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Inzinerine Ekonomika-Engineering Economics, 2014, 25(5), 538–548
Finding a New Way to Increase Project Management Efficiency in Terms of Time
Reduction
Seweryn Spalek
Silesian University of Technology
Gliwice, Poland
E-mail. [email protected]
http://dx.doi.org/10.5755/j01.ee.25.5.8419
There are three basic constraints called ‘the golden triangle’ in each project: time, budget and scope. Researchers and
practitioners are trying to find a way to increase the efficacy of the project’s outcomes in terms of shortening the project’s duration, lowering budgetary costs and meeting the scope. Although several publications have been written on that topic,
there is still no common solution in place. However, more in-depth research related to the specific type of projects or
industries is being conducted and this paper seeks to incorporate additional knowledge into that contemporary field.
Furthermore, this is of growing importance in modern, turbulent times, where the expectations of profiting from lessons
learned are ever increasing.
Following the current market demands, in the article a new, innovative approach is proposed to manage the expectation
that future projects will have a shorter duration. Therefore, the idea of creating specific roadmaps is proposed, which
should help the decision makers improve the efficacy of project management in the company, where the process of such
improvement is measured using the maturity levels assessment concept.
Based on the world-wide quantitative studies in the construction, information technology and machinery industries,
specific roadmaps for each industry were determined. The purpose of these roadmaps is to indicate the most effective
investment sequence in the increase of project management maturity, which should result in a decrease of future projects’ duration. Moreover, discussions on the limitations of such investments are examined.
Keywords: Investment, Time pressure, Company, Projects, Improvement, PM, Implementation of Strategies, Knowledge,
Innovation.
Introduction
Three of the world’s most recognized Latin words,
“Citius, Altius, Fortius,” meaning “Faster, Higher,
Stronger”, have been the Olympic motto since 1894. At the
time: Pierre de Coubertin proposed the motto, having
borrowed it from his friend Henri Didon, a Dominican priest who taught sport close to Paris (IOC, 2007, p. 5).
These three words encourage the athlete to give his or
her best during competition. To better understand the
motto, we can compare it with the Olympic creed: “The
most important thing in life is not the triumph, but the
fight; the essential thing is not to have won, but to have
fought well.” Together, the Olympic motto and the creed
represent an ideal that Coubertin believed in and promoted
as an important life lesson that could be gained from
participation in sport and the Olympic Games: that giving
one’s best and striving for personal excellence was a worthwhile goal. It is a lesson that can still be applied
equally today, not just to athletes but to each one of us
(IOC, 2007, p. 5).
Modern project management has its roots (Lenfle and
Loch, 2010, p. 33) in the atomic bomb Manhattan project
(Morris, 1994, p. 18) and the ballistic missile projects,
Atlas and Polaris (Kerzner, 2013). The term “modern
project management” is used by some authors and relates
mostly to the project management approach started in the
1950s and continuing through today (Chen et al., 2011;
Hill, 2004; Lenfle & Loch, 2010; Shenhar, 2001).
Remarkably, from the very beginning of contemporary
project management, projects were, and continue to be,
under similar time pressure (Campos Silva et al., 2012;
Chen et al., 2012; Griffin, 1993, 1997; Herroelen & Leus,
2005; Omorede et al., 2013; Radziszewska-Zielina, 2010; Zavadskas et al., 2010). Kach and colleagues (2012, p.
377) state: The speed of technological change and
shortened product life cycles have made the time-to-market
requirements for developing new products increasingly
stringent (Kessler and Chakrabarti, Langerak et al., 2010;
Heightened competitive forces have motivated many firms
to move their new products through the design and
manufacturing pipeline at a faster rate, encouraging greater
focus on accelerated development and compressed time
lines (Prasnikar & Skerlj, 2006; Wright et al., 1995).
The above statement for NPD (new product development) projects can be applied to most projects,
whatever their nature. In our current, turbulent times, the
pressure of time seems to increase to an ever greater
extent. Project managers and their teams experience
similar pressure to athletes: to beat the record and to go
faster and faster. The first word, “Citius”, of the Olympic
hendiatris1 seems to dominate projects managed by
1 Hendiatris is a figure of speech in which three words are used to
emphasize one idea, for example, Wine, women, and song. Hendiatris is
often used to create mottos for organizations; for example, The motto at
West Point is “Duty, Honor, Country.” see K. Wilson and J. Wauson, the
AMA handbook of business writing: the ultimate guide to style, grammar,
Inzinerine Ekonomika-Engineering Economics, 2014, 25(5), 538–548
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companies today. The majority of project stakeholders
(executives, sponsors, clients, managers) expect new
projects to be completed faster than ever.
Moreover, companies are always careful about
spending money. Therefore, they also want to know
where to invest their typically limited funds. This issue
also arises when deciding where to invest to shorten the
time of future projects.
This paper gives new insight into the dilemma of where and when to invest limited company funds to
achieve the best approach to time reduction of future
projects.
(R)evolution in Managing Projects
Project management has evolved throughout history.
This evolution has occurred mostly due to the tools and
techniques applied to single projects and the gradual
improvement of human resource management (Avots,
1969). This situation lasted until the 1990s, when the number of projects executed by companies increased.
Moreover, companies’ operating environments became
turbulent (Keil & Mahring, 2010). Furthermore, the
influence of project outcomes on the success of an entire
company increased (Baron & Hannan, 2002). As a result,
companies placed a greater emphasis on project
management. To speed up time-to-market, companies
experienced increased pressure to reduce the duration of
projects. The existing methods for reducing the time of
ongoing single projects (e.g., application of modern
scheduling techniques supported by computer technology)
(Brucker et al., 1999; Iacovou & Dexter, 2004) are significant; however, they are no longer sufficient. A new,
more efficient and revolutionary approach to managing
companies’ portfolios of projects was needed (Cooper et
al., 1999). This need generated new approaches for how to
better manage projects to face the new challenges
appearing in multi-project (Hofman, 2014), dynamic
environments (Spalek, 2013). Accordingly, new concepts
in project management were introduced, focusing on
project environment and knowledge management (del
Cano and de la Cruz, 2002; Ethiraj et al., 2005;
Neverauskas and Stankevicius, 2008; Pemsel & Wiewiora, 2013). Among these concepts is the idea of assessing the
maturity level of project management in the company
(Cooke-Davies, 2007; Fraser et al., 2003; Spalek, 2014;
Tan et al., 2011).
Assessing Maturity: the Purpose
To gain competitive advantage in executing projects,
companies wanted to know how well they manage projects
while taking into consideration different aspects
influencing the effective execution of projects (Kerzner,
2013; Rudzianskaite-Kvaraciejiene et al., 2010). The
assessment outcomes should note the areas for potential improvement (Ahmad et al., 2013).
This expectation can be fulfilled by project
management maturity assessment. There are several
existing models of maturity assessment; however, their
main purpose remains the same: to identify weak and
usage, punctuation, construction, and formatting (New York: American
Management Association, 2010, p. 216).
strong areas in an organization (Belt et al., 2009). By
knowing its strengths and weaknesses, a company can
undertake actions to improve activities related to the
management of projects, resulting in an increased maturity
level and improved project outcomes.
The majority of existing models assess project
management maturity level on a scale from 1 to 5, where 1
represents the lowest and 5 the highest level (Khoshgoftar
& Osman, 2009). The assessment is performed in different areas related to project management. Therefore, the
assessment results in a matrix with maturity scores and
testing areas (Spalek, 2011).
Increasing Maturity: the Investment in the Future
It is critical to understand that an increase of maturity
level will mostly benefit future projects. Increasing by one
maturity level up also has some impact, however limited,
on existing projects. Because the planning phase in each
project is crucial (Wyrozebski & Spalek, 2014), the biggest
possible improvements are associated with future projects.
There is even a well–known quotation saying2:
“Show me how your project starts and I can tell you how it will end.”
If the level of maturity is increased, e.g., from level 1
to 2, the outcomes of that action will be beneficial in new
projects. For existing projects, it will usually be too late to
have an impact.
Therefore, the decision to assess and then increase the
project management maturity level in a company is an
investment in future projects.
Investment “in maturity” is time consuming and
money intensive; therefore, to achieve the highest possible
time reduction of future projects, it is crucial to decide where and when (in which sequence) a company
investment should be placed. Moreover, a company’s
investment funds are often very limited, making the issue
even more important. Therefore, it is essential for
companies today to have a road map guiding decision
makers on the following topic:
“In which areas and in what sequence should limited
funds be most effectively invested in my company?”.
The Research Method
The prediction of the future is a complex issue (Glenn
& Gordon, 2003), often involving various methods and
techniques, and thus has a wide record in
publications.(Booth, 2006; Galbraith & Merrill, 1996;
Lacher et al., 1995; Landeta, 2006; Onkal et al., 2013).
To predict reduction of the future time of projects, questionnaire-based cross-impact analysis was used
following the ideas presented by Fabiana Scapolo and Ian
Miles (2006, p. 680–681). The method included the
following steps:
choosing the object of studies;
selection of subject to study;
choosing the experts to participate;
gathering the data using questionnaires;
data analysis and conclusions.
2 Author unknown, also exists as: “Show me how your project starts and I
can tell you how it will finish” or “Show me how your project starts and I
show you how it will end”.
Seweryn Spalek. Finding a New Way to Increase Project Management Efficiency in Terms of Time Reduction
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Three Industries: Construction, Information
Technology and Machinery The research presented in this article was part of a
larger effort supported by the National Science Centre, focusing on world-wide studies of maturity in project
management in the chosen industries. The overall research
was designed in two major steps.
The first step was to conduct quantitative empirical
studies on project management maturity levels in three types
of industries: machinery, construction and information
technology. Data from 447 global companies, mostly
medium- and large-sized ones, was collected. Ninety-eight
per cent of them earned over € 2,000,000 per year, and 99,5
% of them employed over 49 people.
The second step, which is discussed in this study, was
designed to investigate the relationship between the increase in maturity level in project management and the
predicted duration of forthcoming projects.
The Assessment
For the purpose of this study, a model was used that
assesses the company’s project management maturity in
four areas (Spalek, 2011):
methods (M) (Gary et al., 2011; Ji & Sedano, 2011);
human resources (HR) (Levin, 2010; McDonough, 2000);
project environment (E) (Elbanna, 2013; Killen & Kjaer, 2012);
knowledge management (KM) (Basu, 2014; Gasik, 2011).
The description of the maturity assessment areas is
shown in table 1.
Table 1
Project Management Maturity Assessment Areas
ID Area Description
1. Human Resources staffing, career paths, motivation, training, team work
2. Methods (incl. Tools &
Techniques)
methods, tools, techniques and means used for project planning and execution; risk, requir ements, scope, costs, time
and quality management
3. Environment organizational structures, top management support, stakeholders’ management, company culture
4. Knowledge Management lessons learned approach, gathering data and experience for on-going operations and future references
In the applied project management maturity model the
results of the assessment are reported from level 1 to 5:
LEVEL 1: Initial;
LEVEL 2: Standardized;
LEVEL 3: Appliance;
LEVEL 4: System Management;
LEVEL 5: Self-improvement. The experts were asked to express their opinion on an
increase of maturity level by one increment (from 1 to 2,
from 2 to 3, etc.) and how it influences the time reduction
of future projects in their companies. The possible impact
was measured on a scale from 1 to 5, as shown in table 2.
The assessment was made separately in each testing area.
Table 2
The Impact on Time Reduction on Future Projects
Impact Level Description
1 No influence
2 1–10 % 3 11–20 % 4 21–30 % 5 over 30 %
In our study, the experts were practitioners from the
investigated companies. The invitation to participate in this
study was sent to 308 chosen experts as a follow-up to the
major global study on project management maturity. The
experts were chosen based on the demographic information obtained in the first step of the overall research. They were
experienced managers that had at least five years of
experience and possessed a deep knowledge of the projects
executed in their companies. Moreover, the invitations
were sent to experts from companies that reported a
maturity level of at least 2 in each of the testing areas.
The response rate was 63 %. Such a high rate was
obtained as there were individually approached named
persons who expressed, in the first step of overall research,
their willingness to participate in the second step of the
studies. Non-response bias was tested by comparing the demographic data of participating and non-participating
experts. No significant difference was found, proving that
survey respondents represent the overall sample accurately.
Results and Discussion
Data from 39 information technology, 48 construction
and 107 machinery industry global companies was
collected. The reliability of data was checked using
Cronbach’s alpha, resulting in a value of over 0,9 in each
testing area. Data analysis using mean, median and mode values
was performed. The equality of variances was tested using
Levene's test and the equality of means using a t-test, and
satisfactory results were obtained as the significance for
Levene’s test was greater than 0,05. Additionally,
Spearman's rho correlation coefficients and factor analysis
using rotated component matrix were calculated for further
rigid data analyses. The data analysis was performed using
Statistical Package for the Social Sciences (IBM SPSS
build V21.0.0).
The results revealed differences in the predicted
impact of change in maturity level on the duration of future projects. This level of impact depended on the specific
change in the designated area of assessment (e.g., a change
from level 1 to 2 in the knowledge management area) and
varied between industries.
The Impact on Future Projects: by Maturity Area
and Type of Industry
The data analysis revealed that the dispersion of the
acquired data is low; consequently, the mean value was
chosen to explain the results. Using the mean value allows
Inzinerine Ekonomika-Engineering Economics, 2014, 25(5), 538–548
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the results to be presented more clearly, without going into
deep statistical details and allows the development of a
full, more comprehensive picture. The mean values of
impact on the future projects are shown in table 3.
The Impact on Future Projects by Industry (CONS, IND, IT) and Change of Maturity Level in the Areas of Methods (M), Human Resources (HR), Project Environment (E) and
Knowledge Management (KM).
Table 3
Statistics (Mean Value of Impact on Future Projects)
M
1–2
M
2–3
M
3–4
M
4–5
HR
1–2
HR
2–3
HR
3–4
HR
4–5
E
1–2
E
2–3
E
3–4
E
4–5
KM
1–2
KM
2–3
KM
3–4
KM
4–5
CONS 3,96 3,96 3,21 2,46 3,96 3,96 3,21 2,46 3,21 3,21 2,46 1,71 3,21 3,21 2,46 1,71
IND 3,88 3,88 3,16 2,44 3,88 3,88 3,16 2,44 3,16 3,16 2,44 1,72 3,16 3,16 2,44 1,72
IT 3,90 3,90 3,15 2,41 3,90 3,90 3,15 2,41 3,87 3,87 3,13 2,38 3,87 3,87 3,13 2,38
The impact on future projects depends on the type of
industry. The highest impact levels (3,96) were observed
for the change from level 1 to 2 and from 2 to 3 in the methods (M) and human resources (HR) areas in the
construction industry (CONS). The lowest impact (1.71)
also appeared in the construction industry; however, it was
observed in the areas of environment (E) and knowledge
management (KM) for the change of maturity from level 4
to 5. Note, in each type of the studied industries, the
potential impact on future projects is the highest if the
company is at the initial (1) or standardized (2) level of
maturity in project management and wants to increase it by
going one level up. This observation is true for each
assessment area: methods, human resources, environment
and knowledge management. When a company reports already having appliance (3)
or system management (4), maturity levels and wants to
increase it by one level, the impact on future projects’
duration subsequently decreases. However, this reduction
is greater in the construction and machinery industries than
in information technology companies. Therefore, which
project management maturity area the company should first
invest funds in to reduce the future project’s duration
depends on the type of industry. Moreover, the investment
sequence depends on the current level of maturity in the
company in each testing area, meaning that for a chosen industry, one should consider a different roadmap, showing
where and in which sequence the investment in project
management maturity should be placed.
Different Industries, Different Approach?
This study on the influence of the increase of project
management maturity on future projects’ time reduction was focused on three types of industries:
Construction, one of the “world present” sectors that has projects that are, to some extent, inherited in its
long history. The projects associated with these sectors are
described and considered by numerous authors (Davies et
al., 2009; Dominguez et al., 2009).
Information Technology (IT), the companies of which are admittedly spread throughout the globe. The IT
projects are described and discussed for many different
types, such as agile (Thomke and Reinertsen, 1998) or
Internet projects (Mahadevan, 2000).
The machinery industry, which operates in the background of the above two industries as well as many
others, is present in many countries and is a backbone
industry for the other sectors. Therefore, its importance for
the global economy is very high. However, a limited amount
of research in this particular sector can be found in the
literature. The examples are mostly qualitative case studies describing new product development (NPD) practices
(Ahmad et al., 2013; Huang et al., 2004; Matsui et al.,
2008), which are crucial for machinery industry companies.
The Investment Road Map: The Idea
Based on the factor analysis (as shown in Table 4), a
road map is proposed showing the investment path in
maturity areas, which should result in the biggest pay-offs
in reduction of time in future projects. Analysing the
groups of factors in the table, the proposed roadmaps for
the companies was built. This road map operates in the
areas of methods and techniques (M), human resources
(HR), project environment (E) and project knowledge management (KM), as well as the associated change in
level of maturity (e.g., from 2 to 3), which is noted, for
example, as follows: M 2–3 (the change in maturity from
level 2 to 3 in the area of methods and techniques).
In general, note that at the beginning of the road map,
the investment has the biggest impact on the duration of
future projects and subsequently decreases along the way.
Table 4
Factor Analysis
Rotated Component Matrixa,b
Component
1 2 3
M2-3 ,964 ,245 -,088
HR1-2 ,964 ,245 -,088
HR2-3 ,964 ,245 -,088
M1-2 ,964 ,245 -,088
HR3-4 ,953 ,265 ,146
E1-2 ,953 ,265 ,146
KM2-3 ,953 ,265 ,146
KM1-2 ,953 ,265 ,146
E2-3 ,953 ,265 ,146
M3-4 ,953 ,265 ,146
M4-5 ,719 ,253 ,648
HR4-5 ,719 ,253 ,648
E3-4 ,719 ,253 ,648
KM3-4 ,719 ,253 ,648
KM4-5 -,019 ,093 ,992
E4-5 -,019 ,093 ,992
Extraction Method: Principal Component Analysis.
Rotation Method: Varimax with Kaiser Normalization.
a. BRANCH=CONS; b. Rotation converged in 18 iterations
Rotated Component Matrixa,b
Component
1 2 3
HR1-2 ,964 ,250 -,074
HR2-3 ,964 ,250 -,074
Seweryn Spalek. Finding a New Way to Increase Project Management Efficiency in Terms of Time Reduction
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Rotated Component Matrixa,b
Component
1 2 3
M1-2 ,964 ,250 -,074
M2-3 ,964 ,250 -,074
E2-3 ,945 ,265 ,191
KM2-3 ,945 ,265 ,191
E1-2 ,945 ,265 ,191
M3-4 ,945 ,265 ,191
KM1-2 ,945 ,265 ,191
HR3-4 ,945 ,265 ,191
KM4-5 -,102 ,049 ,990
E4-5 -,102 ,049 ,990
KM3-4 ,641 ,226 ,733
HR4-5 ,641 ,226 ,733
M4-5 ,641 ,226 ,733
E3-4 ,641 ,226 ,733
Extraction Method: Principal Component Analysis.
Rotation Method: Varimax with Kaiser Normalization.
a. BRANCH=IND; b. Rotation converged in 18 iterations.
Rotated Component Matrixa,b
Component
1 2 3
HR1-2 ,947 ,148 ,272
HR2-3 ,947 ,148 ,272
M1-2 ,947 ,148 ,272
M2-3 ,947 ,148 ,272
KM2-3 ,934 ,190 ,297
E2-3 ,934 ,190 ,297
E1-2 ,934 ,190 ,297
KM1-2 ,934 ,190 ,297
HR3-4 ,817 ,227 ,519
M3-4 ,817 ,227 ,519
E3-4 ,783 ,284 ,543
KM3-4 ,783 ,284 ,543
HR4-5 ,288 ,327 ,891
M4-5 ,288 ,327 ,891
E4-5 ,254 ,387 ,872
KM4-5 ,254 ,387 ,872
Extraction Method: Principal Component Analysis.
Rotation Method: Varimax with Kaiser Normalization.
a. BRANCH=IT; b. Rotation converged in 9 iterations.
The Investment Road Map: Construction Industry
The construction sector is one of the largest industries
traditionally associated with using project management.
The proposed road map revealed that the biggest pay-off is
associated with an increase of maturity level from 1 to 2
and from 2 to 3 in the methods (M 1–2, M 2–3) and human
resources areas (HR 1–2, HR 2–3).
The road map can be described in the following four
steps: Step One
Assuming that the company is at an initial (1) maturity
level in all areas, the first step in investment should be an
increase of maturity in the methods (M) and human
resources areas (HR) until they reach the level 3.
Step Two
Then, in the second step, the investment should be
placed in gradually reaching level 3 in the environment and
knowledge management areas (E 1–2, E 2–3, KM 1–2,
KM 2–3), and the increase in methods and human
resources should continually increase until they reach level
4 (HR 3–4, M 3–4). Step Three
The third step should include activities improving the
maturity in the environment and knowledge management
areas until they report level 4 (E 3–4, KM 3–4) and the
methods and human resources areas until level 5 (M 4–5,
HR 4–5).
Step Four
Finally, in the fourth step, the increase should be from
level 4 to 5 in the environment and knowledge
management areas (E 4–5, KM 4–5).
Figure 1 shows the investment road map for the construction industry.
Figure 1. Investment road map for the construction industry
M 1–2,
M 2–3,
HR 1–2,
HR 2–3
E 1–2,
E 2–3,
KM 1–2,
KM 2–3,
HR 3–4,
M 3–4
E 3–4,
KM 3–4,
M 4–5,
HR 4–5
E 4–5,
KM 4–5
STEP 1 STEP 2 STEP 3 STEP 4
Inzinerine Ekonomika-Engineering Economics, 2014, 25(5), 538–548
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Comparing Steps in Construction Companies: the
Reduction of Impact
Remarkably, if assuming that in the first step, the impact
on reduction of time of future projects is 100, then in the
second step, it equals 81; in the third, 62; and in the fourth,
43.
This information is useful for investments in a specific
company, as companies in the same industry differ from one
another. After performing the first step, the total investment and detailed impact on a project’s duration can be measured.
Then, knowing the predicted reduction of impact, one can
estimate the possible outcomes in the second step toward the
investment effort and decide if it is worth it to proceed. The
same approach can be performed before step three and four.
Following this advice means, of course, that time gaps are
needed between successive steps to reassess the situation.
The Investment Road Map: Information Technology
After construction, the second industry traditionally
associated with the project management sector is
information technology (IT). Moreover, both industries
report a long project management application history.
The investment road map for information technology
consists of six steps.
Step One
In step one, the investment in increasing project
management maturity levels should be performed to go from
level 1 to 2 and from 2 to 3 in the methods and human
resources areas (M 1–2, M 2–3, HR 1–2, HR 2–3).
Step Two
After the first investments, the company should consider
investments to increase the level of maturity from 1 to 2 and
then from 2 to 3 in the remaining two areas: environment
and knowledge management (E 1–2, E 2–3, KM 1–2, KM
2–3).
Step Three In the third step, the project management maturity level
should be increased from 3 to 4 in the methods and human
resources areas (M 3–4, HR 3–4).
Step Four
In this step, as in step three, one goes up from level 3 to
4 in the environment and knowledge management areas (E
3–4, KM 3–4).
Step Five
The fifth step is designated for improvement in the
methods and human resources areas from level 4 to 5 (M 4–
5, HR 4–5).
Step Six In the last step, the investment should be placed to
increase the maturity level from 4 to 5 in the areas of
environment and knowledge management (E 4–5, KM 4–5).
Figure 2 shows the investment road map for information
technology companies.
Figure 2. Investment road map for information technology companies
Comparing Steps in IT Companies: The Reduction of
Impact
The highest potential impact on future projects in the
IT industry occurs when investing in the areas indicated in
the first step and then gradually decreases.
Assuming that the impact in the first step equals 100,
the impact on future projects’ duration of investment in step two is 99. Accordingly, in step three, the duration is
81; in step four, 80; in step five, 62; and, finally, in step
six, 61.
Despite a relatively greater number of steps in
comparison to the construction industry, the differences
between some pairs of steps (1 and 2, 3 and 4, 5 and 6) are
small. Thus, the decision to continue the investment after
each step is of a different nature than in the construction
industry where differences are bigger.
The Investment Road Map: Machinery Industry The products of the machinery industry are the
machines, tools, parts and devices used by the other
industries. This industry, like construction and IT, also has
a wide representation among companies world-wide.
M 1-2,
M 2-3,
HR 1-2,
HR 2-3
E 1-2,
E 2-3,
KM 1-2,
KM 2-3
M 3-4,
HR 3-4
E 3-4,
KM 3-4
STEP 1 STEP 2 STEP 3 STEP 4
M 4-5,
HR 4-5
STEP 5 STEP 6
E 4-5,
KM 4-5
Seweryn Spalek. Finding a New Way to Increase Project Management Efficiency in Terms of Time Reduction
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However, a limited amount of project management related
research exists for this sector.
The investment in project management maturity in the
machinery industry should be considered in the following
four steps:
Step One
In the first step, the investment in an increase of
maturity levels from 1 to 2 and, successively, from 2 to 3
should be considered in the areas of methods and human resources (M 1–2, M 2–3, HR 1–2, HR 2–3).
Step Two
The next investments should be focused on further
increases in the methods and human resources areas up to
level 4 and, in parallel, they should be focused on further
increases in the areas of environment and knowledge
management from levels 1 to 3 (M 3–4, HR 3–4, E 1-2, E
2–3, KM 1–2, KM 2–3).
Step Three
In step three, an increase of project management
maturity up to level 4 is advised in the environment and
knowledge management areas (E 3–4, KM 3–4).
Additionally, investment in the methods and human areas
is suggested until they reach level 5 (M 4–5, HR 4–5).
Step Four This final step is dedicated to the environment and
knowledge management areas to reach a level 5 of project
management maturity (E 4–5, KM 4–5).
Figure 3 shows the investment road map for the
machinery industry.
Figure 3. Investment road map for the machinery industry
Comparing Steps in Machinery Industry Companies:
the Reduction of Impact
If one assumes that the possible impact on future
project time reduction in the machinery industry equals 100
as a result of performing step 1, the execution of step 2 will
thus result in an impact level of 81. For steps 3 and 4, there
is a further reduction of impact to 64 and 44, respectively.
After performing each step and comparing the effort
undertaken to the reported project outcomes, this
information can be used to decide whether to continue with
further investments in increasing maturity levels.
Conclusions
The Traditional vs. Agile Approach to Managing
Projects
The outcomes for the construction and machinery
industries are alike, whereas the results of the study in
information technology companies differs. This could be
the result of similarities between the type of projects
undertaken by both the construction and machinery
industry and the different projects undertaken by IT companies. The construction and machinery industry
companies execute projects in a more traditional approach,
whereas IT projects have a greater tendency to migrate
toward agility in project management. The concepts of
traditional vs. agile approach in project management are
widely discussed by (Fernandez & Fernandez, 2008;
Shenhar & Dvir, 1996). That contrast would explain the
major differences between the proposed investment
roadmaps that are associated with either a more traditional
or agile approach to project management.
Construction and Machinery: the Traditional
Approach
In the traditional approach, the general advice is to
invest gradually in all four areas of maturity with some advance investment in project management standards, tools
and techniques and in the competencies of people involved
in projects. The investment in company structures
supporting project execution or project knowledge
management areas should be considered in direct relation
to progress in the methods and human resources areas.
Information Technology: The Agile Approach
Agility, as defined by its founders3, is less focused on
strict methods and tools and more on knowledge flow,
creating an appropriate working environment and team
building processes (Dingsoyr et al., 2012). Therefore,
3 see: http://agilemanifesto.org/; accessed November 2013.
M 1–2,
M 2–3,
HR 1–2,
HR 2–3
E 1–2,
E 2–3,
KM 1–2,
KM 2–3,
HR 3–4,
M 3–4
KM 4–5
E 4–5
E 3–4,
KM 3–4,
M 4–5,
HR 4–5
STEP 1 STEP 2 STEP 3 STEP 4
Inzinerine Ekonomika-Engineering Economics, 2014, 25(5), 538–548
- 545 -
explaining why the investment in that type of project
should be performed gradually and in parallel in each of
the following areas: methods, human resources,
environment and knowledge management.
Although this study is limited to three industries, the
suggested roadmaps can be used by other industries as
well, especially by a wide range of manufacturing
companies (NAICS, 2012). A company can choose a road
map by assessing the similarities of the projects being executed to construction and machinery or IT projects.
However, for some sectors, such as healthcare (Adler et
al., 2003) or education (Palacios-Marques et al., 2013),
more studies are needed to develop their specific road
maps systems.
Continuous Improvement? Not Necessary!
The majority of academics and practitioners think that
continuous improvement is vital for a company’s
operations and its survival in the turbulent market. This
approach is also a noticeable concept of increasing project
management maturity levels as a continuous process,
which should result in reaching the highest maturity level
in all assessment areas.
However, based on existing studies of maturity
(Becker et al., 2009; Grant & Pennypacker, 2006; Mullaly
&Thomas, 2010; Pasian, 2011; Rohrbeck, 2010), the vast
majority of companies today report, on average, the second or third level of maturity in different industries and
assessment areas. Therefore, more effort is put into
discussing how the company should proceed with maturity
improvement to achieve “the top.” However, some doubts
exist if “the top” is even obtainable as there is a risk that
assessment criteria can be changed over time as project
management develops further. The first signs of such an
approach are described in the model proposed by PMI
(2008), in which no levels of maturity are defined. Instead,
maturity is measured using a best practices list, which is
continuously expanded by the PMI. This approach results
in a “never ending” continuous effort to increase project management maturity in the company.
Continuous improvement, however appropriate in
theory, cannot always be the best solution for a company.
As our study revealed, the impact of the increase of
maturity on the reduction of future projects decreases over
subsequent levels of maturity. The biggest impact occurs
at the very beginning when a company is making its first
steps on the road map. Then, the impact decreases by more
than 50 % in the traditional approach to project
management (represented by construction and machinery
industries) and by approximately 40 % in the agile approach (represented by information technology
companies). This brings into the question whether
continuous improvement in maturity is effective for the
company in terms of invested funds and achieved
outcomes.
Therefore, there should be time breaks in between
steps on the investment road map to measure, ex post4, the
4 The translation from Latin means "after the fact". The use of historical
returns has traditionally been the most common way to predict the
probability of incurring a loss on any given day. Ex-post is the opposite of
real impact of the increase in project management maturity
on the recorded project’s time reduction.
Based on this study, one knows the size of the
predicted decrease of impact in the next step for traditional
and agile projects. Therefore, one can compare possible
benefits with the estimated effort needed to continue with
the next step on the road map. As a result, one can
conclude that the investment in further progress in project
management maturity does not pay off and the limited company’s investment funds can be spent on other more
promising and vital company activities.
Limitations and Future Directions
The research is limited to three types of industries,
represented by 194 organisations with 107 belonging to
machinery, 48 to construction and 19 to IT companies. In quantitative analysis, the size of this sample, especially of
the latter two industries, can be assumed to be rather small.
Therefore, definitive conclusions and generalisations
supporting the outcomes of our study should await a larger
sample, especially of IT and construction industries.
The method of the prediction of the costs of
forthcoming projects by investigating experts’ opinions is
constrained by the quality of the respondents’ judgment, as
is the case in all surveys of experts. Generally, predicting
future outcomes is an extremely difficult issue (Glenn &
Gordon, 2003). However, it was a conscious decision to use this method, despite its limitations, to advance the
current stage of knowledge.
Some of the limitations of the study can be
strengthened to show the directions for future research.
The mainstream method would be to investigate the other
industries for which projects are a vital part of their
operations with the same method. Therefore, follow-up
research could be dedicated to the automotive, aerospace
or mining sectors.
Moreover, a study on the relationship between
different investment types and an increase in maturity level
would be advised. It would also be desirable for a new study to investigate the direct influence of different types
of investments on projects’ outcomes.
It may also be worth considering research into
checking whether project management is not
geographically sensitive, as the other recent studies on
project management performance areas suggest.
The results of this study advance the current state of
knowledge in the project management area. However, the
problem of linking investments in project management to
outcomes for an entire company is complex. Hence, the
considerations presented in the paper provide a better understanding of this complexity in the area of project
duration and could be a trigger point for further studies of
other types of project outcomes.
Acknowledgments
This work was supported by the National Science
Centre grant.
ex-ante, which means "before the event". Source:
http://www.investopedia.com/ retrieved on November 2013.
Seweryn Spalek. Finding a New Way to Increase Project Management Efficiency in Terms of Time Reduction
- 546 -
References
Adler, P. S., Riley, P., Kwon, S. W., Signer, J., Lee, B., & Satrasala, R. (2003). Performance improvement capability:
Keys to accelerating performance improvement in hospitals. California Management Review, 45(2).
http://dx.doi.org/10.2307/41166163
Ahmad, S., Mallick, D. N., & Schroeder, R. G. (2013). New Product Development: Impact of Project Characteristics and
Development Practices on Performance. Journal of Product Innovation Management, 30(2), 331–348.
http://dx.doi.org/10.1111/j.1540-5885.2012.01002.x
Avots, I. (1969). Why Does Project Management Fail. California Management Review, 12/1, 77–82.
http://dx.doi.org/10.2307/41164208
Baron, J. N., & Hannan, M. T. (2002). Organizational blueprints for success in high-tech start-ups: Lessons from the
Stanford Project on emerging companies. California Management Review, 44(3). http://dx.doi.org/10.2307/41166130
Basu, R. (2014). Managing quality in projects: An empirical study. International Journal of Project Management, 32(1),
178–187. http://dx.doi.org/10.1016/j.ijproman.2013.02.003
Becker, J., Knackstedt, R., & Poppelbuss, J. (2009). Developing Maturity Models for IT Management - A Procedure
Model and its Application. Business & Information Systems Engineering, 1(3).
Belt, P., Oiva-Kess, A., Harkonen, J., Mottonen, M., & Kess, P. (2009). Organisational maturity and functional
performance. International Journal of Management and Enterprise Development, 6(2), 147–164.
http://dx.doi.org/10.1504/IJMED.2009.022624
Booth, H. (2006). Demographic forecasting: 1980 to 2005 in review. International Journal of Forecasting, 22(3), 547–
581. http://dx.doi.org/10.1016/j.ijforecast.2006.04.001
Brucker, P., Drexl, A., Mohring, R., Neumann, K., & Pesch, E. (1999). Resource-constrained project scheduling: Notation,
classification, models, and methods. European Journal of Operational Research, 112(1), 3–41.
http://dx.doi.org/10.1016/S0377-2217(98)00204-5
Campos Silva, D. D., Santiago, L. P., & Silva, P. M. S. (2012). Impact of Premature Information Transfer on Cost and Development Time of Projects. IEEE Transactions on Engineering Management, 59(4), 692–704.
http://dx.doi.org/10.1109/TEM.2012.2187661
Chen, B., Liou, F. M., & Huang, C. P. (2012). Optimal Financing Mix of Financially Non-Viable Private-Participation
Investment Project with Initial Subsidy. Inzinerine Ekonomika-Engineering Economics, 23(5), 452–461.
Chen, Y. Q., Liu, J. Y., Li, B., & Lin, B. (2011). Project delivery system selection of construction projects in China.
Expert Systems with Applications, 38(5), 5456–5462. http://dx.doi.org/10.1016/j.eswa.2010.10.008
Cooke-Davies, T. (2007). Project Management Maturity Models. In P. W. G. Morris and J. K. Pinto (Eds.), Wiley Guide to
Managing Projects (pp. 1234–1255). Hoboken, NJ, USA: John Wiley & Sons. http://dx.doi.org/10.
1002/9780470172391.ch49
Cooper, R. G., Edgett, S. J., & Kleinschmidt, E. J. (1999). New product portfolio management: Practices and performance.
Journal of Product Innovation Management, 16(4), 333–351. http://dx.doi.org/10.1016/S0737-6782(99)00005-3
Davies, A., Gann, D., & Douglas, T. (2009). Innovation in Megaprojects: Systems Integration At London Heathrow
Terminal 5. California Management Review, 51(2). http://dx.doi.org/10.2307/41166482
del Cano, A., & de la Cruz, M. P. (2002). Integrated methodology for project risk management. Journal of Construction
Engineering and Management-Asce, 128(6), 473–485. http://dx.doi.org/10.1061/(ASCE)0733-9364(2002)128:6(473)
Dingsoyr, T., Nerur, S., Balijepally, V., & Moe, N. B. (2012). A decade of agile methodologies: Towards explaining agile
software development. Journal of Systems and Software, 85(6). http://dx.doi.org/10.1016/j.jss.2012.02.033
Dominguez, D., Worch, H., Markard, J., Truffer, B., & Gujer, W. (2009). Closing the Capability Gap: Strategic Planning
For The Infrastructure Sector. California Management Review, 51(2). http://dx.doi.org/10.2307/41166479
Elbanna, A. (2013). Top management support in multiple-project environments: an in-practice view. European Journal of
Information Systems, 22(3), 278–294. http://dx.doi.org/10.1057/ejis.2012.16
Ethiraj, S. K., Kale, P., Krishnan, M. S., & Singh, J. V. (2005). Where do capabilities come from and how do they matter? A study in the software services industry. Strategic Management Journal, 26(1), 25–45.
http://dx.doi.org/10.1002/smj.433
Fernandez, D. J., & Fernandez, J. D. (2008). Agile Project Management - Agilism Versus Traditional Approaches. Journal
of Computer Information Systems, 49(2), 10–17.
Fraser, P., Farrukh, C., & Gregory, M. (2003). Managing product development collaborations - a process maturity
approach. Proceedings of the Institution of Mechanical Engineers Part B-Journal of Engineering Manufacture,
217(11), 1499-1519. http://dx.doi.org/10.1243/095440503771909890
Galbraith, C. S., & Merrill, G. B. (1996). The politics of forecasting: Managing the truth. California Management Review,
38(2). http://dx.doi.org/10.2307/41165831
Inzinerine Ekonomika-Engineering Economics, 2014, 25(5), 538–548
- 547 -
Gary, K., Enquobahrie, A., Ibanez, L., Cheng, P., Yaniv, Z., Cleary, K., Kokoori, S., Muffih, B., & Heidenreich, J. (2011).
Agile methods for open source safety-critical software. Software-Practice & Experience, 41(9).
http://dx.doi.org/10.1002/spe.1075
Gasik, S. (2011). A Model of Project Knowledge Management. Project Management Journal, 42(3), 23–44.
http://dx.doi.org/10.1002/pmj.20239
Glenn, J. C., & Gordon, T. J. (2003). Futures Research Methodology. Washington D.C.: The United Nations University.
Grant, K. P., & Pennypacker, J. S. (2006). Project management maturity: An assessment of project management capabilities among and between selected industries. IEEE Transactions on Engineering Management, 53(1), 59–68.
http://dx.doi.org/10.1109/TEM.2005.861802
Griffin, A. (1993). Metrics for Measuring Product Development Cycle Time. Journal of Product Innovation Management,
10(2), 112–125. http://dx.doi.org/10.1016/0737-6782(93)90003-9
Griffin, A. (1997). The effect of project and process characteristics on product development cycle time. Journal of
Marketing Research, 34(1), 24–35. http://dx.doi.org/10.2307/3152062
Herroelen, W., & Leus, R. (2005). Project scheduling under uncertainty: Survey and research potentials. European Journal
of Operational Research, 165(2), 289–306. http://dx.doi.org/10.1016/j.ejor.2004.04.002
Hill, G. M. (2004). Evolving the project management office, a competency continuum. Information Systems Management,
21(4), 45–51. http://dx.doi.org/10.1201/1078/44705.21.4.20040901/84187.6
Hofman, M. (2014). Models of PMO functioning in a multi-project environment. Procedia - Social and Behavioral
Sciences, 119, 46–54.
Huang, X. L., Soutar, G. N., & Brown, A. (2004). Measuring new product success: an empirical investigation of Australian SMEs. Industrial Marketing Management, 33(2), 117–123. http://dx.doi.org/10.1016/S0019-
8501(03)00034-8
Iacovou, C. L., & Dexter, A. S. (2004). Turning around runaway information technology projects. California Management
Review, 46(4). http://dx.doi.org/10.2307/41166275
IOC (2007). The Olympic symbols, 2nd ed.. The Olympic Museum: International Olympic Committee.
Ji, F., & Sedano, T. (2011). Comparing Extreme Programming and Waterfall Project Results. IEEE.
Kach, A., Azadegan, A., & Dooley, K. J. (2012). Analyzing the successful development of a high-novelty innovation
project under a time-pressured schedule. R & D Management, 42(5), 377–400.
Keil, M., & Mahring, M. (2010). Is Your Project Turning into a Black Hole?. California Management Review, 53(1).
http://dx.doi.org/10.1525/cmr.2010.53.1.6
Kerzner, H. (2013). Project management: a systems approach to planning, scheduling, and controlling (11 ed.). Hoboken,
NJ: John Wiley & Sons.
Kessler, E. H., and Chakrabarti, A. K. (1996). Innovation speed: A conceptual model of context, antecedents, and
outcomes. Academy of Management Review, 21(4), 1143–1191.
Khoshgoftar, M., & Osman, O. (2009). Comparison of Maturity Models. New York: IEEE.
Killen, C. P., & Kjaer, C. (2012). Understanding project interdependencies: The role of visual representation, culture and process. International Journal of Project Management, 30(5), 554–566. http://dx.doi.org/10.1016/ j.ijproman.
2012.01.018
Kowalska-Styczen, A., & Sznajd-Weron, K. (2012). Access to information in word of mouth marketing within a cellular
automata model. Advances in Complex Systems, 15(8), 17 http://dx.doi.org/10.1142/S0219525912500804
Lacher, R. C., Coats, P. K., Sharma, S. C., & Fant, L. F. (1995). A Neural-Network for Classifying the Financial Health of a Firm. European Journal of Operational Research, 85(1), 53–65. http://dx.doi.org/10.1016/0377-2217(93)E0274-2
Landeta, J. (2006). Current validity of the Delphi method in social sciences. Technological Forecasting and Social
Change, 73(5), 467–482. http://dx.doi.org/10.1016/j.techfore.2005.09.002
Langerak, F., Griffin, A., & Hultink, E. J. (2010). Balancing Development Costs and Sales to Optimize the Development
Time of Product Line Additions. Journal of Product Innovation Management, 27(3), 336–348.
http://dx.doi.org/10.1111/j.1540-5885.2010.00720.x
Lenfle, S., & Loch, C. (2010). Lost Roots: How Project Management Came to Emphasize Control over Flexibility and
Novelty. California Management Review, 53(1), 32–55. http://dx.doi.org/10.1525/cmr.2010.53.1.32
Levin, G. (2010). Interpersonal skills for Portfolio, Program, and Project Managers: Management Concepts.
McDonough, E. F. (2000). Investigation of factors contributing to the success of cross-functional teams. Journal of
Product Innovation Management, 17(3), 221–235. http://dx.doi.org/10.1016/S0737-6782(00)00041-2
Mahadevan, B. (2000). Business models for Internet-based E-commerce: An anatomy. California Management Review,
42(4). http://dx.doi.org/10.2307/41166053
Seweryn Spalek. Finding a New Way to Increase Project Management Efficiency in Terms of Time Reduction
- 548 -
Matsui, Y., Abdallah, A. B., Sato, O., & Kitanaka, H. (2008). International Comparison of Manufacturing Practices and
Performance.
Mian, A., Takala, J., & Kekale, T. (2008). Role of Organizational Culture for Knowledge Sharing in Projects. 2008
Portland International Conference on Management of Engineering & Technology, Vols 1-5, 962–968.
Morris, P. W. G. (1994). The Management of Projects. London: Thomas Telford. http://dx.doi.org/10.1680/mop.16934
Mullaly, M. E., & Thomas, J. (2010). Re-thinking project management maturity. Paper presented at the PMI Research and
Education Conference, Washington, D.C.
NAICS. (2012). North American Industry Classification System Washington, DC: U.S. Census Bureau.
Neverauskas, B., & Stankevicius, V. (2008). Project Management: Research and Studies at the Faculty of Economics and Management of Kaunas University of Technology. Inzinerine Ekonomika-Engineering Economics(4), 59–66.
Omorede, A., Thorgren, S., & Wincent, J. (2013). Obsessive passion, competence, and performance in a project
management context. International Journal of Project Management, 31(6), 877–888. http://dx.doi.org/10.
1016/j.ijproman.2012.09.002
Onkal, D., Sayim, K. Z., & Gonul, M. S. (2013). Scenarios as channels of forecast advice. Technological Forecasting and
Social Change, 80(4), 772–788. http://dx.doi.org/10.1016/j.techfore.2012.08.015
Palacios-Marques, D., Cortes-Grao, R., & Carral, C. L. (2013). Outstanding knowledge competences and web 2.0
practices for developing successful e-learning project management. International Journal of Project Management,
31(1), 14–21. http://dx.doi.org/10.1016/j.ijproman.2012.08.002
Pasian, B. L. (2011). Project management maturity: a critical analysis of existing and emergent contributing factors (pp.
266). Sydney: Faculty of Design, Architecture and Building, University of Technology.
Pemsel, S., & Wiewiora, A. (2013). Project management office a knowledge broker in project-based organisations. International Journal of Project Management, 31(1), 31–42 http://dx.doi.org/10.1016/j.ijproman.2012.03.004
PMI. (2008). Organizational Project Management Maturity Model (OPM3®), Knowledge Foundation - 2nd Edition (pp.
204). Newtown Square, PA, USA: Project Management Institute.
Prasnikar, J., & Skerlj, T. (2006). New product development process and time-to-market in the generic pharmaceutical
industry. Industrial Marketing Management, 35(6), 690–702. http://dx.doi.org/10.1016/j.indmarman.2005.06.001
Radziszewska-Zielina, E. (2010). Analysis of the Impact of the Level of Partnering Relations on the Selected Indexes of
Success of Polish Construction Enterprises. Inzinerine Ekonomika-Engineering Economics, 21(3), 324–335.
Rohrbeck, R. (2010). Towards A Maturity Model For Organizational Future Orientation. Academy of Management
Proceedings, 2010(1), 1–6.
Rudzianskaite-Kvaraciejiene, R., Apanaviciene, R., & Butauskas, A. (2010). Evaluation of Road Investment Project
Effectiveness. Inzinerine Ekonomika-Engineering Economics, 21(4), 368–376. Scapolo, F., & Miles, I. (2006). Eliciting experts' knowledge: A comparison of two methods. Technological Forecasting
and Social Change, 73(6), 679–704. http://dx.doi.org/10.1016/j.techfore.2006.03.001
Shenhar, A. J. (2001). One size does not fit all projects: Exploring classical contingency domains. Management Science,
47(3), 394–414. http://dx.doi.org/10.1287/mnsc.47.3.394.9772
Shenhar, A. J., & Dvir, D. (1996). Toward a typological theory of project management. Research Policy, 25(4), 607–632.
http://dx.doi.org/10.1016/0048-7333(95)00877-2
Spalek, S. (2011). Managing projects in an industrial environment. An overview of the project management maturity
models. Paper presented at the 16th Annual International Conference on Industrial Engineering Theory, Applications
and Practice – IJIE, Stuttgart, Germany.
Spalek, S. (2013). Improving Industrial Engineering Performance through a Successful Project Management Office.
Inzinerine Ekonomika-Engineering Economics, 24(2), 88–98. Spalek, S. (2014). Does Investment in Project Management Pay Off? Industrial Management & Data Systems, 114(5),
832–856. http://dx.doi.org/10.1108/IMDS-10-2013-0447
Tan, W. P., Sauser, B. J., & Ramirez-Marquez, J. E. (2011). Analyzing Component Importance in Multifunction
Multicapability Systems Developmental Maturity Assessment. IEEE Transactions on Engineering Management,
58(2), 275–294. http://dx.doi.org/10.1109/TEM.2010.2071877
Thomke, S., & Reinertsen, D. (1998). Agile product development: Managing development flexibility in uncertain
environments. California Management Review, 41(1). http://dx.doi.org/10.2307/41165973
Wilson, K., & Wauson, J. (2010). The AMA handbook of business writing : the ultimate guide to style, grammar, usage,
punctuation, construction, and formatting. New York: American Management Association.
Wright, R. N., Rosenfield, A. H., & Fowell, A. J. (1995). Report on Federal Research in Support of the US Construction
Industry. Washington, DC.: National Science and Technology Council.
Wyrozebski, P., & Spalek, S. (2014). An Investigation of Planning Practices in Select Companies. Management and
Production Engineering Review, 5(2), 78–87. http://dx.doi.org/10.2478/mper-2014-0020
Zavadskas, E. K., Turskis, Z., Ustinovichius, L., & Shevchenko, G. (2010). Attributes Weights Determining Peculiarities
in Multiple Attribute Decision Making Methods. Inzinerine Ekonomika-Engineering Economics, 21(1), 32–43.
The article has been reviewed.
Received in October, 2014; accepted in December, 2014.
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