k). Also, the plant

represented in Table 1 is a different plant than the

one in Table 2, therefore, only trends can be compared

between the two, but not costs.

To better explain the simulations, let us take, for

example, scenario 1 of Part 1 (line item #13 in Table 1)

and analyse its input variables: one building system

represents all building systems and components. The

estimated life cycle of this system is 45 years (line item

#1), and it originally cost $10 000 to install (line item

#4). The annual average increase in the System

Replacement Value is 3%, distributed as 2% for infla-

tion (line item #11) plus an additional 1% increase

due to environmental/safety laws/other concerns (line

item #12). This rate, however, is an estimated average,

and is subject to variability according to the beta distri-

bution; therefore, it is possible that in any single year,

the combined rate could be 2% or 4%, or anything in

Life CycleSimulation of aSingle System

Set initial values forvariables in Table 1

Do S = 1 to TotalNumber of Simulations

Calculate CI, MEI, REIfor the year

Calculate the exponentialdistribution for

maintenance costs basedon SRV when new and

the future projectedsystem replacement

value

Do P = 1 to TotalNumber of Years

End of Years Loop

Calculate the NetPresent Value of the

amount spent onmaintenance andamount spent on

replacement

End of Simulations Loop

Update SRV based onrates of inflation forsystem/environment.

Rates randomly drawnfrom Beta Distribution

Update total deferredmaintenance as

previous DFM plus costsdue to inflation (raterandomly drawn fromBeta) plus this year’s

DFM

Calculate this year’sdeferred maintenanceas difference betweencalculated costs and

calculated amount spent

Calculate amount spenton maintenance basedon budget subject toGaussian variability

Is ittime toreplace

thesysem?

Calculate maintenancecosts based on

exponential costschedule subject toGaussian variability

Replace system andrecord money spent onnew systems. Update

exponentialmaintenance cost

schedule

Yes

NO

Figure 1 Life cycle simulation of a single system

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between. This specific scenario assumes that the system

is kept for 45 years (variable X, line items #6 and 13),

which means that it would be replaced at the end of its

service life (similar to line item #1). It also assumes that

the maintenance rate in the first year is 2% of the

System Replacement Value (line item #2), increasing

to 100% of the System Replacement Value in its last

year of service life (45) (line item #3). However, only

70% of its maintenance needs are funded each year

throughout its entire service life (variable Y, line items

#8 and 13). The unfunded maintenance goes into a

deferred maintenance backlog, which increases at a rate

of 2% per year (line item #10). The annual discount

rate, or the rate for opportunity cost of capital, is con-

sidered to average 2% (line item #9), and is also subject

to variability according to the beta distribution. The

value shown for the Current System (Plant) Replace-

ment Value (end of Year 1) (line item #5) is the value

in line item #4 plus the average rate of inflation (line

items #11 and 12). The Renewal Rate Factor of PRV

Table 1 Input data for Part 1

Item # Variables/Parameters Value field

1 Estimated Life Cycle (Years) 45

2 Initial Maintenance Rate or Maintenance Rate in the First Year (%) of PRV 2

3 Maintenance Rate in the Final Year of Life (%) of PRV 100

4 System (Plant) Replacement Value when New/Replaced $10 000

5 Current System (Plant) Replacement Value (end of Year 1) $10 300

6 Years Until Planned Replacement X

7 Renewal Rate Factor of PRV for Replacement 1.05

8 *Current Budget for Maintenance as a Percent of Yearly Maintenance Y

9 *The Discount Rate; Rate for Opportunity Cost of Capital (%) 2

10 *Rate of Inflation for Deferred Component Maintenance (%) 2

11 *Rate of Inflation for System (%) 2

12 *PRV Rate of Increase Due to Environmental/Safety Laws/Concerns (%) 1

Scenario

13 Scenario 1: X = 45, Y = 70% X = 45, Y = 70%

14 Scenario 2: X = 50, Y = 50% X = 50, Y = 50%

15 Scenario 3: X = 23, Y = 50% X = 23, Y = 50%

16 Scenario 4: X = 45, Y = 99.9% X = 45, Y = 99.9%

Note: *These variables are subject to variability according to the beta distribution in the simulation.

Table 2 Input data for Part 2

Variables/Parameters

Value fields for each system

Roof HVAC Plumbing Electrical Other

Estimated Life Cycle (Years) 20 30 20 18 20

Initial Maintenance Rate (%) 2 1.5 0.25 0.65 2

Final Maintenance Rate (%) 100 100 100 100 100

PRV when New/Replaced $110 $1400 $800 $1200 $2100

Current PRV (end of Year 1) $113 $1442 $824 $1236 $2163

Years Until Planned Replacement X X X X X

Renewal Rate Factor 1.2 1.1 1.17 0.84 0.77

*Budget for Maintenance Y Y Y Y Y

*The Discount Rate (%) 2 2 2 2 2

*Rate of Inflation for DFM (%) 2 2 2 2 2

*Rate of Inflation for System (%) 2 2 2 2 2

*Environmental, etc. Rate (%) 1 1 1 1 1

Scenario

Scenario 1: X = (Estimated Life Cycle), Y = 70%

Scenario 2: X = (Estimated Life Cycle + 5), Y = 50%

Scenario 3: X = ((Estimated Life Cycle/2) + 1), Y = 50%

Scenario 4: X = (Estimated Life Cycle), Y = 99.9%

Note: *These variables are subject to variability according to the beta distribution in the simulation.


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for Replacement (in line item #7) represents the

additional cost factor to replace one system with

another (the cost to remove, haul, and dispose of the

old system), if a replacement is pursued (for example,

in scenario 3, where the value of variable X is less than

the building’s life cycle). Similarly, scenarios 2 through

4 in Part 1 (line items #14 through 16, respectively), as

well as scenarios 1 through 4 in Part 2 (as shown in

Table 2) can be analysed in terms of their input

variables.

The simulation program was constructed using

Microsoft Excel 2010. Based on user inputs, a period

of 50 years for the facility was simulated up to 100

times. The KPIs were calculated and graphed based

upon the simulated results.

Assumptions

While the simulation scenarios represent an oversimpli-

fied example of an educational building, extensive col-

laboration and debate should be employed for

analysing actual cases. The following assumptions were

made in this study for the development of the simula-

tion scenarios:

(1) The Facility Condition Index is an aggregate of

the CI of each individual system weighted only

by costs. Therefore, if the HVAC system is

more expensive than the Roof serving class-

rooms, by default, the HVAC system would

have a larger influence on the CI. Nevertheless,

the authors recognize that there may be other

methods to weight the individual systems

rather than cost (such as importance to the

overall mission of the organization) in calculat-

ing the CI.

(2) The life of a building system is based upon an

exponential rate of deterioration model where

the costs of maintenance increase exponentially

as the system ages (New Zealand National

Asset Management Steering Group, 2011).

The maintenance model adopted in this study

is shown in Figure 2. The maintenance costs

in the first and the final year of a system’s

service life are assumed as a percentage of its

System Replacement Value (SRV). According

to the maintenance model:

(a) Maintenance Cost Index = 1000 �{(Maintenance Cost) / (SRV)}

(b) Figure 2 shows three examples of compo-

nents with 10, 20, and 30 years of service

life. Each one presents two scenarios: (1)

initial maintenance cost is 0.1% of SRV

and final maintenance cost is 100% of

SRV; and (2) initial maintenance cost is

10% of SRV and final maintenance cost

is 100% of SRV. The exponential equa-

tions and trend lines in Figure 2 are set

simply by commanding Microsoft Excel

to fit an exponential curve between the ini-

tial and final maintenance costs. Neverthe-

less, the authors recognize that the values

used in this assumption for both initial

and final maintenance costs, as well as

the pattern of deterioration, may differ on

a case-by-case basis.

(3) The total cost of maintenance is the sum of

preventive and corrective maintenance. The

fraction of preventive and corrective mainte-

nance in the total maintenance cost was

assumed based on the inferences of the survey

conducted by Whitestone Research Corpora-

tion (1999). According to the survey results,

each building system has a unique value rang-

ing from 21% to 63% of the total maintenance

cost that occurs due to corrective maintenance.

(4) The cost and probability of corrective mainte-

nance are not functions of the accumulated

deferred maintenance to date. One might

hypothesize that both the cost and probability

of corrective maintenance would increase with

the accumulation of deferred maintenance.

One reason that we assume an exponential rate

of deterioration model for the life of building

systems is that we do not currently have a

quantifiable estimate of the relationship

between the cost and probability of corrective

maintenance and the accumulation of deferred

maintenance.

(5) A user of the simulation program could specify

the years until replacement of a system even if

the length of time exceeds the estimated service

life of the system. At this point in time, the

maintenance cost in a given year could not

exceed the SRV for that year.

(6) The SRV is assumed to increase in time as a

function of the rate of inflation and the rate

of cost increase due to environmental factors,

safety regulations, etc.

(7) The budget for maintenance is set as a percent-

age of expected yearly maintenance, and dis-

tributed as a random variable that follows the

beta probability distribution. In addition, the

rates of inflation are distributed as random vari-

ables following the beta probability distribu-

tion. The beta distribution is a family of

continuous probability distributions. It is

defined on an interval greater than 0 and less

than 1, with the shape of the distribution

1190 Lavy et al.

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defined by two positive shape parameters,

denoted by α and β. The advantage of the beta

distribution is that it is flexible in characterizing

percentages and rates. Also, the interest

rate converges in law to a beta distribution

(Delbaen and Shirakawa, 2002).

(8) While the simulation could start at any life

cycle stage for each system, in this study, all

systems are assumed to start as new systems.

(9) Traditional life cycle cost analysis (LCCA) is a

technique used to determine the total cost of

facility ownership, including the first cost of

acquisition and/or construction (Langston,

2005). For existing facilities, the total cost of

ownership incorporates the sunk costs of

acquiring the facility, the ongoing costs of

operations and maintenance, and the future

costs of disposing of a facility. To minimize

Maintenance Cost Index (y) Parameterized by the ExponentialDistribution as a Function of Years (x), Initial Maintenance Rate and

Expected System Life1000

900

800

700

600

500

400

300

200

100

00 5 10 15

Years Since New/Replacement

y = 77.426e y = 88.587e

y = 0.6952e

y = 0.788e

y = 92.367e

y = 0.4642e

0.2558x 0.1212x

0.7675x

0.3636x

0.2382x

0.0794x

20 25 30

Mai

nten

ance

Cos

t In

dex

App

roac

hing

Sys

tem

Rep

lace

men

t Val

ue

Figure 2 Maintenance cost model based on the assumption of an exponential rate of deterioration

$50 000.00

$45 000.00

$40 000.00

$35 000.00

$30 000.00

$25 000.00

$20 000.00

$15 000.00

$10 000.00

$5 000.00

$0.000 5 10 15 20

Age of Plant or System in Years

25 30 35 40 45 500.00

0.10

0.20

0.30

0.40

0.50

0.60

PRV $DFM $$ Spent on MaintenanceCondition Index (0 to 1)

0.70

0.80

0.90

1.00

Figure 3 Scenario 1 showing KPI relationships over the building’s service life (NPV of $ spent = $121k)


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the variability associated with land acquisition

and initial construction costs, as well as future

disposal or resale costs, this paper focuses

solely on operation and maintenance costs for

a performance period of 50 years.

(10) There are three independent appraisal method-

ologies commonly used to evaluate real estate

in the United States (Ling and Archer, 2012):

(a) Cost approach: sums the value of real estate

(land) and the depreciated value of the

improvements to the land (facilities, equip-

ment, infrastructure, etc.) to determine a

total replacement cost for a property. The

cost approach is commonly used when

appraising specialty properties (e.g. facto-

ries, public works, laboratories, etc.).

(b) Sales comparison: assumes that a prop-

erty’s market value is closely correlated to

the current value of a substitutable compa-

rable property. The sales comparison

approach depends on an assessment of

$50 000.00

$45 000.00

$40 000.00

$35 000.00

$30 000.00

$25 000.00

$20 000.00

$15 000.00

$10 000.00

$5 000.00

$0.000 5 10 15 20 25 30 35 40 45 50


0.00

0.10

0.20

0.30

0.40

0.50

0.60

0.70

0.80

0.90

1.00

PRV $DFM $$ Spent on MaintenanceCondition Index (0 to 1)

Figure 4 Scenario 2 showing KPI relationships over service life (NPV of $ spent = $130k)

$50 000.00

$45 000.00

$40 000.00

$35 000.00

$30 000.00

$25 000.00

$20 000.00

$15 000.00

$10 000.00

$5 000.00

$0.000 5 10 15 20 25 30 35 40 45 50

0.00

0.10

0.20

0.30

0.40

0.50

0.60

0.70

0.80

0.90

1.00

PRV $

DFM $

$ Spent on MaintenanceCondition Index (0 to 1)



1192 Lavy et al.

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similar marketplace transactions to estab-

lish value, and is commonly used in resi-

dential markets.

(c) Income capitalization approach: develops a

financial pro forma based on the antici-

pated market return of a property, and

establishes cost based on the discounted

cash value of that property over a given per-

formance period. The income approach is

commonly used to evaluate commercial or

investment properties.

To minimize the variability associated with

real estate market cycles and regional land

values, this paper utilizes the cost approach

to establish the Plant Replacement Value

(PRV). For the purpose of this analysis,

$50 000

$45 000

$40 000

$35 000

$30 000

$25 000

$20 000

$15 000

$10 000

$5 000

$00 5 10 15 20 25 30 35 40 45 50

0.00

0.10

0.20

0.30

0.40

0.50

0.60

0.70

0.80

0.90

1.00


PRV $ DFM $ $ Spent on Maintenance

Condition Index (0 to 1) NPV of $ spent =$166507


1.000.950.900.850.800.750.700.650.600.550.500.45

CI

0.400.350.300.250.200.150.100.050.00

1 3 5 7 9 11 13 15 17 19 21 23 25

Years Since Facility Baseline Measurement27 29 31 33 35 37 39 41 43 45 47 49 51

Figure 7 Simulated CI under scenario 1 based on user inputs and assumptions


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PRV is based on the depreciated value of

the improvements to the land (facilities,

equipment, infrastructure, etc.) and

excludes the acquisition cost of the land

itself, in order to focus the discussion on

operation and maintenance costs.

Results

Part 1

In Part 1, we simulated various input variables in

order to calculate the Condition Index (CI), Plant

Replacement Value (PRV), Deferred Maintenance

(DFM), and Dollars Spent on Maintenance in four dif-

ferent scenarios over a facility’s service life of 50 years.

Figures 3 through 6 show the results of the simulations

for the four scenarios of Part 1. The left side axis repre-

sents all the KPIs with monetary values (PRV, DFM,

and $ Spent on Maintenance), while the right side axis

represents the Condition Index, with values ranging

from a minimum of 0 (not functioning at all) to a

maximum of 1 (condition is as good as new). It must

be emphasized that the minimum acceptable threshold

for the Condition Index is up to the facility manager in

each specific organization to determine, based on the

organization’s mission, goals, policies, and standards.

As seen in Figures 3 through 5, as DFM increased,

the value of CI decreased. At the point that DFM

equalled PRV, the CI reached its minimum value, but

funds were still increasingly being spent on mainte-

nance. This is demonstrated in Figures 3 and 4. After

the point where DFM equals PRV, the spending con-

tinues but the CI cannot be improved until system

replacement. The situation is akin to making minimum

payments on credit card debt: while payments may be

the smallest they can be without going into default,

the debt can never be paid and the amount spent over

the life of the loan adds up to many times the cost of the

original loan. The key to understanding and using these

graphs is knowing that, while taking on some deferred

maintenance (akin to taking on debt) is advantageous

due to the time value of money, too much deferred

maintenance (akin to too much debt) leads to poor

conditions that are increasingly expensive to maintain.

One might expect that the best overall CI over the

50 years would have occurred under scenario 4 (highest

budget of 99.9%), and the worst under scenario 2 (low-

est budget and postponed replacement). This assump-

tion was shown to be correct, as seen in Figures 3

through 6. The least amount of money spent on

maintenance occurred in scenario 3 ($47k), followed

by scenario 1 ($121k). Maintenance budgeting and

replacement strategies in these scenarios prevented an

excessive build-up of DFM. In this part, it was interest-

ing to note that the strategy in scenario 3 led to much

less maintenance expenditure than in scenario 1: this

was due to an early replacement strategy that included

the least years until planned replacement. Note that

this strategy did not work in all scenarios: it only

worked because of the assumption of the exponential

1.00

0.95

0.90

0.85

0.80

0.75

0.70

0.65

0.60

0.55

0.50

0.45

0.40

0.35

0.30

0.25

0.20

0.15

0.10

0.05

0.00

CI

Years Since Facility Baseline Measurement1 2 3 4 5 6 7 8 9 101112131415161718192021222324252627282930313233343536373839404142434445464748495051


1194 Lavy et al.

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rate of deterioration, which may or may not be true.

Scenarios 2 and 4 were the most expensive because

they did not have the correct balance of taking on some

deferred maintenance (scenario 2) and too much

deferred maintenance (scenario 4), as mentioned

above.

Even though the overall maintenance costs over the

entire building’s service life were lowest under scenario

3, the CI was under 80% for 20 of the 50 years of ser-

vice life. Scenario 1 had its CI below 80% for 28 of the

50 years of service life, and in scenario 2, the CI was

below 80% in 37 of the 50 years of service life. It is evi-

dent that the best scenario in terms of CI was scenario

4, in which the CI was always close to 100%. However,

this was also the most expensive scenario ($166k,

which is 253% more expensive than scenario 3), and,

if such a high CI was not necessary, a significant

amount of maintenance dollars could have not been

spent. The key is always to find the strategy that main-

tains a desired CI while spending the least amount on

maintenance. This strategy should also be more robust

to changes in assumptions than other strategies. In

other words, a strategy that is slightly more expensive,

but more insensitive to slight changes to the budget,

the interest rate, or the discount rate might be better

than a strategy that is optimal under assumed condi-

tions, but changes drastically in response to any

changes in the assumptions.

Part 2

In Part 2, we looked at the relationships between CI,

PRV, DFM and Dollars Spent on Maintenance, but

the calculations were rolled up over five different

building systems (Roof, HVAC, Plumbing, Electrical,

and Other). Random variability was incorporated into

the calculations and the results of the simulations were

plotted in order to highlight the variability. Once

again, four different maintenance budget and replace-

ment strategy scenarios were considered, similar to

those in Part 1. Studying a facility at both facility

and individual system levels generated similar results

in terms of CI and Dollars Spent on Maintenance.

Using a rollup approach required more upfront work

because each system, and even sub-system, was char-

acterized and assessed individually. The incorporation

of variability into the simulation also improved the

relationship to actual practice, and allowed for more

‘real-world’ robust and sensitivity analysis. In this

part, the original cost to install the building (all five

systems combined) was $5610; and therefore, the val-

ues below cannot be compared to those calculated in

Part 1.

The least amount of money spent on maintenance

occurred in scenarios 3 (NPV over 50 years of $35k)

and 1 ($51k), with scenario 3 being the lowest. The

best scenario for Condition Index was, again, scenario

4 (~$69k) where CI was always close to 100%. How-

ever, it was almost twice as expensive as scenario 3

(the least expensive). The CI, rolled up over all sys-

tems, under scenarios 1 through 3 is shown in Figures

7 through 9, respectively. The variability in the CI cal-

culations can also be seen in these Figures. It can be

noticed that the variability in the estimated CI

increased as the age of the system moved further away

from the point when it was new or replaced; that hap-

pened because there is more uncertainty as we move

further into forecasting the future.

Due to an early replacement on a system-by-system

basis, the CI under scenario 3 did not fall below 80%

(see Figure 9). Overall, it appeared to be the best strat-

egy that resulted in a balanced maintenance cost and

CI. However, care must be taken in assessing scenario

3; as mentioned previously, this strategy worked well

due to the assumption of the exponential rate of deteri-

oration. This strategy also worked because the facility’s

CI was calculated as an aggregate of the CIs of all indi-

vidual systems weighted only by their costs. This means

that the facility’s CI could be over 80% even if some

individual system’s or multiple systems’ CIs were still

below 80%. This strategy could be misleading from

the standpoint of organizational goals: for instance, if

the facility’s CI was above 80% but the CI of the

HVAC system dropped below 60%, it might adversely

affect factors such as students’ performance and/or

absenteeism more than for any other system.

Two other KPIs that could help find a robust strat-

egy that maintained an appropriate CI while spending

the least amount of money on maintenance were the

Maintenance Efficiency Indicator (MEI) and the

Replacement Efficiency Indicator (REI). It was no

coincidence that the best overall MEI and REI, given

that we were targeting 100% for these KPIs, belonged

to scenario 3, as seen in Figures 10 through 12 (note

that the MEI for scenario 4 is not shown because it is

extremely high and distorts the graph). The target CI

for the MEI graphs was 80%. Therefore, in this sce-

nario, MEI of 100% meant that the exact amount nec-

essary to maintain the target CI was being spent in the

given year on DFM. MEI below or above 100% dem-

onstrated a lack, or a surplus, of maintenance expendi-

ture, respectively. Conventionally, a facility’s CI greater

than the targeted CI resulted in an MEI higher than

100%, and vice versa. However, note that even if the

facility’s CI was greater than the target CI, the MEI

could drop below 100%, if the necessary preventive

maintenance actions were ignored.


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Maintenance rate sensitivity analysis

As seen from the results of Parts 1 and 2, a simulation

approach based on hypothetical data can be utilized for

understanding the interrelationships and interdepen-

dence of KPIs and key variables affecting them. This

approach can also show the combined effects of KPIs

over the higher level organizational goals such as stu-

dents’ scores and absenteeism. However, these results

were based on some assumptions such as the initial

maintenance rate, the final maintenance rate, the

renewal rate, inflation rate, and life of systems after

replacement. Through sensitivity analyses, the best

solution over a range of assumptions can be calculated.

For example, Figure 13 shows the Net Present Value

(NPV) as a function of the number of years until

replacement (x axis), the initial maintenance rate (set

at 2% in all but one scenario) and the maintenance rate

1.00

0.95

0.90

0.85

0.80

0.75

0.70

0.65

0.60

0.55

0.50

0.45

0.40

0.35

0.30

0.25

0.20

0.15

0.10

0.05

0.00

Years Since Facility Baseline Measurement

CI

1 2 3 4 5 6 7 8 9 101112131415161718192021222324252627282930313233343536373839404142434445464748495051


1000%REI MEI

950%900%850%800%750%700%650%600%550%500%450%400%350%300%250%200%150%100%50%0%

1 3 5


RE

I an

d M

EI

7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51

Figure 10 MEI and REI under scenario 1 based on user inputs and assumptions

1196 Lavy et al.

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in the final year, over a period of 50 years. In all scenar-

ios, the annual maintenance budget was assumed at

99.9% (maximum possible rate to keep the CI at 1 at

all times), and the initial cost of the system (PRV)

was considered at $10 000. The maintenance rate in

the final year ranged from 100% to 80%, 60%, 40%,

20%, 10%, 4%, and 2% (the last value yields a mainte-

nance rate constant over the entire service life).

The following is observed from Figure 13:

(1) The NPV was found to behave in a saw-tooth

pattern because it is a function of how many

replacements occur over 50 years of service life,

in addition to years until replacement, initial

maintenance rate, and final maintenance rate.

So any jumps that occur are due to a different

1000.00%

RE

I an

d M

EI


950.00%

900.00%

850.00%

800.00%

750.00%

700.00%

650.00%

600.00%

550.00%

500.00%

450.00%

400.00%

350.00%

300.00%

250.00%

200.00%

150.00%

100.00%

50.00%

0.00%1 2 3 4 5 6 7 8 9 101112131415161718192021222324252627282930313233343536373839404142434445464748495051

REI

MEI


Years Since Facility Baseline Measurement1 2 3 4 5 6 7 8 9 101112131415161718192021222324252627282930313233343536373839404142434445464748495051

REI

MEI

1000.00%

RE

I an

d M

EI

950.00%

900.00%

850.00%

800.00%

750.00%

700.00%

650.00%

600.00%

550.00%

500.00%

450.00%

400.00%

350.00%

300.00%

250.00%

200.00%

150.00%

100.00%

50.00%

0.00%



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number of replacements within the 50-year

period. If the NPV were calculated over a

period of infinite years, there would be no

saw-tooth pattern. However, calculating over

a period of infinite years is not necessarily the

best method because optimizing over infinity

is not realistic. Therefore, it is very important

that the NPV be optimized over the true period

of study, whether that is 25, 50 or 100 years.

(2) The optimum NPV for a maintenance rate of

100% and 80% in the final year was achieved

when replacement was performed at 18 years,

i.e., two replacements over a period of 50 years.

The near-optimum NPV for these scenarios

was 26 years.

(3) The optimum NPV for all other maintenance

rates in the final year was achieved when

replacement was performed at 26 years, i.e.,

5

NP

V

$200 000

2%, 100%

2%, 10%

2%, 80%

2%, 4%

2%, 60%

2%, 2%

2%, 40%

0.1%, 4%

2%, 20%

$180 000

$160 000

$140 000

$120 000

$100 000

$80 000

$60 000

$40 000

$20 000

$010 15 20 25 30 35 40 45 50

Years Until System Replacement for a System with a Life Expectancyof 50 Years

Figure 13 Net Present Value over a period of 50 years under various maintenance rate assumptions

5

NP

V

$70 000$65 000$60 000$55 000$50 000$45 000$40 000$35 000$30 000$25 000$20 000$15 000

$5 000$10 000

2%, 100%

2%, 10%

2%, 80%

2%, 4%

2%, 60%

2%, 2%

2%, 40%

0.1%, 4%

2%, 20%

$010 15 20 25



1198 Lavy et al.

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one replacement over a period of 50 years. The

reader should keep in mind that this may not be

optimal for any rates over a period of 52 years

because of the added cost of the system being

replaced twice instead of once.

(4) NPV was flat, or close to flat, from the replace-

ment periods of 26 to 50 years for all final year

maintenance rates below 10%.

This, basically, leads to the following two major

conclusions:

(1) For a service life of 50 years, the optimum, or

near-optimum, NPV was achieved with a

replacement at 26 years in all scenarios.

(2) If there is a high degree of confidence that the

initial maintenance rate was 2%, and the final

maintenance rate was no more than 4% of the

System Replacement Value, there was found

to be no financial justification to replace a sys-

tem expected to last 50 years. In these scenar-

ios, if we only consider economic reasoning,

the recommendation would be to keep main-

taining the system; however, this might severely

impact on the condition of that system, which

might result in other negative effects to the

building and/or the building users.

As mentioned earlier, there is a saw-tooth pattern in

the NPV, which means that it is very important to opti-

mize the NPV over the true period of service life of the

building. While optimizing it over a five-year period is

too short term because it only serves ‘selfish’ short-term

benefits, a period of 1000 years, on the other hand, is

unrealistic. Simulations may be performed over a range

of periods to identify the optimum solution to this

question.

In Figures 14 and 15, the NPV is calculated over a

period of 25 years and 150 years, respectively. Even

though the life expectancy of the system is the same

in Figures 13 through 15, it can be seen that the num-

ber of years until replacement to optimize NPV

depends on the number of years over which the NPV

is to be optimized (25, 50 or 150).

Conclusions

Under the specific set of assumptions utilized in this

study, Part 1 shows that scenario 4 (full maintenance)

maintained the highest Condition Index for the build-

ing throughout its service life; however, scenario 3

(early replacement) was the least expensive to imple-

ment while still maintaining a better overall CI than

scenarios 1 or 2. Scenario 3 was also found to be the

least expensive in Part 2 of the simulation, where one

building was substituted by five separate building sys-

tems that together rolled up to the building level. The

conclusions themselves may not be important because

they may differ somewhat from real-world settings

and assumptions. However, it is important to note that

the analysis and conclusions are not all obvious and

could not have been drawn without the use of simula-

tion. Is early replacement of a system always the best

strategy? Of course it is not. The best strategy depends

5

NP

V

$1 000 000

$900 000

$800 000

$700 000

$600 000

$500 000

$400 000

$300 000

$200 000

$100 000

$010 15 20 25 30 35 40 45 50


2%, 100%

2%, 10%

2%, 80%

2%, 4%

2%, 60%

2%, 2%

2%, 40%

0.1%, 4%

2%, 20%



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on all of the circumstances, inputs, influences, and

variabilities considered and analysed simultaneously.

Simulation is the tool that enables this pursuit.

As facility managers are faced with increasing pres-

sure to prioritize the direction of limited resources, this

paper develops a tool that may assist them in their

decision-making regarding maintenance and replace-

ment of building systems. Better prioritization of main-

tenance and capital renewal actions could be achieved

by using a simulation (modelling) tool to examine the

interrelationships between the variables that are to be

considered for making more informed decisions. The

significance of using the KPI approach for assessing a

facility’s performance has been highlighted in the liter-

ature. This paper builds upon the proposed set of five

KPIs, as identified in Lavy et al. (2014a, 2014b). The

analysis performed in this paper shows that the interde-

pendence and the relationships of KPIs can be investi-

gated using the simulation approach. Moreover, it can

also be used to understand the combined impact of

KPIs over organizational goals. However, to truly

understand the state of a facility and how the facility’s

maintenance expenditure is optimized while keeping

the CI at a desired level, a simulation based upon actual

data is needed. The process begins with understanding

goals, objectives, functions and inputs, as well as cap-

turing and analysing relevant data. Key performance

indicators (KPIs) are an important part of the data to

be captured, but much more information needs to be

collected and stored in order to understand the cause

and effects among various KPIs.

This study had four main objectives. The first

objective was to simulate the identified core KPI out-

puts for facility performance assessment using hypo-

thetical input data. This is clearly demonstrated in

both Parts 1 and 2, where computer simulations were

conducted to analyse the effect of core KPIs on facility

performance assessment. The second objective of this

study was to demonstrate how simulation allows for

the study of correlations and relationships between

and among KPIs. This was conducted in both Parts 1

and 2, and is evident in Figures 3 through 12, where

correlations between KPIs are presented and analysed.

The third objective of this paper was to highlight the

sensitivity of outputs and outcomes to input variable

sensitivity. Evidence of this may be found in Figures

13 through 15, where the sensitivity of an output vari-

able (Net Present Value over a 50-year period) was

tested against changes in one input variable (final year’s

maintenance rate).

The fourth objective of this study was to demon-

strate that due to variability and future uncertainty,

simulation is a valuable tool for generating future pos-

sible scenarios and making decisions based upon

forecasts and logic. The accomplishing of this objective

is evident throughout the paper, as simulation is a

powerful tool in understanding and projecting relation-

ships among KPIs, their inputs, and their outputs.

Simulations may be used to play out complex relation-

ships and interactions, test sensitivity to changes in

inputs and assumptions, predict the effect of various

scenarios, and optimize objectives. However, as with

any predictive tool, the results are only as good as the

assumptions and inputs. Extremely important ques-

tions to answer include whether or not we have identi-

fied all the potential variables that influence KPIs and

whether the assumptions regarding variable relation-

ships and behaviours are reasonable. In this paper,

the authors demonstrate the power and potential of

understanding and using KPIs through computer sim-

ulations. Additionally, output variability increases as

the prediction refers to a point which is further away

from the present time. This happens due to the fact that

there is more uncertainty as one moves further into

forecasting the future.

There is no ‘right or wrong’ answer to the question

of how to better prioritize available resources. Is a five-

year solution preferred over a 10-year, 20-year, or 50-

year solution, or vice versa? We argue that individual

users should determine the best solution for their spe-

cific circumstances. As long as decision-makers under-

stand the hierarchy of how key performance indicators

may affect facility performance assessment decisions,

which in turn, may affect the achievement of organiza-

tional goals, simulation tools can be used to examine

various scenarios. In our simulation, we estimated a

building’s life cycle of 50 years, which is considered

as the economic service life of a building, but the tool

is flexible enough to work with different assumptions

as well.

One additional area of concern involves short- and

long-term objectives, goals, and decision-making. It is

not illogical for a president, chief executive officer, chief

financial officer, facility manager, or a person holding

any other function/position in an organization to plan

their actions based on the amount of time (‘term’) they

will serve in their position. However, this type of plan-

ning strategy may result in deferring decisions or

actions, such as for maintenance and/or replacement

of building systems and components, to a later date.

This planning may be akin to scenario 2 in our exam-

ples, and could lead to the situation of taking on too

much deferred maintenance wherein the amount of

maintenance expenditure is poor; a point at which the

Condition Index cannot be improved will be reached,

and this might be a very expensive strategy.

Composite KPIs can more directly link KPIs such

as those addressed in this paper to ultimate

performance goals/metrics. For example, while there

are assumed correlations between some of these KPIs

1200 Lavy et al.

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to facility performance assessment and ultimately, to

organizational goals, e.g., student academic perfor-

mance, there needs to be a balance of critical systems’

KPIs (such as HVAC), and functional KPIs (such as

space, technology, access, air quality) to better link to

organizational performance metrics (in the case of edu-

cation facilities: test scores, promotion rates, etc.).

For future research, the authors propose:

(1) Validating the identified core KPIs and the sim-

ulation results using actual data. Data to be

used for such a study could be obtained from

different organizations spread over various

industries.

(2) Selecting actual facilities with available histori-

cal data supporting these KPIs, and comparing

assumptions yielding optimum simulated

results (using the simulation methods described

in this paper) with simulation results using

actual data. This will be the first step to charac-

terize the relationship between the simulation

models and actual operations to better define

facility asset management decision support.

(3) Identifying composite KPIs to better link KPIs

defined in this paper to organizational perfor-

mance.

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Appendix

List of the variables and abbreviations used

in this paper

Estimated Life Cycle (Years)

This is the estimated life of the system of interest.Changing the life cycle affects the rate of deteriorationof the system and the rate of maintenance costs. Thesimulation may start at any point of the life cycle forthe system, but for the purposes of this paper, all sys-tems start as new.

Initial Maintenance Rate (%)

This is the expected rate of maintenance in the first yearof the system. The amount of maintenance ($) expectedfor the system in the first year is calculated by multiply-ing the rate by the System Replacement Value at the endof the first year.

System Replacement Value (SRV)/Plant Replacement Value(PRV)

This is the cost to replace the system/plant withoutaccounting for the Renewal Rate. It is assumed toincrease due to the Rate of Inflation for System andEnvironmental Rate.

Final Maintenance Rate (%)

This is the expected rate of maintenance in the final yearof the system. The amount of maintenance ($) expectedfor the system in the final year is calculated by multiply-ing the rate by the projected System Replacement Valueat the end of the final year.

Years Until Planned Replacement

This is when we plan to replace the system, which doesnot have to be at the end of the life of the system. Thesimulation allows us to study how KPIs are affected byaltering this value.

Renewal Rate Factor

This rate is multiplied by the SRV/PRV to estimatethe cost of replacement accounting for the use ofexisting infrastructure or the cost of tear down and haulaway.

Budget for Maintenance

Treated as a random variable in the simulation (we havean expected target, but it fluctuates from year to yeardue to variability). This is the rate of the SRV to deter-mine how much is spent during the year on mainte-nance. The difference between the amount spent onmaintenance and the amount needed to be spent onmaintenance is the Deferred Maintenance. If the budgetis 100%, the amount spent on maintenance is simplythe current SRV, and there is no Deferred Maintenancefor that particular year.

Deferred Maintenance (DFM)

The difference between the amount ($) spent on main-tenance and the amount ($) needed to be spent onmaintenance.

Targeted Deferred Maintenance

The amount of DFM ($) tolerated to maintain a targetCI.

The Discount Rate (%)

Treated as a random variable in the simulation (we havean expected target, but it fluctuates from year to yeardue to variability). This is the rate for opportunity costof capital, which is used in the calculation of the NetPresent Value.


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Net Present Value of Dollars Spent

The equivalent present value of all future cash outflowsthat enable to conduct maintenance and replacement(capital renewal) activities, given a specific discountrate.

Rate of Inflation for DFM (%)

Treated as a random variable in the simulation (we havean expected target, but it fluctuates from year to yeardue to variability). The cost of repair, for maintenancethat is not performed, is estimated to increase by thisrate of inflation each year.

Rate of Inflation for System (%)

Treated as a random variable in the simulation (we havean expected target, but it fluctuates from year to year

due to variability). The cost of replacement for a systemis estimated to increase by this rate each year due toinflation.

Environmental Rate (%)

Treated as a random variable in the simulation (we havean expected target, but it fluctuates from year to yeardue to variability). The cost of replacement for a systemis estimated to increase by this rate each year due toenvironmental laws and regulations, updated buildingcodes, etc.

Condition Index (CI)

CI is a metric to assess the readiness of a system, plant,and/or facility. If the CI equals 1, the system is ‘as goodas new’; if the CI equals 0, the system life is expired.

CI ¼ 1� Total Current Deferred Maintenance

Total Current SRV

� �

Maintenance Efficiency Indicator (MEI)

MEI is a metric to assess the efficiency of maintenanceexpenditure. MEI of 100% means that the exactamount necessary to maintain the target CI was spentin the given year on deferred maintenance. MEI below100% means that not enough was spent, and MEIabove 100% means that more was spent than needed.If MEI equals 60%, for example, that means we under-spent on what was needed to be spent on deferred main-tenance to either maintain the target CI or take care ofpreventative maintenance costs by 40%. If MEI equals110%, then we spent 10%more than necessary to main-tain the target CI.

Replacement Efficiency Indicator (REI)

REI is a metric to assess the efficiency of system replace-ment expenditure. Given that systems typically do notexpire each year, REI is not a smooth continuous func-tion. REI below 100% means that not enough was spenton replacement and REI above 100% means that morewas spent than needed. In this paper, the REI is cappedat a maximum of 200%.

REI ¼ 100

� $Amount Spent on SystemReplacement þ 0:001

Total $Cost of Expiring Systemsþ 0:001

� �

MEI ¼ 100� $Amount Spent onMaintenance ðExcluding ReplacementÞDFM � Targeted DFM

� �

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Key performance indicators for facility performance...

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