factors affecting cost performance of construction in india
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1.
Editorial board Page CO2
2.
Transferring projects to their final users: The effect of planning and preparations for commissioning on project success Pages 257-265 Dov Dvir
3.
The effect of systemic errors on optimal project buffers Pages 267-274 Dan Trietsch
4.
Simulation-based estimation for correlated cost elements Pages 275-282 I.-T. Yang
5.
Factors affecting cost performance: evidence from Indian construction projects Pages 283-295 K.C. Iyer and K.N. Jha
6.
Cost estimation of a software product using COCOMO II.2000 model a case study Pages 297-307 R. Dillibabu and K. Krishnaiah
7.
Designbuild pre-qualification and tendering approach for public projects Pages 309-320 Khaled Al-Reshaid and Nabil Kartam
8.
Evaluation of stakeholder influence in the implementation of construction projects Pages 321-328 Stefan Olander and Anne Landin
9.
Framework for project managers to manage construction safety Pages 329-341 Evelyn Ai Lin Teo, Florence Yean Yng Ling and Adrian Fook Weng Chong
International Journal of Project Management Copyright 2006 Elsevier Ltd and the International Project Management Association (IPMA). All rights reserved
Volume 23, Issue 4, Pages 257-341 (May 2005)
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The International Journal of Project Management is devoted to the publication of papers which advance knowledge ofthe practical and theoretical aspects of project management. The Journal aims to: provide a focus for worldwideexpertise in the required techniques, practices and areas of research; present a forum for readers to share commonexperiences across the full range of industries and technologies in which project management is used; cover all areas ofproject management from systems to human aspects; and link theory with practice by publishing case studies andcovering the latest important issues in special series.
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INTERNATIONAL EDITORIAL BOARDProfessor D Arditi Professor and Chairman,Department of Civil and ArchitecturalEngineering, Illinois Institute of Technology,3201 South Dearborn StreetChicago, IL 60616-3793, USAE-mail: [email protected]
Professor Karlos A Artto Helsinki Universityof Technology, PO Box 9500FIN02015 HUT, FinlandE-mail: karlos.artto@hut.
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Professor Christophe Midler Centre deResearche de Gestion, Ecole Polytechnique1 rue Descartes, 75005 Paris, FranceE-mail: [email protected]
Professor Peter Morris Professor of Constructionand Project Management, The Bartlett Schoolof Graduate Studies, UCL The Faculty of theBuilt Environment, Torrington Place Site,Gower Street, London WC1E 6BT, UKE-mail: [email protected]
Professor Jerey Pinto School ofBusiness, Penn State Erie, Station Road,Erie PA 16563, USAE-mail: [email protected]
Professor Nigel J Smith Department of CivilEngineering, University of Leeds,Leeds LS2 9JT, UKE-mail: [email protected]
Hiroshi Tanaka Project Services Division,Yokohama World Operations Center,3-1, Minato Mirai 2-chome, Nishi-ku,Yokohama 220-6001, JapanE-mail: [email protected]
Willi Vonrufs Project and ProcessManagement, Larchenstrasse 20,CH-8903 Birmensdorf, SwitzerlandE-mail: [email protected]
Professor Terry M Williams Departmentof Management Science, Graham Hills Building,40 George Street, Glasgow, G1 1QEE-mail: [email protected]
Professor K-T Yeo Nanyang TechnologicalUniaversity, School of Mechanical andProduction Engineering, Nanyang Ave,Singapore 2263
EDITORProfessor J R TurnerProfessor of Project ManagementISGI-Groupe ESC LilleAvenue Willy BrandtF 59777 EURALILLEFrance
Postal Address: EuroProjExWildwoodManor CloseEast HorsleySurrey KT24 6SA, UK
E-mail Address: [email protected]
INTERNATIONAL JOURNAL OF
PROJECTMANAGEMENT
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NOTES FOR AUTHORS
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[1] Cooper DF. Chapman CB. Risk analysis for large projects:models, methods and cases. New York: Wiley, I987.
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This paper examines the relationship between planning and preparing the project for transfer to its nal users and project success.
Four planning and preparation aspects are considered (development of operational & maintenance requirements, customer partic-
ipation in the development process, developers preparations for turning over the project to its nal users, and nal user preparations
out a project: extinction, addition, integration, and star-
unsuccessful or obsolete, may be terminated by starva-
tion, or in other words, by cutting out the funds for its
completion. Starvation is usually used when manage-
inclusion, integration, or extinction; a plan must be
sign responsibility for product support, if necessary [1].
Although the use of a termination manager for ensur-
ing that the project is complete and to deliver its out-
come (if successful) to its customers, is advocated by
several authors, and though project terminationE-mail address: [email protected].
International Journal of Project Manag
INTERNATIONAL JOURNAL OF
PROJECT0263-7863/$30.00 2005 Elsevier Ltd and IPMA. All rights reserved.vation [1]. Termination by extinction means the project
has been successful and achieved its goals: the new prod-
uct has been developed and handed over to the client; or
the building has been completed and accepted by the
purchaser. Projects terminated by extinction may have
been successful or unsuccessful. A project may be termi-
nated by institutionalizing it as a formal part of the
organization (addition) or by distributing the personnel,equipment and functions among the existing elements of
the parent organization (integration). Projects which are
developed to terminate it. The process of project termi-
nation is not an easy task. It is to be planned, budgeted
and scheduled like any other phase of the project life cy-
cle. Sometimes a special termination manager, whose
primary responsibility is to eectively and eciently
complete the termination process, is appointed. The du-
ties of a termination manager may include the following:
Ensure the project is complete, ensure delivery and clientacceptance, prepare a nal report, redistribute person-
nel, materials, equipment, and any other resources, as-for introduction into operational use), along with three measures of project success (project eciency, customer benets, and overall
success). The study is based on data from 110 defense projects performed in Israel and includes regression and correlation analysis
between the two sets of variables. The ndings suggest that customer participation in the development process and nal user prep-
arations have the highest impact on project success. Customer participation in the development process is highly correlated with
project eciency (0.45), while nal user preparations are highly correlated with customer benets (0.46).
2005 Elsevier Ltd and IPMA. All rights reserved.
Keywords: Project planning; Project commission & close out; Project success
1. Introduction
There are four fundamentally dierent ways to close
ment is reluctant to admit that the project actually
failed.
Regardless of a successful project is completed byTransferring projects to their and preparations for comm
Do
School of Management, Ben Gurion Uni
Received 20 April 2004; received in revised
Abstractdoi:10.1016/j.ijproman.2004.12.003l users: The eect of planningsioning on project success
vir
, P.O.B. 653, Beer Sheva 84105, Israel
22 June 2004; accepted 1 December 2004
www.elsevier.com/locate/ijproman
ement 23 (2005) 257265
MANAGEMENT
-
existing literature. Based on the review we propose four
rojechypotheses on the contribution of planning and prepar-ing the project for transfer to its nal users to project
success. A description of the research methodology is
presented in the next section followed by presentation
of the data structure and the reliability of the various
constructs. The next section contains the analysis of
the correlations between planning and preparations
variables and success variables, and the regression re-
sults between the three success measures and the plan-ning and preparing variables. We conclude with a
discussion of the ndings and their implications for
the practice of project management.
2. Theoretical background
The research body on project termination is rela-tively small in comparison to other research areas of
project management such as project planning, control,
success measurement, and risk assessment. Buell [3] in
an early article claims that the main reason for so little
information on the subject is simply because it is hard
to spell out specic guidelines for termination of
projects.
Most research on project termination focused onreasons for premature termination and not on the
introduction of the outcomes of successful projects into
use. Although, the decision to terminate a project may
be in certain situations more important than the deci-
sion to go on with the project, there is almost a unan-
imous agreement [1] that the termination stage of the
project rarely has much impact on technical success
or failure of the project. It has though, a great dealto do with residual attitudes toward the project constitutes a signicant part in the total project, it is of-
ten overlooked by project managers [2].
This paper examines the relationship between plan-
ning and preparing the project termination and com-
mission and project success. Our objective is to
analyze the relationship between the amount of eortinvested in planning and preparing the project for
transfer to its nal users and the degree of success
achieved, as seen from dierent points of view. The
analysis is based on data collected from 110 defense
R&D projects performed in Israel and includes four
planning and preparing for transfer aspects (develop-
ment of operational & maintenance requirements, par-
ticipation of the customer in the development process,developers preparations for turning over the project toits nal user, and nal user preparations for receipt of
the project and starting its operational use), along with
three measures of project success (project eciency,
customer benets, and overall success). The paper is
organized as follows: we begin with a review of the
258 D. Dvir / International Journal of Pthe taste left in the mouth of the client, senior man-agement, and the project team, which is important for
future projects, but of course have no impact on the
current one.
Among the studies on project termination we can
nd for example a study by Dean [4], who provides,
based on a small-scale survey, the frequencies of fac-tors reported as reasons for termination of R&D pro-
jects. Balachandra and Raelin [5,6] performed a
discriminant analysis of variables aecting R&D pro-
jects termination. De et al. [7,8] did a detailed quanti-
tative work on taking abandonment decisions from a
nancial point of view at dierent contexts. Shafer
and Mantel [9] developed a decision support system
(DSS) for project termination. The DSS is able to ana-lyze the sensitivity of various parameters of project ter-
mination, but the requirement for an extensive
database on projects of dierent types, limits its use
in practice. Archibald [10] prepared a check-list for
project termination. Stallworthy and Kharbanda [11]
categorized the problems involved in project termina-
tion into emotional and intellectual problems. Several
other studies on the same issue are those of Pintoand Mantel [12], Green et al. [13], Broockho [14],
Black [15], and Chi et al. [16].
Another stream of research closely related to the re-
search on project premature termination is the research
on project critical success factors (CSF). The list of crit-
ical success factors is often used as a yard stick for
assessing the chances of a project to end successfully
when encountering problems. Pinto and Slevins work[17,18] and other lists of critical success factors devel-
oped over the years, can be used for that purpose. When
several CSFs do not exist in a project, management mayconsider terminating it in order to cut the potential
loses.
Only few researchers see project commission, when
the projects outcome is handed over to its customersfor use, as an integral part of the project life-cycle. Thatis probably the reason for the lack of research on that
issue. The importance of the transfer phase to the suc-
cess of projects (not only the residual attitudes toward
the project), is indirectly evident from some of the stud-
ies on critical success factors of projects which have
identied the act of selling the project to its nal users
as one of the critical success factors [19,20]. Kleinsch-
midt [21], who studied the dierences in project manage-ment practices between Europe and North America,
noticed that in Europe project managers more actively
encourage customer involvement in the project execu-
tion than in the US. Customer involvement is clearly
one of the most important ingredients that contribute
to an ecient and smooth transfer of the project out-
come to its users.
Hadjikhanis [22] perception that every project is anepisode in project marketing is one of a few exceptions.
t Management 23 (2005) 257265The goal of marketing is dened as repetitive selling to
-
rojecthe same buyer. Following Hyden [23] who dened pro-
jects life to begin when one rm draws up a contractwith another rm, and it is dissolved at the end of the
operation phase when the transaction is completed,
Hadjikhani focused his study on the management of
the relationship left after project completion and thedevelopment and marketing activities after project sell-
ing. His hypothesis was that every project leaves sedi-
ment, and accordingly studied cases focused on the
phases before negotiation and after project completion.
This view is also shared by Faulkner and Anderson [24]
who claim that a project cannot be regarded as isolated
from former projects; projects are connected to each
other somehow.In summary, there are many studies on project termi-
nation, but only few of them deal (implicitly) with the
impact of the termination activities on the project suc-
cess. The act of selling the project to its future users
and customer involvement in the project execution were
already identied as factors contributing to project suc-
cess. It is obvious that the main reason for customer
involvement is to increase the chances that the projectsoutcome will meet the customer requirements and de-
sires. But why sell the project to the customer who al-
ready paid for it. The answer is probably that there are
other factors that help the customer to accept and start
using the new product. Furthermore, in our studies of
projects we have seen other activities that were meant
to improve the orderly transfer of projects to their nal
users and not only to establish successful and lastingrelations with the customer. Active involvement in the
development process and preparations made by the con-
tractor for turning over the project to its users are exam-
ples for such activities.
Some of these activities are aimed at improving the
eciency of the project execution, but most of them
are done to ensure that the customer will get the product
he needs and will start using it as soon as possible. Basedon the literature review and our own observations, we
propose the following hypotheses:
1. User involvement in the development of operational
& maintenance requirements is positively contribut-
ing to the overall success of the project and the cus-
tomer benets from its outcomes.
2. The involvement during the project execution of anescorting team representing the customer, positively
contributes to the eciency of the project and to
the customer benets from its outcomes.
3. The developers support for turning over the projectto its nal user positively contributes to the customer
benets from the project.
4. The nal user preparations for receipt of the project
and starting its operational use positively contributeto the customer benets from the project and to its
D. Dvir / International Journal of Poverall success.3. Research methodology
Correlation analysis is often used when it is necessary
to relate a set of variables to other sets of variables.
When one set or both contain a large number of vari-
ables, it is dicult to interpret the results and to ndcausal relationships among the variables. To simplify
the analysis, factor analysis is very often employed to
represent the large data set by fewer, easy-to-interpret
factors. The current study follows that methodology.
First, we employ factor analysis to reduce the large
number of planning variables pertaining to the hand-
over of the project to its nal users. The resultant factors
are correlated with the three main success dimensions(which were found in an earlier study to be more impor-
tant than the other dimensions [25]) and the results are
analyzed and discussed. Regression analysis is used to
enhance our understanding of the relationship between
the planning and preparing variables and success
dimensions.
3.1. Data
Data on 110 defense projects performed in Israel over
the last two decades were gathered using structured
questionnaires [26]. The questionnaires were lled out
by at least three key personnel related to the project
and representing the various stakeholders (the customer,
project management and procurement organization).
The projects in the sample were performed by a vari-ety of rms in the areas of electronics, computers, aero-
space, mechanics and others. The respondent
population included many types of defense projects:
new weapon systems, communication, command and
control systems, electronic warfare equipment, and sup-
port equipment development projects. The sample in-
cludes almost all defense development projects
performed under the hospices of the Israeli Ministry ofDefense at that period. Since the processes and proce-
dures used in the Israeli defense industry are similar to
those used in the US, the sample is probably a good rep-
resentation of that population too, nevertheless, it might
not be a good representation of defense projects per-
formed in other countries.
The questions solicited subjective evaluations on a
seven-point scale. For example, the level of involve-ment of the team escorting the development and inte-
gration of the project was determined by asking the
respondent the following question: According to your
assessment, how active was the escorting team in devel-
opment, integration and testing activities during the
project execution? The answer was given on the scale:
1 (not active at all) to 7 (extremely active). The ques-
tionnaires were administered in face to face sessionsby specially trained interviewers, all of whom had been
t Management 23 (2005) 257265 259previously involved with this type of projects in various
-
rojeccapacities. Most of the interviewers were retired ocers
from the Israeli Defense Forces with technical back-
ground and the others were MBA students in the man-
agement of technology track. For each project there
were three respondents: the project manager (or a se-
nior representative from the project oce); a represen-tative from the customer or end-users community; and
a representative from the government sponsoring orga-
nization. Although an eort was made to locate the
most informed interviewees for each project, it did
not always succeed. In some cases, instead of the pro-
ject manager, only a team member could be located
and interviewed, in other cases the contracting ocer
was replaced with a new ocer whose knowledge onthe project was only second hand. To compensate for
the less reliable answers, the interviewer, who became
well acquainted with the project history, completed a
separate questionnaire integrating the three sets of re-
sponses, giving a lower weight to the less reliable re-
sponses. The analysis presented here is based on the
integrative questionnaire.
Out of the data collected by this questionnaire, rele-vant data to project success and planning and prepara-
tion activities for the transfer of the project to its nal
users were used in this study to examine the relationship
between the development of the project operational and
logistic requirements, the eort invested in escorting the
development, the developer support for transferring the
project to its users and the user preparations for receiv-
ing the product and starting using it, and projectsuccess.
3.2. Measures
3.2.1. Success criteria
Project success was measured along three criteria
(two constructs measuring success from two dierent
points of view and an overall success measure) that werevalidated in previous research by Lipovetsky et al. [25].
1. Meeting planning goals (project eciency).
2. Customer benets (success from the customers pointof view).
3. Overall success (an integrative measure of project
success).
Table A.1 in Appendix A describes the responses to
the questionnaire items that addressed the two dimen-
sions of success used in this study.
3.2.2. Overall success measure
In addition to the two sets of success measures de-
scribed above, the questionnaire included an item
reecting the overall success of the project. Overall suc-cess was also measured on a 1 to 7 scale, where 1 repre-
260 D. Dvir / International Journal of Psents a complete failure and 7 represents outstandingsuccess. There were 108 responses, ranging from 1 to
7, with an average of 4.85 and a SD of 1.54.
3.2.3. Planning the Transfer of the project to its nal users
Twenty-two variables were used for estimating the
investment in planning and preparing for transfer ofthe project to its nal user. Factor analysis with Vari-
max rotation on all items was carried out in order to val-
idate previous grouping of the variables into four factors
and examine their internal consistency. The results of
the factor analysis closely resemble the original grouping
of variables in the questionnaire and include only vari-
ables with loadings of 0.55 or higher (Table A.2 in the
Appendix A). The independence of the four factors isdemonstrated by the low loadings of the variables com-
prising each factor on the other factors. Cronbachscoecient a was also calculated for each group. Thefour resultant factors are listed below. A short title for
each dimension and Cronbachs coecients are inparenthesis.
1. Development of operational & maintenance require-ments (O&M requirements, a = 0.76).
2. Escorting the development process (escorting,
a = 0.82).3. Developers support for turning over the project to its
nal user (developer support, a = 0.90).4. Final user planning and preparing for introduction
into operational use (user preparations, a = 0.95).
While the names of factors 1, 3, and 4 are self-
explanatory, the term escorting the development
process, needs some explanation. In many projects,
customer participation is a common practice; usually
representatives of the nal user are taking part in the
project denition phase and in the nal testing. Here,
in addition to representatives of the nal users, techni-
cal experts knowledgeable in the specic technologicalareas related to the project were an integral part of
the contractors development team. The main purposeof building such a team is to gain in-depth knowledge
of the nal product to enable maintaining and even
improving the product by the user organization with-
out any external help.
The list of items comprising each factor and their
descriptive statistics are shown in Tables A.3A.6 inAppendix A.
3.3. Data analysis and results
The central part of the data analysis consists of exam-
ining the correlations between the four composite mea-
sures of planning and preparing for transfer of the
project to its nal users with the three measures of pro-ject success. The factors scores were computed as the
t Management 23 (2005) 257265average scores of all measures comprising a specic fac-
-
Table 1
Correlations between average planning and preparing for transfer to nal user scores and average success scores
Escorting Developer support User preparation Project eciency Customer benets Overall success
O&M requirements Corr. 0.37 0.08 0.26 0.22 0.22 0.33
Sig. 0.000 0.531 0.053 0.093 0.031 0.001
N 102 64 58 101 92 101
Escorting Corr. 0.19 0.30 0.45 0.27 0.28
022
52
000
D. Dvir / International Journal of Project Management 23 (2005) 257265 261tor. The results of the correlation analysis appear in Ta-
ble 1.
There are 21 correlation coecients, and it is possible
that some will appear to be statistically signicant due to
the compounded eect of Type I error. Consequently,we have adjusted the critical signicance level to the
rather conservative value of 0.001. This means that the
compounded Type I error, i.e., the probability that
Sig. 0.127 0.
N 64 58
Developer support Corr. 0.
Sig. 0.
N 57
User preparations Corr.
Sig.
N
Project eciency Corr.
Sig.
N
Customer benets Corr.
Sig.
N
Bold font represents p 6 0.001.one of the 21 correlations will appear to be statistically
signicant while in fact it is not, is about 0.021, well be-
low the commonly applied threshold of 0.05.
Several interesting results emerge from the correla-
tions presented in Table 1. First, there is a high correla-tion between escorting and O&M requirements. There is
also a high correlation between user preparations and
developer support. But, there is no correlation at all be-
tween the developer support and preparation of O&M
requirements and no signicant correlation with escort-
ing. On the other hand there is a correlation (although
not signicant at the 0.001 level) between user prepara-
tions and O&M requirements and escorting. Althoughthe four dimensions are almost independent (as dis-
cussed earlier), the correlations between some of the
dimensions is not surprising. For example, the developer
Table 2
Multiple step-wise regression results (N = 56)
Success dimension Intercept point O&M requirements Escorting
Project eciency 2.138 0.372
Customer benets 3.714
Overall success 4.033
Bold font represents p < 0.05.support is a complementary activity to the user prepara-
tions; it is not likely that the user (probably via the
escorting team) will make preparations for introduction
into use without the developers support. These activitiesare dierent but performed with the same goal in mind.
Each of the three success measures is highly and sig-
nicantly correlated only with one of the planning and
preparations factors. The project eciency dimension
0.001 0.009 0.005
101 93 101
0.30 0.23 0.17
0.024 0.070 0.168
65 62 65
0.38 0.46 0.28
0.003 0.000 0.032
59 59 59
0.62 0.57
0.000 0.000
94 105
0.71
0.000
95is correlated with escorting (0.45), customer benets
dimension is correlated with user preparations (0.46)
and overall success is correlated with O&M require-
ments (0.33). Furthermore, project eciency is also cor-
related, though to a lesser extent with the developersupport and user preparations (0.30 and 0.38, respec-
tively). Customer benets dimension and overall success
are positively, but not signicantly, correlated with all
other planning and preparation factors.
Finally, all three success measures (project eciency;
customer benets; and overall project success) are highly
inter-correlated, implying that projects perceived to be
successful are considered successful by all their majorstakeholders.
Table 2 presents the results of a step-wise regression
analysis between the three success dimensions and the
Developer support User preparations p Adjusted R2
0.269 0.0003 0.239
0.447 0.0006 0.185
0.272 0.0424 0.057
-
User preparations are positively and signicantly cor-
related with all three success dimensions. This result is
rojecalso supported by the regression analysis (Table 2).
User involvement in development of O&M require-
ments is positively and signicantly correlated with the
overall success of the project as hypothesized and posi-tively correlated but to a signicance level of only
0.031 with customers benets.Escorting the development process by a dedicated
team positively and signicantly contributes to project
eciency as hypothesized while the contribution to the
customer benets and overall success, although positive,
is only signicant to the level of 0.009 and 0.005, respec-
tively. The positive relationship between escorting andproject eciency is also supported by the regression
analysis.
Finally, developer support is positively but not signif-
icantly correlated with project success. Counter to our
hypothesis, the highest correlation is with project e-
ciency (0.30) and not with customer benets or overall
success.
4. Discussion and conclusions
Planning is considered a central element of modern
project management. Commonly accepted professional
standards, such as the PMI Guide to the Project Man-
agement Body of Knowledge, recommend investing in
project management processes and procedures thatsupport planning in order to reduce uncertainty and
increase the likelihood of project success. Nevertheless,
planning of the termination phase, especially planning
for commissioning, has not received proper attention.
As we have seen in the literature review, most studies
on project termination focus on premature termination
and even those who see projects as somehow con-
nected to each other [22,24] focus on the managementof the relationship with the customers left after projectplanning and preparations factors. Only signicant be-
tas are presented (p < 0.05).
User preparations factor is the only factor that comes
out signicant in all three regression analyses. The
escorting factor is also signicant in the regression anal-
ysis of the planning variables with project eciency. Theregression analysis of the overall success with the plan-
ning and preparation variables has very little explana-
tory power while the other two are explaining each
about 20% of the variability of project eciency and
the customer benets from the project.
Referring now to the hypothesized relationships be-
tween the planning and preparation variables and pro-
ject success, we can see that only hypothesis 4 is fullysupported by the study results while hypotheses 2, 3
and 4 were only partially supported.
262 D. Dvir / International Journal of Pcompletion or on the development and marketingactivities after project selling and their eect on project
success.
Our study focuses on the planning and preparing the
introduction of the project into use. It shows that the ef-
fort invested in these activities directly aects project
success both from the eciency point of view and fromthe customer benets perspective.
From the correlations table we can see that all plan-
ning and preparation eorts positively aect project suc-
cess, the escorting team and the nal user preparations
have the greatest impact. Project eciency is signi-
cantly aected by the involvement of an escorting team
and by the nal user preparations for introduction of the
project into use, while customer benets are mainly theresult of the nal user preparations. These ndings are
in line with Dvir et al. [27] conclusions that studied
the eect of planning at large on project success and rec-
ommended that end-user involvement should start at the
rst stage of the project execution and continue until its
successful end. Kleinschmidts [21] observation that inEurope customers are more actively involved in the pro-
ject execution than in the US, represents probably anintuitive understanding of the crucial role of customersinvolvement in project success.
The current study goes one step further and helps to
understand specically how to improve the chances for a
successful commission. By establishing a team that will
escort the development and a team that will plan and
prepare the project commission, the nal user can di-
rectly improve the chances for success, his eorts havea greater inuence on project success than the activities
done by the developer himself.
Furthermore, these nal user activities have a much
larger eect than the up-front formulation and deni-
tion of operational and maintenance requirements.
The main conclusion of this study is that projects per-
formed under contract for a specic customer, either an
external customer or an internal one, should devote con-siderable eorts for planning and preparing in advance
the hand-over of the project to its nal users. Customer
involvement in all phases of the project can highly con-
tribute to the project success, especially to its ecient
execution.
The term we use in this paper defense projects is
somehow misleading and limiting the applicability of
the study results. Defense projects are usually associatedwith weapon systems which their eectiveness is mainlydetermined by their functional performance and to a les-
ser extent by the way they were introduced into service.
However, defense projects also include command and
control systems, electronic warfare systems, communica-
tions systems and logistic and support devices. These
types of systems are complicated, require continuous
maintenance, and it takes time to learn how to use themeciently, and therefore they can benet from proper
t Management 23 (2005) 257265planning and preparing their introduction into service.
-
Members of the contracting oce are ideal candidates
for the escorting team due to their technical skills. They
can contribute to the project denition, participate in
the development, integration and commissioning and as-
sist in future improvements of the product.
There are many other types of projects, not necessar-ily defense projects that can benet from adopting this
approach. Projects performed within an organization
(i.e., production improvement projects, improving logis-
tic procedures, and IT projects) or government, munici-
pal and state contracted projects are examples.
This study is of an exploratory nature; the limited sam-
ple size, the homogeneity of projects types and the focus
on Israeli defense projects,may limit its applicability. Fur-ther research should be done in other countries and in dif-
ferent industries to study the termination and hand over
phase of projects in order to develop better ways for intro-
ducing projects into service and ensuring their nal users
satisfaction,which is the ultimate proof of project success.
Appendix A
Descriptive statistics for the Project Success Items and
Factor loadings and descriptive statistics of the 22 Plan-
ning and Preparations variables (see Tables A.1A.6).
Table A.1
Descriptive statistics for the project success items
technological
specications
Table A.2
Factor loadings of the 22 planning and preparations variables
Variable Factor 1 Factor 2 Factor 3 Factor 4
Criteria for operational
eectiveness dened
0.24 0.37 0.62 0.22
User representatives
involved in
requirement denition
0.06 0.04 0.83 0.00
Combat mode of
operation dened
0.34 0.12 0.72 0.19
Logistic support
requirements dened
0.02 0.22 0.67 0.39
Human engineering
specications
0.13 0.31 0.59 0.17
Representative of the
nal user
active in the project
escorting team
0.25 0.55 0.05 0.08
Escorting team actively
involved in the
development process
0.09 0.79 0.03 0.05
Key escorting personnel
stayed for
the whole duration of
the project
0.11 0.79 0.19 0.06
Escorting team capable
to maintain
and improve the
operational system
0.11 0.70 0.32 0.01
Escorting team
committed to project
during operation
0.11 0.76 0.11 0.02
Prociency level of
escorting team
0.19 0.79 0.18 0.15
Developer prepared
detailed training
program
0.53 0.01 0.12 0.73
Quality of training by
the developer
0.51 0.03 0.01 0.74
Simulators were used for
operational training
0.01 0.23 0.13 0.57
Quality of training
documentation
0.29 0.28 0.34 0.67
Developers teamssupported
introduction
to operation
0.33 0.06 0.18 0.80
Developers teamsavailable in case of
problems at all times
0.45 0.15 0.04 0.61
Existence of professional
team to escort
introduction to
operational use
0.87 0.05 0.12 0.08
Systematic monitoring
of the introduction to
operation process
0.91 0.23 0.04 0.15
Adaptation of the
introduction process
due to lessons learned
0.93 0.06 0.02 0.19
Evaluation of the
introduction process
after completion
0.84 0.16 0.11 0.37
Formal introduction to
operation program
0.84 0.12 0.10 0.17
D. Dvir / International Journal of Project Management 23 (2005) 257265 263Meeting schedule 103 1 7 3.89 1.78
Meeting budget
goals
102 1 7 4.22 1.74
Meeting
procurement goals
82 1 7 4.62 2.30
Customer
benets
Satisfying customer
operational need
93 1 7 5.56 1.66
Project end-product
is in use
90 1 7 4.83 2.45
Systems delivered to
end user on time
83 1 7 4.24 2.16
System has
signicant usable life
expectancy
86 1 7 5.24 1.99
Performance level
superior to previous
release
75 1 7 6.08 1.47
End user capabilities
signicantly
improved
74 1 7 4.96 2.01
End user satised
from project end-
75 1 7 4.79 2.03Success
dimensions
Success measures N Min. Max. Mean SD
Project
eciency
Meeting functional
requirements as
dened
103 1 7 5.82 1.23
Meeting 101 1 7 5.69 1.31product prepared by user
-
ce re
9
0
5
7
7
rting
264 D. Dvir / International Journal of Project Management 23 (2005) 257265Table A.3
Descriptive statistics for the development of operational & maintenan
Measures N
Criteria for operational eectiveness dened 9
User representatives involved in requirement denition 10
Combat mode of operation dened 9
Logistic support requirements dened 8
Human engineering specications 8
Table A.4
Descriptive statistics for the escorting the development process (esco
Measures
Representative of the nal user active in the project escorting team
Escorting team actively involved in the development process
Key escorting personnel stayed for the whole duration of the projectReferences
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Prociency level of escorting team
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Measures N
Developer prepared detailed training program 64
Quality of training by the developer 62
Simulators were used for operational training 61
Quality of training documentation 60
Developers teams supported introduction to operation 60Developers teams available in case of problems at all times 61
Table A.6
Descriptive statistics for the nal user planning for receipt of the project a
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Formal introduction to operation program prepared by user
Existence of professional team to escort introduction to operational use
Systematic monitoring of the introduction to operation process
Adaptation of the introduction process due to lessons learned
Evaluation of the introduction process after completionquirements (O&M requirements) items
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1 7 4.34 1.89
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100 1 7 5.6 1.21
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1 7 5.23 1.80
1 7 5.03 1.72
1 7 2.87 2.01
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1 7 5.70 1.45
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D. Dvir / International Journal of Project Management 23 (2005) 257265 265
-
rs
rie
l Hall
form
cost
d dam
a mo
e leve
as a data-based fraction of the estimated project duration or budget. We also introduce a new approach for collecting data and
estimating the parameters necessary to implement the model. This approach places a smaller burden on decision makers than tra-
cost control. For example, to prevent wasting earliness,
a project buer is placed at the end of the project sche-
analysis.
buer is the dierence between the due date and the ex-
pected completion time. Similar analysis is directly
the serial structure, if we assume that earliness is uti-lized, then the expected duration of the project is equal
to the sum of the expected durations of the activities.
Our result for the serial structure can serve to develop
insights for more realistic project networks, where such
* Tel.: +649 373 7599x87381; fax: +649 373 7430.
E-mail address: [email protected].
International Journal of Project Manag
INTERNATIONAL JOURNAL OF
PROJECT0263-7863/$30.00 2005 Elsevier Ltd and IPMA. All rights reserved.dule. Similarly, where chains of activities merge withthe critical path, feeding buers may be inserted. Note
that to specify such buers is mathematically equivalent
to deciding when to start each chain of activities. Yet the
size of these buers is usually specied as an arbitrary
fraction of the estimated chain duration. The purpose
of this paper is to provide a rst step towards optimizing
such buers based on plausible theory and relevant data
applicable to the determination of a cost buer as well,by the simple substitution of the due date by a project
budget. The main cost of a typical project is the sum
of the individual activity costs, although it also includes
overhead charges and holding costs that depend on the
project duration. Therefore, at least approximately, we
can say that cost is additive and not sensitive to the pro-
ject network structure. Similarly, due to the simplicity ofditional PERT: they provide single point estimates for means, while variance elements and bias correction are computed electron-
ically using historical data.
2005 Elsevier Ltd and IPMA. All rights reserved.
Keywords: Time; Cost; Operations
1. Introduction
Recent developments in the practice of project sched-
uling focus attention on the correct specication of
safety buers. Time buers are used to protect the sche-
dule, and, similarly, budget contingencies are used for
In this paper we study the most basic case: the opti-mal buering of projects with n (not necessarily indepen-
dent) activities in series, and without intermittent idling.
Thus there is only one chain of activities and it is neces-
sarily also the critical path. Assuming that either the due
date or the start time is a decision variable, the projectThe eect of systemic erro
Dan T
ISOM Department, University of Auckland, Old Chora
Received 7 September 2004; received in revised
Abstract
Existing mathematical models for setting buers for time or
tistically independent. This leads to a highly counterintuitive an
negligible for projects with long chains of activities. We present
caused by estimation bias. We show that if relatively high servicdoi:10.1016/j.ijproman.2004.12.004on optimal project buers
tsch *
, 7 Symonds Street, Auckland City 1001, New Zealand
22 October 2004; accepted 3 December 2004
in project management assume that project activities are sta-
aging conclusion that project buers should become relatively
del that considers the statistical dependence between activities
ls are desired, this imposes a positive lower bound on the buer
www.elsevier.com/locate/ijproman
ement 23 (2005) 267274
MANAGEMENT
-
assumption is that planned idling should not occur with-
in the critical path or within any chain of activities.
Nonetheless, it is incorporated where such chains merge
Project Management 23 (2005) 267274chains are often embedded. In such a case, the (serial)
project buer becomes a feeding buer for non-critical
chains feeding other project activities; e.g., feeding the
critical path. Such chains are sub-projects with a serial
structure. A useful approach for extending the results
we obtain here to more complex project networks isby simulation, but this is beyond our scope.
Henceforth, we will discuss time buers, keeping in
mind that the results also apply to contingency budget-
ing. For this, we need to know the cumulative distribu-
tion function of the project completion time, which, in
turn, is a function of the individual activities distribu-tions. In Malcolm et al. [1], the PERT developers dis-
cussed the need for stochastic estimation of activitydurations. Nominally, they proposed tting a beta dis-
tribution. Practically, they required a triplet of estimates
for each activity {min, mode, max} and then arbi-
trarily set standard deviation = (max min)/6 andmean = (min + 4 mode + max)/6. Assuming indepen-
dence, they noted that the sum of a serial chain of activ-
ities is approximately normal by the central limit
theorem. Although they recommended it for tractability,the team recognized that the use of the most likely crit-
ical path is optimistic: it ignores the statistical depen-
dence between paths that share activities. They already
had a computationally intensive remedy: Clark [2] pro-
vided approximate but unbiased calculations for the
project duration distribution. Finally, they highlighted
a major concern about systemic bias which is at the
heart of this paper and we will discuss it later. Armedwith 20/20 hindsight, one may criticize invoking the beta
distribution, considering that it was not really utilized,
but this is harmless. More seriously, it is dicult, per-
haps impossible, to obtain reliable triplets from process
owners. Anecdotal evidence to this eect, related to
work place politics, was given by Woolsey [3]. To this
we must add systemic human error as studied by Tver-
sky and Kahneman [4]; e.g., they observed that 98%condence intervals estimated by experts tend to fail in
about one third of the cases (i.e., experts tend to grossly
underestimate the true variance).
Britney [5] presented optimal buers for stochastic
project activities on a one-by-one basis. Planned idling
occurs between activities unless the preceding buer is
exceeded. He did not, however, optimize the project as
a whole. Also, he did not consider the possibility ofcombining serial buers. The combined buer approach
was used by Yano [6], who obtained the optimal single
buer for a project (or supply chain) composed of inde-
pendent serial activities. Yano [7] inserted individual
buers between the serial activities. Ronen and Trietsch
[8], Kumar [9], and Chu et al. [10], independently, opti-
mized the ordering times of n parallel project (or supply
chain) items with stochastic lead times, where the latestone determines the completion time; i.e., they showed
268 D. Trietsch / International Journal ofhow to coordinate n parallel feeding buers.(yielding feeding buers). The size of protective buers is
determined as an arbitrary fraction of the expected
chain leading to the buer (e.g., 50%). Leach [12] sug-
gested the maximum of a buer based on the traditionalindependence assumption and the arbitrary fraction.
Leach [15] listed 11 reasons why projects are typically
longer than expected. He then proposed a larger buer
based on the sum of the two elements mentioned before.
Although Clarks approximation can handle depen-dent activities, none of the existing mathematical models
for buer setting accounts for systemic error. We intro-
duce an elementary model for this purpose and showthat it yields dependency between activities. We show
that if relatively high service levels are desired, this im-
poses a positive lower bound on the project buer as a
data-based fraction of the expected duration. In con-
trast, the independence assumption leads to a highly
counterintuitive and damaging conclusion that project
buers should become relatively negligible for projects
with long chains of activities. We also recommend anapproach for collecting data and estimating the param-
eters necessary to implement our model. This approach
places a smaller burden on users than traditional PERT:
single point estimates are required instead of triplets.
Variance and bias estimates can then be computed by
a decision support system that uses historical data,
and we show how.
The remainder of the paper is organized as follows.After introducing the main notation, Section 2 presents
the [existing] basic buer optimization model subject to
a given project distribution. Section 3 introduces our
model with the bias-induced correlation. Section 4 ad-
dresses the estimation of the necessary model inputs.
Section 5 provides numerical illustrations. Section 6 is
the conclusion.
2. Notation and basics
B the bias of time or cost estimates of project
activities (a random variable)
b E(B), where E() is the expected value functionV b r2b V(B), where V() is the variance functionC the cost to postpone the due date (per timeThis idea using planned project- and feeding buers
to achieve improved reliability and avoid [implicit] pro-
ject delay penalties had recently been popularized un-
der the title Critical Chain, and many practitioners
nd it useful. Reviews and discussions of this modern
development abound; e.g., Herroelen and Leus [11],Leach [12], Raz et al. [13], Trietsch [14]. The basicunit)
-
ties are independent by nature, their estimated durations
are often subject to the same estimation error. Thus,
ProjD the nominal project duration/due date (a deci-
sion variable)
P time unit cost during delay, including penalty
(where P > C)
nj the number of activities in project j
(j = 1, . . ., J). (Here and elsewhere, j is usedexclusively to index projects, and may be sup-
pressed)
SL service level, the probability of meeting or beat-
ing the due date
Yij the duration of activity i in project j
(i = 1, . . ., nj; j = 1, . . ., J) a random variablewith the realization yij
Yj the set {Yij} for all i in project jYj
PYij, the true project duration
Fj(t) the cumulative distribution function of Yjlij E(Yij)rij the standard deviation of Yijrj the standard deviation of the completion time
of project j
Xij a random variable that models Yij (but not nec-
essarily correctly)Xj the set {Xij} for all i in project j
eij the nominal estimate of lij, assumed propor-tional to E(Xij)
We assume that we have to determine a project due
date, D, or that one is given with enough slack to make
possible a delayed start. The time from the project start
to the due date is the planned duration. For conve-nience, we will treat the start as time zero, so the
planned duration is equal to D. We also assume that
there is an economic cost per time unit, C, which pro-
vides an incentive to reduce the planned duration; e.g.,
the customer is more likely to award us the contract if
we quote an early (but reliable) due date. If the customer
is not likely to pay before the due date when the project
is early, C should also include any xed charge the pro-ject incurs while on the books. Any delay beyond D in-
curs a cost (including penalty) of P(Y D)+, whereY =
PYi and h
+ = max{0, h}. P > C, or it would be
optimal to set D = 0 and pay P as long as necessary.
The objective is to minimize the expected total cost
(TC),
ETC CD EP Y D(By dening P 0 = P C and subtracting CY from bothsides of the equation a value which is not a function
of D we can show that minimizing this objective func-tion is equivalent to minimizing
ECD Y P 0Y D:Thus both earliness and tardiness have economic losses
associated with them.)
Denote the optimal probability of meeting or beating
D. Trietsch / International Journal ofthe due date, i.e., the service level, by SL* (we will usewhen the same optimist estimates many activities, they
will all tend to be underestimated. Equivalently, pres-
sure by management may inuence people to give too
short estimates that cannot be met later. Similarly, badweather during the project or the loss of a key employee
may impede several activities. A booming economy may
increase queueing time for more than one activity, etc.
The same or similar causes also apply in the reverse
direction; e.g., pessimists will tend to overestimate dura-
tions (and severe punishment for missing due dates in
the past will turn optimists to pessimists). The result is
a systemic bias across a project. But the magnitude ofthis bias is a random variable and even its orientationasterisks to denote optimality in general). For continu-
ous distributions, a straightforward application of the
newsboy model shows that SL* = (P C)/P. Thus,D* = arg{SL* = F(D*) = (P C)/P}, where F(t) is theproject duration distribution. For discrete due dates,
where it is possible to hit the due date exactly and thusbe neither early nor late, we must set D such that the
probability of delay will not exceed C/P and the proba-
bility of earliness will not exceed (P C)/P. Either way,we need F(t). The technical focus of this paper is obtain-
ing a correct duration distribution (or expenditure distri-
bution) based on the estimates we have for each activity,
but subject to systemic error.
3. Modeling positive dependence due to systemic error
It is well known that project activities are sometimes
statistically dependent. Nonetheless, with one exception,
the literature on setting buers assumes statistical inde-
pendence. This includes academic papers and practi-
tioner books. But if we increase the number ofactivities along the critical path, the independence
assumption leads to so-called optimal project buers
that, as a fraction of the mean, approach zero asymptot-
ically. Because this is a highly counterintuitive conclu-
sion, the practical approach has always been to set the
buer, or the contingency, as a fraction of the antici-
pated duration; e.g., see OBrien [16]. In addition toleading to such a highly counterintuitive conclusion,and to ying in the face of experience, the independence
assumption should be challenged on theoretical grounds
as well. Leach [15], after citing empirical evidence dem-
onstrating that the independence assumption is not valid
in practice, identies positive bias as the culprit. He then
provides 11 causes of positive bias mostly related to
large projects with multiple paths. Our focus here is
on a single cause of positive or negative bias that applieseven to serial projects and that, arguably, explains the
bulk of the problem. The crux is that even when activi-
ect Management 23 (2005) 267274 269is not known in advance. This introduces a strong
-
Projdependence as far as deviations from plan are concerned.
But, operationally, the only variation that concerns us is
relative to plan!
Notably, the need to account for estimation bias by
some calibrating program has been on the agenda from
the earliest PERT days. Malcolm et al. [1] stated that theproblem had been raised by many. They then suggested
a program of comparing estimates with actual perfor-
mance over a period of time to permit calibration ofthe estimators. Alas there is no evidence that such cal-
ibration was ever implemented on a wide scale, if at all.
Instead, subsequent papers about bias focused mostly
on the optimistic bias due to ignoring near-critical
parallel paths that may become critical in reality; e.g.,Klingel [17], Schonberger [18]. Thus, the clear recom-
mendation that the PERT team expressed in [1] was
ignored, while an issue that they considered more minor
and for which [2] had already provided an approxi-
mate remedy was highlighted.
Furthermore, [1] stated objective with respect to bias
was to calibrate the estimates to make them accurate.
But there is also a statistical dependence issue thatarises. For example, suppose we suspect that there is a
random bias error of 25%, and we nd that the rst se-
ven activities took 85% of their combined estimated
time, then we would probably consider it likely that
the next activities will also tend to consume around
85% of their estimate in other words, we form an infor-
mal estimate of 85% for the necessary calibration. This
means that we perceive a correlation between the rstfew activities and the ones that are yet to follow. This,
by denition, is a case of statistical dependence (because
independent variables are not correlated). In practice,
we need to know not only the average bias but also its
impact on the covariance of activities. Our purpose here
is to address both the need for calibration and the mag-
nitude of the correlation that is involved, so we can draw
conclusions for the optimal combined buer (includingelements for bias correction and for safety).
Let the true activity times compose the random vec-
tor Y = {Yi}. If decision makers would have the distri-
bution of Y, there would be no need for this paper. In
reality, however, decision-makers never know Y, so they
must use some estimate that acts as a model of Y.
Accordingly, let X = {Xi} be a vector of the decision-
makers models of the actual activity durations, {Yi}.So Xi is a random variable that represents another ran-
dom variable. Estimates are based on X, and not directly
on Y. Systemic error arises because X is not a perfect
model of Y. Mathematically, perhaps the simplest possi-
ble model for systemic error involves the introduction of
an additional independent random variable, B, that
multiplies X to obtain Y. In this paper, we limit our-
selves to this basic model. Other potential approachesexist, however, and further research may be justied to
270 D. Trietsch / International Journal ofidentify the best one. We will use b and V b r2b to de-note the mean and variance of B. We assume that ei,
the nominal (single point) estimate of Yi, is proportional
to E(Xi). For example, if a particular provider of esti-
mates includes a hidden 100% buer in her estimates,
then ei = 2E(Xi). Nonetheless, for simplicity (and with-
out loss of generality), we will set ei = E(Xi). As longas the assumption that ei is proportional to E(Xi) holds,
there always exists a B that corrects any deviation intro-
duced by this simplication.
In broad terms, the randomness of Xi relates to
chance events that are specic to Yi (as perceived),
e.g., problems with raw materials or power supply. X
also includes some (but not all the) eects of random-
ness in estimation, since estimates are the result of pro-cesses that are not deterministic. Even with the same
data, dierent people at dierent times typically come
up with dierent estimates. For example, they may for-
get or neglect dierent aspects of the job. Random esti-
mation errors are operationally equivalent to random
deviation from plan. Because perceived activity-specic
chance events and some estimation errors are con-
founded with each other, they must be represented bythe same random variable, Xi. In contrast, B models ef-
fects that are common to all activities, such as pressure
to produce attractive estimates quickly (leading to opti-
mistic bias and omissions), personal safety buers,
weather, economic conditions, etc. Note that B is impor-
tant even if b = 1; i.e., if we take steps to remove bias onaverage, as suggested by [1], and thus achieve accuracy,
B would still capture important information about sys-temic imprecision. Specically, those estimation errors
that are not confounded with activity-specic chance
events.
To present the most basic model, we assume that the
elements of X are independent of each other. This
assumption is often realistic, by which we mean that
decision makers typically estimate the elements of Y
(by the elements of X) as if they are independent andtherefore the elements of X are indeed independent
after all, X is a model only. (Modeling dependence into
X may be useful for some purposes, but requires further
research.) Because B and X are independent of each
other, li = b ei, but the multiplication by the same real-ization of B introduces [positive] dependence between
the elements of Y even if the elements of X are indepen-
dent. Specically, r2i b2 V X i V X i V b V b e2i ,and COV(Yi, Yk) = Vb ei ek; "i 6 k. We can separater2i to two parts, b
2 V(Xi) + V(Xi) Vb and V b e2i . Theformer equals E(B2) V(Xi) and the latter is a specialcase of Vb ei ek. Thus the covariance matrix of Y isthe sum of a diagonal matrix with elements V(Xi) E(B
2)
and a full symmetric matrix with elements Vb ei ek;"i, k (i.e., the vector product {rb ei}{rb ei}T). To cor-rect for the average bias we add a bias correction of(b 1)Pei to the nominal makespan estimate
Pei. Thus
ect Management 23 (2005) 267274we obtain a relative bias correction of b 1. If B = 1, we
-
note the covariance matrix of Y, and let 1 be a column
vector in Rn with elements of 1, then the result is
Projobtained directly by the matrix product 1TV1. To study
the eect of the standard deviation on the optimal
buer further, let q1(e) = E(B2) P
"iV(Xi), and let
q2(e) = (rb P
"iei)2, where e={ei}, then
maxfq1e
p;q2e
pg 6
q1e q2e
p
6q1e
p
q2e
p:
The central element in the inequality is r.pq2 is propor-
tional to the nominal makespan estimate (before the
bias correction),Pei. So there exists a fraction of this
estimate, namely rb, that acts as a lower bound on r.Therefore, if we wish to specify a safety buer against
random variation of kr for some k > 0, this safety bueris bounded from below by k rb
P"iei, which consti-
tutes a fraction of k rb of the nominal makespan esti-mate. Furthermore, when n!1 then q1/q2! 0 so thesame bound serves as the approximate optimal safety
buer. Recall that Leach [12] and Leach [15] suggest rel-
ative buers that are associated with Max{pq1,
pq2}
andpq1 +
pq2, respectively, so for a positive k these
values provide a lower- and an upper bound for the cor-
rect result. (There is no theoretical reason to limit our-
selves to k > 0, so we do not limit our analysis to this
case. Nonetheless, most project managers are uncom-
fortable with negative buers and the low service levels
they entail.)
4. Estimating model parameters
A tempting approach is to estimate Xi by the classical
3-point method of PERT, thus yielding ei and V(Xi). It
would then only remain to estimate B. However, there
are major diculties with this approach (as discussed
in Section 1), even without the new requirement to dis-tinguish consciously between systemic bias and individ-
ual activity variation causes. Therefore we propose to
limit the information elicited from process owners to
single point activity estimates, ei, and obtain all the
other necessary estimates from historical data with theobtain the classic model with independence. Similarly, if
V(B) = 0 but b 6 1, then after the bias correction weagain obtain the classic model. Therefore, our model
generalizes the classic approach. Finally, monitoring a
project over time during its execution always involves
estimating the bias that applies to it, either explicitlyor implicitly.
Bias correction is the rst response to bias, but the
standard deviation of the project completion time is vital
for rational determination of the safety buer. Since the
e