Seongkyu
un Cho and Gle
enn Ballard (2
2011) Last Planner
and Integgrated Projectt Delivery Lean Constructio
on
Journal 2
2011 pp 67-78 - Lean and In
ntegrated Projject
Delivery SSpecial issue www.leancon
w
structionjourn
nal.org
Last Planner and In
ntegrate
ed Project De
elivery
y
Se
eongkyun C
Cho1 and Glenn Ballarrd2
Abstrract
Researc
ch Question
ns: 1) Doess the use of Last Planner (LP) imp
prove projec
ct performa
ance?
2) D
Does Integrated Projecct Delivery (IPD) show different project
p
perfformance? 3)
3 Do
IPD
D projects use LP?
Purpose
e: The firstt objective iis to figure out the rellationship between
b
IPD
D, LP, and project
p
perrformance.
Researc
ch Method:: survey of ‘Lean’ projects known
n to adopt LP,
L includin
ng IPD proje
ects, to
dettermine the
e correlation between LP impleme
entation an
nd Project performanc
p
e (cost
red
duction + tim
me reductio
on); and a T test betw
ween IPD and non-IPD projects.
p
Findinggs: 1) There
e is significa
ant correlattion betwee
en the degrree of imple
ementation of LP
and
d project pe
erformance
e; 2) IPD pro
ojects do no
ot show sign
nificantly different
d
perrformance from
f
that of others not adopting IPD; and 3) IPD projec
cts do not sh
how
sign
nificantly different implementation of LP fro
om that of others but their
imp
plementatio
on is near to
o significan
nce
Limitations: Limitations in sa
ample size and
a data qu
uality reducce the crediibility of
neralization
ns.
gen
Implica
ations: This exploratoryy research revealed in
nteresting and
a importa
ant relationships
bettween proje
ect structurres and pracctices on th
he one hand
d and proje
ect performance on
the
e other.
Value fo
or practitio
oners: The findings fro
om this pap
per can be used
u
by industry practitioners
to d
design proje
ect deliveryy systems fo
or better performance
e.
Keyworrds: Integra
ated Projecct Delivery, Last Plann
ner, Lean Co
onstruction, survey.
Paper type:
t
Full paper.
p
Litera
ature Review
R
According to the American
A
Insstitute of Architects
A
(A
AIA), the In
ntegrated Prroject Delivvery
(IPD) co
ontract form
m includes:
ƒ
ƒ
1
2
Early involvement of Key particiipants;
Shared risk and rewa
ard;
PhD C
Candidate, Civvil and Env. En
ngineering. De
epartment, 40
07-A McLaughllin Hall, Univ. of California,
Berke
eley, CA 94720
0-1712, USA, Phone
P
+1 510/
/725-7929, seo
ongKyuncho@
@berKeley.edu
u
Directtor, Project Production System Laborato
ory, http://p2sl.berKeley.edu, and Adjun
nct Associate
Professsor, Civil and
d Env. Enginee
ering. Departm
ment, 215-A McLaughlin
M
Ha
all, Univ. of Ca
alifornia, Berk
keley, CA
94720
0-1712, USA, Phone
P
+1 415/
/710-5531, [email protected]
keley.edu
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C & Ballarrd: Last Plan
Cho
nner and Inte
egrated Project Deliverry
ƒ
ƒ
ƒ
ƒ
Multi partyy contract;
Collaborattive decisio
on making and
a control;;
Liability waivers
w
amo
ong key participants; and
a
Jointly developed and validated
d project go
oals (Cohen et al., 2010).
Similarlly, the Natio
onal Association of Sta
ate Facility Association
n (NASFA), Constructio
on
Owners Association
n of Americca, Associattion of High
her Educatio
on Facilitiess Officers,
Associatted Genera
al Contracto
ors in Ameriica, and Am
merican Insttitute of Arcchitects defined
IPD as a project de
elivery syste
em using a multi partyy contract that
t
has mo
ore than two
o
parties selected byy qualification based procuremen
p
nt, managed
d/shared risk, compen
nsation
based o
on team perrformance without
w
GM
MP, and open book acco
ounting (NA
ASFA et al 2010).
2
According to CMAA
A, the purpo
ose of IPD iss to solve currently
c
accknowledged problemss in the
construction industry such as low rates of
o productivvity, high ra
ates of ineffficiency an
nd
rework,, frequent disputes,
d
exxcessive cosst, and exce
essive dura
ation--all ca
aused by
organiza
ational, com
mmercial, and
a operatiional proble
ems in curre
ent projectt delivery syystems
(Thomse
en et al., 2009)
2
The Last Planner (LP) is a production
n planning and
a control system imp
plemented on
construction proje
ects to imprrove plannin
ng and prod
duction perfformance. It has four main
processes:
ƒ
ƒ
ƒ
ƒ
hedule;
Master sch
Phase sche
edule;
Look ahea
ad Plan; and
d
Weekly Pla
an (Hamzeh
h, 2009).
Many re
esearchers have
h
proved
d reducing plan variab
bility helps increase
i
prroductivity, such as
Liu et a
al (2008) sug
ggesting a regression
r
line
l 3 between plan reliiability and
d productivity, and
Alarcon et al (1997
7) showing difference
d
i
ing LP.
in productivity before and after implementi
Again, tthe LP has been
b
create
ed to maxim
mize reliabiility of the work/mate
erial/inform
mation
flow to minimize waste
w
in tim
me/money in project p
processes an
nd to maxim
mize custom
mer
value (B
Ballard, 200
00)
While IPD has tried
t
to inte
egrate project particip
pants’ roless and relatio
ons contracctually
in orderr to improve
e project o
outcomes, LP
L has enforrced system
matic production contrrol
reducing plan varia
ability for the
t same pu
urpose. Ourr question iss if having project
organiza
ation integrrated by ussing contrac
ctual alignm
ment, such as
a IPD, is en
nough to maximize
desired outcomes, such as cost/time red
duction. If it
i is not eno
ough, our next concern
n is
whether the imple
ementation of LP can achieve
a
those outcome
es. To find out
o the ansswers to
those questions, we
w did some
e hypothesiss testing in this researrch.
Hypothesis testting regardiing project performance based on a large nu
umber of prrojects
is a welll establishe
ed methodo
ology. For example,
e
Ch
hoi (2008) used
u
one wa
ay ANOVA (A
Analysis
of Varia
ance) to invvestigate if there is sig
gnificant diffference in schedule performance
p
e among
three diifferent con
ntract types, selected from a govvernment da
atabase of more than 1,700
projectss. More sim
milar to our research de
esign, Sanvido et al (1998) made a survey
questionnaire, sent it to 7600
0 projects, and
a got 378
8 responsess on which they
t
did
multivariate t-testt, chi square
e test, ANO
OVA, and regression to identify pe
erformance
differen
nces among
g three project deliverry systems.
3
Labou
ur Productivityy = 0.530 + 1.0
095*Weekly Plan Percent Completion
C
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C & Ballarrd: Last Plan
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Resea
arch De
esign
Resea
arch Hypo
othesis
Our rese
earch assum
mption: pro
oject perforrmance variies with Lasst Planner (LP)
(
implementation. Based
B
on thiis assumptio
on, we diag
gnosed the degree of LP
L impleme
ented in
mine the co
orrelation between
b
LP
Integratted Projectt Delivery (IPD) projectts to determ
implementation an
nd IPD proje
ects’ perforrmance. Th
his assumptiion must be
e supported
d by
hus, our first research hypothesiss is:
general hypothesiss testing. Th
If a pro
oject imple
ements Lastt Planner (LLP) more, itt achieves better
b
pro
oject perfo
ormance better than th
hose emplo
oying LP lesss.
If the first hypothe
esis had nott been supp
ported, it would
w
be me
eaningless to
t go furthe
er
comparring IPD projjects with others
o
in te
erms of LP and
a our rese
earch would have been
redirectted to a qua
alitative ex
xploration seeking
s
wha
at caused LP
P to fail. Ho
owever, the
e first
hypothe
esis was sup
pported, ma
aking it meaningful to test the se
econd and third
t
hypoth
hesis.
The seccond hypoth
hesis is:
If a proje
ect adopts Integrated Project
P
Delivery (IPD),, its perform
mance is
diffferent from
m those of other projeccts.
A
And the thirrd hypothessis is:
If a proje
ect adopts IPD,
I
its deg
gree of impllementation
n of Last Pllanner is
diffferent from
m those of other projeccts.
This pap
per is devotted to the interpretati
i
ion of the results
r
from
m the first, the
t second and the
third hyypothesis te
esting.
Resea
arch Meassuremen
nt
The firsst thing thatt we have to
t do after forming hypotheses is to specify the measurrement
of varia
ables. We co
onceptualizzed our variiables as sh
hown in Figu
ure 1, follow
wing Adcocck et al
(2001).
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C & Ballarrd: Last Plan
Cho
nner and Inte
egrated Project Deliverry
Figure
e 1: Concep
ptualizatio
on and measurement: Levels and
d task (Adcock et al., 2001)
We stru
uctured the variables in the hypottheses so th
hey could be measured
d in the folllowing
parts.
The independe
ent variable
e of the firstt hypothesis is the deg
gree of implementation of
Last Pla
anner (LP). To measure
e this abstrract concept, we developed indicators to be scored
based o
on the follow
wing five ellements:
1
Pullin
ng productiion: each worker
w
invesstigates the
e readinesss of the
next workers
w
(im
mmediate cu
ustomers) before
b
execcution of ta
asks
(Tomm
melein, 199
98)
2
Looka
ahead proccess: each front
f
line supervisor removes
r
con
nstraints
(prere
equisite wo
ork, contracctual appro
ovals, seque
ential
inapp
propriatenesss, insufficient resourrce as well as
a labour &
equip
pment, inad
dequate durration, fund
ding proble
em, problem
ms found
in firsst run studyy, etc) befo
ore executio
on of its ta
asks. Constrrained
tasks are not elig
gible for in
nclusion on daily or we
eekly work plans
p
(Balla
ard, 2000)
3
Learn
ning from breakdowns
b
s: failures to complette planned tasks
t
are
analyz
yzed to roott causes and
d actions arre taken to prevent
reoccurrence (Ba
allard, 2000
0)
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C & Ballarrd: Last Plan
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4
Phase
e schedulin
ng: every ha
andoff in a phase shou
uld be defin
ned by
collab
boration of all relevan
nt specialistts in the ph
hase before
e the
hando
off is produ
uced (Ballarrd et al., 20
003)
5
Distriibuted control: Work is planned in greater detail as yo
ou get
closerr to execution, and pla
anning is do
one collabo
oratively byy those
who are
a to do th
he work. (Ba
allard et all., 2003)
Th
he indicators in the bo
ox above are transform
med into surrvey questio
ons:
Table 2: Survey que
estions mea
asuring Lasst Planner
#
1
Que
estions
Wha
at percentag
ge of specialtty contractors participatted in
sche
eduling the project
p
phase(s) in which
h they were to do their
work?
2
To w
what extent was
w the prin
nciple followed that onlyy work that
was ready to be
e performed could be pla
aced on a we
eekly work
plan
n? Bear in miind that work is ready to
o be performed when all
consstraints are removed.
r
3
To w
what extentt was the principle
p
folllowed that work
shou
uld be done
e in responsse to a requ
uest from an
imm
mediate cusstomer, such
h as the nex
xt trade?
4
Did the project measure the
e extent to which
w
you ‘did what you
said
d you were go
oing to do?’ (The measurre is the perccentage of
wee
ekly work pla
an tasks com
mpleted as planned. If the
ere were
100 tasks on weekly work pllans and 70 were
w
comple
eted as
plan
nned (no parrtial credit), the percenttage would be
b 70%)
How
w often were
e reasons for not completting planned
d tasks (on
wee
ekly work pla
an) analyzed to root causses and actio
on taken to
prevvent reoccurrrence?
5
Answer tyype & Scoring Rule
Percentag
ge ⇒
None: 1/6
6; 0-25%:2/6
6;
25-50%:3/
/6; 50-75%:4
4/6;
75-100%:5
5/6; and All: 1
Frequencyy ⇒
Never: 1/
/5; Rarely: 2/5;
Sometime
es: 3/5; Ofte
en: 4/5;
And Alwayys: 1
Frequencyy ⇒
Never: 1/
/5; Rarely: 2/5;
Sometime
es: 3/5; Ofte
en: 4/5;
And Alwayys: 1
Yes/No ⇒
Yes: 1; an
nd No: 1/6
Frequencyy ⇒
Never: 1/
/5; Rarely: 2/5;
Sometime
es: 3/5; Ofte
en: 4/5;
And Alwayys: 1
So far, we
w have specified the measurement of the independen
i
nt variable in the first
hypothe
esis. Next, we
w address the depend
dent variab
ble of the sa
ame hypoth
hesis, proje
ect
perform
mance. We decided
d
to use the sum
m of cost re
eduction rattio (%) (actual cost under
final ap
pproved bud
dget) + dura
ation reduction ratio (%) (actual duration
d
relative to fin
nal
approve
ed schedule
e) as a meassure of projject perform
mance beca
ause of the low probab
bility of
getting good data on other pe
erformance
e dimensions.
Th
he depende
ent variable
e of the second hypoth
hesis is the same as the
e dependen
nt
variable
e of the firsst hypothesis. The dep
pendent varriable of the
e third hypo
othesis is sa
ame as
the inde
ependent variable of the
t first hyp
pothesis. An
nd the inde
ependent va
ariable of th
he
second hypothesis is the same
e as the ind
dependent variable
v
of the third hypothesis. Thus,
T
the lastt concept th
hat we define is the in
ndependent variable off the second and the third
t
hypothe
esis; i.e., to
o what exte
ent a projecct adopts In
ntegrated Project
P
Delivvery (IPD), or
whether a project adopts IPD
D. We decide
ed to take the binary variable, whether
w
a prroject
adopts IPD, as the type of this variable because
b
we
e could not get enough
h IPD projeccts to
measure
e the exten
nt of implem
mentation. In addition
n, it would be difficultt for respon
ndents
to score
e the degree of adopting IPD structures if we
e had used continuouss variables.
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C & Ballarrd: Last Plan
Cho
nner and Inte
egrated Project Deliverry
Hypotthesis tessting methodolog
gy
The hyp
pothesis tessting was pe
erformed diifferently a
according to
o the type of
o variable. The
indepen
ndent variab
ble (degree
e of Last Pla
anner imple
ementation) of the firsst hypothessis is a
quantita
atively conttinuous ord
dinal variable because the sum off scores of the
t five que
estions
in Table
e 2 is the to
otal degree of Last Pla
anner imple
ementation of a projecct, represen
nted as
a real n
number. The
e dependen
nt variable (cost reducction + time
e reduction)) of the sam
me
hypothe
esis is a ratiio variable represente
ed as a real number. Th
hus, regression betwee
en the
two varriables is ap
ppropriate for
f testing the
t hypothe
esis. Howevver, the ind
dependent variable
v
of the second
s
and the third hypothesis iss a binary categorical
c
variable, ‘w
whether or not a
project adopts IPD
D’, for which
h regression
n analysis iss not appro
opriate. In this
t
case, we
w used
a T-testt, to determ
mine whether the cate
egorization (IPD or othe
erwise) hass a significa
antly
differen
nt influence
e on depend
dent variables: projectt performan
nce in the second
s
hypo
othesis,
and the
e degree of implementtation of Last Planner in
i the third
d hypothesiss.
Samplling Strattegy
In comm
mon sense, the most appropriate form of sam
mpling to su
upport a hyypothesis is
randomized sampliing. Howevver, Last Pla
anner (LP) is
i a very spe
ecific tool for
f producttion
control so that we need the very
v
specific
c respondents who can
n determine
e the degre
ee of LP
implementation in their proje
ects. Thus, we decided
d to use a purposive
p
sa
ampling tak
king
age of e-ma
ail lists in re
elevant groups such ass general IG
GLC group in
n Yahoo4, or
advanta
particip
pants in worrkshops succh as those sponsored by
b the Project Producttion System
m
5
Laborattory . The same applie
es to selectiion of IPD p
projects. If we were to
o select pro
ojects
randomly from anyywhere in the world, very
v
few, if any, IPD projects wou
uld be inclu
uded.
Purposivve sampling
g is widely used in stud
dying unusu
ual critical cases. For example,
e
itt can be
used efffectively in
n identifying
g communitties across the
t United States thatt have voted
d for
the winner in the past,
p
or it is used in se
electing keyy informantts for ethno
ographic stu
udies
such as one describ
bing gangstter’s lives (B
Bernard, 20
000)
Results
Regre
ession mo
odel from
m testing
g the firstt hypothesis
There iss a significa
ant correlattion betwee
en the implementation
n of Last Pla
anner (LP) and
a
project performance—the sum
m of cost an
nd schedule
e reduction percentage
es. That me
eans we
have successfully supported
s
t first hyp
the
pothesis. Th
his is repressented as a regression model
in Table
e 3 in the Appendix.
A
Figu
ure 2 is a grraphical rep
presentation
n including scatter plo
otting and a linear regrression
line. Evven though we
w used a straight
s
line
e, the scattter plot see
ems to show
w a curve is more
approprriate in describing beh
haviour of variables. Th
hus, we trie
ed several linear
l
regre
essions,
whose independen
nt variables are ‘square
e of indepe
endent varia
able (X) in Figure
F
2’ orr X2 and
‘cube of X’ or X3
The result is en
ncouraging.. The regresssion model with ‘squa
are of X’ orr X2 is ‘Y(Sum
m of
cost red
duction and
d time reducction) = 0.7
7371101×X2-3.89088’ with
w
its P<|
|t(2.98)| is 0.005,
4
5
http:/
//finance.dir..groups.yahoo
o.com/group/iiglc/message/
/677
http:/
//p2sl.berkele
ey.edu/
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C & Ballarrd: Last Plan
Cho
nner and Inte
egrated Project Deliverry
-20
-10
0
10
20
30
40
which iss less than 0.009,
0
the P<|t(2.71)| in Table 3 with mere
e X. The lesss p value off t
(P<|t|) means there is greate
er significan
nce in the coefficient
c
of
o the regre
ession line.
3
3
Furtherrmore, the regression
r
m
model
with X is ‘Y = 0.1484254×
0
×X -1.617307’ with its p value
of t is 0
0.004, which
h is less tha
an 0.005 in the regresssion model with X2. Bu
ut, X to the fourth
does no
ot show morre significan
nce than X3.
.5
1
1.5
5
2
2.5
3
3.5
Implem
mentation of Las
st Planner (X)
Sum
m of cost reducttion and schedu
ule reduction(Y)
4
4.5
5
Y=4.141356**X-9.003641
Figu
ure 2: Regre
ession of Last
L
Planner on Projec
ct Performa
ance
The fina
al regressio
on line with X cubed, saying that the
t projectt performan
nce is
proporttionate to th
he degree of
o Last Plan
nner’s imple
ementation
n cubed, is visually
v
represe
ented as blu
ue diamond type plots in Figure 3. We decide
ed to call itt ‘Cho-Balla
ard
curve’, which show
ws that Projject Perform
mance (sum
m of cost re
eduction and
d schedule
3
reductio
on) = 0.1484254 ×(Imp
plementatio
on of Last Planner)
P
-1..617307.
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C & Ballarrd: Last Plan
Cho
nner and Inte
egrated Project Deliverry
Cho-Ballard Curve
C
"more reduction of cost and schedule" Bottom to Top
Sum of Cost reduction and Schedule reduction: Y (%),
5
50
4
40
3
30
2
20
1
10
0
0
1
2
3
4
-1
10
-2
20
5
6
Y=0.14
484254*X^3-1.6
617307
X: Score
S
of Last Planner Impllementation (LPI), "more regorous LPII" L->R
Figure 3: Cho-Ballard
C
d Curve b/w
w (Last Plan
nner)3 and Project pe
erformance
e
Summ
mary of Hypothesi
H
is testing
g6
The folllowing box summarizes the resultts of hypoth
hesis testing
g so far.
Hy
ypothesis 1
If a pro
oject implem
ments Last Planner (LP) more, itt achieves project
p
performa
ance betterr than those
e employing
g LP less
=> Stron
ngly supporrted by the regression model: Pro
oject Perforrmance
(sum
m of cost red
duction and
d schedule reduction) = 0.148425
54 ×
3
(Imple
ementation of Last Pla
anner) -1.61
17307
Hy
ypothesis 2
If a proje
ect adopts Integrated Project
P
Delivery (IPD),, its perform
mance is
diffferent from
m those of other projeccts.
=> Failed
F
to be supported
d definitive
ely
Hy
ypothesis 3
If a proje
ect adopts IIPD, its deg
gree of impllementation of LP is different
d
from thosse of other projects.
=> Fa
ailed to be supported.
s
However, IPD
I projectts in our sam
mple
implemented LP to a certaiin degree evven though
h the level is
i not
significcant statistically.
6
For de
etail of hypothesis testing, please see Ap
ppendix
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C & Ballarrd: Last Plan
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Conclusion
We foun
nd in this re
esearch tha
at project performance
p
e improves with the im
mplementattion of
Last Pla
anner. Howe
ever, we diid not find a strong rellationship among
a
Last Planner, Prroject
Perform
mance, and Integrated Project De
elivery (IPD)).
Thiss research does
d
not pre
event us fro
om believin
ng that if IPD, aligning goals of
particip
pants, and LP,
L reducingg project va
ariability, a
are combine
ed, any pro
oject can acchieve
better p
performanc
ce. Indeed, this is the claim
c
put fo
orward by Lean
L
Constrruction adh
herents,
criticizing forms off IPD that rely only on alignment of commerrcial interessts and
organiza
ational inte
egration, wh
hile neglectting the lea
an ‘operatin
ng system’, which addresses
how the
e work is ac
ctually done
e. Future re
esearch is needed
n
to validate
v
thiss claim.
Appe
endix
Detaill of the first
f
hypo
othesis te
esting
Table 3 is the resu
ult produced
d by STATA v.10, a sta
atistics pack
kage, using data from the 49
projectss. Simply, we
w need to see the ‘co
oefficient’, written on
n the right side
s
of ‘Y in
n Figure
2’ in Ta
able 3. This is the grad
dient of the regression line. Y is ‘ssum of costt reduction and
duration
n reduction
n’ and X is ‘the degree
e of implementation off Last Plann
ner’. The
significa
ance of thiss coefficien
nt is determ
mined by P > |t|, 0.009
9 (red-unde
erlined number in
Table 3). Usually, if P>|t| is less
l
than 0..05, we can
n say this co
oefficient (tthe regression
nt. In our case, the regression mo
odel is Y=4..141356×X--9.003641
model) is significan
Table 3: Re
esult of Re
egression fo
or the first hypothesiss
Sourc
ce
SS
D
DF
M
MS
Number
N
of object
o
= 49
Mode
el
543.294059
1
543.294059
F(1, 47) = 7.36
Residu
ual
3467
7.23372
47
73.7709302
P
Probability
> F = 0.0093
Tota
al
4010
0.52778
48
83.552662
R-squared = 0.1355
Adjjusted R-squ
uared = 0.117
71
Root Mean Squarre Error = 8.859
S Errors
Std
T
P>|t|
X in Figu
ure2
Coefficient
4.1
141356
1
1.526046
2.71
0.009
1.071347
7.211366
Consta
ant
-9.0
003641
5
5.279548
-1.77
0.095
-19.62
2472
1..61744
Y in Figu
ure2
95% confidence
c
In
nterval
Detaill of the second
s
hy
ypothesiis testing
g
The seccond hypoth
hesis is < If a project adopts
a
Integ
grated Proje
ect Deliveryy (IPD), its
perform
mance is diffferent from
m those of other
o
projeccts>. Before
e T test, we
e needed to
o see if
the two
o groups (IPD and Non IPD) have significantlyy different variance
v
in project
perform
mance becau
use generall T test is performed
p
b
based
on eq
qual varianc
ce. If not, T test
should be
b performed under th
he unequal variance co
ondition. Ta
able 4 is the variance ratio
test, na
amed as “sd
dtest” in ST
TATA v.10. The
T f value stands for the ratio between
b
the
e
variance
e of IPD and
d that of No
on IPD, whiich is expre
essed as ‘Ra
atio’ in Tablle 4. When the
probabiility, expresssed as p (F
F<f), p (|F|>|f|), and p (F>f) in Table
T
4, is less than 0.0
05, the
alternattive hypoth
hesis, locate
ed right abo
ove the pro
obability, is chosen. In this test, the
t
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C & Ballarrd: Last Plan
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egrated Project Deliverry
target a
alternative hypothesis is Ha: ratio
o!=1. The probability right
r
under the alterna
ative
hypothe
esis is 0.084
43, which iss bigger tha
an but near to 0.05 so that we cam
me to decid
de to do
anotherr T test with
h unequal variance
v
forr more assu
urance
Table 4: Variance
V
Ra
atio Test on
n performance between IPD and
d otherwise
e
Grou
up
Ob
bs.
M
Mean
Std. Err.
Std. Dev.
[95% Con
nf. Interval]
Non IPD
40
5
5.105027
1.55351
9.825258
1.962757
7
8.2472
297
IPD
9
4
4.160776
1.822258
5.466773
-.041357
77
8.3629
909
Com
mbined
49
4
4.931593
1.305816
9.140715
2.306073
3
7.557113
Ratio = standard
d deviation (Non
D)
IPD))/standard deviation (IPD
Nulll hypothesis: Ratio =1
Alte
ernative Hypo
othesis (Ha):: ratio <1
Prob
bability: p (F
F<f)=0.9578
f = 3.230
02
degrees of freedom = 39
(=40-1), 8 (=9-1)
Ha: ratio !=
=1
2*p (F>f) = 0.0843
Ha: Ratio > 1
P (F > f) = 0.0422
Table 5 is the resu
ult of T-testt with equal variance of
o STATA v.10. The ‘t’ value stands for
‘the rem
mainder of the Perform
mance mean of Non IPD after sub
btracted by the Mean of
o IPD’,
which iss expressed
d as ‘Differe
ence’ in Tab
ble 5. When
n a probability, expresssed as p (T
T<t), p
(|T|>|tt|), and p (T
T>t) in Table 5, is lesss than 0.05,, the alternative hypotthesis, loca
ated
right ab
bove the pro
obability, iss chosen. In
n our case, the target alternative
e hypothesiss is Ha:
Differen
nce!= 0, a different
d
ex
xpression bu
ut one having the same
e meaning as that of our
o
second hypothesis.. p (|T| >|tt|) right be
elow the altternative hyypothesis, Ha:
H Differen
nce!=0,
is 0.782
28, much bigger than 0.05
0
so thatt we cannott choose the
e alternativve hypothessis, our
second hypothesis
Ta
able 5: T-te
est with eq
qual varianc
ce on perfo
ormance be
etween IPD
D and Non IPD
Group
Non
n IPD
Obs.
0
40
Mean
M
5
5.105027
Std. Err.
1.55351
Std. Dev.
9.825258
[95% Co
onf. Interval]
1.962757
7
8.2472
297
IPD
9
4
4.160776
1.822258
5.466773
-.041355
57
8.3629
909
Com
mbined
9
49
4
4.931593
1.305816
9.140715
2.306073
3
7.557113
.6551067
3.15756
-5.90614
44
7.7946
646
Diffe
erence
Diffe
erence = Mean (Non IPD))-Mean
(IPD
D)
Nulll hypothesis: Difference = 0
Alte
ernative Hypo
othesis (Ha)::
Diffe
erence < 0
Prob
bability: p (T
T<t)=0.6086
t = 0.277
73
degrees of freedom = 47
Ha: Difference !=0
p (|T|>|t|)) = 0.7828
Ha: Diffe
erence > 0
P (T > t)) = 0.3914
As we m
mentioned, we did ano
other T – tesst with une
equal varian
nce, whose result is sim
milar to
that of equal varia
ance. Unequ
ual T test sa
ays the probability p (|T|>|t|) iss 0.6972, much
t
0.05.
bigger than
Detaill of the third
t
hyp
pothesis testing
t
The thirrd hypothessis is <If a project
p
adopts IPD, its degree of implementa
i
ation of Lasst
Plannerr (LP) is diffferent from
m those of other projeccts> The varriance test said there is no
significa
ant differen
nce betwee
en the varia
ance of the two groupss (IPD and Non
N IPD) in LP
implementation byy showing th
he probabillity, used in
n determiniing whetherr to choose the
alternattive hypoth
hesis (standa
ard deviatio
ons of the two
t
groups are differe
ent), is 0.19
948,
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C & Ballarrd: Last Plan
Cho
nner and Inte
egrated Project Deliverry
bigger than
t
0.05.
Table 6 shows the result of
o t test witth equal variance in te
esting our third hypoth
hesis.
Similar to Table 5, if a probab
bility right under the alternative
a
hypothesis, representted as p
(T<t), p (|T|>|t|), and p (T>t), is less than 0.05, we
w can choo
ose the alte
ernative hypothesis,
located right above
e the proba
ability. Our alternative
e hypothesis is ‘Differe
ence (betwe
een
means o
of IPD and Non
N IPD)!=0
0’, a differe
ent expressiion but one
e having the
e same mea
aning as
that of our third hyypothesis. Even
E
though P (|T| >|t|), 0.074 is
i bigger th
han 0.05, it is not
clear fo
or us whethe
er to discarrd our third
d hypothesiss. As for seccond hypothesis, it is clear
c
in
that the
e probabilitty, P (T<t), is 0.7828, much
m
bigge
er than 0.05
5. But, the third hypotthesis is
at the border.
b
In short, even though Inte
egrated Pro
oject Delive
ery projectss do not sho
ow
implementation off Last Plann
ner significa
antly differe
ent from ottherwise, it seems to employ
e
Last Pla
anner to a certain
c
degrree
Ta
able 6: T-te
est with eq
qual varianc
ce on perfo
ormance be
etween IPD
D and Non IPD
Grou
up
Non IPD
Obs.
40
Mean
3.266667
Std. Err.
.1321312
2
Std. De
ev. [95% Conf. Interval]]
.835671
14 2.999406
6
3.53927
7
IPD
9
3.801471
.1802989
9
.540896
68 3.385701
1
4.21724
42
Com
mbined
49
3.364896
.116053
.812370
07 3.131556
6
3.59823
36
-.5348048
.2926632
2
Diffe
erence
-1.12356
67
.053957
76
Diffe
erence = Mea
an (Non IPD)-Mean (IPD)
Null hypothesis: Difference = 0
t = -1.82
274
degrees of freedom = 47
Alterrnative Hypo
othesis (Ha): Difference <0
< Ha: Diffe
erence !=0
Prob
bability: p (T<t)=0.0370
p (|T|>|
|t|) = 0.0740
0
erence > 0
Ha: Diffe
P (T > t) = 0.9630
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