Construction waste management model for contractors using lean and BIM tools

Uni e sidade do Minho
Escola de Engenha ia
Mahmoud Abdel Fa ah Abdel A him Ka az
Cons uc ion Was e Managemen Model o
Con ac o s Using Lean and BIM Tools
Oc obe 2024
UMinho | 2024 Mahmoud Abdel Fa ah Abdel A him Ka az
Cons uc ion Was e Managemen Model
o Con ac o s Using Lean and BIM Tools
Uni e sidade do Minho
Escola de Engenha ia
Mahmoud Abdel Fa ah Abdel A him Ka az
Cons uc ion Was e Managemen Model
o Con ac o s Using Lean and BIM Tools
Oc obe 2024
Philosophy Doc o a e Thesis
Ci il Enginee ing
Wo k conduc ed unde supe ision o :
P o esso Doc o José Manuel Ca doso Teixei a
ii
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iii
Acknowledgemen s
This esea ch esul s om a c i ical li e s age, demons a ing pe sis ence and consis ency owa ds
pu suing knowledge. Du ing he pas yea s, many people ha e been me wi h a ou able and di ec ly and
indi ec ly in luenced my li e, he e o e his in es iga ion. I am g a e ul o mysel and all o hem, as hey
ha e pushed me and shaped he way I am oday, o mula ed he cu en knowledge, and sha ed beau i ul
expe iences.
Fi s ly, I would be speci ically p o oundly indeb ed o my ad iso , P o esso José Manuel Ca doso Teixei a,
whose unwa e ing guidance, in aluable ad ice, pa ience, and de o ed suppo ha e been ins umen al
h oughou his jou ney. His men o ship was undamen al in shaping his academic endea ou and
inspi ed pe sonal g ow h and esilience.
I ex end hea el g a i ude o he membe s o his disse a ion commi ee membe s, o hei expe ise,
cons uc i e eedback, and commi men o excellence. Thei collec i e wisdom has en iched his esea ch
and con ibu ed o i s schola ly igou .
I am g a e ul o he Uni e si y o Minho o p o iding a conduci e en i onmen o in ellec ual inqui y and
schola ly pu sui . The uni e si y's esou ces, acili ies, and oppo uni ies ha e been indispensable in
comple ing his disse a ion.
I wish o hank my colleagues and pee s o hei cama ade ie, encou agemen , and s imula ing
discussions. You cama ade ie has made his academic jou ney mo e en iching and enjoyable.
Special hanks a e dedica ed o my mo he , a he , sis e s, and b o he s o hei uncondi ional lo e,
encou agemen , and unwa e ing belie in my abili ies. Thei s ead as suppo has been my ancho du ing
challenging imes. I would exp ess my g a i ude o my wi e, who was always beside me o mo i a e me
when I was down.
I wan o hank my iend Mohammed Al Da abseh o his company while li ing ab oad and du ing his
PhD jou ney.
This esea ch has been suppo ed by he Po uguese Founda ion o Science and Technology (FCT) unde
he doc o al g an SFRH/BD/04751/2021.

i
STATEMENT OF INTEGRITY
I he eby decla e ha ing conduc ed his academic wo k wi h in eg i y. I con i m ha I ha e no used
plagia ism, any o m o undue use o in o ma ion, o alsi ying esul s along he p ocess leading o i s
elabo a ion.
I u he decla e ha I ha e ully acknowledged he Code o E hical Conduc o he Uni e si y o Minho.
The esea ch is no di ec ed owa ds any speci ic demog aphic wa an ing dis inc i e e hical a en ion.
Gi en i s ocus on p o essionals such as con ac o s, a chi ec s, ades, and lean p ac i ione s, he p ima y
e hical conce ns cen e a ound sa egua ding he p i acy, con iden iali y, and anonymi y o pa icipa ing
companies, subjec s, and ques ionnai e esponden s. The li e a u e e iew upheld me iculous ci a ion
and elec onic managemen o all schola ly sou ces. Addi ionally, he sou ces o da a we e duly
acknowledged du ing he dissemina ion o esea ch ou comes o enhance he eliabili y o he da a.
Resumo
Os esíduos de cons ução con inuam a se um desa io signi ica i o pa a o sec o , di icul ando a
p odu i idade e desenco ajando a ino ação. Vá ios ela ó ios desc e em os seus impac os ad e sos, ais
como o obs áculo ao desen ol imen o da sus en abilidade ambien al, económica e social. Os
académicos, os egulado es e as sociedades p o issionais apelam con inuamen e a uma indús ia sem
esíduos. No en an o, a conc e ização des e obje i o ambicioso con inua a se con á ia à si uação a ual,
uma ez que a egulamen ação, as polí icas e as p á icas exis en es ainda êm o igem em p ocessos
adicionais de ges ão da cons ução que se cen am no a amen o dos esíduos al como são, no inal,
em ez de começa em na on e de p odução de esíduos. Es a ese cen a-se na explo ação do p oblema
gene alizado do despe dício de Making-Do (MD) em p ojec os de cons ução, com a u ilização dos
p incípios da Lean (LC), em pa icula o Las Planne Sys em (LPS) e o çado pela Modelação da
In o mação da Cons ução (BIM), pa a desen ol e es a égias de mi igação do MD. Os esíduos de MD
ge ados pela ealização de a e as de cons ução sem os p é- equisi os necessá ios causam
ine iciência, aumen o de cus os e edução da qualidade num p oje o. Inicialmen e, a e isão
sis emá ica da li e a u a iden i icou um conhecimen o limi ado dos esíduos de MD e a al a de
uma abo dagem in eg ada pa a os mi iga . Es a in es igação analisa en ão os ac o es subjacen es aos
esíduos de MD, os elemen os dos esíduos de MD e os seus e ei os, sin e izando os esul ados da
e isão da li e a u a e dos ques ioná ios es u u ados. Com base nes a a aliação, as causas po enciais
do despe dício de MD podem es a elacionadas com o planeamen o, a comunicação e o
conhecimen o en e os abalhado es. Com base nes es esul ados, a in es igação c iou um no o
Modelo Dinâmico de Sis ema - o Modelo Dinâmico pa a a Mi igação do Making-Do (D3M) pa a modela
e a alia os e ei os do LPS e do BIM na edução dos esíduos de MD. O D3M combina os esul ados
dos inqué i os po ques ioná io e es udos de caso de p ojec os de condomínios de á ios anda es, em
que os aspec os écnicos e sociais são quan i icados pa a o nece endências ge ais na ge ação e
p opagação de esíduos de MD. O modelo pe mi e es a á ios cená ios de implemen ação do LPS-
BIM em e mos da sua e icácia na edução dos esíduos de MD e na melho ia dos esul ados do
p oje o. Os esul ados des acam a impo ância do planeamen o colabo a i o e da análise das es ições,
da pa ilha anspa en e de in o mações a a és do BIM e de uma cul u a que dê p io idade à
p e enção e esolução dos esíduos de MD. Assim, es a in es igação o e ece um modelo de aplicação e
uma e amen a de simulação baseada em modelos (D3M) pa a a p á ica de in eg ação LPS-BIM
pa a a minimização dos esíduos de MD, a im de bene icia os p o issionais e os in es igado es no
aumen o das possibilidades de sucesso nos p ojec os de cons ução.
Pala as-cha e: Cons ução Lean (LC); Modelação de In o mação de Cons ução (BIM); Resíduos de
Cons ução; Modelagem Dinâmica de Sis emas (SDM); Resíduos de imp o isação.
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Abs ac
Cons uc ion was e emains a signi ican challenge o he indus y, hinde ing p oduc i i y and
discou aging inno a ion. Va ious epo s ou line i s ad e se impac s, such as hu dling en i onmen al,
economic, and social sus ainabili y de elopmen . Academics, egula o s and p o essional socie ies
con inuously call o a ze o-was e indus y. Howe e , achie ing his ambi ious objec i e emains con a y
o he ac ual si ua ion, wi h he exis ing egula ions, policies and p ac ices s ill o igina ing om adi ional
cons uc ion managemen p ocesses ha ocus on ea ing was es as hey a e, in he end, ins ead o
s a ing om he sou ce o was e gene a ion. This hesis ocuses on he explo a ion o he pe asi e
p oblem o Making-Do (MD) was e in cons uc ion p ojec s, wi h he use o Lean Cons uc ion (LC)
p inciples, pa icula ly he Las Planne Sys em (LPS) enhanced by Building In o ma ion Modelling (BIM),
o de elop mi iga ion s a egies o MD. MD was e gene a ed by unde aking cons uc ion asks wi hou
he equi ed p e equisi es causes ine iciency, inc eased cos and educed quali y wi hin a p ojec .
Ini ially, he sys ema ic li e a u e e iew iden i ied limi ed knowledge o MD was e and he lack o an
in eg a ed app oach o mi iga ing i . This esea ch hen e iews he ac o s behind MD was e, elemen s
o MD was e, and i s e ec s by syn hesizing he ou comes o li e a u e e iew and s uc u ed
ques ionnai es. Based on his e alua ion, po en ial causes o MD was e can pe ain o planning,
communica ion, and knowledge among wo ke s. Based on hese esul , he esea ch has c ea ed a new
Sys em Dynamic Model - he Dynamic Model o Making-Do Mi iga ion (D3M) o model and assess he
e ec s o LPS and BIM on MD was e educ ion. D3M combines esul s om he ques ionnai e su eys
and case s udies o mul is o ey condominium p ojec s whe e echnical and social aspec s a e quan i ied
o p o ide gene al ends in gene a ing and p opaga ing MD was e. The model enables a ious LPS-BIM
implemen a ion scena ios o be es ed in e ms o hei e ec i eness in educing MD was e and enhancing
p ojec ou comes. Findings highligh he impo ance o collabo a i e planning and cons ain analysis,
anspa en in o ma ion sha ing h ough BIM, and a cul u e p io i ising MD was e p e en ion and
esolu ion. Thus, his esea ch o e s an applica ion model and a model-based simula ion ool (D3M), o
he LPS-BIM in eg a ion p ac ice o MD was e minimisa ion o bene i p ac i ione s and esea che s in
enhancing he chances o success in cons uc ion p ojec s.
Keywo ds: Lean Cons uc ion (LC); Building In o ma ion Modelling (BIM); Cons uc ion Was e; Sys em
Dynamic Modelling (SDM); Making-Do was e.
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TABLE OF CONTENTS
1. In oduc ion ________________________________________________________ 1
1.1. Mo i a ion __________________________________________________________ 2
1.2. Objec i es __________________________________________________________ 4
1.3. Thesis Ou line _______________________________________________________ 5
2. Li e a u e Re iew ___________________________________________________ 8
2.1. In oduc ion _________________________________________________________ 9
2.2. Sys ema ic Re iew Resea ch Ques ions ____________________________________ 12
2.3. Ma e ials and Me hods ________________________________________________ 13
2.4. Da a E alua ion _____________________________________________________ 15
2.5. Resul s ___________________________________________________________ 18
2.5.1. Was e Elimina ion and Lean Cons uc ion _____________________________________ 18
2.5.2. Lean Cons uc ion P inciples o Was e Elimina ion ______________________________ 20
2.5.3. In eg a ed Lean P ojec Deli e y ____________________________________________ 21
Lean Supply Chain Managemen _______________________________________________ 22
Lean Design Managemen ____________________________________________________ 24
Las Planne Sys em _________________________________________________________ 25
Loca ion-based Managemen Sys ems __________________________________________ 27
2.5.4. Rela ional and Legal S uc u es o Was e Elimina ion ____________________________ 31
2.5.5. Lean Sus ainabili y _____________________________________________________ 31
2.5.6. Value S eam Mapping __________________________________________________ 33
2.5.7. Building In o ma ion Modelling as Lean Cons uc ion Enable _______________________ 34
B ie BIM his o y ____________________________________________________________ 35
BIM de ini ion ______________________________________________________________ 35
BIM and Lean ______________________________________________________________ 36
BIM cons uc ion managemen ________________________________________________ 37
BIM and Lean P oduc ion Planning and Con ol ___________________________________ 37
BIM o lean supply chain managemen _________________________________________ 38
2.5.8. Lean Cons uc ion Resea ch in he Po uguese Con ex ___________________________ 39
2.6. Discussion _________________________________________________________ 41
2.6.1. How does lean cons uc ion concep ualise was e? _______________________________ 41
xi
exponen ial decay a he igh when he balancing loop is dominan . .................................................. 70
Figu e 3-9 - Common e e ence modes (Ki kwood, 1998) .................................................................. 72
Figu e 3-10: Basic S ock and Flow Diag am ...................................................................................... 73
Figu e 4-1- A concep ual diag am o making-do phenomena. ............................................................. 85
Figu e 4-2- A diag am illus a ing he ela ionships among p e equisi es, MD ca ego ies and impac s
based on he p o ocols de eloped by Dos San os e al. (2020) and Somme (2010) .......................... 91
Figu e 4-3-- Casual Loop Diag am illus a es he s uc u al ela ionships wi h p e equisi es, MD
ca ego ies and impac s. .................................................................................................................. 102
Figu e 5-1 - A F amewo k o Policy o Imp o e he Pe o mance o MD mi iga ion models. ............... 107
Figu e 5-2 -- Educa ional a ainmen among he su ey pa icipan s. ................................................ 115
Figu e 5-3 -- Occupa ional oles wi hin he esponden g oup. .......................................................... 116
Figu e 5-4 -- The pe cen age o Lean and BIM Educa ion and T aining among he Responden s ....... 117
Figu e 5-5 -- The Responden s Expe ience in Lean and BIM (yea s) .................................................. 117
Figu e 5-6 -- The knowledge and use o Making-Do e minology and simila concep s. ...................... 118
Figu e 5-7 -- Es ima ed Pe cen age o MD in cons uc ion wo k lows acco ding o he esponden s. .. 119
Figu e 5-8: Examina ion o esponden s' pe spec i es on he en i ies accoun able o Making-Do (MD)
was e gene a ion. ........................................................................................................................... 119
Figu e 5-9 -- Unadjus ed SEM measu emen model ......................................................................... 125
Figu e 5-10 -- Adjus ed measu emen model ................................................................................... 126
Figu e 5-11 -- Boo s ap se ings ..................................................................................................... 129
Figu e 5-12 --- A g aphical ep esen a ion o he applied Media ion Analysis o LPS, BIM, COO, MDK130
Figu e 5-13 -- F amewo k o an LPS and BIM o MD was e mi iga ion. ........................................... 132
Figu e 6-1 -- Gene ic iew o he dynamic sys ems o making-do ..................................................... 135
Figu e 6-2 – Example o aw da a collec ed and classi ied in o Phases, Subs ages, P e equisi es, MD
ca ego ies, and MD impac s. This igu e is no men ioned in he ex ............................................... 138
Figu e 6-3 -- Gene al Wo k B eakdown S uc u e used o he h ee p ojec s ..................................... 139
Figu e 6-4-- An examina ion o he dis ibu ion pa e n wi hin he phases p ima ily impac ed by MD
inciden s in he analysed cases (acco ding o da a collec ed). .......................................................... 140
Figu e 6-5-- An analysis o he dis ibu ion among he sub-s ages mos a ec ed by MD inciden s in he
examined cases (acco ding o da a collece ed). ............................................................................... 141
Figu e 6-6 -- Analysing he ask p e equisi es (no esol ed cons ain s) caused MD inciden s: a s udy o
a ec ed asks dis ibu ion in examined cases (acco ding o da a collece ed) .................................... 142

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Figu e 6-7 -- Analysing Ca ego ies o MD: a s udy o a ec ed ask dis ibu ion in examined cases
(acco ding o da a collece ed). ........................................................................................................ 143
Figu e 6-8 -- Analysing he Impac o MD Inciden s on P oduc ion Sys ems: A S udy o A ec ed
Dis ibu ion in Examined Cases ....................................................................................................... 144
Figu e 6-9 -- G aphical ou pu s o a single MLRA oo small ............................................................... 148
Figu e 6-10-- S ock and Flow Diag am o T adi ional P oduc ion Planning and Con ol .................... 152
Figu e 6-11 -- Snippe o Planning and Con ol Subsys em ............................................................... 153
Figu e 6-12 -- Was e subsys em ...................................................................................................... 154
Figu e 6-13 -- Simula ed o a e ages o planned, backlog and comple ed asks based on he adi ional
planning se ings. ........................................................................................................................... 155
Figu e 6-14: Sys em dynamics simula ion o p ojec A s ages in ec ed by MD inciden s. Is his he same
da a o igu e 6.4? .......................................................................................................................... 157
Figu e 6-15: The accumula ed numbe o simula ed asks included MD inciden s. ........................... 157
Figu e 6-16: The di e ence be ween simula ed (expec ed) and collec ed da a o S ages in ec ed by MD.
...................................................................................................................................................... 158
Figu e 6-17: The a iance be ween simula ed asks and obse ed da a ac oss ime. ....................... 159
Figu e 6-18: Simula ion esul s o he numbe o cons ain s Case A. ............................................. 160
Figu e 6-19: The a iance be ween simula ed cons ain s and obse ed da a ac oss ime. ............... 161
Figu e 6-20: Simula ion esul s o he ca ego ies o MD encoun e ed in Case A. .............................. 162
Figu e 6-21: The a iances be ween obse ed and simula ed MD ca ego ies. ................................... 162
Figu e 6-22: Simula ion o MD impac s on he p oduc ion sys em. ................................................... 163
Figu e 6-23: The a iance be ween simula ed and obse ed impac s ac oss ime. ............................ 164
Figu e 7-1 – The esea ch design used o his chap e ................................................................... 173
Figu e 7-2 -- Wo kp og ess subsys em wi h MD and was e subsys ems. ........................................... 180
Figu e 7-3 -- The dynamic a iables a ec ing he cons uc ion p oduc i i y. ...................................... 183
Figu e 7-4 -- Impac o wo kspace limi a ion on p oduc i i y. ............................................................ 185
Figu e 7-5 -- The dynamic subsys em o Resou ces. ....................................................................... 189
Figu e 7-6: The LPS dynamic subsys em ........................................................................................ 190
Figu e 7-7 - The echnical LPS dynamic subsys em ......................................................................... 191
Figu e 7-8 -- BIM unc ionali ies subsys em ...................................................................................... 192
Figu e 7-9 - Time analysis o cons ain s in P ojec M ..................................................................... 197
Figu e 7-10 -- Time analysis o MD in P ojec M .............................................................................. 198
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Figu e 7-11 -- Time se ies analysis o was e in P ojec M ................................................................ 199
Figu e 7-12 -- Time analysis o cons ain s in P ojec N ................................................................... 199
Figu e 7-13 -- Time se ies analysis o was e in P ojec N ................................................................. 200
Figu e 7-14 -- Time analysis o was e in p ojec N. .......................................................................... 200
Figu e 7-15 - Compa ison be ween cons ain s P1 - P6 in p ojec M ................................................. 201
Figu e 7-16-- MD ca ego ies CAT1 o CAT5 compa ison in p ojec M ................................................ 202
Figu e 7-17 -- Compa a i e analysis o he impac o MD I1-I5 in P ojec M ...................................... 204
Figu e 7-18 -- Compa ison be ween cons ain s P1 - P6 in p ojec N ................................................ 205
Figu e 7-19 – MD ca ego ies CAT1 o CAT5 compa ison in p ojec N ............................................... 206
Figu e 7-20 -- Compa a i e analysis o he impac o MD I1-I5 in P ojec N ....................................... 207
Figu e III-1 -- Cloud applica ion o D3M. ........................................................................................... 249
Figu e III-2 -- D3M Welcome Message ............................................................................................. 249
Figu e III-3 -- The dynamic amewo k o MD was e analysis. .......................................................... 250
Figu e III-4 -- Use inpu window. ..................................................................................................... 252
Figu e III-5 -- p ese bu ons o ex eme alue es s. ........................................................................ 252
Figu e III-6 -- Slide con olle s o se pa ame e alues. ................................................................... 253
Figu e III-7 -- D3M dashboa d. ......................................................................................................... 254
Figu e III-8 -- Con ol cha s o D3M................................................................................................. 255
Figu e III-9 -- Wo k p og ess and MD subsys ems. ........................................................................... 256
Figu e III-10 -- Loca ion subsys em. ................................................................................................. 256
Figu e III-11 -- Resou ces subsys em ............................................................................................... 257
Figu e III-12 -- P oduc i i y subsys em ............................................................................................. 257
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INDEX OF TABLES
Table 2-1: Inclusion and exclusion logic used o il e esea ch documen s ......................................... 15
Table 2-2 -- Cons uc ion p oduc ion was e ca ego ies acco ding o (Fo moso e al., 2020)................. 19
Table 2-3 -- Compa a i e analysis o he in eg a ed me hods wi h VSM o in es iga e cons uc ion
was es. ............................................................................................................................................. 33
Table 2-4-- Cu en con ibu ions o BIM Cons uc ion Managemen So wa e and Lean Cons uc ion
Based BIM ....................................................................................................................................... 38
Table 2-5--Lean cons uc ion ac o s o was e elimina ion ................................................................... 43
Table 3-1 -- Cons uc ion managemen wo ld iews adap ed om (C eswell & C eswell, 2017; Fellows &
Liu, 2015) ........................................................................................................................................ 55
Table 3-2 -- Model i ness measu es .................................................................................................. 63
Table 3-3-- Explo a o y and Con i ma o y Fac o Analysis ................................................................... 65
Table 3-4: The componen s o S ock and Flow Diag am ..................................................................... 72
Table 3-5 -- Compa ison be ween a ailable so wa e packages o Sys em Dynamics Modelling unc ions
........................................................................................................................................................ 80
Table 4-1 - Summa y o Causal Fac o s Con ibu ing o Making Do in Cons uc ion P ojec s ............... 86
Table 4-2 - P e equisi es o Cons uc ion P ojec Execu ion and Thei Explana ions, Sou ced om
Koskela (2000) and Somme (2010) ................................................................................................ 91
Table 4-3-- Making-Do Ca ego ies and Desc ip ions in Cons uc ion P ojec s ...................................... 93
Table 4-4 -- Impac o Making-Do P ac ices on Cons uc ion P ojec s: ................................................ 94
Table 5-1 -- Demog aphic cha ac e is ics o he su ey esponden s ................................................. 115
Table 5-2 -- Reliabili y analysis able wi h means and anking o LPS and BIM s a egies o MD
mi iga ion. ...................................................................................................................................... 120
Table 5-3 -- KMO and Ba le 's Tes ................................................................................................ 122
Table 5-4 -- Componen labelling and co esponding c i e ia om ac o analysis ............................. 122
Table 5-5 -- Model i ness measu es ................................................................................................ 124
Table 5-6 -- Model i ness measu es o he adjus ed measu emen model ....................................... 126
Table 5-7 -- Loadings, Reliabili y and Con e gen Validi y ................................................................ 127
Table 5-8. HTMT Analysis ............................................................................................................... 128
Table 5-9--Mode a ion analysis o he s uc u al model and model i indices ................................... 129
Table 5-10 -- A summa y o Media ion Analysis esul s .................................................................... 130
Table 6-1-- Cons ain s, MD, and MD impac s .................................................................................. 135
x iii
Table 6-2-- Compa a i e O e iew o Mul is o ey Condominium P ojec s: Case A, Case B and Case C
...................................................................................................................................................... 137
Table 6-3 -- Associa ion abula ion es o Task p e equisi es (Le Column) and MD ca ego ies (Top
Row) 6241 ..................................................................................................................................... 145
Table 6-4 - Chi-Squa e es o p e equisi es and MD ca ego ies ....................................................... 146
Table 6-5 -- Associa ion abula ion es o MD Ca ego ies (Le Column) and cons uc ion (impac s)
was e (Top Row) ............................................................................................................................. 146
Table 6-6 -- Chi-Squa e es o MD ca ego ies and MD impac s ....................................................... 147
Table 6-7 – Summa y o he Mul iple Reg ession Models (P1-P6, CAT1-CAT5, and I1-I5) ................. 148
Table 6-8 -- Reg ession abula ion o ask p e equisi es (P1-P6) in ela ion o sub-s ages (SS1-SS11) 149
Table 6-9 – Reg ession abula ion o MD ca ego ies (CAT1-CAT5) conce ning cons ain s (P1-P6). .. 150
Table 6-10 -- Reg ession abula ion o MD impac (I1-I5) conce ning MD ca ego ies (CAT1-CAT5) .... 150
Table 6-11 -- LPS -BIM model pa ame e s ....................................................................................... 167
Table 7-1 - Compa a i e O e iew o Selec ed Cons uc ion P ojec s A and B ................................... 174
Table 7-2- Time o disco e cons ain s wi h mas e planning comple ion ......................................... 182
Table 7-3 - Lookup unc ion o he impac o wo kspace a ailabili y on p oduc i i y ........................... 184
Table 7-4 - Lookup unc ion o he impac o a igue on p oduc i i y ................................................. 185
Table 7-5 - Lookup unc ion o schedule p essu e e ec ................................................................... 186
Table 7-6 -- Lookup unc ion o he impac o applying he BIM p ocess . ........................................ 187
Table 7-7 -- Table unc ion o esou ces alloca ion needed ollowing he ac ion o p ojec comple ed.
...................................................................................................................................................... 189
Table 7-8 -- Lookup he commi men unc ion o implemen BIM p ocesses (Po wal, 2013). ............. 192
Table 7-9 -- Da a en e ed in AnyLogic o c i ical a iables o he baseline scena io (P ojec s A and B)
...................................................................................................................................................... 194
Table 7-10 -- Inpu Da a o baseline scena io ................................................................................... 194
Table 7-11 -- Compa ison o sys em dynamic model wi h p ojec da a .............................................. 195
Table 7-12. The mix o a iables o be es ed in Scena ios I o IV. .................................................... 196
Table 7-13 – Cons ain s P1 o P6 in P ojec M Simula ion .............................................................. 201
Table 7-14-- Making Do Ca ego ies CAT1 o CAT 5 in P ojec M Simula ion ...................................... 203
Table 7-15 -- Was e I1 o I5 P ojec M Simula ion ............................................................................ 203
Table 7-16 -- Cons ain s P1 o P6 in P ojec N Simula ion ............................................................... 204
Table 7-17 -- Making Do Ca ego ies CAT1 o CAT 5 in P ojec N Simula ion ..................................... 205
xix
Table 7-18 -- Was e I1 o I5 P ojec N Simula ion ............................................................................. 206
Table 8-1 -- D3M s adi ional MD analysis ..................................................................................... 213
Table III-1 -- Pa ame e s o LPS-BIM wi hin D3M .............................................................................. 251
Table IV-1 -- Pa ame e s explana ion and scale ................................................................................ 258
Table IV-2 -- A ay dimensions used in he Anylogic.......................................................................... 262
Table IV-3 -- Dynamic equa ions ...................................................................................................... 263
Table IV-4 -- Table unc ions in D3M ................................................................................................ 270

xx
LIST OF ABBREVIATIONS
2D-CAD Two-Dimension Compu e ised-Aided-Design __________________________________ 33
4D 3D BIM model elemen s associa ed wi h ime ____________________________________ 38
AECO A chi ec u e, Enginee ing, Cons uc ion and Ope a ions ___________________________ 33
AHP Analy ical Hie a chy P ocess ______________________________________________ 75
AON Ac i i ies On Nodes _____________________________________________________ 27
AVE A e age Va iance Ex ac ed _______________________________________________ 121
BIM Building In o ma ion Modelling ______________________________________________ 33
CBA Choosing-By-Ad an age __________________________________________________ 24
CCC Cons uc ion Consolida ion Cen e s __________________________________________ 22
CDW Cons uc ion and Demoli ion Was e ___________________________________________ 2
CFI Compa a i e Fi Index ___________________________________________________ 119
CLD Casual Loop Diag am ___________________________________________________ 98
CPM C i ical Pa h Me hod ____________________________________________________ 37
CR Composi e Reliabili y ____________________________________________________ 121
CSC The Cons uc ion Supply Chain _____________________________________________ 21
FL Flow Line ______________________________________________________________ 32
GIS Geog aphic In o ma ion Sys em _____________________________________________ 39
HTMT He e o ai -Mono ai __________________________________________________ 122
ICT In o ma ion and Communica ion Technology ____________________________________ 94
IFOA In eg a ed Fo ms o Ag eemen s ____________________________________________ 25
IGLC In e na ional G oup o Lean Cons uc ion ______________________________________ 41
IPD In eg a ed P ojec Deli e y _________________________________________________ 25
ISO In e na ional O ganiza ion o S anda disa ion ___________________________________ 33
xxi
JIT Jus -In-Time ____________________________________________________________ 22
KMO Kaise -Meye -Olkin _____________________________________________________ 116
KPIs key pe o mance indica o s ________________________________________________ 73
LBMS Loca ion-Based Managemen Sys ems _______________________________________ 29
LBS Loca ion-Based-S uc u e _________________________________________________ 30
LC Lean Cons uc ion _______________________________________________________ 37
LDM Lean Design Managemen ________________________________________________ 16
LPC Lean Planning and Con ol ________________________________________________ 16
LRA Linea Reg ession Analysis ________________________________________________ 60
LSCM Lean Supply Chain Managemen ___________________________________________ 16
NaN No a Numbe ________________________________________________________ 182
NFC Nea -Field Communica ion ________________________________________________ 39
NVA Non-Value-Added ______________________________________________________ 23
PERT P og am E alua ion and Re iew Technique ____________________________________ 2
PPC Pe cen Plan Comple e __________________________________________________ 98
PPC Plan Pe cen Comple e ___________________________________________________ 28
QT Queuing Time __________________________________________________________ 23
RCA Roo Cause Analysis _____________________________________________________ 25
RFI Reques o In o ma ion ___________________________________________________ 40
RFID Radio F equency Iden i ica ion _____________________________________________ 38
RMSEA Roo Mean Squa e Residual ____________________________________________ 119
SBD Se -Based-Design ______________________________________________________ 24
SRMR S anda dized Roo Mean Squa e Residual ___________________________________ 119
STELLA Expe imen al Lea ning Labo a o y wi h Anima ion _____________________________ 75
TFV T ans o ma ion-Flow-Value _________________________________________________ 18
xxii
TLI Tucke -Lewis Index ______________________________________________________ 119
TPS Toyo a P oduc ion Sys em _________________________________________________ 17
TT Tak Time ____________________________________________________________ 23
TTP Tak Time Planning ______________________________________________________ 32
TVD Ta ge -Value-Design _____________________________________________________ 24
WBS Wo k B eakdown S uc u e ______________________________________________ 30
WIP Wo k-In-P og ess ______________________________________________________ 23
WMP Ma e ial Was e Managemen Plan ___________________________________________ 25
WWP Weekly Wo k Planning ___________________________________________________ 27
1. In oduc ion
1
1.
In oduc ion
This chap e p esen s a gene al o e iew o he hesis, opening wi h he main objec i es o he esea ch
mo i a ion. A b ie desc ip ion o he esea ch me hodology includes he main heo e ical and
me hodological app oaches o achie e he hesis objec i es. The hesis s uc u e is ou lined, wi h sho
desc ip ions o each chap e illus a ed.
2. Li e a u e Re iew
8
2.
Li e a u e Re iew
This chap e p esen s he s a e o he a o he s udied opic and p o ides a hema ic analysis o lean
cons uc ion me hods. Finally, i iden i ies he esea ch gaps in he li e a u e and p esen s a heo e ical
amewo k.
Excep o some changes execu ed o he o ma ing and o ganisa ion pu poses o global
in o ma ion in his documen , his chap e in eg ally p esen s he wo k:
(Ka az & Teixei a, 2023b). Was e Elimina ion based on Lean Cons uc ion and Building In o ma ion
Modelling: A Sys ema ic Li e a u e Re iew. U. Po o Jou nal o Enginee ing, 9(3), 72–90.
h ps://doi.o g/h ps://doi.o g/10.24840/2183-6493_009-003_001808

2. Li e a u e Re iew
9
2.1. In oduc ion
Cons uc ion was e is a high-le el concep behind poo p oduc i i y and low inno a ion le els in he
indus y, which is challenging o measu e sys ema ically. The ple ho a o epo s shows he massi e
consequences o cons uc ion was e, which hu dles sus ainabili y de elopmen in i s h ee dimensions
(en i onmen al, economic, and social). Rega ding ma e ials, one- hi d o he global cons uc ion
ma e ial is land illed wi hou ea men (Yuan & Shen, 2011). The second ype o was e is
en i onmen al, he indus y's g eenhouse gas (GHG) oo p in ; 33% o he global GHG is eleased om
cons uc ion and anspo a ion p ojec s (UN,2017). Cons uc ion and Demoli ion ma e ial Was e (CDW)
and GHG emissions conce n many egula o s, such as he Eu opean Commission o publish se e al
di ec i es o impose s a egies o he nega i e en i onmen al impac o he cons uc ion (i.e., EU was e
di ec i e (2008/98/EC), The Ene gy Pe o mance o Buildings Di ec i e (2010/31/EU) among o he s).
Howe e , none o he p o ided di ec i es ha e discussed he oo causes o he eme ging issues,
and mos o he p oposed egula ions and guidelines o e end-o -pipe solu ions wi hou p oposing
coun e measu es ha ackle was e a sou ce (Osmani e al., 2008). Hence, mos de eloped egula ions
deal wi h symp oms o was e a he han a ge ing how was e is p oduced in e ms o p oduc ion was e.
Tha akes ime uni s in o he o mula o was e gene a ion. P oduc ion was e is usually called non- alue
added (NVA), de ined as any ac i i y ha abso bs esou ces (e.g., ime, loca ion, ma e ial, ene gy, and
o he s) wi hou adding alue o in e nal o ex e nal cus ome s. Acco ding o he me a-analysis o
Ho man & Kenley (2005), NVA ac i i ies cons i u e 49.6% o he cons uc ion ope a ions. The li e a u e
has in es iga ed di e en ypes o NVA, including ewo k (Lo e & Li, 2000a), p oduc de ec s (Josephson
& Hamma lund, 1999), wai ing (Sacks, 2016), anspo a ion (Belayu ham e al., 2016), in ui ional and
in ui ional was e (Sa han e al., 2017),. And he ela ion be ween p oduc ion and en i onmen al was e
(Ca ajal-A ango e al., 2019; Golza poo e al., 2017; Golza poo & González, 2013; Nahmens &
Ikuma, 2012). This dispa i y in measu ing and de ining was e measu es inc eases he di icul y o
o mula ing holis ic and hu dles e o s o p o iding gene al guidelines o oo causes analysis (Fo moso
e al., 2020). Addi ionally, many epo ed ypes o was e a e measu ed empi ically a an ope a ional
le el o p o essional expe ience, which challenges a comp ehensi e judgmen on he na u e o
gene a ed was es and hei ela ionships wi h o he ypes.
Toyo a P oduc ion Sys em (TPS) i s a emp ed a axonomy o p oduc ion was e, which was
2. Li e a u e Re iew
10
supposed o be achie ed by a acking o e p oduc ion and educing in en o y, ope a ional ad an ages
a he p oduc ion le el, and inc easing p o i s a he o ganisa ional le el wi h minimum in es men
(Ohno, 1988). On his basis, lean p oduc ion was ounded as a was e elimina ion- ocused philosophy
o gene a e cus ome alue wi h ze o was e ideally. Lean p oduc ion conside s was e an ac ionable
language ha b ings s akeholde s' a en ion o ac ual causes o ine iciency;Womack and Jones (2003)
d ew om a se o p inciples adop ed in a ious indus ies, including cons uc ion. Lean Cons uc ion
(LC) adop ed hese p inciples o educe cycle ime and p oduc and p ocess a iabili y, en o ce
con inuous imp o emen , and inc ease anspa ency (Koskela, 2000; A. San os, 1999). In addi ion,
o he p inciples, including 'map o alue s eam,' 'es ablish o pull planning,' and 's uc u e he
cons uc ion p ocess in o lows,' a e also used (Koskela e al., 2002).
F om a heo e ical poin o iew, LC is ounded on he T ans o ma ion-Flow-Value (TFV) heo y,
which s a es ha adi ional cons uc ion managemen sees he p ocess as inpu -p ocess-ou pu is a
coun e p oduc i e pe spec i e because i is a black-boxed de ini ion ha hides was es inside he p ocess
which esol es p oduc ion p oblems a e occu ing. The TFV sugges s adding wo mo e concep s, Flow
(F) and Value (V). Tha o ms a new pa adigm in cons uc ion p ojec s, whe e F aims o b eak down he
cons uc ion p ocess in o NVA and VA, as shown in Figu e 2-1, o p o ide echnical me ics o unde s and
and con ol he cons uc ion p og ess and p oblems p oac i ely. The V pe spec i e adds a social
dimension by encou aging he ups eam o unde s and he downs eam equi emen s, which should
wo k collabo a i ely o eap global p ojec op imisa ion ins ead o ocusing on local op imisa ion.
Figu e 2-1 -- T ans o ma ion-Flow-Value heo y
2. Li e a u e Re iew
11
In p ac ice, lean cons uc ion canno be applied only om he ea ly s ages un il hando e bu also
a he end-o -li e/ex ension-o -li e s ages o a building (e.g., demoli ion, eno a ion, e o i ing,
expansion). This applica ion ex ends ac oss he whole cons uc ion supply chain. The ideal Lean Supply
Chain Managemen aims o deli e cons uc ion p oduc s on ime, which elies on pulling in o ma ion
abou he p oduc and p ocess om p oduc ion signals a he han solely o ecas ing demand (Bo olini
e al., 2019; V ijhoe , 2020). P oduc ion signals should be pulled om eliable plans ha a e e ie ed
om LC planning and con ol sys ems (i.e., Las Planne Sys em® (LPS) and Loca ion-Based-
Managemen Sys em (LBMS)). The LPS is a con ex -speci ic and socio- echnical sys em ha aims o
imp o e planning eliabili y h ough successi e collabo a i e sessions o shield he downs eam om
ups eam a iabili y (Balla d, 2020). A he same ime, LBMS is a spa ial, empo al sys em ha
echnically plans and s uc u es he cons uc ion ope a ions acco ding o hei loca ions (i.e., loo ,
zones, sec ions, and loo s) (Kenley & Seppänen, 2010). LPS and LBMS can be used concu en ly o
add ess was e such as wo k-in-p og ess, wai ing, space conges ion, and o e p oduc ion(F andson e
al., 2014). Those was es will be discussed and emo ed collabo a i ely a e acknowledging wo k
s uc u e, sequencing, esou ce alloca ion, and associa ed cons ain s.
Due o he in ensi y o he p oduc ion in o ma ion, labou and manual inpu s o LPS and LBMS can
hinde planning eliabili y. So, Building In o ma ion Models (BIMs) a e essen ial o communica e eal-
wo ld da a s eamlined om LC planning and con ol sys ems and suppo he decisions bo h sys ems
ake. Acco ding o Sacks e al. (2010), BIM unc ionali ies a e ecognised as a cohe en and consis en
sou ce o in o ma ion ha can p o ide up o 56 posi i e in e ac ions wi h lean p inciples. A signi ican
example o LC-BIM is he in eg a ion o LPS in o he BIM 4D model, which assis s in il e ing ade
ac i i ies acco ding o ask eadiness and enables manage s o ack he p oduc ion p og ess and
bo lenecks using he p oduc ion me ics (Sacks e al., 2010). Those me ics include cons uc ion
p oduc ion a e, esou ce consump ion, low index, Plan Pe cen Comple e (PPC), and ask ma u i y,
which can be ansla ed in o Andon signals o b ing s akeholde s' a en ion o p oduc ion was es (Da e,
2013). The empi ical esea ch shows ha LPS-BIM's e ec i eness o was e elimina ion is pa ial
wi hou eal- ime acking, which is imp o ed h ough digi al moni o ing, a i icial in elligence, and linked
da a, among o he echnologies (Da e & Sacks, 2020).
Addi ionally, LBMS can bene i om BIM wo k lows suppo ing LBMS o ecas s wi h accu a e
ma e ial ake-o s and loca ion in e e ence analysis. Mo eo e , LC-BIM has a posi i e impac on
accele a ing he adop ion o o he ini ia i es, including 4.0 cons uc ion, ci cula economy, design-ou -
2. Li e a u e Re iew
12
was e (Ka az e al., 2021), and Design o Manu ac u ing and Assembly (D MA) (Gbadamosi e al.,
2019). The planning and con ol-based LC-BIM app oach holds as was e elimina ion oppo uni ies o
sus ainabili y (Saieg e al., 2018).
The objec i es o his chap e a e o explo e lean cons uc ion app oaches o concep ualise
cons uc ion was e, o iden i y was e elimina ion ac o s p esen ed by lean cons uc ion a a ious s ages
o he cons uc ion supply chain, and o e iew he ela ionship be ween Lean p inciples and BIM
unc ionali ies o add ess cons uc ion was e. The chap e u ilises he sys ema ic li e a u e e iew
me hod o achie e hese objec i es. The sys ema ic e iew ound ou gaps ega ding he concep o
was e in cons uc ion managemen esea ch as ollows: i) pe sis en cons uc ion managemen heo ies
in he cu en esea ch and p ac ice, ii) a holis ic heo y ha explains was e p opaga ion and i s
cha ac e is ics is no p esen ed ye , iii) ambigui y in he epo ed da a collec ion me hods o was e, i )
a ie y in concep ualisa ion o add ess simila ypes o was e.
2.2. Sys ema ic Re iew Resea ch Ques ions
A ple ho a o esea ch a emp s o in es iga e he impac o LC and BIM on imp o ing p oduc ion and
planning using he concep s o was e elimina ion and alue gene a ion. Howe e , LPS-based and LBMS-
based BIM u ilisa ion emains pa allel whe e models a e p esen ed while collabo a i e planning akes
place wi h low au oma ion and ma u i y le els. Addi ionally, he p esen ed esea ch lacks explici
de ini ions and measu es o cons uc ion was e, which is a ely cap u ed sys ema ically. A
comp ehensi e and in eg a i e li e a u e e iew is needed o unde s and how lean cons uc ion and BIM
in e ac o e eal and elimina e was e sys emically. The e o e, his chap e in es iga es hese gaps by
adop ing he SLR me hodology. The o mula ed SLR ques ions a e de ined as ollows:
1. How does lean cons uc ion concep ualise was e?
2. Wha a e was e elimina ion ac o s imposed by lean cons uc ion (LC)?
3. Wha a e he ac o s o LC-BIM ha con ibu e o was e elimina ion?
This chap e is s uc u ed as ollows: Sec ion 2.3 p esen s a de ailed desc ip ion o he sys ema ic
li e a u e e iew me hodology and a desc ip i e analysis o he publica ions in his ield o e ime.
Sec ion 2.4 p esen s con en and hema ic analysis o clus e he exis ing esea ch in o ou esea ch
hemes. Sec ion 2.5 concludes he pe o med analysis and discusses u he de elopmen needed o
2. Li e a u e Re iew
13
imp o e he cu en unde s anding and applica ion o was e elimina ion in he cons uc ion indus y.
2.3. Ma e ials and Me hods
Yea by yea , eno mous esea ch is conduc ed wi h con lic ing unde s andings o cons uc ion was e
and a ious in e en ions o ackle i . A Sys ema ic Li e a u e Re iew (SLR) is a me hod o unde s and
a con ex -speci ic p oblem and laud he sugges ed in e en ions by he li e a u e o add ess ha p oblem
by syn hesising he dispe sed esul s om e idence-based li e a u e. An SLR should be a anspa en ,
upg adable, ans e able, and quali y exclusi e e iew Denye & T an ield, (2009). Howe e , he
cons ain s o SLR a e limi ed o a ime-consuming e iew me hodology ha equi es addi ional
esou ces compa ed o adi ional me hods (Mul ow, 1994; Wohlin & Claes, 2014).
The adop ed SLR me hodology is illus a ed in Figu e 2-2, which shows ha SLR comp ises ou
i e a i e s ages: (1) planning o he e iew, (2) ma e ial collec ion, (3) da a e alua ion, and (4) esul s
epo ing and dissemina ion (T an ield e al., 2003). Du ing 'planning o he e iew,' he e iew ques ion
is de eloped in he ollowing sec ion as an ea ly-s age p ocess ha concep ualises and o mula es
complex p oblems in o a con ex ual ame (Flemming e al., 2019).
Figu e 2-2-- Sys ema ic Li e a u e Re iew (SLR) Me hodology
The li e a u e da a we e collec ed using he Scopus da abase, IGLC, and snowballing echniques
du ing he ma e ial collec ion s age. Mos e ie ed documen s a e om he Scopus da abase ha was
que ied by using wo en ies: he i s que y is a p elimina y sea ch s ing ha combines only he s udy
STAGE 1
PLAN FOR THE
REVIEW
STAGE 3
DAT A EVALUATION
STAGE 4
REPORTING AND
DISSEMINATION
Ɩ S o ing, classi ying and so ing eco ds
Ɩ Da a base c ea ion
Re e ence Managemen
SLR
Me hodology SLR Tasks
Desc ip i e
Analysis
Re e ences
Manage
Mendeley
Resea ch
Da abase
XMind
Keywo d map
Mic oso Excel
Thema ic
Analysis
Quali a i e
Analysis
MaxQDA
Fo mula e esea ch ques ion
Elabo a ed in sec ion 1
Conduc
SLR?
STAGE 2
MATERIAL
COLLECTION
Ɩ Ti le-Keywo d-Abs ac Sea ch
Ɩ Sea ch s ings in sec ion 1.2
Loca ing s udies in Scopus
Mee s
C i e ia?
P elimina y Re iew
Ti le, abs ac & keywo ds Sc eening
Ɩ Full- ex sc eening
Ɩ Desc ip i e e alua ion
Ɩ Full- ex analysis
Ɩ Da abase upda e
Ɩ Inclusion and exclusion
In-dep h Re iew
Ɩ Pe o m Thema ic analysis
Ɩ Resul s e alua ion and ex ac ion
Repo ing he e idence
Ɩ Ma e ial syn hesis
Ɩ Repo and dissemina e he e idence
De elop he e iew p o ocol
Ɩ Iden i y he ques ion ele ance
Ɩ De e mine da abases, keywo ds,
and exclusion and inclusion c i e ia
Quali a i e Analysis
Da abase, So wa e
and Tools

2. Li e a u e Re iew
14
keywo ds' lean cons uc ion' and 'BIM' and 'was e' using he 'AND' ope a o ; he second que y was
o mula ed using he 'AND 'OR' ope a o s o ex end he sea ch ange and also included he Ti le-
Abs ac -Keywo ds using he s eamlined keywo ds in he ee map (Figu e
2
-
3
). The keywo ds used
we e adap ed om (Tezel e al., 2020; Viana e al., 2012). A he same ime, he snowballing echnique
ollowed he p ocedu e sugges ed by (Wohlin & Claes, 2014).
Figu e 2-3 -- A mind map o s eamlined keywo ds
A e que ying he selec ed da abases, he numbe o included documen s o a p elimina y
analysis was (411) eco ds, as illus a ed in Figu e 2-4. The inclusion/exclusion c i e ia a e summa ised
in Table 1 o il e he numbe o documen s included. The equi emen s a e applied o publishing yea ,
esea ched concep s, esea ch domains, and language. A e using he speci ied including/exclusion
c i e ia in Table 2-1, he numbe o esea ch documen s was na owed o (190) a e sc eening Ti les
and Abs ac s. In con as , (136) documen s we e excluded a e ull- ex analysis, so he numbe o
ele an documen s (54) is o be analysed in his pape .
Keywo ds
BIM
LC
Lean cons uc ion
Las Planne Sys em
Jus In Time
Kaizen
Kanban
CW
Visualisa ion
Vi ual Design
Building In o ma ion Model* (BIM)
Non- alue-add* (NVA)
Cons uc ion was e
2. Li e a u e Re iew
15
Figu e 2-4 – P isma diag am applied o esea ched documen s
Table 2-1: Inclusion and exclusion logic used o il e esea ch documen s
C i e ia Inclusion Exclusion
Publishing Yea 1999-2022
Ea lie han 1999 and la e han
2022.
Discussed
Topics
Combine LC and was e concep s, OR
LC, BIM, and was e.
S udies a e on only one concep :
s udies on BIM and was e only.
Resea ch
Domain
Cons uc ion managemen domain
wi h a ocus on LC concep s.
O he domains han LC
managemen .
Publica ion
Language The English language only. O he han English.
.
2.4. Da a E alua ion
This sec ion employs desc ip i e analysis o p o ide a b oad o e iew o was e elimina ion de elopmen
h ough LC and LC-BIM o e ime. Fi s ly, he s udy aimed o depic how he opic quan i a i ely
de eloped o e ime. Figu e 2-5 illus a es ha he numbe o was e elimina ion pape s has isen. F om
Iden i�ca ion
Sc eening
Eligibili y
Included
Reco ds iden i�ed
h ough scopus
(n= 394)
Reco ds a e duplica es
emo ed
(n= 307)
Reco ds sc eened
(n= 258)
Reco ds emo ed by
au ho ƎMahmoudƐ
(n= 49)
Reco ds iden i�ed
h ough IGLC*
(n= 7)
Reco ds excluded i le,
abs ac , o keywo ds
(n= 78)
A icles excluded
(n= 136)
Full a icles assessed
o eligibili y (n= 180)
Reco ds included
(n= 54)
Reco ds included using
snowballing (n= 10)
2. Li e a u e Re iew
16
ea ly 1992 un il he i s qua e o 2022, mos li e a u e ecognised was e elimina ion as a cen al
concep o LC. In 2008, he impac o LC-BIM on was e elimina ion eme ged (Eas man e al., 2008).
Figu e 2-5-- The numbe o documen s con ibu ed o was e elimina ion based on LC and LC-BIM om 1992 o
2022
The p opo ion o ele an documen s ha used he LC-BIM app oach is 53.70%, while 46.30% o
selec ed eco ds u ilised LC heo y and me hods. Figu e 2-6 p esen s an analysis o he objec i e o
was e elimina ion esea ch, which was classi ied in o (i) concep ual, (ii) li e a u e e iew, and (iii)
empi ical.
The i s class includes pape s on heo y and p edominan ly on his o ical and concep ual analysis
o cons uc ion was e. The second class ep esen s li e a u e e iew pape s ha de elop in eg a i e
solu ions based on seconda y da a om e idence-based li e a u e. Empi ical pape s a e hose aimed
o p o ide applica ions o adap a ions o LC and LC-BIM solu ions in a speci ic con ex o he
cons uc ion supply chain, o example, (i) epo ing p oblems and p esc ibing a solu ion o ha
p oblem, (ii) implemen a ion o LC p inciples, me hods, ools, and echniques, (iii) de ine he
equi emen s o was e elimina ion solu ions-based LC-BIM (i ) e alua ion o LC and LC-BIM solu ions,
( ) use o IT a e ac s; among o he s.
2. Li e a u e Re iew
17
Figu e 2-6-- A dis ibu ion o he esea ch me hods om 1992-2021
The analysis demons a es ha he ocus o he li e a u e has been mainly empi ical a he han
heo e ical and li e a u e e iew, as shown in Figu e 2-7. Addi ionally, his highligh s ha heo y is no
e ol ing a he same a e as p ac ical implemen a ion, and he e a e limi ed success ul examples, hence
weak suppo o was e elimina ion in LC-BIM implemen a ion. The analysis in Figu e 2-7 classi ied he
empi ical esea ch in o ou unc ional a eas o he cons uc ion supply chain managemen , namely (i)
Lean Supply Chain Managemen (LSCM), (ii) Value s eam Mapping, (iii) Lean design managemen
(LDM) and (i ) Lean planning and con ol (LPC). Tha shows ha li le empi ical esea ch was conduc ed
on pos -occupancy s ages such as demoli ion (Elma aghy e al., 2018), ehabili a ion (Pe ei a &
Cachadinha, 2011), and acili y managemen (Bascoul e al., 2018).
Figu e 2-7 -- Time analysis o empi ical esea ch om 2002 o 2021.
2. Li e a u e Re iew
24
Figu e 2-10-- Illus a ion o he ole o Cons uc ion Consolida ion Cen es
Lean Design Managemen
Lean Design Managemen (LDM) p o ides simul aneous design p ocesses ha ocus on diminishing
was e collabo a i ely a he ea lies s ages o BIM p ojec s using social, echnical, and socio- echnical
dimensions (Ba kokebas e al., 2021; Uusi alo e al., 2019). The social dimension u ges people o
adop BIM and enhance hei collabo a i e p oduc ion skills based on us and sha ed unde s anding
among he in ol ed pa ies (A ayici e al., 2011). A he same ime, he socio- echnical dimension
elimina es was e in e ms o planning and con ol, cus ome equi emen managemen , decision-
making me hods, and p oblem-sol ing echniques (He e a e al., 2021; Uusi alo e al., 2017). Again,
he p oduc ion indica o s a e used in LDM o quan i y NVA in design wo k low and in o ma ion low.
The cu en p ac ice plan o design asks is Kanban based on so wa e bu wi hou comple e
e lec ion o LC-BIM in eg a ion (Mahalingam e al., 2015). This p ac ice dis ega ds oppo uni ies o
BIM unc ionali ies speci ic o he cons uc ion design con ex . This esea ch ecommends mo e
esea ch on applying LDM planning and con ol me hods in a BIM en i onmen o apply was e
elimina ion concep s. A he same ime, he esea ch lacks in eg a ion o was e elimina ion in LDM
cus ome managemen me hods, which can po en ially s ee design p ocesses and p oduc s owa d
cus ome s' alue and was e elimina ion by u ilising echniques such as Ta ge -Value-Design (TVD),
Choosing-By-Ad an age (CBA) and Se -Based-Design (SBD) (Ka az & Teixei a, 2023a).
Ti e 3
Ti e 2Ti e 1
O -si e Supply Chain On-si e Cons uc ion
Di ec Supply
Demand/ eedback, design in o ma ion,
p oduc ion in o ma ion, Re e e Logis ics
O de s
Re e se Logis ics
Pull Supply
SupplyJIT Allo
Push Supply
Consolida ion
Cen e

2. Li e a u e Re iew
25
Las Planne Sys em
The p ima y objec i e o he LPS® is o p o ide eliable p oduc ion planning ha shields downs eam
om ups eam a iabili y (Balla d, 2000). LPS is de ined as a socio- echnical sys em ha uni es he
s akeholde s, including ' he las planne s' o he las esponsible people, o op imise p oduc ion plans
in successi e de ailed le els (Balla d, 2020). Figu e 2-11 illus a es he s uc u e o LPS, which di ides
he planning in o ou successi e le els: Mas e scheduling (Should), Phase planning (Can), Lookahead
planning (Will), weekly, bi-weekly, o daily planning and lea ning (Did). Mas e scheduling is an o e all
p ojec schedule om s a o inish, which iden i ies Ac i i ies On Nodes (AON) using he CPM me hod
ha delimi s he p ojec scope. The knowledge o a mas e schedule is ed by his o ical da a o
p ac i ione s' expe ience; a mas e schedule is used as a con ac ual schedule o suble wo ks o
subcon ac o s, assess he p ojec easibili y, iden i y long lead imes, and iden i y miles ones o be
used in he nex s age "phase planning" (F andson e al., 2013).
Lookahead planning spans 4-6 weeks, collabo a i ely de i ing sub- asks om he miles one
schedule, de ec ing and delega ing esponsibili y o cons ain s, and hus, ades commi o emo ing
hese cons ain s (Balla d 2000). In he weekly wo k planning (WWP), he eam sc eens cons ained
asks and excludes hem om he execu ion schedule. To s eamline con inuous and s able low, LPS
p ac i ione s commence wo king on ma u e ac i i ies only, and a quali y check o wo k backlog is
applied o wo k package de ini ion, soundness, sequence, size, and lea ning (Balla d & Howell, 1998).
In he lookahead planning window, cons ain s analysis is a c i ical unc ion o LPS. Acco ding o
(Koskela, 1999), he cons ain s o he cons uc ion p oduc s a e se en (1) design, (2) componen s, (3)
ma e ials, (4) ma e ials, (5) space, (6) connec ing wo ks and (7) ex e nal condi ions. Simila ly, (Balla d
& Howell, 1998) classi ied he cons ain s in o h ee clus e s: (1) di ec i es: necessa y in o ma ion o
commence p ocessing (i.e., speci ica ions, p oduc designs, c ew numbe s, and speciali y, among he
o he s), (2) p e ious wo ks: p e equisi e wo k ha mus be inished be o e s a ing new wo k and (3)
esou ces: including labou , equipmen , suppo acili ies, ma e ials, and space.
2. Li e a u e Re iew
26
Figu e 2-11-- A gene ic amewo k o he Las Planne Sys em
(Balla d & Howell, 2003)
Du ing mee ings o sc een asks and cons ain s, a language/ac ion can be used o o malise he
communica ion o commi men s be ween ades o desc ibe, eques , decla e, p omise, o asse
speci ic in o ma ion abou wo k packages (MacOmbe e al., 2005). Con ol indica o s such as PPC a e
used o measu e he eliabili y o p omises, which is he pe cen age o wo k comple ed di ided by he
p omised wo k, which helps in es iga e and lea n po en ial p oduc ion bo lenecks (Sacks, Ko b, e al.,
2018). The LPS p ac ice can diminish was es such as making do, mo ing, wai ing, anspo a ion,
in en o y, ewo ks, and de ec s. Hence, LPS o malises communica ion be ween mul idisciplina y
ades du ing wo k s uc u ing, sequencing, cons ain emo al, con ol, and lea ning.
In o ma ion synch onisa ion is necessa y o implemen LPS, p ima ily by inc easing he planning
de ails o enla ge he immense amoun o in o ma ion ha can be e ie ed in o he sys em, which can
be ackled using BIM unc ionali ies such as 4D planning, clash de ec ion, and si e layou planning
(Da e & Sacks, 2020). Lean-BIM-based p oduc ion planning and con ol sys ems PCS can s eamline
lexible p oduc ion sys ems ha ac i ely espond o bo lenecks. Was e should be e ealed and
P ojec
Objec i e
In o ma ion
Resou ces P oduc ion DID
Planning he
Wo k
CAN WILL
SHOULD
Las Planne
P ocess
2. Li e a u e Re iew
27
unde s ood by imp o ing si ua ional awa eness, ac i a ing oo causes o was e, pe o ming wha -i
scena ios, and ac i a ing eal- ime acking o p oduc ion bo lenecks.
Figu e 2-12 -- Las Planne Sys em s ages, du a ions, de ails, main ac i i ies, add essed was es, and
esponsible pa ies.
Addi ionally, loca ion-based managemen sys ems (i.e., ak -planning, low line, Line-o -Balance) can
be applied o s uc u e e en and s ead wo k lows ac oss loca ions o he cons uc ion p oduc ,
suppo ed by 4D unc ionali y o p o ide addi ional insigh s abou wo k sequencing, ela ed ime, and
spa ial con lic s (Bjö n o & Jongeling, 2007). The a ailable digi al acking me hods a e enabled o
si e condi ions by LPS and BIM, including su eying me hods, lase scanning, indoo posi ioning, GPS,
BLE Beacon, IoT echs, s a ional ouch sc eens, PDAs, mobiles, able s, and RFID (Chen e al., 2020;
2016; Sacks e al., 2010; on Heyl & Teize , 2017). These echnologies deli e addi ional p ospec i e
oppo uni ies in au oma ic was e elimina ion decisions h ough a i icial in elligence (AI) algo i hms,
acco ding o he inc easing was e da a ha can be ex ac ed om Lean planning and con ol sys ems,
which a e equi emen s o sys em lea ning, es ing, and alida ion (McHugh e al., 2022).
Loca ion-based Managemen Sys ems
The goal o Loca ion-based Managemen Sys ems (LBMS) is simila o ha o LPS, which is o s eamline
eliable planning and con ol by planning o con inuous wo k low and p e en ing in e e ence be ween
ades. Ne e heless, LBMS is a scheduling and con ol me hod a he han LPS and depends on
echnical measu es, unlike LPS, whe e social measu es a e appa en . LBMS is p eplanning, planning,
Phase (Pull)
Ɩ Apply e e se planning
Ɩ Iden i y hando s, du a ions,
and o e laps
Ɩ De�ne deli e y condi ions
Ɩ De�ne key miles ones
Ɩ Iden i y c i ical pa h (CPM)
Ɩ Assign s a -�nish ela ions
CAN
SHOULD WILLDID
Mas e Sho - e m Lea ning
Main ac i i ies
Add essed was es
Ac o s
*PPC = Plan Pe cen Comple e; RNC = Reason o Non-Compliance
Ɩ P ojec Manage s
Ɩ Si e/p oduc ion Manage Ɩ P ojec Manage s
Ɩ Si e/p oduc ion Manage
Ɩ P ojec Manage s
Ɩ Si e/p oduc ion Manage
Ɩ C ew manage s
Ɩ P ojec Manage s
Ɩ Si e/p oduc ion Manage
Ɩ C ew manage s
Ɩ Owne
Ɩ Po olio Manage s
Ɩ P ojec Manage s
Planning de ail
Planning S age
Planning Window
Lookahead
Ɩ Cons ain analysis
Ɩ B eakdown P ocesses
Ɩ Design o ope a ions Ɩ Make eliable p omises
Ɩ Measu e PPC*
Ɩ In es iga e RNC**
Ɩ S anda dize
4-6 weeksPhases
Miles ones
Ɩ Budge o e un
Ɩ Schedule o e head
Ɩ ENV measu es
Ɩ Value measu es
Ɩ De ec s
Ɩ Budge o e un
Ɩ Schedule o e head
Ɩ ENV measu es
Ɩ Value measu es
Ɩ De ec s
Ɩ Makind-do
Ɩ Wo k-in-P og ess
Ɩ In en o y
Ɩ C ews absen , inju ies
Ɩ T anspo a ion
Ɩ In o ma ion delay
Ɩ Rewo k
Ɩ Idle (wai ing)
Ɩ Un�nished wo ks
Ɩ Rewo k
Ɩ Mo ing
Ɩ PPC Failu es
Weekly/Bi-Weekly/Daily assignmen s
Pebbles (Assignmen s)
Boulde s (P ocesses)
Rocks (Phases)Rocks (Miles ones)Pebbles (Ope a ions)
2. Li e a u e Re iew
28
scheduling, and con olling sys ems o p oduc ion uni s based on hei physical loca ion (Kenley &
Seppänen, 2010).
The LBMS p ocess comp ises scheduling, schedule op imisa ion, o ecas ing, ala ming, and con ol.
The i s s ep o scheduling using LBMS is de ining Loca ion-Based-S uc u e (LBS). LBS is he me hod
o b eaking he p ojec in o manageable loca ions, simila o Wo k B eakdown S uc u e (WBS), bu
applied o loca ion logic ins ead o ac i i y-based logic. Each ask is de ined a a speci ic hie a chy le el
and includes one o mo e loca ions (Kenely and Seppanen, 2010; pp 125-126). De ining LBS is a
c i ical decision because i is no only necessa y o LBMS pu poses (e.g., de ining logical ela ionships,
isualising low line, and con olling he p og ess), bu i could also be applicable o classi ying quan i y-
ake-o s and signalling logis ics and deli e ies (Kenley & Seppänen, 2010: PP 203-204). In he example
elabo a ed in Figu e 2-13, LBS i s b eaks down he p ojec in o in e dependen s uc u es ha can be
buil sepa a ely, hen na ows i down o manageable loca ions whe e one ade can ope a e
con inuously wi hou in e up ion (wai ing). Whe e ac o s can il e and communica e ac i i ies based
on his LBS code.
Figu e 2-13 -- A gene ic example o LBS hie a chy o a building p ojec (Kenley & Seppänen, 2010)
Accu a e es ima ions o quan i ies a e c i ical o calcula e he schedule du a ions. Calcula ing
quan i ies is a subsequen s ep in LBS de ini ion in LBMS scheduling. The ask quan i ies mus de ine
all he wo k o be comple ed in a loca ion be o e a c ew mo es o he ollowing loca ion. Then, LBMS
builds asks om quan i ies, de ines op imum c ew size, and uses laye ed logic o o he asks.
Du a ions a e compu ed by mul iplying quan i ies wi h labou consump ion and di iding by he c ew
size.
Schedule alignmen and op imisa ion h ough isk managemen expand ade-o s be ween ime,
isk, and cos sough by alignmen , making planning asks con inuous and bu e ing asks agains
in e e ence. LBMS op imises he plan o ensu e wo ke s do no wai o wo k and wo k does no wai
P ojec I
A B C
I II
1
III I II
1
III I
2 3 1 2 1 1 2 2 4 3 4 3 4 3 4 2 3 1 2 3 4 4 4
II III
4 3 2 1 4 3 2 1 3 2 1
P ojec
S uc u e
Floo
Apa men
2. Li e a u e Re iew
29
o wo ke s. LBMS needs mo e da a han CPM, and LBS needs o be de ined be o ehand. A he same
ime, CPM me hods a e a ee o m o planning; hey epo weekly o daily, while CPM is mon hly and
LBMS is eal- ime con ol. The s a egies used o schedule op imisa ion a e 1) changing p oduc ion
a es, 2) adding mo e esou ces, 3) spli ing asks, 4) allowing discon inuous wo k, and 5) adding mo e
o he scope o educing esou ces.
Besides scheduling in o ma ion, LBMS also p o ides con olling in o ma ion. This in o ma ion
includes baseline schedule, cu en s age, p og ess, o ecas ing, and ala ms. The baseline schedule is
an owne epo ing ool ha se s limi a ions o he cu en schedule. The cu en s age enables changing
quan i ies, p oduc i i y a es, logic, and planning du ing p oduc ion, whe e each ask (de ailed ask) is
linked o one baseline ask o compa ison easons. The p og ess s age moni o s he ac ual
pe o mance o he p ojec /loca ion/ asks. In his s age, ac ual da es do no eplace planned ones bu
a e used o compa e and de ec a ia ions. (Inpu ) in s a and inish da es, suspended days, and ac ual
esou ces, while he (ou pu ) is calcula ed as 1) ac ual esou ce consump ion and 2) ac ual p oduc ion
a e (uni s/day/ ade). The o ecas combines he cu en and p og ess s ages in o ma ion o signal
ea ly wa ning abou p oduc ion p oblems by assuming ha he p oduc ion will con inue wi h he same
p oduc i i y and wi h he planned esou ces and ollow he cu en logic. LBMS ala ms show when he
p edecesso delays he o ecas o a successo . Ala ms a e wo weeks be o e he planned ime (Kenley
& Seppänen, 2010: 123-126), ha a emp o p e en cascading delays by concen a ing p oduc ion
con ol esou ces o a e hese ala ms om happening and co ec ing he p edecesso 's p oduc ion
a e o slowing down he successo .
In LBMS, s akeholde s can educe he numbe o in e dependencies be ween ac i i ies p o ided by
CPM me hods, emo e loa be ween asks and synch onise p oduc ion a es. LBMS can apply se e al
con ol indica o s o o ecas p oduc ion capabili ies and p o ide in o ma ion abou oo causes o
cascading delay (i.e., wo k-in-p og ess, wai ing, ewo k, and conges ion) (Kenley & Seppänen, 2010).
Finally, LBMS educes p oduc ion complexi y by s eamlining con inuous low ac oss loca ions, s ee ing
planning a ge s owa ds s able and in e up ed p oduc ion, gi ing clea di ec ions o ade c ews, and
educing isk and wai ing ime (Bio o & Kagioglou, 2020).
The compa ison conduc ed by Bio o and Kagioglou (2020) highligh s Lean-Based Managemen
Sys em (LBMS) echniques as p ominen managemen me hodologies in bo h he mas e and phase
planning p ocesses. Wi hin mas e planning, he Line o Balance (LOB) and Flow Line (FL) echniques
a e commonly applied. LOB p ima ily ocuses on moni o ing he deli e y a e o comple ed uni s, wi h

2. Li e a u e Re iew
30
planne s alloca ing p oduc ion capaci y bu e s wi hin he du a ion o wo k packages pe uni be ween
ac i i ies. This moni o ing unc ion enables LOB o balance p oduc ion by adjus ing he numbe o c ews
assigned o speci ic ac i i ies and modi ying he composi ion and quan i y o wo k o be execu ed.
In con as , he Flow Line (FL) echnique is mo e o ien ed owa d p oduc ion pace planning, wi h
ac o s alloca ing bu e s o p oduc ion capaci y wi hin ask du a ions. The p incipal s a egy o FL
in ol es modi ying he c ew's composi ion. The slope o FL de ines he p oduc ion a e, calcula ed by
di iding he wo k quan i y by he du a ion. FL is ypically employed in epe i i e cons uc ion scena ios
bu can also be applied in complex p ojec s, p o ided he p ojec can be e ec i ely segmen ed in o
equal loca ions.
On he o he hand, he Tak Time planning (TTP) echnique is commonly used in phase planning
because i p o ides mo e signi ican planning de ails han FL and LOB and equi es a highe le el o
collabo a ion. TTP is de ined as ' he uni o ime wi hin which a p oduc mus be p oduced (supply a e)
o ma ch he a e a which ha p oduc is needed (demand) a e' (F andson e al., 2013). I is measu ed
using he Tak Time (TT) indica o , which can be ob ained by di iding he a ailable p oduc ion ime by
he p oduc uni s demanded by he cus ome . The p ima y bu e s a egy used in TTP is like he FL
me hod, which uses a p oduc ion capaci y bu e ha u ges ac o s o inse di e en composi ions and
numbe s o wo ke s wi hin he wo k package.
Figu e 2-14 – Visualisa ion compa ison be ween h ee LBMS echniques: a) Line o Balance; b) Flow Line; c)
Tak Time
(Bio o & Kagioglou, 2020)
2. Li e a u e Re iew
31
2.5.4. Rela ional and Legal S uc u es o Was e Elimina ion
Legal bonds be ween pa ies a e essen ial in guiding cons uc ion o ganisa ions owa d alue c ea ion
and was e gene a ion (Koskela, 2000). Rela ional con ac s eme ged and lou ished in he la e 20 h
cen u y o acili a e a oad map o cons uc ion imp o emen s. In eg a ed P ojec Deli e y (IPD),
In eg a ed Fo ms o Ag eemen s (IFOA), Pa ne ing, and alliance con ac s a e examples o Rela ional
con ac s ha may ackle cons uc ion was e by cul i a ing collabo a ion in de ining clea assignmen s
and esponsibili ies owa d p ojec goals ha align he in e es s o mul iple s akeholde s o op imise
hei p ojec s globally ins ead o ocusing on local op imisa ion o he p oduc ion.
Pa ne ing con ac s educe he liaisons be ween ac o s o esol e issues and in ol e he
downs eam in ups eam decisions h ough imp o ed collabo a ion and anspa ency. Ma e ial was e
is highligh ed in pa ne ing con ac s by imposing a ma e ial was e managemen plan (WMP), bu he
e ms o pa ne ing con ac s lack ocus on p oduc ion was e elimina ion (Ma hews e al., 2000). In
con as , alliance con ac s suppo ask comple ion, manage in en o y and oolbox sha ing, and
alloca e esponsibili ies whe e lean p inciples apply o decompose complex was es and disca d NVA
h ough ools such as 5whys, was e walks 'Genchi nembu su,' spaghe i diag ams, quali y con ol
his og ams and Roo Cause Analysis (RCA). Combining lean p inciples and ela ional con ac s can
elimina e abou 24% o NVA by acili a ing double lea ning, imp o ing p ocess quali y, and implemen ing
sa e y measu es (Vilasini e al., 2014). Howe e , alliance p ocu emen lacks explici e ms o was e
elimina ion, which causes an absence o legal commi men and measu es owa d was e ecogni ion,
analysis, and esponsibili y o was e elimina ion. Mo e esea ch is needed o include he concep s o
was e elimina ion in ela ional con ac s, and his gap can also be applied o IPD, alliance con ac s,
and collabo a i e Design-Build con ac s.
2.5.5. Lean Sus ainabili y
F om he LC pe spec i e, en i onmen al was e encompasses ad e se ou comes wi hin he p oduc ion
sys em ha ail o con ibu e alue o he inal cus ome s (Fo moso e al., 2015; Koskela e al., 2013).
P ac ices associa ed wi h LC ha e demons a ed he po en ial o enhance p oduc ion e iciency and
sus ainabili y pe o mance (Kim & Bae, 2010; Rosenbaum e al., 2014; Saieg e al., 2018). P o ides
e idence elucida ing a ious app oaches o s udying he impac o lean cons uc ion on educing
en i onmen al was e, showcasing educ ions achie ed in each s udy. Howe e , i is wo h no ing ha
2. Li e a u e Re iew
32
speci ic lean me hods, such as Jus -in-Time (JIT), may inad e en ly lead o inc eased ca bon emissions
due o equen shipmen s equi ed o main ain ze o-in en o y condi ions (Hussein & Zayed, 2020).
Mo eo e , a comp ehensi e solu ion o e ec i e acili y managemen wi h a lean managemen
philosophy has no ye been in es iga ed (Elma aghy e al., 2018).
2. Li e a u e Re iew
33
Table 2-3 -- Compa a i e analysis o he in eg a ed me hods wi h VSM o in es iga e cons uc ion was es.
Au ho (s) Me hod Ma e ial
was e
Ca bon
Emission
Ene gy
consump ion Wa e
Land i
lls
Fuel Elec ic
(Golza poo e al.,
2017) DES-I/O ● 41% 41% - - ●
(Fu e al., 2015) LCA - 42-44% - - - -
(Wu e al., 2013)
Low ca bon
lean
sys em
● 29.39% ● - - ●
(Kim & Bae, 2010) CEDST ● 10-20% ● ● - ●
(Rosenbaum, Toledo
and González 2014) VSM 50 o
100%
● ● - - 100%
(Belayu ham,
González and Yiu
2016)
VS-PM ● - - - ● -
(Vil en han, Ram, and
Suguma an, 2019) VSM 13.18-
57.37% - - - - ●
(Nahmens and Ikuma
2012) SLIK 64% ● ● ● ● ●
DES-I/O = Disc e e E en Simula ion – Inpu /ou pu ; LCA = Li e Cycle Analysis; CEDST = Cons uc ion En i onmen al Decision-Suppo Tool; VSM=
Value S eam Mapping; VS-PM = Value S eam-P ocess Map; SLIK = Sa e y and Lean In eg a ed Kaizen; ● = epo ed educ ions wi hou pe cen age
2.5.6. Value S eam Mapping
Applying he Value S eam Mapping (VSM) me hod is indispensable o lean ini ia i es, o e ing a
s uc u ed app oach o isualize in o ma ion and ma e ial lows while iden i ying po en ial imp o emen s
h ough was e elimina ion p inciples (Womack & Jones, 2003). Th ough da a collec ion o p oduc ion
indica o s sou ced om in ol ed s akeholde s o ia di ec su eys and obse a ions, wo dis inc VSMs
can be de eloped: cu en and u u e maps. Bo h maps ely on key pe o mance indica o s such as
NVA (Non-Value-Added), CT (Cycle- ime), WIP (Wo k-In-P og ess), Tak Time (TT) and Queuing Time
2. Li e a u e Re iew
40
in eg a ed in o uni e si y cu icula h ough esponden s' en husiasm o he po en ial o lean
cons uc ion. Howe e , ew esponden s in ended o each lean cons uc ion p inciples in u u e
cou ses. Su p isingly, 42% o p o essional esponden s do no know wha lean cons uc ion is, while
58% con i med ha lean is pa ially o ully applied in hei wo k low.
Ma ias & Cachadinha (2010) E alua ed he po en ials and ba ie s o LC adop ion in Po ugal by
in e iewing o ep esen en con ac o companies and iden i ied ha he p ima y cause o delays and
was e is Reques o In o ma ion (RFI). The LC is no well-hea d in Po ugal, bu esponden s ha e
shown in e es in adop ing i and en husiasm o i s po en ial.
Ma ins & Cachadinha (2013) In e iewed designe s and owne s o in es iga e he oo causes o
was e and e alua e LC po en ials o ackle hese p oblems. Su p isingly, mos esponden s did no know
abou he LC; hey ag eed ha adi ional p ocu emen and RFI a e he mos equen causes o
cons uc ion was e.
In in e iews wi h owne s and designe s in Po ugal, only a ew companies a e es ing LC, and mos
companies a e unawa e o hei po en ial. Po ugal's lack o con ac ual s uc u e leads o lean
cons uc ion and he pe cep ion ha mee ing deadlines does no allow hem o use LC (Ma ins, 2011).
Despi e awa eness o LC po en ials, ano he su ey showed ha he lean cons uc ion philosophy is no
widely applied in Po ugal (Pe ei a, 2014). 53% o lean applica ions in Po ugal a e 5s, and kaizen is
mainly used in logis ics managemen ; 55% o esponden s we e no in e es ed in lea ning lean
cons uc ion philosophy (Casei o, 2016).
Ac ion esea ch was applied in a Po uguese company, and posi i e esul s ha e been epo ed o
applying lean me hods such as Kaizen, isual managemen , 5s, sa e y, and eam boa ds (de Sousa,
2019). H. Jus -in- ime LPS; hei wo k isualised he p ocesses using VSM (Gonçal es, 2009).
Compa a i e esea ch is also pe o med. Fo ins ance, he adop ion o LC philosophy expe ience
was compa ed be ween Po uguese and B azilian pe cep ions o and (Casei o, 2016). Ano he example
was s udying he Danish expe ience in LC and a emp ing o p opose a model o he Po uguese con ex
based on his expe ience (Sil a, 2008).
LC is ela i ely unknown in Po ugal (Gonçal es, 2009), and many c i ical ba ie s ha e been
epo ed. 1) In o mal planning and esis ance o change we e epo ed as c i ical ba ie s. 2) lack o op
managemen suppo bu eauc acy, 3) lack o lean awa eness and unde s anding, 4) ea o new hings,
and 5) poo cons ain s analysis and lack o commi men o sho - e m planning (Gonçal es, 2009), 6)

2. Li e a u e Re iew
41
he esea ch encoun e ed esis ance o change and blockage om companies o sha e in o ma ion,
which a e he mos anked ba ie s (Ped osa e al., 2023).
2.6. Discussion
By e iewing 54 ele an pape s as he sys ema ic li e a u e e iew amewo k, e o s we e made o
ensu e a comp ehensi e unde s anding o he co e ed opics, namely, was e elimina ion, Lean
cons uc ion, and BIM, by add essing he disad an ages and limi a ions o a adi ional li e a u e e iew.
These included designing a e iew p o ocol o a oid ambigui y, inc easing sea ch anspa ency, and
iden i ying he s udy's sui abili y o eplica ions. Tha said, i may be he case ha a small numbe o
he da abases used (i.e., Scopus and IGLC) may ha e p oduced a sligh ly lowe numbe o ound
documen s. Ne e heless, he indings o his e iew indica e ha was e elimina ion is a co e concep
o lean cons uc ion philosophy, bu when i is in eg a ed wi h BIM, ew esul s ha e been epo ed,
especially ega ding p oduc ion was e. Addi ionally, changing cu en esea ch on lean cons uc ion and
BIM does no show explici guidelines o was e elimina ion. Unde s anding he unde lying oo causes
o cons uc ion was e was a signi ican hu dle o building a holis ic e iew o he was e elimina ion
app oaches. This asse ion ag ees wi h p e ious esea ch such as (Fo moso e al., 2020; Viana e al.,
2012), who iden i ied he di icul y o composing a sys ema ic li e a u e e iew on was e in cons uc ion
because o ou easons: 1) pe sis en cons uc ion managemen heo ies in he cu en esea ch and
p ac ice; 2) a holis ic heo y ha explains was e p opaga ion and i s' cha ac e is ics is no p esen ed
ye .; 3) ambigui y in he epo ed collec ion me hods o was e da a; 4) a ie y in concep ualisa ion o
add ess simila ypes o was e.
2.6.1. How does lean cons uc ion concep ualise was e?
The concep o was e in lean cons uc ion has been in eg al since i s eme gence in 1992, emphasising
he impe a i e o was e elimina ion o ensu ing su i al and enhancing he compe i i eness o
companies (Sacks e al., 2018). Wi hin he cons uc ion li e a u e, was e is concep ualised as a se o
symp oms o an ine icien p oduc ion sys em, including ma e ial was e, inju ies, capi al was e, and
ene gy was e. In add essing was e, he lean cons uc ion app oach di e ges om adi ional end-o -pipe
and eac i e s a egies, which me ely espond o symp oms o in e nal was e a e i s occu ence wi hin
a p oduc ion sys em. Koskela, 2013 c i icised he poo ounda ions in he cons uc ion managemen
esea ch in concep ualising was e and p oduc ion concep s; e en he ela ionship be ween hem is he
2. Li e a u e Re iew
42
pu ely economic iew ha only sees he wo ld as inpu and ou pu , which e used o include was e
concep s wi h he exis ing cons uc ion managemen heo ies.
Based on he ans o ma ion- low- alue heo y, lean cons uc ion unde s ands was e conce ning
alue and low concep s in concep ualizing cons uc ion was e. The o me ends o e ine he p ocess
o cap u ing he cus ome alue om any misconcep ion, and he la e u ilises a mix u e o social and
echnical me hods o e eal he cons uc ion lows and expand he anspa ency o see was e ul (NVA)
and no was e ul ac i i ies. These NVAs a e in oduced as a lis o was e and ca ego ized as a “was e
lis .” The lis helps s akeholde s p o ide ano he laye o analysis o was e oo causes in a cons uc ion
sys em.
Was e is concep ualised in lean li e a u e as a communica ion ool and uni o measu emen . The
i s conside a ion o was e is o be concep ualised as an ac ionable language ha acili a es discussion
among s akeholde s, whe e a s o y elling app oach desc ibes he gene a ed was e, he na u e o his
was e, and he consequences o his gene a ed was e. This app oach inc eases si ua ional awa eness
among s akeholde s who can collabo a i ely iden i y and add ess he oo causes o p oduc ion
bo lenecks in hei p ojec s o o ganisa ions. Secondly, was e is a me ic used o measu e he oo
causes o ine iciency wi hin a p oduc ion sys em o o ganisa ion, and i p o ides insigh s in o he key
p ocesses and ope a ions ha equi e u he a en ion o imp o emen . This concep ion o was e also
enables he s akeholde s o ecognise po en ial gains ha can be ealised h ough was e elimina ion
ini ia i es (Bøl iken e al., 2014). Thus, wi hin a lean cons uc ion amewo k, was e elimina ion
ca alyses p oac i e lea ning, p oblem-sol ing, and con inuous imp o emen a he han me ely being a
byp oduc o p oduc ion p ocesses.
2.6.2. Wha a e was e elimina ion ac o s imposed by lean cons uc ion?
The was e elimina ion measu es p o ided by lean cons uc ion can be ca ego ized in o wo dis inc
ace s: social and echnical, as shown in Table 2-5. The was e elimina ion concep is cen alised in lean
cons uc ion ounda ions ope a ionalised in p oduc ion planning and con ol. Due o was e elimina ion
concep s and unc ions embedded wi hin lean p oduc ion planning and con ol, planne s can p o ide
mo e eliable plans and ades wo k wi hin mo e s able wo k lows. The li e a u e e iew e ealed eigh
undamen al p inciples iden i ied: (1) educing cycle ime, (2) minimising a iabili y, (3) os e ing
con inuous imp o emen , (4) enhancing anspa ency, (5) dec easing ba ch size, (6) minimising ewo k,
(7) educing in en o ies and (8) mi iga ing de ec s (Koskela, 2000; A. San os, 1999). Table 2-5 poin ed
2. Li e a u e Re iew
43
o he ac o s o LC o was e elimina ion based on lean p inciples in he Lean Founda ions column. The
second old is s eamlined om p oduc ion planning and con ol unc ions; his s udy sho ens he lean
unc ion o his ole because i can be applied o a ious s ages o In eg a ed Lean P ojec Deli e y
(design, supply, assembly, and use). The second aspec o he esponse s ems om insigh s de i ed
om p oduc ion planning and con ol unc ions. Gi en i s e sa ili y in applica ion ac oss a ious s ages
o In eg a ed Lean P ojec Deli e y (including design, supply, assembly, and u ilisa ion), his s udy
simpli ies he lean unc ion o his ole.
Table 2-5--Lean cons uc ion ac o s o was e elimina ion
Measu es
Lean Founda ions
P oduc ion Planning &
Con ol Func ions
S able and Reliable
P oduc ion Plans
Social •
Respec People
• Decide by consensus.
•
Ex end he ne wo k o
pa ne s.
•
Cap u e he cus ome
alue.
• Align in e nal and ex e nal
clien s.
•
Feedback om downs eam
• Collabo a i e planning and
con ol
•
Use Language/Ac ion
pe spec i e.
• Hand-o Managemen
• P o ide eliable p omises.
• Commi men planning
•
Inc ease Si ua ional
awa eness.
• Feedback loops
•
Ups eam unde s and
downs eam wo k
• Measu e commi men
Technical
•
Simpli y p ocesses.
• Use Reliable Technology
• Reduce Cycle Time
• Reduce ba ch size.
• Inc ease T anspa ency
• S anda dise
• Con inuous Imp o emen
•
Pull Planning
• Wo k s uc u ing.
•
Visualise ma e ial and
in o ma ion low.
• Pull Planning
• Cons ain Analysis
• In e e ence Analysis
• Alignmen Analysis
• Roo Cause Analysis
• Schedule bu e s alloca ion
• Con ol Me ics
• Con ol Cha s
• Resou ces Alloca ion
•
P oduc and low
isualisa ion
• P oduc ion S abili y
• Con inuous one-piece low
• People commi men
• Va iabili y Reduc ion
• Wo kable backlogs
• Quali y Imp o emen
• Con ol Ac ions
•
Wo k low eliabili y
me ics
2. Li e a u e Re iew
44
2.6.3. Wha a e was e elimina ion ac o s imposed by lean cons uc ion (LC) and
Building In o ma ion Modelling (BIM)?
Mos collec ed documen s used BIM as a pa allel p ocess wi h lean cons uc ion implemen a ion
ega ding was e elimina ion based on LC-BIM. This asse ion ag ees wi h he esul s p esen ed by
Ra ajczak e al. (2017). Rega ding was e elimina ion measu es o e ed by adding BIM o LC, bo h
sys ems appea ed o ackle he oo causes o was e caused by agmen a ion be ween p ocess and
p oduc in o ma ion (Da e & Sacks, 2020). Howe e , he li e a u e ne e quan i ied was e elimina ion,
which appea s somewha necessa y.
F om a s a egic poin o iew, ew lean design s udies ha e ac i ely pu sued implemen ing BIM
s anda ds (ISO, 2018) while execu ing BIM wo k lows in Lean cons uc ion p ojec s. Howe e , no
co ela ion was de e mined be ween hese s anda ds and was e elimina ion. The ex an li e a u e ailed
o iden i y he impac o was e elimina ion p ac ices on BIM s anda ds. This gap would sugges u u e
esea ch agendas ocusing on inding he po en ial was e elimina ion-based BIM s anda ds cu en ly
no p io i ised in he e iewed li e a u e.
Again, he li e a u e conside ed BIM's po en ial signi ican in s eamlining LC p inciples, me hods,
echniques, and ools; he e o e, many was e elimina ion measu es we e ini ia ed o imp o e
in o ma ion low, as shown in Figu e 2-16. I is in e es ing o no e consis en o e laps o 'simula ion'
and 'quan i y- ake-o 'du ing p oduc ion planning and con ol by s eamlining eliable and eal- ime
p oduc ion In o ma ion o s akeholde s ha use LPS and LBMS. Howe e , only de eloping LC-BIM
in eg a ion du ing lean supply, planning, and con ol unc ions. Fo example, li le esea ch has been
conduc ed o s udy he impac o LC-BIM on he "use" and "lean design" s ages, which include
eno a ion, e o i ing, expansion, and demoli ion (Elma aghy e al., 2018). This asse ion would
sugges ha he li e a u e conside ed ha mos cons uc ion was e is caused by planning and con ol
in he lean assembly s age and is a ely gene a ed di ec ly o indi ec ly du ing he lean design and
supply s ages. This pe cep ion is a denial acknowledged by (Cheng & Ma, 2013; Uusi alo e al., 2017),
whose indings e eal ha he BIM p ocess con ibu es signi ican ly o cons uc ion p oduc ion planning
and con ol eliabili y.
The li e a u e has a emp ed o de elop so wa e ha ha nesses BIM unc ionali ies o au oma e LPS
implemen a ion (Da e, 2013; Heige mose e al., 2019; Sacks e al., 2010; Schimanski e al., 2021).
These sys ems sha e some cha ac e is ics: 1) isualising he p ocess and p oduc in o ma ion; 2)
p o iding digi al kanban ca ds; 3) digi ising cons ain analysis wo k low and managing cons ain s da a;
2. Li e a u e Re iew
45
4) enabling communica ion be ween design managemen , p ojec managemen , si e managemen , and
c ews; 5) moni o p ojec , p ocess and ope a ion p og ess, 6) enable he p oduc ion me ics o highligh
was es. Such so wa e ad ances he planning unc ionali ies o e ed by comme cial BIM sys ems by
inc easing he planning de ails om mas e schedules only o medium- and sho - e m planning.
Addi ionally, hey enable he eedback om labou e s a he coal ace when da a en y o ms a e
a ailable o each planned ask. Mo eo e , acking echnologies such as IoT (Da e e al., 2016), GPS,
beacons, and au onomous d ones (Lin & Golpa a -Fa d, 2021) enable au oma ic in o ma ion cap u e
o moni o p oduc ion p og ess and ma e ial supply and logis ics s a uses. Such echnologies would be
undamen al o ealise ini ia i es such as a i icial in elligence, Big Da a, Digi al wins o cons uc ion
(DTC), and Indus y 4.0 (McHugh e al., 2022).
Based on he abo e discussion, his chap e p esen s a concep ual amewo k (Figu e 2-16) ha
assembles p oduc ion planning and con ol unc ions based on lean cons uc ion and he exis ing
unc ionali ies o BIM o s eamline was e elimina ion measu es, which e en ually p o ide mo e s able
and eliable p oduc ion plans. The links be ween concep s in he p esen ed amewo k, Figu e 2-17,
will be a basis o hypo hesis gene a ion in he ollowing chap e (Chap e 5).

2. Li e a u e Re iew
46
Figu e 2-16-- C i ical ac o s o BIM unc ionali ies suppo ing lean cons uc ion unc ions.
Figu e 2-17-Gene ic concep ual F amewo k o was e elimina ion model based on Lean cons uc ion and BIM.
BIM P ocesses
P oduc�on Planning
& Con ol Func�ons
S able and Reliable
P oduc�on plans
Was e Elimina�on
Lean Founda�ons
2. Li e a u e Re iew
47
2.7. Conclusions
This chap e p esen s an in-dep h li e a u e e iew based on he sys ema ic li e a u e e iew
me hodology acco ding o (T an ield e al., 2003). The e iew objec i e was o unde s and: i) how lean
cons uc ion (LC) concep ualises was e and ii) wha was e elimina ion ac o s a e imposed by LC and
Building In o ma ion Modelling (BIM). Based on p ede ined exclusion and inclusion c i e ia, he numbe
o eco ds has been il e ed om 406 o 54 ele an documen s. A ime analysis was p esen ed o
illus a e he accumula ion o was e elimina ion knowledge based on LC and BIM be ween 1992-2021.
This analysis shows ha he eme gence o he was e elimina ion concep based on LC has been e ol ing
since 1992, while an in eg a i e app oach o LC-BIM was i s epo ed in 2008. The analysis also
indica es a small quan i y o heo e ical and e iew esea ch compa ed o empi ical esea ch. This
assessmen shows a lack o heo e ical ounda ions o LC-BIM opics, and e en i i exis s, was e
elimina ion was implici ly discussed o sepa a ely analysed.
Was e is a high-le el and con ex -speci ic concep hinde ing p oduc ion pe o mance and nega i ely
impac ing social, economic, and en i onmen al dimensions. The heo e ical pape s discussed he idea
o cons uc ion was e in e ms o na u e, concep ualisa ion, axonomies, and p opaga ion and e iewed
cause-e ec ela ionships be ween di e en ypes o was e. The lean cons uc ion communi y seeks a
holis ic unde s anding o he oo causes o p oduc ion was e and he consequences on he o e all
supply chain. The na u e o p oduc ion was e inhe i s he cha ac e is ics o he cons uc ion p ocesses
in e ms o dependencies be ween asks. (Fo moso e al., 2015) I shows ha he pa e n o was e
p opaga ion can be mapped in o ne wo ks o was e cycles, ypically ini ia ed by ailu es in cons uc ion
managemen unc ions (p oduc ion planning and con ol, p oduc design, si e layou planning, p e ious
s ages, and quali y managemen ). The axonomy o cons uc ion was e has been adop ed om
manu ac u ing esea ch (i.e., Ohno's lis o was e (Ohno, 1988). This lis has been ans e ed in o
p ac ice wi hou e lec ion on he cons uc ion peculia i ies. Hence, a con ex -speci ic axonomy is
needed o he cons uc ion, as Fo moso e al. (2020) sugges ed. Bøl iken & Koskela (2016) epo ed
ha he concep o was e had been igno ed in he cons uc ion managemen esea ch, and he p ac ice
lowe s he impo ance o was e elimina ion in hei agenda. This asse ion shows ha u u e esea ch
should ocus on de eloping new heo ies based on p oduc ion heo ies o unde s and and cap u e he
cha ac e is ics o p oduc ion was e in he cons uc ion con ex .
Howe e , he consensus on he dominance o Making Do and i s eno mous impac no only
gene a es p oduc ion was e (NVA) bu can also hold esponsible beha iou o hinde ing he o e all
2. Li e a u e Re iew
48
p ojec pe o mance. The cu en was e axonomies do no di ec ly add ess he Making Do was e
gene a ion and i s e ec on he cons uc ion indus y. Fo example, Fo moso e al. (2020) iden i y
Making Do as one o he ypes o p oduc ion was e ha occu s a he beginning o con inua ion o a
ask wi h incomple e esou ces, esul ing in eac i e decisions such as ‘ i e igh ing’ o ‘low- ui
g ipping,’ hey do no desc ibe i s peculia i ies in de ail. While hese axonomical amewo ks help
concep ualize cons uc ion was e, such an app oach is insu icien in analysing Making Do. A dedica ed
axonomy o Making Do was e could conside ac o s like inaccu a e ask planning o he p ojec
schedule, ine ec i e communica ion be ween ades, unp edic able a i al o ma e ials, and imp ope
con ol o he esou ces. In o de o gain insigh and p og ess in he comp ehension o Making Do,
esea che s can g ound i in a pa icula axonomy, measu e i s du a ion and he ex en o i s in luence,
and c ea e e ec i e s a egies o i s educ ion.
The empi ical li e a u e hemes ha e been classi ied ollowing he p oposed model o In eg a ed
Lean P ojec Deli e y (ILPD) (Balla d & Howell, 2003). The hema ic analysis shows ha was e
elimina ion is a p ima y pa o lean cons uc ion p ojec s om he ea ly s ages, which should be de ined
in ela ional con ac s as p ojec pu pose in he p ojec de ini ion s age. Howe e , he exis ing
con ac ual s uc u e (e.g., Design-Bid-Build, In eg a ed P ojec Deli e y (IPD)) does no include implici
clauses o was e elimina ion in i s empla es. This gap can hinde s akeholde s' commi men o was e
elimina ion due o he absence o egula o y measu es (Ma hews e al., 2000; Vilasini e al., 2014).
Conce ning he sus ainabili y dimensions, he li e a u e epo ed "imp o ing sus ainable pe o mance"
as an explici objec i e wi hin some lean cons uc ion p ojec s. The consensus by using Value S eam
Mapping (VSM) shows ha p oduc ion was e elimina ion is no only educing en i onmen al was e (such
as ma e ial was e, land ills, gas emission, ene gy consump ion, and was ewa e ) (Kim & Bae, 2010;
Rosenbaum e al., 2014). Ne e heless, social bene i s can also eaped by educing he likelihood o
acciden s and inju ies on-si e o in cons uc ion ac o ies (Gamba ese e al., 2017). The economic
impac s o was e elimina ion a e p esen ed in e ms o educing ope a ion cos s and sho ening he
lead ime o o e all p ojec deli e y, which a e objec i es o any lean ini ia i e (V ijhoe , 2020).
The lean design has an essen ial ole in gene a ing in o ma ion o ab ica ion, logis ics, assembly,
and acili y use, bu he li e a u e lacks app oaches ha s udy he e ec o LC-BIM on he whole supply
chain. The ole o BIM is signi ican i lean cons uc ion is o be con inued as he dominan cons uc ion
managemen me hod in he u u e; he cu en s a e o queue shows ha he only possible way o
au oma e lean sys ems is by mel ing he bounda ies wi h BIM. Thus, he pa allel use o bo h concep s
is no enough. A ypical in eg a ion be ween LC-BIM exis s in p oduc ion planning and con ol sys ems
2. Li e a u e Re iew
49
(i.e., Las Planne Sys em (LPS) and Loca ion-Based-Managemen -Sys em (LBMS)). LPS and LBMS
ha e complemen a y unc ions in c ea ing eliable and s able plans oge he . The po en ial o
ha monised use o bo h sys ems has been e ealed and applied in p e ious s udies (Da e e al., 2016;
Seppänen e al., 2010, 2015), which show ha bo h sys ems a e highly co ela ed; o example, LBMS
become de icien wi hou e ec i e social communica ion o in o ma ion, on he o he hand, LPS
equi es mo e me ics han PPC o measu e he quali y o he cons uc ion low which LBMS o e s.
Bo h sys ems ely on in ensi e p oduc ion in o ma ion in e ms o quan i y, du a ion, and ask de ini ion,
and BIM can p o ide ha , which suppo s p oduc ion planning and con ol wi h he necessa y
in o ma ion when needed.
A mo e c i ical was e elimina ion- ocused esea ch agenda is cu en ly equi ed o in es iga e he
in eg a ion mechanisms o he BIM and LC concep s ac oss he supply chain. Gi en ha he di usion
o BIM and LC ha e been s udied since 2008, i is possibly su p ising ha so ew ha e in es iga ed
hei combined implemen a ion o was e elimina ion pu poses. A comp ehensi e unde s anding o his
combined BIM and LC equi es conside a ions o he p ocesses, echnology, and di usion mechanisms
o in o m p ac i ione s and policymake s o he indus y. To o e come he limi a ions o his s udy in
e ms o co e ing eco ds collec ed om da abases o he han Scopus, da abases such as Web o
Science (WOS), Google Schola , and EBSCO li e a u e collec ion and e iew can be implemen ed in he
u u e.
3. Resea ch Me hodology
56
Resea ch
app oach
Mixed, o en
Quali a i e Quali a i e Quan i a i e Mixed me hods
This esea ch will mix cons uc i ism and posi i ism on ologies, and he o me seeks o cons uc he
knowledge based on p ac i ione s' iews on cons uc ion p ojec s. The la e di ec ly obse es he
making-do phenomena h ough collec ed da a om he cons uc ion p ojec s. Bo h wo ld iews decide
he mixed esea ch app oach, and he quali a i e esea ch app oach p o ides he con ex o he
signi ican pe spec i e. The ole o he esea che can be objec i e in e ms o using closed-ended
ques ions o he esea ch subjec s. Al e na i ely, open-ended ques ions ha he ole o he esea che
is an ac o . These wo ld iews and hei ollowed esea ch app oach a e selec ed because o he
a ailable da a collec ed.
3.2.2. Resea ch Design
The e a e se e al s a egies o conduc ing esea ch, and he li e a u e abounds wi h con adic o y
claims ega ding he igh app oach o achie e speci ic esea ch objec i es o p oblems (C eswell and
C eswell, 2017). As such, conside able e o is equi ed o choose he app op ia e esea ch app oach
and da a collec ion me hods in esponse o he esea ch ques ions. This in es iga ion enables he
esea che o plan while p ope ly conside ing he esea ch pa adigm, s a egies, and echniques. Th ee
esea ch app oaches a e commonly used in cons uc ion managemen esea ch: quan i a i e,
quali a i e, and mixed me hods.
Quan i a i e me hods
Engaging in quan i a i e esea ch en ails he me iculous measu emen o da a h ough sui able
scales. Two undamen al ques ions guide his p ocess: Wha aspec s should be measu ed, and how
should hese measu emen s be execu ed? (Fellows & Liu, 2015). The choice o measu emen scale is
pa amoun , ensu ing ease o da a collec ion, accu acy, and he alida ion o subsequen analyses. In
an ideal quan i a i e app oach, he esea che 's in luence on he collec ed da a is minimised o uphold
objec i i y and mi iga e he po en ial impac o pe sonal belie s.
Quan i a i e me hods adop an empi ical s ance o measu e da a abou key a iables using
app op ia e scales. Building on exis ing indings o enable eplica ion, esea che s o mula e inqui ies.
P edominan ly ollowing a deduc i e esea ch pa adigm, quan i a i e me hods emphasise hypo hesis
and heo y es ing alongside explo ing a iable ela ionships. I commences wi h a hypo hesis o
concep ual model cons uc ion g ounded in exis ing li e a u e and heo ies; subsequen s ages

3. Resea ch Me hodology
57
encompass da a collec ion and analysis o subs an ia e hese ini ial cons uc s. This app oach esona es
wi h he posi i is philosophical s andpoin (Fellows & Liu, 2015).
E iciency in da a collec ion and p o iciency wi h ecognised quan i a i e da a analysis echniques
cons i u e he p ima y s eng hs o quan i a i e esea ch. No ewo hy examples include expe imen al
designs and su ey esea ch. Expe imen al design assesses he impac o independen a iables on
one o mo e dependen a iables. In his con ex , he dependen a iable ep esen s he measu ed
esponse. Be ween-subjec s designs in ol e andomly assigning subjec s o expe imen al condi ions,
enabling da a collec ion ac oss a ious g oups.
On he o he hand, su ey esea ch gauges ends, a i udes, o consensus wi hin a popula ion
sample o gene alise indings o a b oade con ex . I acili a es he ex ension o conclusions om a
sample o he en i e popula ion. Su ey esea ch p edominan ly employs ques ionnai es as da a
collec ion ools, encompassing closed-ended and open-ended que ies. While sel -adminis e ed and
pos ed ques ionnai es a e s anda d, he popula i y o web-based ques ionnai es, u ilising pla o ms like
Su eyMonkey, Ques ionP o, Google Su eys, and Mic oso Fo ms, has g own. Le e aging web-based
ques ionnai es enhances bes p ac ices, encou ages pa icipa ion, educes po en ial e o s, and
s eamlines da a p ocessing and analysis.
Quali a i e me hods
Quali a i e esea ch p edominan ly adop s an induc i e app oach cen ed a ound heo y gene a ion.
Wi hin his esea ch amewo k, ex ual analysis is a common p ac ice. The esea ch p ocess ini ia es
wi h da a collec ion and subsequen analysis o disce n pa e ns, culmina ing in cons uc ing a
heo e ical amewo k. Quali a i e esea ch o e s a no able ad an age by del ing deeply in o he
phenomena unde in es iga ion, al hough i o en demands a conside able ime in es men . Quali a i e
esea ch encompasses di e se me hodologies, including e hnog aphy, g ounded heo y,
phenomenology, and case s udy.
Quali a i e esea ch me hods ind hei p ima y u ili y in comp ehending he unde lying causes,
p inciples, and beha iou s associa ed wi h a gi en p oblem o issue, as exp essed by pa icipan s
(Fellows and Liu, 2015). The esea che 's ole includes de e mining a iables and he co esponding
measu emen me hodology. Fo ins ance, phenomenological esea ch en ails an inqui y design o
elucida e indi iduals' expe iences o a phenomenon, equen ly accomplished h ough in e iews
(Gio gi, 2009). G ounded heo y is a esea ch s a egy in which a heo y o p ocess o beha iou is
de ised and g ounded in he pa icipan s' iews (C eswell & C eswell, 2017).
3. Resea ch Me hodology
58
On he o he hand, case s udies acili a e an in-dep h examina ion o a case, p ocess, e en , o
indi idual wi hin he bounds o speci ic empo al and ac i i y pa ame e s, u ilising an a ay o da a
collec ion echniques (Yin, 2018). The in o ming case s udies will be used in his hesis as a me hod o
esea ch ha includes a de ailed examina ion o Making-Do was e as a single ins ance o conclude
b oade insigh s and heo ies while unde s anding complex phenomena om di e en pe spec i es (F.
Chen e al., 2020; Dixon-Woods e al., 2007; Ickis & Omazić, 2013).
Mixed me hods
The mixed me hods app oach ha monises quan i a i e and quali a i e da a, add essing he limi a ions
o indi idual me hods and o e ing mo e p o ound insigh s in o he esea ch ques ion. While e ms like
mul i-me hods, in eg a ed me hods, and quan i a i e and quali a i e me hods a e used, "mixed
me hods" is he mos p e alen designa ion (C eswell & C eswell, 2017). This app oach le e ages
iangula ion, whe ein di e se da a sou ces a e sc u inised o es ablish esea ch hemes. In line wi h
C eswell's ca ego isa ion based on he sequence o me hod applica ion, mixed me hods exhibi h ee
p ima y ypes: 1) con e gen pa allel mixed me hods, in ol ing simul aneous collec ion and compa ison
o quali a i e and quan i a i e da a; 2) explana o y sequen ial mixed me hods, commencing wi h
quan i a i e da a collec ion and analysis be o e p oceeding o quali a i e da a collec ion and analysis;
and 3) explo a o y sequen ial mixed me hods, ini ia ing wi h quali a i e da a collec ion and analysis,
ollowed by co esponding quan i a i e phases. These ypologies p o ide s uc u e o da a in eg a ion
and o e esea che s a amewo k o conduc ing comp ehensi e mixed me hods s udies.
3.3. Da a analysis me hods
3.3.1. Pa e o (80/20) ule
A Pa e o cha combines ba s and a line g aph, wi h indi idual alues a anged in descending o de on
he ba s and he cumula i e o al ep esen ed by he line. The cha is named a e he Pa e o p inciple,
which, in u n, is de i ed om he economis Vil edo Pa e o. The p ima y objec i e o using a Pa e o
cha is o highligh he mos signi ican ac o s. I is commonly u ilised in quali y managemen as a ool
o quali y con ol. The cha p o es help ul in iden i ying he mos p obable causes o de ec s in
p oduc s and p ocesses, aiding in ocused p oblem-sol ing and decision-making wi hin a ious
indus ies.
3.3.2. Fac o Analysis
3. Resea ch Me hodology
59
Fac o analysis is a s a is ical me hod o disco e ing a o a ed subse o p incipal componen s ha ,
ins ead o he o iginal a iables, educe he amoun o noise in he da a while e aining he in o ma ion
ha s ands o he ac ual a iables in he "Mul i a ia e Da a Analysis" (Hai e al., 2019). I is mainly
used in da a educ ion and dimensionali y educ ion, whe e he objec i e is o dec ease he numbe o
a iables in o ewe ac o s, subjec o he bes p ese a ion o he o iginal a iabili y. Fac o analysis
in ends o disco e he unde lying s uc u e in he da a using i s abili y o ecognise common pa e ns
among he obse ed a iables. These unknown ac o s a e he basis ha encompasses he aspec s o
he subjec ha a e no di ec ly measu ed bu a e pe cei ed h ough he ga he ed a iables. The ac o
analysis p ocedu e di e s in how he ac o s ha e been ex ac ed om he co ela ion ma ix o he
obse ed a iables wi h he o a ed ac o s ha yield a mo e s aigh o wa d and easie - o-in e p e
solu ion. The esul ing ac o s shed ligh on he in e connec ions be ween he obse ed a iables and
he main ac o s aiding in unde s anding he ou comes. In summa y, explo a o y ac o analysis is i al
o e ealing he dis inc i e con en o mul i a ia e se s and de e mining he la en ac o s ha p oduce
he complex pa e n o associa ions in he da a se .
3.3.3. Chi-Squa e Associa ion es
The chi-squa e es uses a con ingency able o e alua e hypo heses o s a is ical analyses, especially
when he sample size o he collec ed da a is la ge (Field, 2005; Has ie e al., 2009). This es mainly
examines he ela ion be ween wo ca ego ical a iables in a c oss- abula ion se ing. These c oss-
abula ions classi y he da a in o wo disc e e o g oupings, implying wo independen a iables in he
da ase . The ank o he able e e s o he ca ego ies o he i s a iable, while he column indica es
he ca ego y o he second a iable; each a iable mus ha e mo e han one ca ego y o ac o . Any cell
in he chi-squa e o con ingency able sums up he numbe o occu ences ha all wi hin a pa icula
combina ion o he ca ego ies, making i easie o unde s and he in e ela ion be ween a iables. The
chi-squa e es encompasses wo p ima y ypes o s a is ics: Pea son's and Likelihood a io chi-squa es.
The o mula o each is s a ed below.
In Equa ion 3.1,
pi
ep esen s he summa ion o all p obabili ies pi, ac oss all ca ego ies
i
om 1 o
.
In a andom sample o
n
obse a ions om a popula ion, hese obse a ions a e di ided in o
mu ually
exclusi e classes, each wi h co esponding obse ed numbe s
x
ij ( o i = 1, 2, ...,
). The null hypo hesis
s a es he p obabili y
pi
ha an obse a ion belongs o he i h class.
X2
ep esen s he Chi-squa e (
X2
)
s a is ic, a measu e o how obse ed da a de ia e om expec ed da a. Tha es s he in e dependence
o wo ca ego ical a iables and he goodness-o - i be ween obse ed and expec ed equencies. Fo
each cell in he con ingency able,
X2
calcula es he a io o he squa e o obse ed equency (Oij) o he
3. Resea ch Me hodology
60
expec ed equency (E
ij
). The esul is he
X2
s a is ic, and in o de o decide whe he he di e ences
obse ed a e s a is ically signi ican o no , i is compa ed wi h he c i ical alue om he Chi-Suqa e
dis ibu ion wi h he ele an deg ees o eedom.
�𝑝𝑝𝑖𝑖= 1
𝑟𝑟
𝑖𝑖=1 Equa ion 3.1
𝑋𝑋2=�𝐸𝐸𝑖𝑖𝑖𝑖 =�𝑂𝑂𝑖𝑖𝑖𝑖2
𝐸𝐸𝑖𝑖𝑖𝑖 −𝑛𝑛
𝐶𝐶
𝑖𝑖=1
𝑟𝑟
𝑖𝑖=1 Equa ion 3.2
whe e 𝐸𝐸𝑖𝑖𝑖𝑖 =(𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡 𝑟𝑟𝑡𝑡𝑟𝑟𝑖𝑖) x (𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡 𝑐𝑐𝑡𝑡𝑡𝑡𝑐𝑐𝑐𝑐𝑐𝑐𝑗𝑗)
𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡 𝑐𝑐𝑐𝑐𝑐𝑐𝑛𝑛𝑛𝑛𝑟𝑟 𝑡𝑡𝑜𝑜 𝑡𝑡𝑛𝑛𝑜𝑜𝑛𝑛𝑟𝑟𝑜𝑜𝑡𝑡𝑡𝑡𝑖𝑖𝑡𝑡𝑐𝑐𝑜𝑜 Equa ion 3.3
Likelihood- a io chi-squa e s a is ic ep esen s he a io be ween obse ed and expec ed equencies in
he associa ion able as exp essed in Equa ion 3.5
𝐺𝐺2= 2 ��𝑂𝑂𝑖𝑖𝑖𝑖 ln �𝑂𝑂𝑖𝑖𝑖𝑖
𝐸𝐸𝑖𝑖𝑖𝑖�
𝐶𝐶
𝑖𝑖=1
𝑟𝑟
𝑖𝑖=1 Equa ion 3.4
Whe e 𝑂𝑂𝑖𝑖𝑖𝑖is e e ed o he obse ed alue o equency in he cell (i,j), 𝐸𝐸𝑖𝑖𝑖𝑖 is he expec ed equency
alue o cell (i,j).
A con ingency able's deg ee o eedom (DF) is he numbe o pa ame e s ha can a y independen ly
in a s a is ical compu a ion. DF is he numbe o cells in he associa ion able ha can be changed
eely once he ow and column o als a e ixed. Equa ion 3.6 shows he o mula o DF, whe e R is he
numbe o ows and C is he numbe o columns. DF is essen ial when using
X2
s a is ics because DF
helps de e mine he c i ical alue o he Chi-Squa e dis ibu ion, allowing he esea che o compa e
calcula ed Chi-Squa e alues o decide whe he o ejec he null hypo hesis.
𝐷𝐷𝐷𝐷=(𝑅𝑅−1) × (𝐶𝐶−1)
Equa ion 3.6
3.3.4. Linea Reg ession Analysis
Linea Reg ession Analysis (LRA) is a s a is ical modelling echnique ha examines causal ela ionships
wi hin p ede e mined da ase s ha a e linea in he pa ame e s. I o mula es linea equa ions,
speci ically he bes - i line (commonly known as he leas squa e line), o explica e he associa ions
be ween p edic o and dependen a iables. LRA p o es in aluable o esea che s in p edic ing and
3. Resea ch Me hodology
61
o ecas ing he beha iou o modelled da a, exhibi ing con e gence wi h me hodologies inhe en o
machine lea ning. The Linea Reg ession model se es he pu pose o es ima ing pa ame e s in ol ing
unknown a iables, deno ed as β o pa ame e s; p edic o s, deno ed as 𝑋𝑋𝑖𝑖, dependen a iables
ep esen ed as 𝑌𝑌𝑖𝑖 and e o s signi ied by 𝑒𝑒𝑖𝑖. The esul an linea o mula o he eg ession unc ion 𝑌𝑌𝑖𝑖
is exp essed as ollows:
𝑌𝑌
𝑖𝑖
=𝑓𝑓(𝑋𝑋
𝑖𝑖
+𝛽𝛽)+𝑒𝑒
𝑖𝑖 Equa ion 3.7
The esul ing eg ession models should be es ed o i ness, and se e al s a is ical indica o s desc ibe
he i ness o eg ession models, such as s anda d de ia ion (S), R2, adjus ed R2, and Du bin-Wa son
s a is ic.
S
s ands o he s anda d de ia ion o he esiduals ha measu es he dis ance be ween he da a alues
and he i ed alues p o ided by he eg ession model. This s a is ic is measu ed in he same uni s as
he dependen a iable ( esponse). A lowe alue o S indica es a be e i o he model o desc ibe he
da a. Howe e , a lowe S alue alone does no gua an ee he alidi y o he unde lying model
assump ions. I is essen ial o examine esidual plo s o e i y po en ial iola ions o assump ions,
he eby ensu ing he model's eliabili y.
R2
(R-squa ed), o he coe icien o de e mina ion, ep esen s he p opo ion o a iance in he
dependen a iable accoun ed o by he independen a iables in he model. I is calcula ed as one
minus he a io o he esidual sum o squa es ( ep esen ing he unexplained a ia ion) o he o al sum
o squa es ( ep esen ing he o al a ia ion wi hin he da a). R-sq is a c ucial me ic o assessing he
goodness o i o he eg ession model, wi h highe alues indica ing a mo e signi ican p opo ion o
explained a iance and a be e i o he da a. The R-sq alue anges be ween 0% and 100%, whe e 0%
indica es ha he model explains none o he a ia ions, as shown in Figu e 3-1 (c), 50% indica es ha
he model explains 50% o he a ia ion, as in Figu e 3-1 (b) and 100% indica es he model explains all
he a ia ion Figu e 3-1 (a).
𝑅𝑅2= 1 −𝑆𝑆𝑆𝑆𝑅𝑅
𝑆𝑆𝑆𝑆𝑆𝑆= 1 −𝑆𝑆𝑆𝑆𝑆𝑆 𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑒𝑒𝑆𝑆 𝑅𝑅𝑒𝑒𝑅𝑅𝑆𝑆𝑒𝑒𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑛𝑛
𝑆𝑆𝑆𝑆𝑆𝑆 𝑅𝑅𝑓𝑓 𝑡𝑡𝑅𝑅𝑡𝑡𝑆𝑆𝑡𝑡 𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑒𝑒𝑅𝑅 = 1 −∑(𝑦𝑦𝑖𝑖−𝑦𝑦�𝑖𝑖)2
∑(𝑦𝑦𝑖𝑖−𝑦𝑦�)2
Figu e 3-1-- R squa ed alues ep esen ing he goodness o eg ession line i
(a)
(b)
(c)

3. Resea ch Me hodology
62
The Du bin-Wa son s a is ic es s i s -o de au oco ela ion in ime se ies da a wi hou lagged
independen a iables anging om ze o o ou . A alue close o 2 shows no au oco ela ion, while an
app op ia e ange is abou 1.50 o 2.50. Values below 1.50 indica e posi i e au oco ela ion; alues
abo e 2.50 indica e nega i e au oco ela ion. Howe e , he Du bin-Wa son s a is ic becomes un eliable
in models ha con ain lagged a iables. High au oco ela ion, in se ies wi h seasonal solid pa e ns,
sugges s ha o dina y leas squa es eg ession may no be he mos accu a e me hod o use when
o ecas ing.
𝐷𝐷𝐷𝐷=∑(𝜀𝜀𝑡𝑡−𝜀𝜀𝑡𝑡−1)2
𝑟𝑟
𝑖𝑖=2∑𝜀𝜀𝑡𝑡2𝑟𝑟
𝑖𝑖=1
3.3.5. PLS-SEM S a is ical Technique
The p ima y i ue o s uc u al analysis lies in i s abili y o p opound, explo e, and implemen scien i ic
disco e ies, making i an ine i able passage o con empo a y esea che s o ei he na u al o social
sciences. The Pa ial Leas Squa es S uc u al Equa ion Modelling (PLS-SEM) was a second-gene a ion
app oach among he mul i a ia e analysis me hodologies. On he o he hand, Co a iance-Based
S uc u al Equa ion Modelling (CB-SEM) p e ailed in social science li e a u e ill a ound 2010, whe eas
CB-SEM was a he apex. The e o e, esea che s should be awa e o hese his o ical de elopmen s o
emb ace new possibili ies. A he same ime, PLS-SEM, which is abo e he CB-SEM in popula i y
nowadays, has expe ienced a signi ican spike in he numbe o publica ions made in many disciplines
such as ma ke ing, managemen , in o ma ion managemen sys ems, accoun ing, s a egic
managemen (Hai e al., 2019), cons uc ion managemen (Wang e al., 2024) and many o he s.
Wi hin da a analysis, uni a ia e analysis in ol es s udying a single a iable o cha ac e ise i s dis ibu ion
wi h measu es o cen al endency and dispe sion h ough g aphical ep esen a ion. This kind o
app oach o bi a ia e analysis can be aken o he nex le el by examining he ela ions be ween wo
di e en a iables (o dependen and independen a iables). I is done o ind he pa e ns and
associa ions in he da a. He e, co ela ion analysis and eg ession can be u ilised. Nex , mul i a ia e
analysis deals wi h ela ionships among a ious mul i-dimensional a iables and uses a di e se se o
s a is ical echniques, e.g., ac o analysis, clus e analysis, and logis ic eg ession, o disco e unknown
s uc u es and in ica e pa e ns wi hin he da a. This in eg a i e app oach allows esea che s o une
in o he complexi ies o in e ela ionships and he in e ac ions among nume ous a iables, hus opening
an oppo uni y o a mo e de ailed comp ehension o he phenomena (Hai e al., 2019).
3. Resea ch Me hodology
63
These mul i a ia e s a is ical me hods a e o en e e ed o as i s -gene a ion me hods (Fo nell, 1982)
and can be ca ego ised in o wo main ypes: conduc ing explo a o y and con i ma ion s udies. The
con i ma o y app oach e alua es he heo ies de eloped and accep ed a p io i ia hence o h known
me hods. Di e en a achmen me hods consis o logis ic eg ession, mul iple eg ession, analysis o
a iance, and con i ma o y ac o analysis. While p edic ion echniques aim o o ecas exis ing o u u e
pa e ns, he p ima y ole o explo a o y me hods is o gain insigh s in o he da a when li le o no
p e ious knowledge is a ailable. The explo a o y me hods include K-means clus e ing, mul i-
dimensional scaling, and explo a o y ac o analysis.
Rega ding he choice o so wa e, one has se e al op ions o implemen ing pa ial leas squa es
s uc u al equa ion modelling (PLS-SEM) and co a iance-based s uc u al equa ion modelling (CB-SEM).
Rega ding PLS-SEM, PLS-G aph by (Chin e al., 2003) is he mainly used op ion, and Sma PLS (Ringle
e al., 2005) is also a a ou i e. Besides ha , R and semPLS by Monecke and Leisch (2012) a e also
conside ed. While CB-SEM can be ca ied ou using p og ams such as LISREL, AMOS, CALIS, EQS, and
SEPATH, i comes wi h some cons ain s like measu emen e o , sample size, and unobse able e o s.
The SEM p ocedu e:
• Speci y he measu emen model: his s ep p o ides a igo ous analysis o he i ness
o da a wi h he modelled a iables. Table 3-2 summa ises he model indices and hei
accep able alues. As a composi e o CMIN/d , Compa a i e Fi Index (CFI), Tucke -Lewis Index
(TLI), Roo Mean Squa e Residual (RMSEA), and S anda dized Roo Mean Squa e Residual
(SRMR), hese model- i indices a e commonly used o he o e all e alua ion o he model.
Some calcula ed s a is ics signi ican ly di e ed om he es ablished below-s anda d alue
de ined in p e ious esea ch (Bagozzi & Yi, 1988; Ben le , 1990; Hu & Ben le , 1998; Schumacke
& Lomax, 2004).
Table 3-2 -- Model i ness measu es
Fi indices Th eshold Sou ce
P obabili y le el Insigni ican
(Bagozzi & Yi, 1988)
CMIN/DF Be ween 1 and 3
(Schumacke & Lomax,
2004; Ullman &
Ben le , 2012)
CFI >0.95 (Ben le , 1990)
3. Resea ch Me hodology
64
TLI >0.08
SRMR <0.08
(Hu & Ben le , 1998)
• Iden i y he S uc u al model: a s uc u al model shows he expec ed ela ionships be ween he
cons uc s (also called unmeasu ed o la en a iables) and, in some cases, be ween he
a iables (also called obse ed a iables) ha se e as he measu emen componen s o he
cons uc s. The s uc u al model desc ibes he ela ionship be ween he a iables, including
hei in e media e and di ec ela ionships, and is o en ep esen ed h ough a pa h diag am.
This hesis applies wo ypes o pa h analysis: media ion analysis and mode a ion analysis, as
illus a ed in Figu e 3-2.
Figu e 3-2 -- Media ion analysis and Mode a ion analysis.
• Media ion analysis examines whe he he e ec o an independen a iable (X) on a dependen
a iable (Y) is ansmi ed h ough a hi d a iable known as he media o . I explains how one
a iable a ec s ano he , including pa hs om X o he media o , media o o Y, and X o Y, and
di ec e ec (a*b) (Ba on & Kenny, 1986). The analysis in ol es he signi icance o he indi ec
e ec (a*b) and o en uses me hods such as he Sobel es o boo s apping o he signi icance
o es ing (Hayes, 2013).
• Mode a ion analysis examines whe he he e ec o an independen a iable (X) on a dependen
a iable (Y) is mode a ed h ough a hi d a iable known as he mode a o . I de ines he
condi ions o he ela ion be ween wo a iables changes, and his consis s o he main e ec
o X and Mode a o and he in e ac ion e ec o X*Mode a o on Y. S a is ical analysis is ypically
used o analyse he signi icance o he in e ac ion o he e m (X* Mode a o ) whe eby an
assessmen o i s signi icance is usually ca ied ou ; some imes simple slopes analysis is used
o explaining his in e ac ion (Ba on & Kenny, 1986).
3. Resea ch Me hodology
65
3.4. Explo a o y and Con i ma o y Fac o Analysis
This hesis employs Explo a o y Fac o Analysis (EPA) and Con i ma o y Fac o Analysis (CFA) o
disco e he unde lying s uc u e o LPS and BIM a iables. Table 3-3 summa ises he dis inc ion
be ween he wo echniques. EPA is o en used a ea lie s ages o he esea ch when no p io i
hypo heses a e a ailable abou he ela ionships be ween a iables and ac o s; he da a d i e he
p ocedu e and in es iga e he unde lying ac o s uc u e by iden i ying he numbe o ac o s and
loadings o obse ed a iables wi hou any p io y s uc u e (Leand e R. & Duane T., 2011). EPA o en
u ilises ac o o a ion o achie e a mo e s aigh o wa d and in e p e able s uc u e o ac o s. The CFA
is o en used o con i m he esul s o EPA by ensu ing ha each ac o loads exclusi ely on o one
cons uc ; i inc eases he cla i y and p ecision o he modelled s uc u e o ela ionships and da a.
Based on es ablished esea ch indings and heo ies, CFA es s exis ing hypo heses abou he ac o s'
s uc u e. The esea che assigns he numbe o ac o s, se e al a iables associa ed wi h each ac o ,
and he ela ions be ween ac o s. Model i indices such as Chi-squa e, RMSEA, TLI, and CFI es
whe he he da a i s o ejec s he modeled s uc u e(Hai e al., 2019).
Table 3-3-- Explo a o y and Con i ma o y Fac o Analysis
Explo a o y Fac o Analysis
Con i ma o y Fac o Analysis
Reduc ionis
I is b
ene icial o condensing ex ensi e
da ase s wi h nume ous indica o s. I
a
lso aids in iden i ying any o e lap o
ambigui y in indica o s, pa icula ly in
cases whe e hey may measu e mul iple
cons uc s simul aneously.
Ensu e ha each indica o loads exclusi ely on o
one cons uc , p e en ing ambigui y and
enhancing he cla i y and p ecision o he model.
Ma ices
Co ela ion ma ices a e commonly
u ilized
, ye hey pose challenges when
compa ing pa ame e s ac oss di e en
samples.
I uses a co a iance ma ix and is mo e adep a
handling compa isons ac oss samples. Using
he co a iance ma ix, SEM is be e equipped o
manage compa ison
ac oss samples, o e ing
g ea e obus ness and accu acy in pa ame e
assessmen s.
Ro a ion
Conside s da a o a ion
I does no conside o a ion
3.5. Sys em Modelling and Simula ion
Sys ems hinking p o ides ools o be e unde s anding complex managemen p oblems. A key
ecommenda ion om sys em hinke s o p oblem-sol ing is o shi he ocus om add essing he
3. Resea ch Me hodology
72
Figu e 3-9 - Common e e ence modes (Ki kwood, 1998)
3.5.4. S ock and Flow Diag am (SFD)
In he model o mula ion phases o SDM, CLDs a e ans o med in o S ock and Flow diag ams (SFDs).
In ypical SFD, he e a e six ypes o objec s, as shown in Table 3-4. The S ock elemen e e s o a eal-
wo ld p ocess's le els, accumula ion, and agg ega ions o e ime. S ocks change h ough Flow
elemen s, he e o e de ining he dynamici y o he sys em. The cloud elemen o SFD e e s o i ele an
in o ma ion o he model o in o ma ion ha lea es he model wi hou a ec ing he S ock o he low.
Dynamic a iables a e auxilia y o in e media e unc ions ha dynamically in e ac wi h he s ocks and
low in he sys em. Pa ame e s a e simila o dynamic a iables bu a e s a ic alues ha p o ide he
sys em bounda y a ia ions o condi ions. Dynamic a iables and pa ame e s a e connec ed o SFD
using links iden ical o hose used o o mula e CLDs. Table unc ions ep esen a se ies o collec ed
da a con aining unc ions as a gumen s and alues. Ma hema ical o mulas a e necessa y o d i e
changes in he SFD models, as he nex sec ion discusses he ounda ions o s ock and low calcula ions.
Table 3-4: The componen s o he S ock and Flow Diag am
SFD
componen s
De ini ion Legend
S ock Sys em le els, s a es, accumula ion, and in eg a ions a e only
S ock
Exponen ial G ow h
Oscilla ions
Goal-Seeking
S-Shape
Pe o mance
Time
Pe o mance
Time
Pe o mance
Time
Pe o mance
Time
Goal

3. Resea ch Me hodology
73
con olled by change a es ( low); ano he s ock le el canno
di ec ly in luence he s ock le el.
Flow Changes (o a e o changes) in each si ua ion
Cloud I is a s a e used when he in o ma ion is i ele an o he
pu pose o analysis.
Dynamic
and s a ic
Pa ame e s
In e media e concep s ha consis o unc ions o s ocks and
a ec lows. Cons an alues o m he bounda y condi ions
o he sys em.
Table
Func ions
A unc ion is de ined as a able using an a gumen and
e u ned alue.
Links A di ec line is a low o he han a low ha connec s di e en
SFD componen s.
3.5.5. SDM Ma hema ical Founda ions
The S ock and Flow Diag am (SFD) is unchanged wi hou ma hema ical models wo king in he
simula ion so wa e's back ends. F om he s ock de ini ion as agg ega ion o accumula ion, i is easy o
p edic he p ima y ma hema ical unc ion behind SDM. Figu e 3-10 ep esen s he mos
s aigh o wa d S ock and low diag am ha will be used o explain SDM ma hema ics as exp essed in
Equa ion 3-1, which indica es ha s ock change o e ime equals he accumula ion o di e ence
be ween in lows and ou lows wi h addi ions o ini ial S ock. A nume ical app oxima ion is used ins ead
o di ec in eg a ion, and wo s anda d me hods a e used o app oxima ion, namely Eule and Runge
Ku a me hods, which a e used o app oxima ion exp essed in equa ions.
Figu e 3-10: Basic S ock and Flow Diag am
ow
ou low
s ock
ini ial_s ock
in low
3. Resea ch Me hodology
74
𝑅𝑅𝑡𝑡𝑅𝑅𝑠𝑠𝑠𝑠 (𝑡𝑡) = 𝑅𝑅𝑛𝑛𝑅𝑅𝑡𝑡𝑆𝑆𝑡𝑡_𝑅𝑅𝑡𝑡𝑅𝑅𝑠𝑠𝑠𝑠+∫�𝑅𝑅𝑛𝑛𝑓𝑓𝑡𝑡𝑅𝑅𝑖𝑖(𝑡𝑡)−𝑅𝑅𝑆𝑆𝑡𝑡𝑓𝑓𝑡𝑡𝑅𝑅𝑖𝑖(𝑡𝑡)�𝑆𝑆𝑡𝑡
𝑡𝑡
𝑡𝑡0 ……. (Equa ion 3-1)
When = inal ime s ep, 0 = ini ial ime s ep, ini ial_s ock = he alue ha simula ion begins wi h In low
= he low ha eeds he S ock, ou low = is he s ock ou pu .
1. The Eule app oxima ion me hod, a widely u ilized nume ical echnique, is a ou ed o i s simplici y
and e iciency. Unlike mo e complex p ocedu es, i necessi a es only a single equa ion o
calcula ion. This s aigh o wa dness makes i pa icula ly accessible o a ious applica ions. The
basic o m o he Eule app oxima ion o mula is as ollows:
𝑆𝑆 𝑅𝑅𝑡𝑡𝑅𝑅𝑠𝑠𝑠𝑠 (𝑡𝑡)
𝑆𝑆𝑡𝑡 =�𝑅𝑅𝑛𝑛𝑓𝑓𝑡𝑡𝑅𝑅𝑖𝑖(𝑡𝑡)−𝑅𝑅𝑆𝑆𝑡𝑡𝑓𝑓𝑡𝑡𝑅𝑅𝑖𝑖(𝑡𝑡)�
𝑅𝑅𝑡𝑡𝑅𝑅𝑠𝑠𝑠𝑠(0)=𝑅𝑅(0)
2. The Runge-Ku a app oxima ion o mula, o en called he RK4 me hod, is a nume ical echnique o
sol ing o dina y di e en ial equa ions. This me hod is known o i s accu acy and eliabili y bu
equi es a ela i ely longe compu a ional ime compa ed o mo e s aigh o wa d me hods due o
he necessi y o e alua ing ou in e media e alues, deno ed as k's, o de e mine he alue o he
s ock. The equa ions go e ning he RK4 me hod a e ypically ep esen ed as ollows:
𝑠𝑠1=𝑓𝑓(𝑅𝑅∗(𝑡𝑡),𝑡𝑡)=𝑅𝑅𝑛𝑛𝑓𝑓𝑡𝑡𝑅𝑅𝑖𝑖−𝑅𝑅𝑆𝑆𝑡𝑡𝑓𝑓𝑡𝑡𝑅𝑅𝑖𝑖
𝑠𝑠2=𝑓𝑓�𝑅𝑅(𝑡𝑡)+ℎ𝑠𝑠1
2,𝑡𝑡+ℎ
2�
𝑠𝑠3=𝑓𝑓�𝑅𝑅(𝑡𝑡)+ℎ𝑠𝑠2
2,𝑡𝑡+ℎ
2�
𝑠𝑠4=𝑓𝑓(𝑅𝑅(𝑡𝑡)+ℎ𝑠𝑠3,𝑡𝑡+ℎ)
𝑅𝑅𝑡𝑡𝑅𝑅𝑠𝑠𝑠𝑠=𝑠𝑠1+ 2𝑠𝑠2+ 2𝑠𝑠3+𝑠𝑠4
6
3.5.6. Model Valida ion and Ve i ica ion Me hods
Model alida ion is c i ical in compa ing he model agains eali y and simila models pe o med o he
same p oblem unde in es iga ion. Valida ion should end wi h accep ing o e using he o mula ed
hypo hesis a he i s s ep o SDM. Acco ding o S e man, SDM can be alida ed h ough en me hods:
1) dimensional consis ency es ; 2) bounda y adequacy es ; 3) ex eme condi ion es ; 4) s uc u ed
assessmen es ; 5) pa ame e a ia ion assessmen es ; 6) in eg a ion e o s es ; 7) beha iou
ep oduc ion es ; 8) ailing membe es ; 9) su p ise beha iou es ; 10) s uc u al sensi i i y es
(S e man, 2000). The dimensional consis ency es e i ies whe he connec ed a iables ha e a di ec
ela ionship. I is used when de eloping SFD o expose w ong connec ions be ween model elemen s
and highligh i egula i ies in he model beha iou . The s uc u e es e i ies model beha iou compa ed
3. Resea ch Me hodology
75
o his o ical da a and p oduces ime-based cha s ha can be analysed o de e mine i hey a e ele an
o his o ical da a. Such a es is essen ial o p o ide c edibili y o he o mula ed SFD. Modelle s can
compa e a ious cus om s uc u es (a che ypes) wi h hei model unde de elopmen .
The e a e wo obus ness es ing me hods: (1) Sensi i i y analysis, which is used o e i y how he
de eloped SFD is sensi i e o small changes, and (2) Pa ame e a ia ion, he opposi e es ing me hod
o sensi i i y analysis because i es s he model agains wide a ia ions.
No model has e e been, no will e e be, ho oughly alida ed. The e o e, e ms such as "use ul,"
"illus a i e," o " eliable" a e mo e ap desc ip ions o models han " alid" (Fo es e , 1961).
A emp ing o es he alidi y o a model wi hou a clea dis inc ion o i s pu pose is meaningless; a
sys em dynamics model add esses speci ic p oblems, no gene al sys em issues. The con idence we
place in a model o help us analyze a gi en p oblem should no depend on whe he he model can
analyze a di e en p oblem (Richa dson & Pugh, 1981). The basic s uc u e o his esea ch is alida ed
based on p ojec managemen models, as p e iously discussed. The Ex eme Condi ion Tes
de e mines whe he he newly added componen s can beha e ealis ically unde ex eme alues o
policies (Han e al., 2012).
3.5.7. Cons uc ion managemen dynamics
The complexi y o cons uc ion managemen is a widely ecognized and well- esea ched a ea.
O e budge and schedule o e head a e common indus y p oblems caused by ecu en changes,
wo k low in e up ions, and in o ma ion agmen a ion. As a esul , cons uc ion manage s end o use
policies o a oid hese issues by inc easing esou ces, adding mo e o e ime, push wo k o execu ion
be o e e ining i s cons ain s, which may ha e posi i e ou comes o educe cos and inc ease he pace
o p oduc ion, bu in he longe un can lowe p oduc i i y by demo i a ion, ma e ial was e, ewo k,
wai ing, quali y de ec s among o he s.
SDM has been demons a ed as an e ec i e analy ical ool in cons uc ion p ojec managemen .
To manage such complexi y, he SDM mus be capable o ep esen ing he cons i u ing subsys ems
wi h hei complex in e ela ionships and p ope ies.
In e dependencies among p ojec p ocesses
Cons uc ion p ocesses exhibi in ica e in e dependencies, whe e a change in one p ocess can ipple
h ough o he s, a ec ing esou ce u ilisa ion, spa ial alloca ion, and alloca ed ime. Such changes o
in e up ions can lead o wo k blockages, pa icula ly in a eas di ec ly impac ed by hese al e a ions.
3. Resea ch Me hodology
76
Cons uc ion dynamics
The dynamic na u e o cons uc ion p ojec s is cha ac e ised by a ious ime delays, including schedule
execu ion, e o iden i ica ion, and co ec ion, esponse o changes in scope o speci ica ions, and
wai ing o ma e ials o in o ma ion di ec i es. Addi ionally, p oduc i i y is ad e sely a ec ed by he
di e sion o expe ienced wo ke s' ime o ain ec ui s in he sho un. To e ec i ely ep esen , analyse
and explain he complexi ies o socio- echnical and manage ial sys ems in cons uc ion, Highly
de eloped guidelines ha enable he applica ion o sys em dynamics me hodologies
Nonlinea in e ela ionships
The complex ela ionship be ween causes and e ec s in cons uc ion e en s o en de ies simple linea
modelling. As an analy ical ool, sys em dynamics unde sco es he nonlinea na u e o hese
ela ionships in cons uc ion (Fo d, 2010). Fo example, o e ime may lead o inc eased a igue,
a ec ing mo i a ion and ul ima ely impac ing p oduc i i y (Lyneis e al., 2001). Such ela ionships a e
complex o be ep esen ed using linea ma hema ical unc ions because hey ollow nonlinea
ela ionships.
3.5.8. Applica ion o SDM in Cons uc ion Managemen
In he ield o cons uc ion managemen esea ch, he e is a di e se ange o applica ions, including
decision-making and policy analysis, pe o mance assessmen , ewo k and change managemen ,
scheduling, isk and con ingency planning, esou ce managemen , p oduc i i y enhancemen , cos
planning and es ima ion, p ojec con ol, bidding and p ocu emen s a egies, as well as heal h and
sa e y conside a ions (Kedi e al., 2023).
Policy analysis
Decision-making and policy analysis esea ch u ilises SDM o analyse a ious scena ios and suppo
policy de elopmen ac oss sys em-le el, high-le el, and indus y-le el pe spec i es. Tha includes
employing SDM o manage in as uc u e p ojec s, such as examining highway main enance policies
and assessing sus ainabili y. Fu u e esea ch should explo e inco po a ing eedback delay in o SD
models, e ining hem o analyze policy e ec s on subsys ems and mi iga ing issues ela ed o policy
op imiza ion and scena io analysis. S udying hyb id models can enhance unde s anding o social
impac s in decision-making.
3. Resea ch Me hodology
77
Pe o mance assessmen
Pe o mance assessmen in cons uc ion managemen in ol es e alua ing a ious me ics agains
p ede ined s anda ds o benchma ks. SDM has been u ilised o enhance pe o mance a he p ojec
and o ganisa ional le els, add essing a eas such as p ojec managemen quali y imp o emen and
o e all o ganisa ional pe o mance. Fu u e esea ch should ocus on expanding SDM models o include
mo e key pe o mance indica o s (KPIs) and cap u ing dynamic ela ionships be ween hese pa ame e s
o de e mine o e all pe o mance. Addi ionally, unde s udied ac o s a ec ing pe o mance, such as
ou -o -sequence wo k, wa an u he in es iga ion.
Scope and design changes and ewo k
Rewo k and cons uc ion p ojec changes signi ican ly impac schedule, cos , and quali y pe o mance
me ics. SDM esea ch is well-sui ed o managing hese issues because i cap u es he dynamic
ela ionships be ween planned ac i i ies and causes o ewo k o changes. P e ious s udies ha e
ocused on unde s anding hese ela ionships o p opose e ec i e mi iga ion s a egies. The esea ch
has used SDM o simula e change managemen s a egies and hei e ec s on p ojec planning and
pe o mance (Lo e e al., 1999;D Lo e e al., 2000; Han e al., 2012). Howe e , he e is a s agna ion
in esea ch on ewo k causa ion, possibly due o eliance on adi ional me hods like ques ionnai e
su eys (Lo e e al., 2016). Fu he esea ch is needed o explo e quali a i e and quan i a i e aspec s
o ewo k causa ion in di e en cons uc ion con ex s and o unde s and he dynamic impac o changes
on he ecu si e na u e o ewo k du ing bo h he design and cons uc ion s ages.
Planning and scheduling
SDM has been widely employed in p ojec scheduling, pa icula ly in s udying he e ec s o scheduling
delays. Ea ly s udies ocused on mi iga ing delay and dis up ion in p ojec s by op imising p ojec
du a ion ex ensions. Fu he esea ch explo ed delays caused by comp essing la ge p ojec s o ea lie
deli e y and in es iga ed dynamic planning and he impac o scheduling in mul iple p ojec s. Recen
s udies ha e a emp ed o quan i y he e ec s o a ious ac o s on p ojec schedules, highligh ing he
complexi y o achie ing planned p ojec miles ones solely h ough schedule-d i en managemen (D. N.
Fo d, 2002; Lee e al., 2003; Pa k & Peña-Mo a, 2003).
Resou ce managemen
SDM can be conside ed a ool o managing cons uc ion esou ces, encompassing esou ce alloca ion,
c ew managemen , wo k o ce lea ning e olu ion, and c ew planning. Addi ionally, li e a u e has

3. Resea ch Me hodology
78
employed SDM o assess he in luence o esou ces on a ious aspec s o p ojec pe o mance,
including cos , schedule adhe ence, p oduc i i y, and o e all p ojec success.
P oduc i i y
SDM o e s a comp ehensi e amewo k o cap u ing he dynamics o cons uc ion p ocesses and
assessing he in luence o a ious ac o s on p oduc i i y. Ex ensi e esea ch has examined nume ous
ac o s ha a ec p oduc i i y, including o e ime, a igue, labou mo i a ion, schedule p essu e,
esou ce delays, sho ages, and wo k low in e up ions (Lyneis e al., 2001; Po wal & Hewage, 2012).
Mo eo e , ecen s udies, such as ha by Kaya and Dikmen (2024), ha e speci ically ocused on
e alua ing he impac o di e en wo king hou a angemen s and echnologies on labou p oduc i i y.
This esea ch con ibu es o unde s anding wo k o ce managemen s a egies and hei implica ions o
o e all p ojec pe o mance in cons uc ion con ex s.
3.5.9. SDM and Lean Cons uc ion Resea ch
This sec ion p o ides a comp ehensi e o e iew o s udies employing Sys em Dynamics Modelling
(SDM) o in es iga e Lean Cons uc ion (LC) me hodologies and echniques in he cons uc ion indus y.
Zhan e al. (2022) de ised a concep ual SDM o quan i y su eying p ac ices, in eg a ing Building
In o ma ion Modeling (BIM) and Lean p inciples o delinea e in ica e ela ionships among people
issues, in e nal impe us o BIM and Lean adop ion, ex e nal d i e s o LC and BIM implemen a ion
and impedimen s o implemen a ion. Nguyen and Sha mak (2022) employed he Value S eam
Mapping (VSM) me hod o e alua e en i onmen al pe o mance, demons a ing how Lean s a egies
like he Las Planne Sys em (LPS) and Poka-Yoke educe p ocessing ime and CO2 emissions. Mesh e
e al. (2023) p oposed a decision-making amewo k o managing cons uc ion ma e ial was e
h oughou he li e cycles o indus ial p ojec s, in eg a ing BIM and Lean design in o he design phase.
Omo ayo e al. (2020) diagnosed kaizen cos ing and budge ing p ac ices o cons uc ion p ojec s in
Nige ia using SDM alongside he Analy ical Hie a chy P ocess (AHP). S udies by Ko and Chung (2014),
Ko and Kuo (2019), and Ko (2020) alida ed Lean Design P ocesses in o mwo k wo k lows, aiming o
enhance o mwo k design e iciency h ough Lean p inciples and BIM. Cano and Rubiano (2020)
de eloped a dynamic model o assess imp o emen s in unde s anding non- alue-adding was e wi hin
cons uc ion p ocesses o enhance economic pe o mance and beha iou al aspec s. F ancis and
Thomas (2019) quan i ied he e ec s o Lean cons uc ion p ac ices on sus ainabili y using causal loop
diag amming echniques. Chinda (2009) e alua ed e ec i e Lean policies o os e ing sa e y-o ien ed
cul u es wi hin cons uc ion p ojec s using SDM o explo e di e se scena ios manipula ing pe sonnel,
3. Resea ch Me hodology
79
leade ship dynamics, pa ne ships, and esou ce alloca ion a iables. Collec i ely, hese s udies
unde sco e he e sa ili y and e icacy o SDM in in es iga ing and enhancing a ious ace s o Lean
cons uc ion p ac ices wi hin he cons uc ion indus y.
3.5.10. A ailable So wa e Packages
The e a e se e al so wa e ools a ailable o simula ion ac i i y as sys em dynamic modelling, including
Expe imen al Lea ning Labo a o y wi h Anima ion (STELLA), Vensim, AnyLogic©, Powe sim, iThink®,
TRUE, DynaRisk, and DynamoTM (Kedi e al., 2023). These compu e p og ams a e e alua ed by
compa ing he iendly use in e ace, easy modelling, capabili ies o simula ion enginee ing and ying
al e na i e app oaches, cus omizabili y op ions, in eg abili y, open sou ce, and p og amming language,
which a e displayed in Table 3-5.
The assessmen was done by AnyLogic© 8.7.11 p o essional (conce ning o he sys ems conside ed)
condi ioned by such pa ame e s as e o -checking, compa ibili y, capabili y, accessibili y, and lexibili y.
The e o checke consis s o a Ja a-based se o ules ha accep o ejec a pa icula model s uc u e.
The ules a e designed o iden i y inapp op ia e modelling issues and wa n he use abou he p oblem.
Fu he mo e, i enables he smoo h impo o he models om Vensim, empowe ing he exchange
p ocess and, hus, impo ing hem wi h o he compa ible so wa e. The ool also p o ides a p e-se
en i onmen o model es ing, enabling he esea che o pe sonalise he es wi h Ja a code.
Fu he mo e, he cloud se ice o AnyLogic© allows model sha ing wi h people who do no ha e a license
o an AnyLogic© accoun , hence inc easing he model’s usabili y among hose who lack p io expe ience
in sys em dynamics.
Nume ical sol e s o di e en ial, algeb aic, and mixed equa ions a e designed as an in eg a ion o
he AnyLogic simula ion engine. Simula ion (gene al simula ion, in e ac i e simula ion (games and
pedes ian simula o s)), un compa isons, sensi i i y analysis, calib a ion and op imisa ion, and Mon e
Ca lo p edic ion a e some o he capaci ies o he expe imen . I has been explained ha Any Logic uns
on (Mesh e e al., 2023).
Fu he mo e, he ease o using he AnyLogic© so wa e o simula ion p edic ions can be cus omised
using use inpu o include people ha ing limi ed expe ience o no knowledge o sys em dynamics. I is
wo h men ioning ha AnyLogic may be used o model in disc e e e en simula ion (DES) and agen -
based modelling (ABM), hence inc easing he analyse 's lexibili y conce ning di e en le els o dep h
ha can be equi ed.
3. Resea ch Me hodology
80
Table 3-5 -- Compa ison be ween a ailable so wa e packages o Sys em Dynamics Modelling unc ions
So wa e
Package
STELLA
iThink®
Vensim
PLE Anylogic® Powe sim
s udio TRUE DynaRisk DynamoTM
Use -F iendly
In e ace x x x
In ui i e
Modelling x
Ad anced
Model Building x x
Simula ion
Capabili ies x x x x
Analysis
Fea u es x x
Mul i-Me hod
Simula ion x
Decision
Suppo x x x
Cus omizable x x
In e ope abili y x
Open Sou ce x x
P og amming
Language Py hon Ja a VBSc ip
and C++ Py hon
License Comme cial Comme cial Comme cial Comme cial F eewa e Comme cial Comme cial
3. Resea ch Me hodology
81
3.6. Conclusions
This chap e se es o delinea e he a ionale unde lying he chosen esea ch me hodology. I
commences by dissec ing es ablished me hodologies wi hin cons uc ion managemen , culmina ing in
he disce nmen o an ap app oach ha monising wi h amassed da a and esea ch objec i es. The
adop ion o c i ical ealism and he in eg a ion o mixed me hods s ems om hei capaci y o anscend
singula esea ch pa adigms and philosophical o ien a ions. No ably, he s a egic inclusion o Sys em
Dynamics Modelling (SDM) eme ges as a p omising a enue o lean cons uc ion policy es ing, o e ing
a comp ehensi e amewo k o add ess making-do was e and non- alue-added ine iciencies. This
me hodology amewo k is ein o ced by cons uc ing a obus heo e ical amewo k ha enhances
unde s anding o he making-do challenge and e ol es in o a p ac ical esea ch ins umen closely
in e wined wi h SDM. Acknowledging limi a ions, no ably he abs ac na u e o SDM, we en ision u u e
possibili ies by in eg a ing Agen -Based Modelling (ABM) o enhance ealism. In conclusion, his chap e
signi ies a me hodological selec ion and amewo k cons uc ion, posi ioning us o me hodically un a el
he complexi ies o making-do was e while emb acing i s bounda ies and u u e po en ials.
The nex chap e assesses 1) unde s anding om he li e a u e making-do was e and 2) he
expec a ions o s akeholde s on Lean cons uc ion planning and con ol me hods and BIM o making-
do elimina ion. Unde s anding s akeholde s' expec a ions is essen ial o deploying and accep ing he
bes LC-BIM p ac ices o educing p oduc ion was e wi hin he cons uc ion indus y; he nex chap e
discusses da a collec ed and analysis p ocesses.
4. Unde s anding Making-Do
88
"Eme gen Beha iou ," MD eme ges om in ica e a iable in e ac ions, yielding unexpec ed p oduc ion
ou comes due o unce ain y and a iabili y. The "Equi alence o Success and Failu es" p inciple
highligh s he alue o lea ning om posi i e and nega i e MD inciden s. In "Func ional Resonance,"
MD mani es s ac oss le els—indi idual, eam, and o ganisa ional—s emming om di e se con ex ual
unc ions. Again, his non-linea beha iou o MD o en aces back o a singula oo cause, illumina ing
complex p oduc ion sys em in e connec ions, which is delinea ed as in e ac ion wi hin a complex social
sys em (CSS).
4.1.3. The ela ionship be ween MD and o he cons uc ion issues
This sec ion in e p e s he ela ionship be ween MD and pi o al cons uc ion managemen phenomena,
including bu e ing, a iabili y, unce ain y, imp o isa ion, and un inished wo k. Comp ehending hese
associa ions makes i easible o augmen comp ehension o hese phenomena. While explo ing MD
was e, Koskela (2004) discusses he concep o bu e ing and i s s a k con as wi h MD. Bu e ing
en ails he empo a y hal o ma e ials awai ing p ocessing, in oducing a pause in he wo k low. In
con as , MD in ol es nega i e wai ing ime, depic ing scena ios whe e asks p og ess wi hou he
comple e s anda d inpu s o e en commence wi hou a leas one essen ial inpu .
Fi eman and Sau in (2020) emphasised ha a iabili y in bo h p oduc and p ocess has a
subs an ial connec ion o he occu ence o MD. Pa icula ly in complex sys ems such as cons uc ion,
un o eseen a iabili y is p e alen . Consequen ly, implemen ing a ma gin becomes impe a i e, se ing
as a p oac i e measu e o p e en he immedia e con e sion o "making- eady" ailu es in o ins ances
o MD was e. In his con ex , slack is pi o al as an ea ly indica o ha ope a ional pe o mance di e ges
om he pa ame e s de ined by s anda dised ope a ing p ocedu es. Recognising he ine i abili y o a
conside able deg ee o a iabili y, p uden p epa a ion o such ci cums ances becomes essen ial
(Be elsen and Koskela, 2005). Fu he mo e, he esea ch accen ua es ha o es alling and mi iga ing
he epe cussions o MD can eap bene i s om p oac i ely iden i ying sou ces o a iabili y and
o mula ing co esponding slack esou ces.
Pikas e al. (2012) es ablish a co ela ion be ween unce ain y and he phenomenon o MD. Pikas
ci ed Winch's (2010) de ini ion o unce ain y, which is cha ac e ised as he absence o essen ial
in o ma ion equi ed by he p ojec eam o ask execu ion, which emains una ainable. No ably, 90%
o 345 eco ded MD cases we e a ibu ed o unce ain y (Fo moso, 2011). Unce ain y can engende
de ensi e lose-lose beha iou al pa e ns when planning is obscu ed by unce ain y. Wi hin his con ex ,
MD decisions become p ima ily s ee ed by isk a e sion a he han he pu sui o op imal bene i s o
all pa ies in ol ed.

4. Unde s anding Making-Do
89
The phenomenon o MD is closely in e wined wi h imp o isa ion. Imp o isa ion, a p e alen people
p ac ice, inds i s p esence e en wi hin well-s uc u ed business o ganisa ions (Fo moso, 2011). I s
signi icance becomes pa icula ly p onounced when es ablished ules and me hods all sho (Fo moso,
2011). The equency o imp o isa ion ends o escala e in he ace o unp edic able e en s o p essing
u gencies ha necessi a e immedia e esponses (Cunha, 2004; Hamzeh e al., 2012). Hamzeh e al.
(2012) es ablish a connec ion be ween people's a i udes owa ds imp o isa ion and he cha ac e is ics
o a p ojec , encompassing ac o s such as complexi y, deli e y me hod, ime cons ain s, company
igidi y, design challenges, and p ojec ype. I is wi hin his in e play ha he concep s o MD and
imp o isa ion a e en wined. I is wo h no ing ha imp o isa ion o en in e wines wi h he no ion o MD.
While hese e ms o e lap in speci ic con ex s, hei dis inc di e ences lack p ecise delinea ion wi hin
he li e a u e.
Fi eman (2013) illumina ed a s ong co ela ion be ween MD and he eme gence o Un inished
Wo ks was e. The phenomenon o Un inished Wo ks, as obse ed by Suks e (2005), can o en be
a ibu ed o a lack o ha monisa ion be ween P oduc ion Planning and Con ol (PPC) and Quali y
Managemen (QM) p ocesses. This misalignmen can esul in asks wi hin sho - e m planning
packages being p ema u ely deemed comple ed, lea ing many small asks una ended o he ollowing
week (Fi eman, 2013). These ins ances o un inished wo k a e equen ly associa ed wi h ewo k o
incomple e asks, po en ially con ibu ing o an upsu ge in Wo k-in-P og ess (WIP). Such scena ios can
dis o he accu acy o PPC by e oneously ma king wo k packages as inalised when addi ional wo k is
s ill equi ed in subsequen weeks. The na u e o hese un inished wo ks p edominan ly in ol es new
wo k packages and ewo k, which a e o en excluded om sho - e m planning. These si ua ions
equen ly encompass Non-Value-Added (NVA) ac i i ies, such as se up, mo emen , and esidue
emo al.
Simila ly, Emmi e al. (2012) no ed ha MD equen ly esul s in le un inished asks, leading o
"un inished wo ks." This ci cums ance necessi a es subsequen ec i ica ion o unc ions. Such an
occu ence is o en linked o e alua ing p io wo k as accep able, allowing he ollowing ade o p oceed.
As in oduced by B odeskaia (2010), he concep o Re-en an Flow unde sco es he consequences o
planning based on indi idual asks a he han adop ing a loca ion-based and s anda dised ba ch
app oach. O ganising wo k a ound speci ic loca ions and s anda dised ba ch sizes, acili a ed by a
Loca ion-based Managemen sys em (LBMS), minimises unnecessa y mo emen . This s a egic
o ganisa ion empowe s ades o comple e asks wi hin a designa ed uni be o e ansi ioning o he
subsequen ba ch, he eby os e ing ope a ional e iciency.
4. Unde s anding Making-Do
90
4.1.4. People's beha iou Towa ds MD
As discussed in he i s sec ion o his chap e , he decisions in cons uc ion p ojec s can lead people
o make he ollowing decisions: (1) Abandonmen o he planned wo k, (2) Imp o isa ion o MD (Pikas
e al., 2012). These decisions a e in luenced by beha iou s owa d p oduc ion, which a y among
people in he cons uc ion indus y ega ding a i ude, modes o hinking, and eedom o engage as
necessa y p oac i ely (Hamzeh e al., 2012). Acco ding o he case s udy by Hamzeh e al. (2012),
cons uc ion p ac i ione s di e en ia ed be ween whi e-colla and blue-colla people. Blue colla s we e
ound o be mo e p agma ic and inno a i e bu localised o hei execu ion s age. On he o he hand,
op managemen 's decisions we e ound o be mo e sensi i e o ime and cos es ic ions han blue-
colla people, as well as he e is li le inno a ion in hei decisions, whe e hei e o s o en ocus on
s a egic planning, es ablishing imelines, and scheduling asks.
Acco ding o Ja anma di e al. (2019), a compa ison was made be ween he beha iou o
cons uc ion p ac i ione s in China and he USA conce ning MD. They ound ha cons uc ion manage s
in China may no encoun e a ia ion in p ojec du a ion due o hei p e e ence o MD, while in he
USA, MD may exhibi a signi ican in luence on p ojec du a ion. Ja anma di e al. (2019) show ha
cons uc ion p ac i ione s in China end o ely on making-do p ac ices wi hou signi ican ly a ec ing
p ojec du a ions, whe eas, in he USA, making-do beha iou s subs an ially impac p ojec imelines.
This esul sugges s ha he app oach owa ds making do among cons uc ion manage s di e s
be ween he wo coun ies, po en ially in luencing p ojec ou comes and du a ions acco dingly.
Ja anma di e al. (2019) s udy has ejec ed i he e is any a ia ion o MD impac when di e en
managemen le els a e esponsible o MD decisions. Howe e , Hamzeh sees ha he pe cep ion o
MD decisions can di e be ween managemen le els, whe e he a i ude owa ds MD di e s. Thus,
he e is a di e ence in he pe cep ion o MD decisions among managemen le els, sugges ing po en ial
dispa i ies in a i udes and app oaches owa ds MD wi hin cons uc ion managemen hie a chies.
The e o e, hese indings unde line he complexi y o MD and highligh he need o nuanced analysis
when conside ing i s impac on cons uc ion p ojec s.
4.1.5. Essen ial P e equisi es o Cons uc ion P ocess Execu ion
Figu e 4-2 depic s a p o ocol o managing Making-Do p ac ices and co esponding epo ing
mechanisms. This p o ocol es ablishes he ela ionship be ween he ca ego ies o eme ging Making-Do
p ac ices and hei impac s, highligh ing he essen ial inpu s equi ed o execu e wo k packages based
4. Unde s anding Making-Do
91
on he wo ks o (Dos San os e al. 2020) and (Somme 2010).
Figu e 4-2- A diag am illus a ing he ela ionships among p e equisi es, MD ca ego ies, and impac s based on
he p o ocols de eloped by Dos San os e al. (2020) and Somme (2010)
Table 4-2 summa ises he essen ial p e equisi es o execu ing cons uc ion p ocesses, sou ced
om Koskela's (2000) and Somme 's (2010) wo ks. Table 4-2 de ailed insigh s in o each equi emen
pe hese explo a o y s udies, de ining se en undamen al cons uc ion lows comp ising in o ma ion,
ma e ials and componen s, labou , equipmen and ools, physical space, in e dependen asks, and
ex e nal condi ions. F om he necessi y o comp ehensi e wo k plans ensu ing de ailed p ojec
in o ma ion o he a ailabili y o skilled labou and unc ional ools and equipmen , each p e equisi e
holds signi ican impo ance. Fu he mo e, his able de ails he impo ance o unde s anding
in e dependencies be ween asks, accoun ing o ex e nal ac o s like wea he condi ions, and p o iding
empo a y acili ies. G asping hese p e equisi es is pa amoun o p ojec manage s, as hey ensu e a
well-p epa ed and e icien ly unc ioning cons uc ion en i onmen , he eby enhancing p ojec ou comes
and minimising dis up ions.
Table 4-2 - P e equisi es o Cons uc ion P ojec Execu ion and Thei Explana ions, Sou ced om Koskela (2000)
and Somme (2010)
P e equisi e Explana ion
In o ma ion The p esence o comp ehensi e wo k plans wi h adequa e in o ma ion
Ma e ials and
Componen s
The a ailabili y o ma e ials and componen s ensu es adhe ence o p ojec
speci ica ions and s anda ds ega ding quali y and quan i y.
Does i has impac s
on he p oduc ion
sys em?
Cons ain s
Analysis?
Is he e any MD
p ac ices?
Task
Execu ion
In e up ed
Wo k low
Yes
Yes
No
No
Yes
No
4. Unde s anding Making-Do
92
Labou A ailabili y o he necessa y human esou ces in e ms o bo h quan i y and
quali ica ions.
Equipmen and Tools The a ailabili y and p ope unc ioning o he equi ed ools and equipmen a e
essen ial o success ully execu ing asks.
Space Ensu ing adequa e wo kspace a ailabili y, well-de ined ci cula ion ou es, and
su icien s o age a eas o ma e ials is essen ial o unin e up ed p ojec
ope a ions.
In e dependen Tasks
Ac i i ies wi h s ong in e dependencies can impac he execu ion o
subsequen asks.
Ex e nal Condi ions
Ex e nal ac o s, such as wea he condi ions, including wind and ex eme
empe a u es, play a signi ican ole in p ojec planning and execu ion.
Ins alla ions
A ailabili y o p o isional elec ical and hyd aulic ins alla ions, si e secu i y
acili ies, sca olding, a ea closu es, and s o age zones
4.1.6. Making Do Ca ego ies
Table 4-3 ca ego ises a ious aspec s o Making-Do (MD) p ac ices in cons uc ion p ojec s, shedding
ligh on he complexi ies in ol ed. The i s se o ca ego ies del es e e s o he physical elemen s
essen ial o ask execu ion. 'Access/Mo emen ' delinea es spa ial conside a ions, emphasising he
need o unobs uc ed access and clea pa hways o labou e s. 'Componen Adjus men ' highligh s he
adjus men s made o cons uc ion elemen s, o en unplanned, o ease ins alla ion. 'Wo king A ea'
assesses he app op ia eness o he wo k zone, ensu ing conduci e su oundings o ask execu ion.
'S o age' unde sco es he signi icance o p epa edness, p o iding adequa e s o age o ools, ma e ials,
and componen s. 'Equipmen /Tools' e lec s he adap a ion o de elopmen o ools o e icien usage
du ing asks, while 'Wa e and Elec ici y Supply' ocuses on es ablishing he necessa y in as uc u e
o a seamless ene gy supply. 'P o ec ion' e alua es he a ailabili y o sui able condi ions o
implemen ing p o ec i e measu es. The inal ca ego y, 'Sequence,' del es in o he de ia ion om he
in ended cons uc ion p ocess, pinpoin ing ins ances whe e asks s ay om he planned sequence.
The sou ces, p ima ily Fo moso e al. (2011), Somme (2010), Fi eman e al. (2013), and Leão e al.
(2014), p o ide a comp ehensi e ounda ion o unde s anding hese ca ego ies. This de ailed
ca ego isa ion o e s in aluable insigh s in o he mul i ace ed na u e o MD p ac ices, p o iding a basis
o cons uc ion p o essionals o add ess and mi iga e hese challenges e ec i ely.
4. Unde s anding Making-Do
93
Table 4-3-- Making-Do Ca ego ies and Desc ip ions in Cons uc ion P ojec s
MD Ca ego y Explana ion Sou ce
Access/Mo emen
Rela ing o he spa ial conside a ions equi ed o
execu e asks ega ding su icien access and
clea labou pa hways
(Fo moso e al.,
2011; Somme ,
2010)
Componen
Adjus men
Unnecessa y o unplanned adjus men s o
cons uc ion componen s o elemen s o acili a e
ask comple ion du ing ins alla ion
Wo king A ea
Lack o conside a ion gi en o he app op ia eness
o wo k a eas o suppo ing zones du ing ac i i y
execu ion.
S o age
Inadequa e p epa a ion was gi en o so and
s o e ools, ma e ials, and componen s.
Equipmen /Tools
Inapp op ia e de elopmen o adap a ion is
imp o ised o equipmen and ools du ing usage
o ask execu ion.
Wa e and Elec ici y
Supply
The inapp op ia e adap a ion o es ablishmen o
in as uc u e o supplying wa e and ene gy.
P o ec ion A ailabili y o condi ions o p o ec ion measu es (Somme , 2010)
Sequence
De ia ion om he in ended cons uc ion p ocess
(Fi eman e al.,
2013a; Leão e al.,
2014)
4.1.6. Impac o Making-Do P ac ices in Cons uc ion P ojec s
Table 4-4 p o ides an in-dep h analysis o he di e se impac s o MD p ac ices wi hin cons uc ion
p ojec s. The social consequences encompass demo i a ion among wo ke s, pa icula ly when o ced
in o asks likely o emain incomple e, leading o us a ion and ex ensi e communica ion e o s. This
demo i a ion is exace ba ed by he need o e u n isi s o ec i y e o s, impac ing o e all p oduc i i y.
F om a echnical pe spec i e, MD con ibu es o delays, ex ending p ojec du a ions and p ocessing
imes. These delays o en equi e addi ional inspec ions and co ec ions, leading o o e budge
scena ios due o penal ies, ewo k, and was e. Ma e ial was age occu s when inapp op ia e ma e ials
a e employed o compensa e o MD-induced delays, some imes leading o demoli ions and co ec ions

4. Unde s anding Making-Do
94
upon e o disco e y. Ou -o -sequence ac i i ies u he dis up p ojec low, c ea ing illogical ask
sequences. Rewo k becomes a common consequence o MD, equi ing addi ional esou ces and
inspec ions o ec i ica ion. Un inished wo ks ini ia ed in o mally h ough MD a e o en abandoned due
o hei inabili y o mee quali y s anda ds. Quali y de ia ion is ano he no able issue, mani es ing in
de ec i e p oduc s and necessi a ing ewo k.
Addi ionally, MD ad e sely a ec s p oduc i i y, unde mining he pe o mance o ma e ials,
equipmen , and pe sonnel, comp omising o e all p ojec success. Fu he mo e, inadequa ely managed
spaces con ibu e o inc eased equipmen / ool anspo a ion was e and pe sonnel mo emen ac oss
a ious p ojec loca ions. This comp ehensi e analysis unde sco es he mul i ace ed nega i e impac o
MD p ac ices, emphasising he u gen need o e ec i e mi iga ion s a egies wi hin cons uc ion
managemen p ac ices.
Table 4-4 -- Impac o Making-Do P ac ices on Cons uc ion P ojec s:
MD Impac Explana ion Sou ce
Social
Demo i a ion
MD can diminish he mo i a ion o
wo ke s who a e cognizan o engaging
in o ced asks ha a e unlikely o be
comple ed.
(Ronen, 1992; Koskela,
2004; Fo moso, e al. 2011;
Ama al e al.2020)
F us a ions
Wo ke s dealing wi h challenging asks
may become us a ed, o en engaging
in ex ensi e communica ion, especially
when missing essen ial inpu s. This
us a ion can be exace ba ed when
hey mus e u n o ec i y e o s wi hou
wi nessing any signi ican
inc ease in
p oduc i i y.
(Alha a e al., 2019;
Ne e & Wandahl,
2018)
Reduced e o exe ed
In he ea ly s ages o MD-a ec ed
ac i i ies, e o s ise empo a ily bu
o en shi elsewhe e in he long un
when issues a ise.
(Koskela, 2004)
Viola ions o Heal h, Sa e y
and
En i onmen (HSE)
egula ions.
Wi hou Heal h, Sa e y, and
En i onmen (HSE) ins uc ions, ools, a
clean en i onmen , and adequa e
aining can lead o haza dous ac i i ies.
(Ne e & Wandahl,
2018)
4. Unde s anding Making-Do
95
Technical
Delay (Ex ended p ojec
du a ion) and ex ended
p ocessing ime
MD causes a ce ain amoun o delay,
esul ing in longe lead imes, and is
o en equi ed o e o co ec ion and
addi ional inspec ions, especially when
he e a e s oppages in subsequen
asks.
(Ronen, 1992; Koskela,
2004; Fo moso e al. 2011;
Hamzeh e al. 2012; Alha a
e al. 2019; Ama al e
al.2020)
Ma e ial Was e
MD can con ibu e o ma e ial was e by
necessi a ing inapp op ia e ma e ials o
compensa e o delays o inadequacies
in ma e ial deli e y. MD may also lead
o demolishing o co ec ing mis akes
once he p oblem is disco e ed.
(Koskela, 2004; Fo moso, e
al. 2011; Ama al e al.2020)
Sequence ac i i ies
Tasks ha commence wi h inadequa e
p epa a ion, whe he due o imp ope
p e equisi es o hei absence, end o
se o a chain eac ion o e en s ha
ollow an illogical sequence.
(Ne e and Wnadahl, 2018)
O e budge
Delays, ma e ial was age, penal ies o
delays, ewo k, ene gy ine iciencies,
acciden s, and
inju ies collec i ely
con ibu e o exceeding he alloca ed
budge o each ask a ec ed by MD.
(Ama al e al.2020, 2022)
O e load o acan wo k
loca ions
Wo ke s dealing wi h MD-in ec ed asks,
o en caused by inadequa e space
managemen , may ace dis up ions o
emp y loca ions o conges ion in igh ly
packed wo k a eas.
(Ne e & Wandahl,
2018)
Rewo k
Ini ia ing ac i i ies wi hou adequa e
inpu s can esul in p oduc ion mis akes
and e o s, necessi a ing inspec ions
and alloca ing addi ional esou ces o
ec i ica ion.
(Ronen, 1992; Koskela,
2004; Fo moso e al. 2011;
Hamzeh e al. 2012;
Fi eman e al. 2013; Alha a
e al. 2019; Ama al e
al.2020)
4. Unde s anding Making-Do
96
Un inished wo ks
Small, in o mally ca ied asks may be
abandoned when i becomes appa en
ha hey canno be execu ed co ec ly
h ough he MD app oach.
(É. M. Dos San os e al., 2020a;
Emmi e al., 2012; Fi eman e
al., 2013a)
Quali y De ia ion
MD o en esul s in non-con o mance
wi h p ocess and p oduc s anda ds,
leading o de ia ions in he equi ed
quali y. These de ia ions a e ypically
mani es ed as de ec i e p oduc s ha
may necessi a e ewo k.
(É. M. Dos San os e al., 2020a;
Fo moso e al., 2011; Koskela,
2004; Somme , 2010)
Reduced P oduc i i y
The nega i e impac s o MD a e closely
linked o he poo p oduc ion
pe o mance o ma e ials, equipmen ,
and pe sonnel, ul ima ely a ec ing he
o e all p ojec 's success.
(É. M. Dos San os e al.,
2020a; Fo moso e al.,
2011; Koskela, 2004)
Mo ing and T anspo a ion
was e
Tasks wi h inadequa ely managed
spaces a e closely associa ed wi h
inc eased
was e in equipmen / ool
anspo a ion and he mo emen o
pe sonnel ac oss a ious loca ions
wi hin he cons uc ion p ojec .
(Pe ez e al., 2015)
4.2. MD Mi iga ion S a egies
The inc eased a en ion ha Making Do (MD) was e has ecei ed om bo h he cons uc ion indus y
and academic ci cles, coupled wi h ecen ad ancemen s in Planning and Con ol (P&PC) me hods and
In o ma ion and Communica ion Technology (ICT) echnologies, has pa ed he way o he de elopmen
o se e al me hodologies aimed a aiding p o essionals in conduc ing ho ough p e equisi e analyses
o asks du ing he planning and con ol phases o diminish he nega i e impac s o MD p ac ices.
Indeed, he ole o compu e assis ance is i al in cons uc ion- ela ed asks o a ain lexibili y, eliabili y,
and e iciency (Sacks e al., 2018). Based on hese conside a ions, his sec ion del es in o a
comp ehensi e e iew o exis ing MD esea ch o e alua e he e icacy and limi a ions o ecommended
s a egies.
4. Unde s anding Making-Do
97
This sec ion iden i ies he bes p ac ices om he ga he ed pape s. Subsequen ly, hese iden i ied
pape s we e ho oughly examined by manually sc u inising he e e ences ci ed and placing an
addi ional 25 bes p ac ices. Th ough a ho ough assessmen o he p ima y objec i es o hese
s a egies ex ac ed om he li e a u e, ou b oad classi ica ions o p ac ices eme ged, which can be
summa ised as ollows:
i) Making Do iden i ica ion, ca ego isa ion, and quan i ica ion.
ii) P oduc ion Planning and Con ol
iii) Quali y Managemen and Con ol
i ) In o ma ion Communica ion Technologies
) Social Empowe men Pe spec i es
Koskela's discou se posi ed he impe a i e o a pa adigma ic ealignmen wi hin he domain o
p oduc ion heo ies o espond o Making Do p ac ices. Wi hin his con ex , his ecommenda ions adop
a comp ehensi e app oach o econ igu ing he o ganisa ional landscape. The discou se expounds upon
Koskela's p oposi ions, which a den ly ad oca e o he managing-as-o ganisa ion p inciples, linguis ic-
ac ion heo y, and scien i ic expe imen a ion as ans o ma i e amewo ks. Fu he mo e, his
concep ual pu iew ex ends o he nuanced concep ualisa ion o cons uc ion as an adap i e complex
sys em, d awing p o ound esonance om he insigh s espoused by Be elsen (2004).
4.2.1. P oduc ion Planning and Con ol Sys ems
P agma ic ecommenda ions also succeed in he abo e-men ioned heo e ical pa adigms ad anced by
Koskela. Among hem, he ad oca es o implemen ing he Las Planne Sys em (LPS) as an ope a ional
me hodology while simul aneously emphasising he pi o al objec i es o educ ion o lead ime and
in en o y as pi o al ca alys s o imp o emen . LPS, as a echnical planning sys em, os e s he
o ma ion o a social sys em among c ews. Wi hin LPS, wo p ima y con ol ypes can be iden i ied.
Wi hin LPS, wo p ima y con ol ypes can be iden i ied.
1- P oduc ion uni con ol: his o m o con ol enables imp o ed wo ke assessmen h ough
con inuous lea ning and co ec i e ac ions.
2- Wo k low con ol: This o m o con ol ocuses on op imising he sequence and a e a which
wo k lows a e done ac oss p oduc ion uni s.
(Alha a e al., 2019; Ama al e al., 2022; Dos San os e al., 2020; Emmi e al., 2012; Fi eman e
al., 2013; Fo moso e al., 2020; Pikas e al., 2012; Suks e , 2005) Ad ised o ocus on planning and
con ol necessi a es igo ous con ol o e cons uc ion p ocesses, which iden i ies he minimum o
4. Unde s anding Making-Do
104
he cons uc ion si e condi ions ha can be connec ed o he BIM p ocess o u he isualisa ion and
analysis s eps.
Se e al policies could be applied o egula e he e ec o ma e ial deli e y on p ojec pe o mance.
On he si e, measu es o PP&C associa ed wi h planned si e layou planning (SLP) we e inse ed (Cheng
e al., 2015). These unc ions send 'pull' signals o ma e ials o be imely o de ed and deli e ed unde
he jus -in- ime (JIT) concep . In he PP&C, he ma e ials should be quan i ied and alloca ed be o e
execu ion. Thus, in any p ocess ha commences wi hou he pe quisi e ma e ials, a making-do was e
a ises, esponsible o o he was es such as Wo k-In-P og ess (WIP) and un inished wo ks, and hinde s
labou e s' p oduc i i y. Ano he was e no communica ed in Figu e 4-3 is subs i u ion was e, a
phenomenon ha u ges subs i u ing una ailable ma e ials wi h o he ma e ials o mee scheduled
deadlines; his was e is simila o making do bu hinde s p oduc i i y due o ewo ks and de ec s.
The ma e ial deli e y also impac s he eliabili y o p oduc ion planning and con ol (e.g., when he
equi ed ma e ials a e no a ailable when he planned wo k is eleased, a making-do e ec could be
aised, which can snowball o he eme gence o ewo k and de ec s). The ole o SLP (si e layou
planning) is necessa y o coo dina e physical loca ions be ween he demand o he p oduc ion sys em
and empo a y acili ies, deli e ed ma e ials, c ew mo emen s, machine se ups, and uck a ic.
BIM p ocesses posi i ely educe design changes, a iabili y, ewo k, and RFIs (Da e & Sacks, 2020),
as shown in Loops R11, R12, and B4. BIM unc ionali ies such as quan i y- ake-o , 4D planning,
isualisa ions, clash de ec ion, and in e ope abili y a e a ailable in comme cial so wa e, b inging
aluable in o ma ion con ol o he PP&C sys ems. Ac ual in e ac ion be ween BIM unc ionali ies and
lean-based PP&C is no ma u e. The li e a u e ac i ely p oposed di e en p o o ypes, bu a eal impac
on was e elimina ion has no been p esen ed ye . Howe e , he po en ial e ec s o BIM unc ionali ies
on was e educ ion a e e iden e en on inal was es such as ewo k, ma e ial was e, and capi al was e.
B3, R9, and R10 a e conce ned wi h quali y managemen in con olling a iabili y using quali y
con ol cha s and con inuous imp o emen me hodology based on he PDCA cycle (Deming, 1982).
Cons uc ion a iabili y is he p ima y sou ce o p oduc de ec s and p ocess discon inui y, leading o
ewo ks, poo p oduc i i y, capi al was e, and cascading delays. Loop R7 illus a es he causali y o
Making Do wi h WIP and un inished wo ks. The impac can be explained by wo king on ac i i ies wi hou
i s p e equisi es (e.g., ades ha ocus on local op imisa ion a e eluc an o pick he mos accessible
a ailable wo k packages a he beginning, "a low-hanging ui phenomenon", c ews mo e o open
spaces un il a p oblem appea s, lea e un inished wo k behind hem and p e en s WIP o be p og essed.

4. Unde s anding Making-Do
105
E en ually, his phenomenon led o in e up ions in wo k low and in e e ence wi h o he ades'
schedules, lea ing labou e s o wai un il he p oblem is esol ed, whe e o e ime s a egy would be a
solu ion o compensa e o he delay, which can lead o labou a igue and labou p oduc i i y o be
nega i ely impac ed (Loop R8). Again, he impac o making-do was e is complex and canno be
modelled in a unidi ec ional causali y link (Fo moso e al., 2020), which con adic s CLD p inciples (As
shown in loops R7 and R8, he link be ween making-do and un inished-wo ks is bidi ec ional). Thus,
u he in es iga ion is needed o explo e in e media e a iables be ween making-do and o he was es.
4.5. Conclusions
Despi e he ex ensi e body o li e a u e add essing he causes o Making-Do (MD) was e, a no iceable
dea h o s udies ocus on de eloping planning and con ol s a egies o iden i y and mi iga e MD was e
e ec i ely. The exis ing esea ch p ima ily e ol es a ound quan i ying MD was e and explo ing he
ela ionships be ween i s causes and consequences (Ama al e al., 2022; Dos San os e al., 2020;
Fo moso e al., 2011; Leão e al., 2014). This obse a ion highligh s a signi ican knowledge gap,
indica ing he absence o an es ablished amewo k o aid p oduc ion planne s and ade eams in
de ec ing, es ima ing, and elimina ing MD was e om hei cons uc ion p ojec p ocesses, cul u es,
and p ac ices.
This chap e analysed he heo e ical ounda ions o Making-Do, del ing in o i s oo causes,
de ec ion me hods, and speci ic cha ac e is ics. The explo a ion concluded by c ea ing a causal loop
diag am connec ing p e equisi es, MD causes, MD ca ego ies, and impac s. Recognising he c i ical
impo ance o mi iga ing he ad e se e ec s o MD was e, his chap e is dedica ed o b idging his
knowledge gap by e ealing he causal s uc u e o MD in cons uc ion p ojec s. A gene ic causal loop
diag am connec s he do s be ween p oduc ion planning and con ol, quali y managemen , ha nessing
in o ma ion echnology, and social ac o empowe men .
Based on he buil CLD h ough his chap e , he subsequen chap e aims o simula e he impac s
o e ec i e me hodologies, including he Las Planne Sys em, Loca ion-Based Managemen , and
Building In o ma ion Modelling (BIM) on MD was e, wi hin a comp ehensi e policy amewo k. This
amewo k is designed o p oac i ely minimise he impac o MD was e long be o e he execu ion phase,
o e ing a p omising app oach o enhance bo h lean and non-lean p ojec managemen p ac ices.
5. Su eying LPS-BIM s a egies o MD mi iga ion
106
5.
Su eying LPS-BIM s a egies o MD mi iga ion
This chap e p esen s he esul s o a su ey o in es iga e he implemen a ion le el o Lean
Cons uc ion and BIM in he Po uguese cons uc ion indus y. To achie e his, i es ima es he le els
o awa eness o "making-do" p ac ices among pa icipan s and iden i ies he p ima y g oup o
s akeholde s who suppo such de elopmen . In addi ion, esponden s we e gi en he chance o ank
a se o mi iga i e s a egies o MD, which we e collec ed h ough he quali a i e da a analysis in Chap e
4.
5. Su eying LPS-BIM s a egies o MD mi iga ion
107
5.1. In oduc ion
The signi icance o bo h he Las Planne Sys em (LPS) and Building In o ma ion Model (BIM) is widely
acknowledged in add essing he sho comings o p oduc ion planning and con ol ha led o Making-
Do (MD); howe e , he cu en policies lack alida ion. Acco dingly, his chap e assesses he
s akeholde s' expec a ions abou LPS and BIM o MD mi iga ion s a egies om he exis ing li e a u e
e iew ha used hema ic analysis, a quali a i e da a analysis echnique in Figu e 5-1. The ollowing
s ep was embedding he iden i ied ac o s in a ques ionnai e su ey o explo e new e ms applied o
desc ibe MD p ac ice in he cons uc ion indus y and o classi y he aspec s o he de eloped c i e ia
o e alua ing LPS and BIM me hods o con ol MD was e. The su ey e ealed h ee p incipal g oups
o ac o s: "BIM-based collabo a ion o cons ain analysis," "Medium- e m and Sho - e m MD
analysis," and "En e p ise lea ning and adap a ion," which includes, bu is no limi ed o, componen s
like "imp o ed documen a ion o MD cases" and "Dynamic epo s o MD and cons ain analy ics."
P ope unde s anding and conside a ion o hese ac o s a e signi ican in add essing he s akeholde s'
expec a ions ega ding applying LPS. Along wi h he expec a ions o ele an indus y p ac i ione s, an
LPS-BIM amewo k o MD mi iga ion policy was buil , including he echnological and indus ial needs
o planning and con ol o he p oduc ion and he MD mi iga ion.
Figu e 5-1 - A F amewo k o Policy o Imp o e he Pe o mance o MD Mi iga ion Models.
5. Su eying LPS-BIM s a egies o MD mi iga ion
108
5.2. In-dep h explo a ion o he c i ical ac o s ha mi iga e MD h ough LPS
and BIM
This sec ion will comp ehensi ely examine he dynamic a iables associa ed wi h he LPS and BIM o
limi MD p ac ices and hei ad e se consequences. Conside ing he in e sec ions o hese a iables,
he discussion will explo e he social ac o s inhe en in he LPS in e ms o collabo a ion and adap a ion.
Subsequen ly, his sec ion will discuss he echnical componen s o he LPS, including aspec s such as
isual managemen and medium sho - e m planning. Finally, he unc ionali y o BIM will be explained
wi hin his con ex .
5.2.1 Collabo a ion
The dynamic a iable “Collabo a ion” cons i u es an essen ial aspec o he LPS. Wi hou collabo a ion,
he e icacy o he LPS will emain limi ed, and i s ou comes will be less p omising (Balla d,2020), as i
becomes challenging o os e us and commi men among ades wi hou a collabo a i e app oach o
implemen ing he LPS. This a iable unde sco es he adical inno a ion b ough abou by he LPS,
pa icula ly in i s social dimension. Collabo a ion acili a es deploymen planning and con ol
esponsibili ies h oughou he p ojec o ganisa ion and ocuses on eliable p omises be ween
in e dependen playe s as he key o he success ul applica ion o he LPS. Success ul implemen a ion
o a collabo a i e LPS includes mul iplica ion o he ollowing pa ame e s:
• Handling con lic s among a ious s akeholde s: Add essing con lic s among di e se
s akeholde s is a c i ical me ic ha seeks o gauge he ex en o p ojec managemen 's exe ion
in ha monising in e es s ac oss mul iple sec o s. I also encompasses esol ing dispu es abou
esponsibili y o cons ain s and he mechanisms employed o hei emo al.
• Enabling discussions: Facili a ing discussions is a c ucial dimension ha assesses how
p ojec managemen encou ages dialogue among ele an pa ies du ing he planning phase
and he esolu ion o hei asks wi hin lookahead and make- eady planning sessions.
• Ensu ing a high le el o coo dina ion: This inpu speci ies he pe cen age o coo dina ion
implemen ed du ing Las Planne Sys em (LPS) sessions.
• Engagemen le el in cons ain s analysis: This pa ame e enables use s o designa e he
deg ee o in ol emen o each ade in he cons ain s analysis p ocess. A heigh ened le el o
engagemen can esul in mo e eliable plans and a educ ion in MD p ac ices when cons ain s
a e emo ed co ec ly and collabo a i ely.
5. Su eying LPS-BIM s a egies o MD mi iga ion
109
• Measu e plan pe o mance: This pa ame e is dedica ed o assessing he eliabili y o
measu emen s o planning pe o mance. I examines he po en ial o missing da a o e o s in
epo ing he PPC me ic, including he possibili y o o e es ima ing PPC.
5.2.2 Adap a ion
This dynamic a iable assesses he le el o adap a ion wi hin LPS p ac ices, aimed a os e ing a mo e
common o sha ed unde s anding among he p ojec pa ies while applying LPS and add essing oo
causes o esis ance o changes du ing he LPS implemen a ion (Rooke, 2020). The adap a ion a iable
add esses he oo causes o esis ance o change by ans o ming o ganisa ional alues, encou aging
local adjus men s, and enhancing indi idual capabili ies h ough u he aining and educa ion in lean
cons uc ion philosophy and LPS p inciples; addi ionally, as pa o social LPS, he adap a ion a iable
endea ou s o mi iga e MD p ac ices by gene a ing da a banks in compu e ised o ms h ough ac i e
da a collec ion on MD inciden s. This p ocess in ol es he de elopmen o meaning ul language o s o e,
analyse and o ganise he on ology o MD. The adap a ion a iable is he mul iplica ion o he ollowing
pa ame e s:
• Local adjus men s o o ganisa ions: Local adjus men s o o ganisa ional p ac ices
encompass he modi ica ions made o accommoda e he Las Planne Sys em in alignmen wi h
he o ganisa ion's cul u e, esou ces, p ojec s, p io i ies, goals, and isions. These adjus men s
a e ailo ed o in eg a e seamlessly wi h he ne wo k o s akeholde s associa ed wi h he
o ganisa ion.
• Coaching and aining: E alua ion o coaching and aining e ec i eness pe ains o he le el
o ins uc ion p o ided o supe in enden s and c ew leade s in collabo a i e pull planning. I also
encompasses assessing how planne s p epa e o and sus ain he implemen a ion o he LPS.
• Gene a e da a bank abou LPS me ics and MD inciden eco ds.: Timely da a
collec ion is impe a i e o moni o ing iden i ied MD inciden s and hei esolu ions. The da a
eposi o y o MD inciden s should comp ehensi ely de ail each occu ence, including i s unique
iden i ie (ID), imes amps o he inciden his o y, MD ca ego y, associa ed cons ain s, oo
cause analysis, in ol ed ades, loca ion, p ocess name, ope a ion de ails, ask speci ics, MD
impac and any coun e measu es employed i co ec ions we e made.
• Exchange expe iences wi h o he en e p ises: Exchanging expe iences p o ides a
aluable oppo uni y o sha e insigh s and lea nings. Th ough dissemina ing success ul and
unsuccess ul s o ies, en e p ises can de i e a collec ion o s a egies acqui ed h ough
expe ience, aiding in mi iga ing ad e se MD occu ences in hei p ojec s. This collabo a i e

5. Su eying LPS-BIM s a egies o MD mi iga ion
110
app oach enables he ans e o knowledge and he cul i a ion o a mo e in o med and esilien
p ojec managemen amewo k.
• Compa e MD eco ds and expe ience wi h o he en e p ises: Analysing MD eco ds
and expe iences compa ed wi h o he en e p ises o e s a compe i i e ad an age, allowing
companies o benchma k hei was e educ ion e o s. This compa a i e assessmen p o ides
aluable insigh s in o indus y p ac ices, enabling he iden i ica ion o bes p ac ices and a eas
o imp o emen in managing and minimising MDs.
5.2.3 Visual Managemen
One o he componen s o so Lean Cons uc ion ools in ol es educa ing indi iduals o ecognise was e
and low wi hin hei p ojec s. This concep , known as “lea ning o see,” aligns wi h he p inciples
concep o isual managemen , which employs di e en ools ha a ge he human i e senses o
inc ease he anspa ency o he cons uc ion p ocess and e eal was es (Tezel & Aziz, 2017). In his
dynamic sys em, his a iable is cha ac e ised by he mul iplica ion o h ee pa ame e s, as ou lined
below:
• Communica e ins uc ional and ac ionable in o ma ion.: Communica e he adi ional lis
o was es a ound he si e clea ly and concisely. Also, in og aphics abou he causes o MD and he
possible consequences should be dis ibu ed.
• Keep all plans public: All plans a all le els o he LPS hie a chy should be sha ed and seen wi h
in ol ed pa ies. This s ep inc eases si ua ional awa eness ega ding ask cons ain s, which can
p e en possible MD.
• BIM models a ailable du ing LPS sessions: In LPS sessions, i is impe a i e o employ he
mos up- o-da e BIM models o acili a e he essen ial in o ma ion equi ed o ask planning and
execu ion. I is ecommended ha unc ional analysis, quan i y ake-o schedules, 4D BIM
in o ma ion, and ask-speci ic a ibu ed da a be sha ed wi h he BIM da abase. This collabo a i e
app oach enhances he e ec i eness o planning sessions.
5.2.4 Lookahead Planning
This subsec ion ansi ions o he echnical aspec s o he LPS, which ocuses on lookahead planning,
posi ioned be ween long- e m and sho - e m planning. This s age o he LPS hie a chy en ails b eaking
down p ojec phases and miles ones in o ope a ional asks, as ou lined in Chap e 2. A his s age, he
undamen al unc ion o he LPS lies in he cons ain s analysis, a p ocess hypo hesised by he li e a u e
5. Su eying LPS-BIM s a egies o MD mi iga ion
111
ha signi ican ly impac s ackling MD when i is execu ed success ully. A collabo a i e design o
ope a ions eme ges as ano he pi o al unc ion o LPS, enhancing isual managemen as elucida ed in
subsec ion 5.1.4 and le e aging collabo a ion pa ame e s in subsec ion 5.1.1. The a iable Lookahead
planning comp ises he ollowing cons i uen s:
• Cons ain s managemen : The co e unc ion o he LPS is cons ain s analysis, which can be
pe o med a each le el o planning. Fo example, he mas e schedule is cons ained by cos , ime,
quali y, en i onmen , and social ac o s in a b oade iew. In pull planning, he cons ain s a e
in e dependen among p ocesses, and condi ions o sa is ac ion o each p ocess should be
sc eened be o e mo ing owa ds Lookahead planning.
• B eak down wo k packages in o ac ionable de ails: B eak down p ojec s in o phases and
phases in o miles ones. Wi hin miles ones, de ine p ocesses, p ocesses in o ope a ions, ope a ions
in o asks, and asks in o elemen al mo ions. The LPS hie a chy mus be ollowed, pa icula ly o
highly unce ain c i ical asks. MD p ac ices should be classi ied wi hin he publicly discussed LPS
hie a chy du ing LPS sessions.
• Collabo a i e design o ope a ions: Collabo a i e ope a ion design is ad isable o explo ing
a ious scena ios o cons ain s and an icipa ing po en ial issues MD be o e execu ion. Me hods
ecommended in he li e a u e include i ual p o o yping echniques (such as BIM simula ion DES),
i s - un s udies, and physical p o o yping.
5.2.5 Weekly, Bi-weekly, and Daily Planning
This subsec ion desc ibes he pa ame e s ha comp ise sho - e m planning wi hin he LPS. This
ope a ional s age o he LPS in ol es aligning manage ial di ec i es wi h weekly, bi-weekly, and daily
eedback om ades. I en ails p o iding eedback on wo kable backlogs, engaging indi iduals in daily
cons ain disco e y and emo al, making commi men s o wo k, acili a ing cons ain emo al, and
moni o ing wo k p og ess o calcula e he Plan Pe cen Comple e (PPC). The mul iplica ion o he
ollowing pa ame e s o mula es he sho - e m planning a iable:
• Daily huddle mee ings: This pa ame e speci ies he pe cen age o daily mee ings be ween
wo ke s and supe in enden s. These mee ings in ol e discussions on planned and comple ed
asks, add essing echnical ask execu ion de ails. Wo ke s also di ec ly epo he daily wo k
ou pu om he p e ious day, highligh ing any encoun e ed cons ain s du ing execu ion.
Fu he mo e, whi e-colla pe sonnel a e included in hese mee ings, especially du ing Gemba
walks.
5. Su eying LPS-BIM s a egies o MD mi iga ion
112
• Main ain wo kable backlog: The wo kable backlog is a c ucial pa ame e as i ensu es he
a ailabili y o wo k o execu ion ahead o ime, p e en ing engagemen wi h cons ained asks.
Tasks loca ed in his backlog a e mo e likely o be ee om cons ain s, o hei cons ain s a e
less unce ain. Addi ionally, i aids in classi ying non-wo kable asks, p omp ing indi iduals o
inqui e abou hei cons ain s and a emp o esol e hem so hey can be included in his
wo kable backlog la e .
• Engagemen le el in cons ain s analysis: This pa ame e enables use s o designa e he
deg ee o in ol emen o each ade in he cons ain s analysis p ocess. A heigh ened le el o
engagemen can esul in mo e eliable plans and a educ ion in MD p ac ices when cons ain s
a e emo ed co ec ly and collabo a i ely.
• Daily discussion o cons ain s: The mo e equen ly he cons ain s a e discussed, he
mo e hey a e unde s ood and emo ed. A daily con e sa ion is ecommended o achie e a
highe le el o collabo a ion in emo ing cons ain s and p e en ing MDs.
5.2.6 BIM Func ionali ies
The exis ing BIM unc ionali ies a y widely based on he pu pose o use, acco ding o (ISO, 2018). Fo
MD mi iga ion, he ollowing unc ionali ies a e explained:
• Add MD a ibu e o BIMs: Facili a ing he inco po a ion o MD and cons ain s in o ma ion
wi hin BIM cons uc ion managemen and au ho ing so wa e is essen ial. This pa ame e
measu es he pe cen age o MD in o ma ion a p ojec eam in eg a es in o he pa ame ic BIM
model.
• U ilise 4D BIM o he asks a he las planne hie a chy: This pa ame e quan i ies he
pe cen age o planning de ails inco po a ed in o he 4D BIM. I is ad isable o enhance he le el
o 4D planning in alignmen wi h he LPS hie a chy.
• Apply clash de ec ion analysis o disco e spa ial cons ain s: This alue ep esen s
he pe cen age o applied clash de ec ion ac oss a p ojec . This BIM unc ionali y is
ecommended o p e en spa ial clashes be ween he wo ks o di e en ades. Addi ionally, i
can assis decision-make s in de e mining he sequencing among ope a ions.
• U ilising colou -coded isualisa ion o ep esen ask s a us: This pa ame e deno es
he pe cen age o isualisa ion u ilised o acking ask s a us in esponse o Andon signals. I
encompasses embedded ypes o cons ain s associa ed wi h asks and epo ed MD issues.
Addi ionally, his pa ame e epo s isualised in o ma ion used by whi e-colla pe sonnel o
da a collec ion and o e guidance on ask execu ion o p oblem esolu ion.
5. Su eying LPS-BIM s a egies o MD mi iga ion
113
• Online communica ion ools using BIM cloud se ices: This pa ame e speci ies he
pe cen age o communica ions pe o med o e BIM cloud se ices o exchange p oduc ion
in o ma ion on de eloped BIM models by design eams wi h si e managemen , c ew leade s,
and c ews.
5.3. Resea ch Me hodology
A comp ehensi e li e a u e e iew iden i ied 30 a iables ela ed o using BIM and LPS o MD
mi iga ion. These a iables we e subsequen ly s uc u ed in o a ques ionnai e su ey, and a pilo s udy
was conduc ed o e ine he ques ionnai e be o e i s dis ibu ion o he in ended esponden s. The pilo
s udy in ol ed i e PhD s uden s in Ci il Enginee ing, whose eedback in o med he inal e sion o he
ques ionnai e p esen ed in Appendix A.
5.3.1 Ques ionnai e Design
The ques ionnai e su ey commenced wi h a pilo s udy employing a p elimina y ques ionnai e
con aining a compiled lis o LPS and BIM s a egies o MD mi iga ion. This ini ial phase assessed
ques ionnai e ele ance, leng h, complexi y, and layou . Pa icipan s o he pilo s udy we e selec ed
om wo Po uguese uni e si ies and comp ised PhD s uden s specializing in cons uc ion managemen
and BIM esea ch. Feedback om he pilo s udy pa icipan s was ins umen al in e ining he inal
ques ionnai e.
The inal ques ionnai e su ey comp ises he ollowing h ee sec ions:
SURVEY COVER LETTER – This sec ion explains he pu pose o he su ey, p o ides a
de ini ion o MD p ac ice, and elabo a es on examples om he li e a u e. The esponden s
we e in o med ha he da a collec ed would be used solely o esea ch pu poses o encou age
a high esponse a e. Likewise, he esponden s we e assu ed ha he con iden iali y o all
indi iduals’ esponses would be main ained.
SECTION A: DEMOGRAPHIC OF THE RESPONDENTS – This sec ion cap u es
demog aphic in o ma ion abou esponden s. The esponden s we e asked o indica e hei
expe ience le el, educa ion le el, and designa ion. This sec ion would enable he esea che o
iden i y he esponden s' oles wi hin which indus y.
SECTION B: LEAN AND BIM EDUCATION AND IMPLEMENTATION – This sec ion
cap u es whe he he esponden s ha e been in aining o a BIM and Lean Cons uc ion
5. Su eying LPS-BIM s a egies o MD mi iga ion
120
5.4. Resul s
5.4.1 Reliabili y analysis
Reliabili y analysis assessed he in e nal consis ency o he a iables ela ed o using BIM and LPS o
mi iga e MD p ac ices in cons uc ion p ojec s. A o al o 25 a iables we e es ed i hey a e, and he
Like scale consis en ly e lec s he cons uc o he s udy se ou o measu e. Acco dingly, C onbach’s
alpha coe icien o eliabili y (α) was calcula ed o he a iables using Equa ion 5.1.
𝛼𝛼=
𝑁𝑁
𝑁𝑁−1�1−
∑𝜎𝜎2
𝜎𝜎𝑇𝑇
2� Equa ion 5-1
In his con ex , N ep esen s he o al numbe o ques ions. Each ques ion has a sco e a iance deno ed
as σ whe e i anges om 1 o n. The o e all es sco e's o al a iance, no in pe cen age o m, is
ep esen ed by he
σ T
. C onbach’s alpha αwhich has a alue om 0 o 1, and he highe he alue o
(α), he g ea e he in e nal consis ency o da a (Field, 2005). I is gene ally belie ed ha a alue o α
= 0.7 is accep able, and α > 0.8 depic s good in e nal consis ency. The calcula ed α o his s udy is
0.9475, demons a ing an excellen in e nal consis ency. The 25 a iables we e hen anked using he
desc ip i e s a ical mean as he a io o impo ance. The esul s o he eliabili y analysis and anking
o he a iables a e shown in Table 5-2.
Table 5-2 -- Reliabili y analysis able wi h means and anking o LPS and BIM s a egies o MD mi iga ion.
No Va iable Mean
C onbach’s
Alpha
Rank
VA24
Iden i y and esol e ime and space clashes using BIM
Clash De ec ion ools.
3.736 0.945 1
VA23
Repo ask in o ma ion in alignmen wi h p oduc
speci ica ions o ensu e accu acy.
3.722 0.945 2
VA25 Facili a e he exchange and communica ion o Making-Do
p ac ices h ough online BIM models.
3.722 0.944 3
VA22 U ilise 4D planning o isualise cons ain s and hei impac
on p ojec imelines.
3.681 0.945 4
VA5 P o ide coaching, aining, and
semina s o
supe in enden s and o epe sons.
3.653 0.945 5
VA11 Ensu e he a ailabili y o BIM models, design d awings, and
si e layou plans o e e ence du ing he implemen a ion o
he Las Planne Sys em.
3.611 0.944 6
VA21 Facili a e daily
discussions be ween ades o add ess
cons ain s and coo dina e ac i i ies.
3.583 0.945 7
VA2 Ensu e high-le el coo dina ion among p ojec s akeholde s. 3.542 0.946 8

5. Su eying LPS-BIM s a egies o MD mi iga ion
121
VA20
Collabo a i ely design ope a ions using BIM o digi al
p o o yping.
3.486 0.945 9
VA12
Main ain anspa ency by keeping all plans publicly
accessible.
3.472 0.945 10
VA14
Apply cons ain s analysis p oac i ely o iden i y and
add ess po en ial issues as a eam.
3.472 0.945 11
VA9 Facili a e knowledge exchange and
sha ing expe iences
among di e en companies.
3.458 0.945 12
VA3
Facili a e discussions o add ess conce ns and os e
consensus.
3.444 0.945 13
VA7 Es ablish a da a bank o cla i y misconcep ions ega ding
Lean cons uc ion, Making-Do, and
Las Planne Sys em
p inciples.
3.444 0.946 14
VA1
Handle disag eemen s and in e es s e ec i ely o os e
collabo a ion.
3.431 0.947 15
VA6 P ocess and ansla e knowledge om expe ien ial lea ning
in o ac ionable insigh s.
3.403 0.946 16
VA13 U ilis
e guiding in o ma ion ac oss digi al and physical
en i onmen s o enhance unde s anding.
3.403 0.946 17
VA17 In ol e s akeholde s in cons ain s managemen p ocesses
o enhance collabo a ion in Mi iga ing MD.
3.347 0.944 18
VA8 Lea n om pas inciden s o Making-Do. 3.306 0.946 19
VA16
Encou age s akeholde s o communica e and sha e any
cons ain s ha may impede p og ess.
3.278 0.945 20
VA10 Compa e and analyse mul iple cases o unde s and how
Making-Do is managed.
3.264 0.946 21
VA4
Adap local adjus men s o align wi h o ganiza ional
equi emen s.
3.181 0.947 22
VA18
Main ain a wo kable backlog o asks o p io i ize and
manage wo kload e ec i ely.
3.153 0.944 23
VA15 Delay asks wi h unce ain
cons ain s o a oid po en ial
dis up ions.
2.931 0.947 24
VA19 B eak down asks om p ocesses o ope a ions and u he
o indi idual asks o cla i y o managemen and con ol.
2.889 0.947 25
The mean anking e eals ha “Iden i ying and esol ing ime and space clashes using BIM Clash
De ec ion ools” is he mos signi ican s akeholde expec a ion o using BIM o MD mi iga ion. This
ank is because he cons uc ion indus y is long o e due o BIM-Based ools o iden i y con lic s.
5.4.2 Explo a o y Fac o Analysis (EFA)
The ac o analysis me hod aims o disco e "unde lying" s uc u es associa ed wi h he a iables
e ealed in he li e a u e. I s goal is o de e mine he se o dimensions o ming a iables as he base
o hei s uc u e, using he educ ionis me hod o subs i u e hem wi h ewe unco ela ed p incipal
componen s. The esul ing p ocedu es ha e he added ad an age o dele ing edundan (highly
co ela ed) a iables while a he same ime p ese ing he in eg i y o he o iginal da a. In his esea ch,
5. Su eying LPS-BIM s a egies o MD mi iga ion
122
ac o analysis was done by Mini ab employing p incipal componen analysis (PCA) wi h oblique o a ion
( a imax) o 25 a iables. PCA was employed o ac o ex ac ion, and a imax o a ion was used as a
o a ion p ocedu e. The Kaise -Meye -Olkin (KMO) measu e o he sampling adequacy go a alue o
0.873, which is highe han he ecommended h eshold o 0.5, while Ba le ’s Tes o Sphe ici y
esul ed in a p- alue o 2.45 x 10-104 (less han 0. 5) as shown in Table 5-3, sugges ing subs an ial
e idence agains he null hypo hesis o an iden i y ma ix.
Table 5-3 -- KMO and Ba le 's Tes
Kaise -Meye -Olkin o Sampling Adequacy. 0.873
Ba le 's Tes o Sphe ici y
App ox. Chi-Squa e
1178.643
d
300
Sig.
<0.000
Tha demons a ion p e iously men ioned con i ms ha his da a se is sui able o ac o analysis. The
PCA esul s eo ganise he lis o a iables in o i e ac o s, which accoun o he o al a iance o
67.871%, as shown in Table 5-4. Acco dingly, he g oups we e deduced and ca ego ised based on he
assigned a iables. The g oups include:
• G oup A, deno ed by In eg a ed P oduc ion and P oduc In o ma ion
• G oup B is deno ed by Adap a ion Towa ds LPS and MD- ee cul u e.
• Collabo a i e Commi men owa ds MD Mi iga ion deno es G oup C
• G oup D is deno ed by Cons ain s Analysis wi h Sho and Long- e m Planning
• G oup E deno ed by Ac i e Feedback Mechanisms o MD Inciden Resolu ion
Table 5-4 -- Componen labelling and co esponding c i e ia om ac o analysis
No Va iable Eigen
Values
% o
Va iance
Loading
Fac o
A- In eg a ed P oduc ion and P oduc In o ma ion Managemen 11.338 45.353%
VA24
Iden i y and esol e ime and space clashes using BIM Clash
De ec ion ools.
0.767
VA23 Repo ask in o ma ion in alignmen wi h p oduc speci ica ions
o ensu e accu acy.
0.712
VA25 Facili a e he exchange and communica ion o Making-Do
p ac ices h ough online BIM models.
0.715
VA22 U ilise 4D planning o isualize cons ain s and hei impac on
p ojec imelines.
0.803
B- Adap a ion Towa ds LPS and MD- ee cul u e 1.761 7.043%
VA5 P o ide coaching, aining, and semina s o supe in enden s and
supe iso s.
0.672
5. Su eying LPS-BIM s a egies o MD mi iga ion
123
VA6 P ocess and ansla e knowledge om expe ien ial lea ning in o
ac ionable insigh s.
0.715
VA4
Adap local adjus men s o align wi h o ganiza ional
equi emen s.
0.527
VA11
Ensu e he a ailabili y o BIM models, design d awings, and si e
layou plans o e e ence du ing he implemen a ion o he Las
Planne Sys em.
0.655
VA12
Main ain anspa ency by keeping all plans publicly accessible.
0.739
VA13 U ilis
e guiding in o ma ion ac oss digi al and physical
en i onmen s o enhance unde s anding.
0.511
VA21 Facili a e daily discussions be ween ades o add ess cons ain s
and coo dina e ac i i ies.
0.576
VA19 B eak down asks om p ocesses o ope a ions and u he o
indi idual asks o cla i y o managemen and con ol.
0.698
VA18 Main ain a wo kable backlog o asks o p io i ize and manage
wo kload e ec i ely.
0.688
VA15 Delay asks wi h unce ain
cons ain s o a oid po en ial
dis up ions.
0.759
C- Collabo a i e Commi men owa ds MD Mi iga ion 1.426 5.702%
VA2
Ensu e high-le el coo dina ion among p ojec s akeholde s.
0.743
VA3
Facili a e discussions o add ess conce ns and os e consensus.
0.678
VA1
Handle disag eemen s and in e es s e ec i ely o os e
collabo a ion.
0.748
VA20
Collabo a i ely design ope a ions using BIM o digi al
p o o yping.
0.644
D- Cons ain s Analysis wi h Sho and Long Te ms Planning 1.324 5.297%
VA14
Apply cons ain s analysis p oac i ely o iden i y and add ess
po en ial issues as a eam.
0.644
VA17
In ol e s akeholde s in cons ain s managemen p ocesses o
enhance collabo a ion in Mi iga ing MD.
0.796
VA16
Encou age s akeholde s o communica e and sha e any
cons ain s ha may impede p og ess.
0.738
E- Ac i e Feedback Mechanisms o MD Inciden Resolu ion 1.119 4.476%
VA9 Facili a e knowledge exchange and sha ing expe iences among
di e en companies.
0.528
VA7 Es ablish a da a bank o cla i y misconcep ions ega ding Lean
cons uc ion, Making-Do, and Las Planne Sys em p inciples.
0.512
VA8
Lea n om pas b eakdowns and ins ances o Making-Do.
0.676
VA10 Compa e and analyse mul iple cases o unde s and how Making-
Do is managed.
0.724
16.968 67.871%
5. Su eying LPS-BIM s a egies o MD mi iga ion
124
5.5. S uc u al Equa ion Model (SEM)
Based on SEM, a Con i ma o y Fac o Analysis (CFA) was implemen ed in AMOS so wa e o e i y he
measu emen model's alidi y. The SEM comp ises de ining he measu emen model and he s uc u al
model.
5.4.1. Measu emen Model Fi ness
The measu emen model was es ablished using he g aphical ep esen a ion unc ionali y in AMOS
26.0.0. as shown in Figu e 5-9. The model comp ised ou a iables, COO, MDK, LPS, and BIM,
ep esen ing 25 ac o s loaded on hese a iables. Simila ly, 25 e o s we e associa ed wi h hese
ac o s, and measu ing hese unobse ed e o s o ms he powe o SEM, which o he mul i a ia e
me hods canno measu e. The model assumes co a iances among all unobse ed a iables: COO,
MDK, LPS, and BIM.
As a composi e o CMIN/d , Compa a i e Fi Index (CFI), Tucke -Lewis Index (TLI), Roo Mean
Squa e Residual (RMSEA), and S anda dized Roo Mean Squa e Residual (SRMR), hese model- i
indices we e used o he o e all e alua ion o he model. Some calcula ed s a is ics signi ican ly di e ed
om he es ablished below-s anda d alue de ined in p e ious esea ch (Bagozzi & Yi, 1988; Ben le ,
1990; Hu & Ben le , 1998; Schumacke & Lomax, 2004). The ou - ac o model, comp ising COO, MDK,
LPS, and BIM, demons a ed some unsa is ac o y i o he da a, as indica ed by he ollowing i indices:
CFI = 0.786, TLI = 0.762, SRMR = 0.099, and RMSEA = 0.098. Howe e , he sa is ac o y i o he
da a is as ollows: CMIN/d = 2.133 and p obabili y le el = 0.000 (Table 5-5 includes model- i indices;
please see below o mo e de ails)
Table 5-5 --
Model i ness measu es
Fi indices Th eshold Ob ained alue E alua ion Sou ce
P obabili y le el Insigni ican 0.000 Passed
(Bagozzi & Yi, 1988)
CMIN/DF Be ween 1 and 3 2.133 Excellen
(Schumacke & Lomax,
2004; Ullman &
Ben le , 2012)
CFI >0.95 0.786 Te ible (Ben le , 1990)
TLI >0.08 0.762 Te ible
SRMR <0.08 0.099 Unaccep able
5. Su eying LPS-BIM s a egies o MD mi iga ion
125
RMSEA <0.06 0.098 Te ible
(Hu & Ben le , 1998)
Figu e 5-9 -- Unadjus ed SEM measu emen model
The alidi y es shows ha he measu emen model equi es se e al modi ica ions. Fi s , by looking
a ac o loadings o e e y i em, I was ound ha h ee i ems, i.e., VA4, VA10, VAR21, had low ac o
loadings (VA4 = 0.46, VA10 = 0.45, VA21 = 0.48) which a e all less han he accep ed h eshold o
0.5. The e o e, hey we e aken ou o he s udy. Second, a e looking a he medica ion indices,
se e al co a iances a e sugges ed o be d awn in (BIM: e25 ↔ e24, e25↔e21, e25↔e22,
e23↔e22) and (LPS: e11↔e12, e12↔e13, e16↔e17, e17↔e18, e18↔e19, e11↔e19),
a e wa ds ano he analysis was ca ied ou a e unning he analysis o he model i ness a e
modi ica ions o he measu emen model. These model- i indices we e used o e alua e he model as
a composi e o CMIN/d , CFI, TLI, RMSEA, and SRMR. Signi ican ly, he means o all calcula ed s a is ics
we e wi hin he es ablished s anda d alues. The ou - ac o model, comp ising COO, MDK, LPS, and
BIM, demons a ed a sa is ac o y i o he da a, as indica ed by he ollowing i indices: CMIN/d =
1.490, CFI = 0.932, TLI = 0.911, SRMR = 0.08 and RMSEA = 0.065 (Table 5-6 includes model- i
indices; please see below o mo e de ails).

5. Su eying LPS-BIM s a egies o MD mi iga ion
126
Table 5-6 -- Model i ness measu es o he adjus ed measu emen model
Fi indices Th eshold Ob ained alue E alua ion
P obabili y le el Insigni ican 0.000 Passed
CMIN/DF Be ween 1 and 3 2.133 Excellen
CFI >0.95 0.946 Accep able
TLI >0.90 0.932 Excellen
SRMR <0.08 0.063 Excellen
RMSEA <0.06 0.060 Accep able
Figu e 5-10 -- Adjus ed measu emen model
5.4.2. Model Reliabili y Analysis
Cons uc Reliabili y was e alua ed using C onbach's Alpha and Composi e Reliabili y (CR). The
C onbach's Alpha coe icien s o each cons uc in he s udy exceeded he ecommended h eshold o
0.70 (Nunnally & Be ns ein, 1994). CR alues also anged om 0.813 o 0.839, su passing he 0.70
benchma k (Hai e al., 2019). The e o e, CR was es ablished o each cons uc in he s udy, as
documen ed in Table 5-7.
The con e gen alidi y o he scale i ems was assessed using A e age Va iance Ex ac ed (AVE)
(Hai e al., 2019). The AVE alues o BIM unc ionali ies and LPS echnical measu es exceeded he
5. Su eying LPS-BIM s a egies o MD mi iga ion
127
h eshold alue o 0.5 (Hai e al., 2019). Howe e , Collabo a ion, MD knowledge, LPS Func ions, and
BIM Func ionali ies exhibi ed AVE sco es below 0.5. None heless, gi en ha he CR alues exceeded
he equi ed h eshold, i can be in e ed ha hese cons uc s main ain adequa e con e gen alidi y
o he p esen s udy, as summa ised in Table 5-7.
Table 5-7 -- Loadings, Reliabili y, and Con e gen Validi y
I ems Loadings Alpha CR* AVE**
Collabo a ion 0.815 0.835 0.628
VA1 0.813
VA2 0.806
VA3 0.757
Making-Do Knowledge 0.811 0.813 0.552
VA5 0.696
VA6 0.727
VA7 0.786
VA8 0.676
LPS Func ions 0.873 0. 839 0. 397
VA11 0.588
VA12 0.542
VA13 0.623
VA14 0.589
VA17 0.605
VA18 0.720
VA19 0.625
VA20 0.725
BIM Func ionali ies 0.843 0.838 0.567
VA22 0.730
VA23 0.815
VA24 0.814
VA25 0.638
* CR: Composi e Reliabili y
**AVE: A e age Va iance Ex ac ed
The disc iminan alidi y o he esea ch was assessed by u ilizing bo h he Fo nell and La cke C i e ion
app oach and he He e o ai -Mono ai (HTMT) a io. The Fo nell La cke C i e ion allows disc iminan
5. Su eying LPS-BIM s a egies o MD mi iga ion
128
alidi y when he squa e oo o a cons uc 's A e age Va iance Ex ac ed (AVE) su passes he co ela ion
wi h o he s udy cons uc s (Fo nell & La cke , 1981). Ne e heless, he Fo nell and La cke C i e ion
has been he sou ce o ecen c i icisms, and schola s ha e accep ed new me hods, such as he HTMT
Ra io, as al e na i e echniques. Al hough he Fo nell and La cke C i e ia did no p o ide e idence o
disc iminan alidi y in his s udy, all HTMT a ios we e below he 0.85 h eshold alue, as (Hensele e
al., 2015) ecommended. The e o e, he HTMT a io was used o con i m he disc iminan alidi y. The
p esen ed con en o Table 5-8 is he esul s o a closely ca ied ou disc iminan alidi y analysis, whe e
he coe icien s and signi icance alues o disc imina ion a e highligh ed in Table 5-8.
Table 5-8. HTMT Analysis
COO
MDK
LPS
AVE
COO
0.628
MDK
0.616
0.552
LPS
0.603
0.765
0. 397
BIM
0.332
0.504
0.666
0.567
5.4.3. S uc u al Model Assessmen
The s uc u al equa ion model is gene a ed h ough AMOS and was used o es he ela ionships among
dependen a iables. Simila o he measu emen model c i e ia, an excellen i ing model is accep ed
i he alue o CMIN/d is below i e, which mee s he equi emen s o (Hai e al., 2019), TLI and CFI
below 0.90 (Ben le , 1990). Also, an adequa e- i ing model was accep ed i he AMOS so wa e
compu ed RMR and SRMR be ween 0.05 and 0.080 (Hai e al., 2019). The i ness measu es o he
ell wi hin he accep able ange: CMIN/ d = 1.418, TLI=0.932, CFI= 0.946, SRMR= 0.0597, and
RMSEA =0.060.
5.4.4. Media ion Analysis
Media ion analysis examines hose complex and mul i-dimensional d aws be ween wo cons uc s
because hei in luence likely ope a es no di ec ly bu h ough a hi d a iable called a media o . In his
case, he in e media y a iable demons a es an in e e ing elemen in he ela ionship be ween he wo
a iables, which explains he unde lying mechanisms o he associa ions. The boo s apping me hod
in es iga es media ion wi hin a model wi h se ings o (boo s ap samples = 2000 samples, pe cen ile
con idence le el (PC) = 90%, Bias-co ec ed con idence le el = 90%). Knowing he basic e ms, such as
di ec and indi ec , is i al in media ion analysis.
5. Su eying LPS-BIM s a egies o MD mi iga ion
129
Figu e 5-11 -- Boo s ap se ings
The squa ed mul iple co ela ion was 0.62 o MDK; his shows ha he LPS, COO, and BIM accoun
o 62% o he a iance in MDK. The squa e mul iple co ela ion o COO accoun s o 0.44; his signi ies
ha LPS unc ions and BIM accoun o 44% a iance in collabo a ion. The s udy assessed he impac
o COO, LPS, and BIM on MDK. The e ec o LPS on MDK was posi i e and signi ican ; hence, H2 was
suppo ed. Howe e , he impac o BIM on MDK was nega i e and insigni ican . The e o e, H1 was no
suppo ed. The e ec o he COO on MDK was posi i e and insigni ican , oo. Hence, H1 was no
suppo ed. The impac o LPS on he COO was posi i e and signi ican ; hence, H2 was suppo ed.
On he con a y, he BIM impac on he COO was nega i e and insigni ican . Thus, H1 was no
suppo ed. Model i indices and hypo hesis esul s a e p esen ed in Table 5-9.
Table 5-9--Mode a ion analysis o he s uc u al model and model i indices
Hypo hesised Rela ionship S anda dised Es ima es - alue p- alue Decision
LPS  MDK 0.698 3.15 <0.001 Signi ican
BIM  MDK -0.013 -0.085 0.932 Insigni ican
COO  MDK 0.118 1.030 0.303 Insigni ican
LPS  COO 0.803 4.309 <0.001 Signi ican
BIM  COO -0.257 -1.415 0.157 Insigni ican
R-Squa e
MDK 62%
COO 44%
Re e ences
232
Liu, Z., Osmani, M., Demian, P., & Baldwin, A. (2016).
A BIM-aided cons uc ion was e
minimisa ion amewo k
.
59
(2015), 1–23. h ps://doi.o g/10.1016/j.au con.2015.07.020
Lo e, P. E. D., Edwa ds, D. J., & Smi h, J. (2016). Rewo k Causa ion: Eme gen Theo e ical
Insigh s and Implica ions o Resea ch.
Jou nal o Cons uc ion Enginee ing and Managemen
,
142
(6). h ps://doi.o g/10.1061/(ASCE)CO.1943-7862.0001114
Lo e, P. E. D., & Li, H. (2000a). Quan i ying he causes and cos s o ewo k in cons uc ion.
Cons uc ion Managemen and Economics
,
18
(4), 479–490.
h ps://doi.o g/10.1080/01446190050024897
Lo e, P. E. D., & Li, H. (2000b). Quan i ying he causes and cos s o ewo k in cons uc ion.
Cons uc ion Managemen and Economics
,
18
(4), 479–490.
h ps://doi.o g/10.1080/01446190050024897
Lo e, P. E. D., Manual, P., & Li, H. (1999). De e mining he causal s uc u e o ewo k in luences
in cons uc ion.
Cons uc ion Managemen and Economics
,
17
(4), 505–517.
h ps://doi.o g/10.1080/014461999371420
Lyneis, J. M., Coope , K. G., & Els, S. A. (2001). S a egic managemen o complex p ojec s: A
case s udy using sys em dynamics.
Sys em Dynamics Re iew
,
17
(3), 237–260.
h ps://doi.o g/10.1002/sd .213
MacOmbe , H., G ego y, G. A., & Reed, D. (2005). Managing p omises wi h he las planne
sys em: Closing in on unin e up ed low.
13 h In e na ional G oup o Lean Cons uc ion
Con e ence: P oceedings
, 13–18.
Mahalingam, A., Yada , A. K., & Va ap asad, J. (2015). In es iga ing he ole o lean p ac ices in
enabling BIM adop ion: E idence om wo Indian cases.
Jou nal o Cons uc ion Enginee ing and
Managemen
,
141
(7), 05015006. h ps://doi.o g/10.1061/(ASCE)CO.1943-7862.0000982
Ma ins, J., & Cachadinha, N. (2013, July). An Explo a o y S udy o Lean Cons uc ion in Po ugal:
Owne s and Designe s’ Pe spec i es.
21 h Annual Con e ence o he In e na ional G oup o Lean
Cons uc ion (IGLC 21)
.
Ma ins, J. R. M. (2011).
Lean Cons uc ion na Cons ução e Engenha ia Po uguesas-
Opo unidades e Desa ios Pa a os Donos de Ob a
. Uni e sidade No a de Lisboa.

Re e ences
233
Ma ias, J., & Cachadinha, N. (2010). E alua ing he Po en ial Bene i s and Challenges o LC
Adop ion in he Po uguese Cons uc ion Indus y: A Su ey S udy.
P oceedings IGLC-18
.
h ps:// un.unl.p /handle/10362/10003
Ma hews, J., Pellew, L., Phua, F., & Rowlinson, S. (2000). Quali y ela ionships: Pa ne ing in he
cons uc ion supply chain.
In e na ional Jou nal o Quali y and Reliabili y Managemen
,
17
(4),
493–510. h ps://doi.o g/10.1108/02656710010371191
McHugh, K., Da e, B., & Koskela, L. (2022). On The Role o Lean in Digi al Cons uc ion. In
Indus y 4.0 o he Buil En i onmen
(pp. 207–226). Sp inge .
Mesh e , A. N., Elkasaby, El. A. F. A., & Abdel Kade Mohamed Fa id, A. (2023). Reducing
cons uc ion was e in he cons uc ion li e cycle o indus ial p ojec s du ing design phase by using
sys em dynamics.
Jou nal o Building Enginee ing
,
69
.
h ps://doi.o g/10.1016/j.jobe.2023.106302
Michaud, M., Fo gues, E.-C., Ing, M., Ca ignan, V., Fo gues, D., & Ouelle -Plamondon, C. (2019).
A lean app oach o op imize BIM in o ma ion low using alue s eam mapping.
Jou nal o
In o ma ion Technology in Cons uc ion (ITcon)
,
24
, 472–488.
h ps://doi.o g/10.36680/j.i con.2019.025
Mul ow, C. D. (1994). Sys ema ic e iews: a ionale o sys ema ic e iews.
Bmj
,
309
(6954),
597–599.
Nahmens, I., & Ikuma, L. H. (2012). E ec s o Lean Cons uc ion on Sus ainabili y o Modula
Homebuilding.
Jou nal o A chi ec u al Enginee ing
,
18
(2), 155–163.
h ps://doi.o g/10.1061/(ASCE)AE.1943-5568.0000054
Na h, T., A a zadeh, M., Tiong, R. L. K., Chidamba am, C., & Yu, Z. (2015). P oduc i i y
imp o emen o p ecas shop d awings gene a ion h ough BIM-based p ocess e-enginee ing.
Au oma ion in Cons uc ion
,
54
, 54–68. h ps://doi.o g/10.1016/j.au con.2015.03.014
NBS. (2018). Wha is BIM? | NBS. In
Na ional Building Speci ica ion (NBS)
.
h ps://doi.o g/10.3917/deba.065.0117
Ne e, H. H., & Wandahl, S. (2018). Towa ds iden i ying making-do as lead was e in e u bishmen
p ojec s.
IGLC 2018 - P oceedings o he 26 h Annual Con e ence o he In e na ional G oup o
Re e ences
234
Lean Cons uc ion: E ol ing Lean Cons uc ion Towa ds Ma u e P oduc ion Managemen Ac oss
Cul u es and F on ie s
,
2
, 1354–1364. h ps://doi.o g/10.24928/2018/0236
Nguyen, D. T., & Sha mak, W. (2022). Hyb id Simula ion o Value S eam Mapping o Imp o e
he En i onmen al Pe o mance o he Cons uc ion Phase.
30 h Annual Con e ence o he
In e na ional G oup o Lean Cons uc ion, IGLC 2022
, 421–432.
h ps://doi.o g/10.24928/2022/0145
Nunnally, J., & Be ns ein, I. (1994). Psychome ic heo y. In
(No Ti le)
(3 d Edi ion). McG aw-Hil.
Ohno, T. (1988).
Toyo a p oduc ion sys em: beyond la ge-scale p oduc ion
. CRC P ess.
Oli ie i, H., Seppänen, O., & G anja, A. D. (2018). Imp o ing wo k low and esou ce usage in
cons uc ion schedules h ough loca ion-based managemen sys em (LBMS).
Cons uc ion
Managemen and Economics
,
36
, 109–124.
h ps://doi.o g/10.1080/01446193.2017.1410561
Omo ayo, T., Awuzie, B., Egbelakin, T., Obi, L., & Ogunnusi, M. (2020). AHP-sys ems hinking
analyses o kaizen cos ing implemen a ion in he cons uc ion indus y.
Buildings
,
10
(12), 1–24.
h ps://doi.o g/10.3390/buildings10120230
Oskouie, P., Ge be , D., Al es, T., & Bece ik-Ge be , B. (2012). Ex ending he In e ac ion o
Building In o ma ion Modeling and Lean Cons uc ion.
20 h Annual Con e ence o he In e na ional
G oup o Lean Cons uc ion
,
1
(617), 111–120. h ps://doi.o g/10.1016/j.au con.2010.09.013
Osmani, M., Glass, J., & P ice, A. D. F. (2008). A chi ec s ’ pe spec i es on cons uc ion was e
educ ion by design.
Was e Managemen
,
28
, 1147–1158.
h ps://doi.o g/10.1016/j.wasman.2007.05.011
Pa k, M., & Peña‐Mo a, F. (2003). Dynamic change managemen o cons uc ion: in oducing
he change cycle in o model‐based p ojec managemen .
Sys em Dynamics Re iew: The Jou nal
o he Sys em Dynamics Socie y
,
19
(3), 213–242.
Ped osa, M., A an es, A., & C uz, C. O. (2023). Ba ie s o Adop ing Lean Me hodology in he
Po uguese Cons uc ion Indus y.
Buildings
,
13
(8).
h ps://doi.o g/10.3390/buildings13082047
Re e ences
235
Penei ol, N. L. S. (2007).
Lean Cons uc ion em Po ugal Engenha ia Ci il: Caso de es udo de
implemen ação de sis ema de con olo da p odução Las Planne
[Disse ação pa a ob enção do
G au de Mes e]. Ins i u o Supe io Técnico - ULISBOA.
Pe ei a, C. M. N. (2014).
Implemen ação da Lean Cons uc ion na Cons ução Nacional
[Disse ação de Mes ado]. Uni e sidade do Minho .
Pe ei a, D., & Cachadinha, N. (2011). Lean cons uc ion in ehabili a ion wo ks - Sui abili y
analysis and con ibu ion o he de ini ion o an applica ion model.
19 h Annual Con e ence o
he In e na ional G oup o Lean Cons uc ion 2011, IGLC 2011
, 157–167.
Pe ei a, F. F. (2019).
Implemen ação de P á icas Lean na Ges ão de Subemp ei adas: Es udo de
Caso
[Mas e Thesis, Uni e sidade Do Po o]. h p://www. e.up.p
Pé ez, C. T., & Bas os Cos a, D. (2021). Inc easing p oduc ion e iciency h ough he educ ion o
anspo a ion ac i i ies and ime using 4D BIM simula ions.
Enginee ing, Cons uc ion and
A chi ec u al Managemen
,
2954
. h ps://doi.o g/10.1108/ECAM-02-2020-0132
Pe ez, C. T., Somme , L., Cos a, D. B., & Fo moso, C. T. (2015). A case s udy on causes and
consequences o anspo a ion was e.
Con . o he In ’l. G oup o Lean Cons uc ion
, 444–
453. www.iglc.ne
Pikas, E., Sacks, R., & P i en, V. (2012). Go o No-Go Decisions a he Cons uc ion Wo k ace:
Unce ain y, Pe cep ions o Readiness, Making Ready and Making-Do. In I. D. Tommelein & C. &
Pasqui e (Eds.),
20 h Annual Con e ence o he In e na ional G oup o Lean Cons uc ion
.
Po wal, A. (2013).
Cons uc ion was e managemen a sou ce
: a Building In o ma ion Modeling
based sys em dynamic app oach
. Uni e si y o B i ish Columbia.
Po wal, A., & Hewage, K. N. (2012). Building in o ma ion modeling-based analysis o minimize
was e a e o s uc u al ein o cemen .
Jou nal o Cons uc ion Enginee ing and Managemen
,
138
(8), 943–954. h ps://doi.o g/10.1061/(ASCE)CO.1943-7862.0000508
P asce ic, N., & P asce ic, Z. (2017). Applica ion o uzzy AHP o anking and selec ion o
al e na i es in cons uc ion p ojec managemen .
Jou nal o Ci il Enginee ing and Managemen
,
23
, 1123–1135. h ps://doi.o g/10.3846/13923730.2017.1388278
Re e ences
236
P oença, V. L. S. (2017).
Aplicação do Las Planne Sys em (LPS) em Casos de Es udo
[Mes e
em Engenha ia Ci il]. Ins i u o Supe io de Engenha ia Do Po o (ISEP).
PTPC. (2016).
Guia P á ico: Lean na Cons ução
.
h ps://www.impic.p /impic/asse s/misc/pd /Guia_P a ico_Lean_Con ucao_Se _2016.pd
Ra ajczak, J., Riedl, M., & Ma , D. T. (2019).
BIM-based and AR Applica ion Combined wi h
Loca ion-Based Managemen Sys em o he Imp o emen o he Cons uc ion Pe o mance
.
h ps://doi.o g/10.3390/buildings9050118
Ra ajczak, J., Schimanski, C. P., Ma che , C., Riedl, M., & Ma , D. T. (2017). Mobile Applica ion
o Collabo a i e Scheduling and Moni o ing o Cons uc ion Wo ks Acco ding o Lean Cons uc ion
Me hods.
Lec u e No es in Compu e Science (Including Subse ies Lec u e No es in A i icial
In elligence and Lec u e No es in Bioin o ma ics)
,
10451 LNCS
, 207–214.
h ps://doi.o g/10.1007/978-3-319-66805-5_26
Richa dson, G. P., & Pugh, A. (1981). In oduc ion o Sys em Dynamics Modeling wi h DYNAMO.
Jou nal o he Ope a ional Resea ch Socie y
,
48
, 1146.
h ps://api.seman icschola .o g/Co pusID:62752425
Rod igues, A., & Bowe s, J. (1996). Sys em dynamics in p ojec managemen : A compa a i e
analysis wi h adi ional me hods.
Sys em Dynamics Re iew
,
12
(2), 121–139.
h ps://doi.o g/10.1002/(sici)1099-1727(199622)12:2<121::aid-sd 99>3.0.co;2-x
Rooke, J. (2020). People and knowledge: lean o ganisa ion. In
Lean Cons uc ion
(pp. 85–101).
Rou ledge.
Rosenbaum, S., Toledo, M., & González, V. (2014). Imp o ing En i onmen al and P oduc ion
Pe o mance in Cons uc ion P ojec s Using Value-S eam Mapping: Case S udy.
Jou nal o
Cons uc ion Enginee ing and Managemen
,
140
(2), 04013045.
h ps://doi.o g/10.1061/(ASCE)CO.1943-7862.0000793
Ro he , M., & Shook, J. (2003).
Lea ning o see: alue s eam mapping o add alue and elimina e
muda
. Lean En e p ise Ins i u e.
Rozen eld, O., Sacks, R., & Rosen eld, Y. (2009). “CHASTE”: Cons uc ion haza d assessmen
wi h spa ial and empo al exposu e.
Cons uc ion Managemen and Economics
,
27
(7), 625–638.
h ps://doi.o g/10.1080/01446190903002771
Re e ences
237
Sacks, R. (2016). Wha cons i u es good p oduc ion low in cons uc ion?
Cons uc ion
Managemen and Economics
,
34
(9), 641–656.
h ps://doi.o g/10.1080/01446193.2016.1200733
Sacks, R., Eas man, C., Lee, G., & Teicholz, P. (2018). BIM Handbook. In
BIM Handbook
(Thi d
edi ). John Wiley & Sons, Inc. h ps://doi.o g/10.1002/9781119287568
Sacks, R., & Goldin, M. (2007). Lean Managemen Model o Cons uc ion o High-Rise Apa men
Buildings.
Jou nal o Cons uc ion Enginee ing and Managemen
,
133
(5), 374–384.
h ps://doi.o g/10.1061/(asce)0733-9364(2007)133:5(374)
Sacks, R., Ko b, S., Ba ak, R., & Ge a, G. (2018).
Building lean, building BIM
: imp o ing
cons uc ion he Tidha way
(Issue Decembe 2017). Rou ledge Taylo & F ancis G oup.
Sacks, R., Koskela, L., Da e, B. A., & Owen, R. (2010). In e ac ion o lean and building in o ma ion
modeling in cons uc ion.
Jou nal o Cons uc ion Enginee ing and Managemen
,
136
(9), 968–
980. h ps://doi.o g/10.1061/(ASCE)CO.1943-7862.0000203
Sacks, R., Radosa lje ic, M., & Ba ak, R. (2010). Requi emen s o building in o ma ion modeling
based lean p oduc ion managemen sys ems o cons uc ion.
Au oma ion in Cons uc ion
,
19
(5),
641–655. h ps://doi.o g/10.1016/j.au con.2010.02.010
Saeed, K. (1998).
De ining a p oblem o cons uc ing a e e ence mode
. Depa men o Social
Science and Policy S udies, Wo ces e Poly echnic Ins i u e.
Saieg, P., So elino, E. D., Nascimen o, D., & Caiado, R. G. G. (2018). In e ac ions o Building
In o ma ion Modeling, Lean and Sus ainabili y on he A chi ec u al, Enginee ing and Cons uc ion
indus y: A sys ema ic e iew.
Jou nal o Cleane P oduc ion
,
174
, 788–806.
h ps://doi.o g/10.1016/j.jclep o.2017.11.030
San os, A. (1999).
Applica ion o low p inciples in he p oduc ion managemen o cons uc ion
si es
. h p://usi .sal o d.ac.uk/2231/
Sa han, S., Pasqui e, C., Manu, E., & King, A. (2017). Con ac ual go e nance as a sou ce o
ins i u ionalised was e in cons uc ion.
In e na ional Jou nal o Managing P ojec s in Business
,
10
(3), 550–577. h ps://doi.o g/10.1108/ijmpb-07-2016-0058

238
Sa oshi Hino. (2005).
Inside he Mind o Toyo a: Managemen P inciples o Endu ing G ow h
(Fujimo o Takahi o, Ed.; 1s ed.). Taylo & F ancis.
Sau in, T. A., & Sanches, R. C. (2014). Lean Cons uc ion and Resilience Enginee ing -
Complemen a y Pe spec i es o Va iabili y. In B. T. , Kalsaas, L. Koskela, & T. A. Sau in (Eds.),
22nd Annual Con e ence o he In e na ional G oup o Lean Cons uc ion. Oslo
(pp. 61–71).
Schimanski, C. P., Ma che , C., Monizza, G. P., & Ma , D. T. (2020). The las planne ® sys em
and building in o ma ion modeling in cons uc ion execu ion: F om an in eg a i e e iew o a
concep ual model o in eg a ion.
Applied Sciences (Swi ze land)
,
10
(3).
h ps://doi.o g/10.3390/app10030821
Schimanski, C. P., Pase i Monizza, G., Ma che , C., & Ma , D. T. (2021). De elopmen o a BIM-
based p oduc ion planning and con ol sys em o Lean Cons uc ion h ough ad ancemen and
in eg a ion o exis ing managemen echniques.
F on ie s o Enginee ing Managemen
,
8
(3), 429–
441. h ps://doi.o g/10.1007/s42524-020-0105-5
Schumacke , R. E., & Lomax, R. G. (2004). A beginne ’s guide o s uc u al equa ion modeling,
2nd ed. In
A beginne ’s guide o s uc u al equa ion modeling, 2nd ed.
Law ence E lbaum
Associa es Publishe s.
Seppänen, O. (2020). Loca ion-based managemen sys em now and in he u u e. In
Lean
Cons uc ion
(pp. 276–300). Rou ledge.
Seppänen, O., Balla d, G., & Pesonen, S. (2010). The Combina ion o Las Planne Sys em and
Loca ion-Based Managemen Sys em.
P oceedings IGLC-18
, 467–476. h p://p2sl.be keley.edu
Seppänen, O., Mod ich, R., & Balla d, G. (2015). In eg a ion o Las Planne Sys em and Loca ion-
Based Managemen Sys em.
23 d Ann. Con . o he In ’l. G oup o Lean Cons uc ion
, 123–132.
She amn, B., & Ki wan, B. (2019).
To wha ex en can Lean Cons uc ion p ocesses and p inciples
be u ilised in he design s age o a BIM p ojec in he I ish design and cons uc ion indus y
.
h ps://a ow. udublin.ie/schmuldis cap
Shewha , W. A., & Deming, W. E. (1986).
S a is ical me hod om he iewpoin o quali y con ol
.
Cou ie Co po a ion.
Re e ences
239
Sil a, P. (2008).
Implemen ing Lean Cons uc ion in Po ugal-Adap a ion a e s udying a Danish
case
[Mas e ’s Thesis]. Ins i u o Supe io Técnico.
Somme , L. (2010).
Con ibuições pa a um mé odo de iden i icação de pe das po imp o isação
em can ei os de ob as
.
S e man, J. D. (2000).
Business dynamics: Sys ems hinking and modeling o a complex wo ld,
. McG aw-Hill.
Suks e , R. (2005).
A in eg ação en e o sis ema de ges ão da qualidade e o planejamen o e
con ole da p odução em emp esas cons u o as
.
Tezel, A., & Aziz, Z. (2017). Bene i s o isual managemen in cons uc ion: case om he
anspo a ion sec o in England.
Cons uc ion Inno a ion
,
17
(2), 125–157.
h ps://doi.o g/10.1108/JBIM-06-2016-0127
Tezel, A., Koskela, L., & Tzo zopoulos, P. (2016). Visual managemen in p oduc ion managemen :
A li e a u e syn hesis. In
Jou nal o Manu ac u ing Technology Managemen
(Vol. 27, Issue 6, pp.
766–799). Eme ald G oup Publishing L d. h ps://doi.o g/10.1108/JMTM-08-2015-0071
Tezel, A., Tagga , M., Koskela, L., Tzo zopoulos, P., Hanahoe, J., & Kelly, M. (2020). Lean
cons uc ion and BIM in small and medium-sized en e p ises (SMEs) in cons uc ion: A sys ema ic
li e a u e e iew.
Canadian Jou nal o Ci il Enginee ing
,
47
(2), 186–201.
h ps://doi.o g/10.1139/cjce-2018-0408
Thomas, H. R., Maloney, W. F., Ho ne , R. M. W., Smi h, G. R., Handa, V. K., & Sande s, S. R.
(1990). Modeling Cons uc ion Labo P oduc i i y.
Jou nal o Cons uc ion Enginee ing and
Managemen
,
116
(4), 705–726. h ps://doi.o g/10.1061/(ASCE)0733-9364(1990)116:4(705)
Tommelein, I. D., & Gholami, S. (2012). Roo Causes o Clashes in Building In o ma ion Models.
In I. D. Tommelein & C. L. Pasqui e (Eds.),
20 h Annual Con e ence o he In e na ional G oup o
Lean Cons uc ion
.
T an ield, D., Denye , D., & Sma , P. (2003). Towa ds a Me hodology o De eloping E idence-
In o med Managemen Knowledge by Means o Sys ema ic Re iew.
B i ish Jou nal o
Managemen
,
14
(3), 207–222. h ps://doi.o g/10.1111/1467-8551.00375
Re e ences
240
Ullman, J. B., & Ben le , P. M. (2012). S uc u al Equa ion Modeling. In
Handbook o Psychology,
Second Edi ion
. Wiley. h ps://doi.o g/10.1002/9781118133880.hop202023
UN En i onmen al and In e na ional Ene gy Agency, G. S. R. (2017). Towa ds a ze o-emission,
e icien , and esilien buildings and cons uc ion sec o . In
Global S a us Repo
.
Uusi alo, P., Oli ie i, H., Seppänen, O., Pikas, E., & Pel oko pi, A. (2017). Re iew o Lean Design
Managemen : P ocesses, Me hods and Technologies.
25 h Annual Con e ence o he In e na ional
G oup o Lean Cons uc ion
, 571–578. h ps://doi.o g/10.24928/2017/0224
Uusi alo, P., Seppänen, O., Lappalainen, E., Pel oko pi, A., & Oli ie i, H. (2019). Applying le el o
de ail in a BIM-based p ojec : An o e all p ocess o lean design managemen .
Buildings
,
9
(5), 1–
13. h ps://doi.o g/10.3390/buildings9050109
Viana, D. D., Fo moso, C. T., & Isa o, E. L. (2017). Unde s anding he heo y behind he Las
Planne Sys em using he Language-Ac ion Pe spec i e: wo case s udies.
P oduc ion Planning
and Con ol
,
28
(3), 177–189. h ps://doi.o g/10.1080/09537287.2016.1233360
Viana, D. D., Fo moso, C. T., & Kalsaas, B. T. (2012). Was e in cons uc ion: A sys ema ic
li e a u e e iew on empi ical s udies.
IGLC 2012 - 20 h Con e ence o he In e na ional G oup o
Lean Cons uc ion
.
Vilasini, N., Nei ze , T., & Ro imi, J. (2014). De eloping and e alua ing a amewo k o p ocess
imp o emen in an alliance p ojec : A New Zealand case s udy.
Cons uc ion Managemen and
Economics
,
32
(6), 625–640. h ps://doi.o g/10.1080/01446193.2013.874565
Vil en han, A., Ram, V. G., & Suguma an, S. (2019). Value s eam mapping o iden i ica ion and
assessmen o ma e ial was e in cons uc ion: A case s udy.
Was e Managemen and Resea ch
,
37
(8), 815–825. h ps://doi.o g/10.1177/0734242X19855429
on Heyl, J., & Teize , J. (2017). Lean P oduc ion Con olling and T acking Using Digi al Me hods.
25 h Annual Con e ence o he In e na ional G oup o Lean Cons uc ion
,
II
(July), 127–134.
h ps://doi.o g/10.24928/2017/0238
V ijhoe , R. (2020). Ex ended oles o cons uc ion supply chain managemen o imp o ed
logis ics and en i onmen al pe o mance. In
Lean Cons uc ion
(pp. 253–275). Rou ledge.
Re e ences
241
V ijhoe , R., & Koskela, L. (2000). The ou oles o supply chain managemen in cons uc ion.
Eu opean Jou nal o Pu chasing and Supply Managemen
,
6
(3–4), 169–178.
h ps://doi.o g/10.1016/S0969-7012(00)00013-7
Wang, Y., Zhao, J., Gao, N., & Shen, F. (2024). A Dynamic E alua ion Me hod o he De elopmen
o In elligen Cons uc ion Technology in he Cons uc ion Field Based on S uc u al Equa ion
Model–Sys em Dynamics Model.
Buildings
,
14
(2). h ps://doi.o g/10.3390/buildings14020417
Wohlin, C., & Claes. (2014). Guidelines o snowballing in sys ema ic li e a u e s udies and a
eplica ion in so wa e enginee ing.
P oceedings o he 18 h In e na ional Con e ence on
E alua ion and Assessmen in So wa e Enginee ing - EASE ’14
, 1–10.
h ps://doi.o g/10.1145/2601248.2601268
Womack, J., & Jones, D. (2003). Lean Thinking-Banish Was e and C ea e Weal h in You
Co po a ion. In
F ee P ess
. Simon and Schus e .
Wu, P., Low, S. P., & Jin, X. (2013). Iden i ica ion o non- alue adding (NVA) ac i i ies in p ecas
conc e e ins alla ion si es o achie e low-ca bon ins alla ion.
Resou ces, Conse a ion and
Recycling
,
81
, 60–70. h ps://doi.o g/10.1016/j. escon ec.2013.09.013
Yin, R. K. (2018).
Case s udy esea ch and applica ions
. Sage.
Yuan, H., & Shen, L. (2011). T end o he esea ch on cons uc ion and demoli ion was e
managemen . In
Was e Managemen
(Vol. 31, Issue 4, pp. 670–679). Pe gamon.
h ps://doi.o g/10.1016/j.wasman.2010.10.030
Zhan, Z., Tang, Y., Wang, C., Yap, J. B. H., & Lim, Y. S. (2022). Sys em Dynamics Ou look on BIM
and LEAN In e ac ion in Cons uc ion Quan i y Su eying.
I anian Jou nal o Science and
Technology - T ansac ions o Ci il Enginee ing
,
46
(5), 3947–3962.
h ps://doi.o g/10.1007/s40996-022-00833-w
Zhang, X., Azha , S., Nadeem, A., & Khal an, M. (2018). Using Building In o ma ion Modelling o
achie e Lean p inciples by imp o ing e iciency o wo k eams.
In e na ional Jou nal o
Cons uc ion Managemen
,
18
(4), 293–300.
h ps://doi.o g/10.1080/15623599.2017.1382083
Zla ano ic, D. (2012). Sys em Dynamics Models in Managemen P oblem Sol ing.
Economic
Ho izons
,
14
(1), 25–38. www.ek ak.kg.ac. s
Re e ences
III.Appendix C: D3M Use Guide
249
III. Appendix C: D3M Use Guide
III.1. Con ex
D3M ini ially eme ged h ough a disse a ion wi hin a esea ch ini ia i e ocused on assessing he
po en ial e ec s o Making-Do (MD) on p oduc ion p ac ices and cons uc ion ou comes. Fu he mo e,
i del es in o he possible ad an ages o me ging he LPS and BIM o p oduc ion planning and con ol.
Figu e III-1 -- Cloud applica ion o D3M.
Figu e III-2 is a snapsho o he welcome message once he model is opened; he message con ains a
b ie in oduc ion wi h objec i es o he model de elopmen and simula ion and a sho desc ip ion o
how D3M can be help ul o he use once he un bu on is clicked.
Figu e III-2 -- D3M Welcome Message

III.Appendix C: D3M Use Guide
250
D3M p o ides a di ec assessmen o 25 s a egies om LPS-BIM and sugges s addi ional pa hways o
o mula e lean-based policy, as p esen ed in Figu e III-3.
Figu e III-3 -- The dynamic amewo k o MD was e analysis.
III.Appendix C: D3M Use Guide
251
Table III-1 delinea es each s a egy used by D3M, whe e he pa ame e s and hei coe icien s we e
used in he D3M sys em. All pa ame e s had hei weigh s dis ibu ed by conside ing he es ima ion
esul s o he SEM media ion analysis. Mo eo e , he ela ionship be ween a iables is applied, MDK
in luenced by LPSF_F, COO_F exe ing in luences LPS_F and in luenced by BIMF_F.
Table III-1 -- Pa ame e s o LPS-BIM wi hin D3M
Componen Pa ame e Coe icien Fo mula
LPS_F
VA10 9.8%
( 0.098* VA10 + 0.072 * VA11 + 0.077 * VA12 + 0.074
*VA13+ 0.073 * VA14 + 0.084 * VA15 + 0.072 * VA16 +
0.064 * VA17 + 0.057 * VA18 + 0.071 * VA19 + 0.077 *
VA20) + 0.106 * COO_F
VA11 7.2%
VA12 7.7%
VA13 7.4%
VA14 7.3%
VA15 8.4%
VA16 7.2%
VA17 6.4%
VA18 5.7%
VA19 7.1%
VA20 7.7%
COO_F 10.6%
COO_F
VA1 5.7%
(0.057 * VA1 + 0.067 * VA2 + 0.055 * VA3 + 0.083*
VA4) + 0.160 * BIMF_S
VA2 6.7%
VA3 5.5%
VA4 8.3%
BIMF_S 16%
MDK
VA5 8.5%
( 0.85 * VA5 + 0.047 * VA6 + 0.055 * VA7 + 0.057 * VA8
+ 0.081 * VA9) + 0.116 * LPSF_F
VA6 4.7%
VA7 5.5%
VA8 8.1%
LPSF_F 11.6%
BIMF_F
VA21 12.5%
(0.125 * VA21 + 0.070 * VA22 + 0.055 * VA23 + 0.061
*VA24 + 0.077 * VA25)
VA22 7%
VA23 5.5%
VA24 6.1%
VA25 7.7%
III.Appendix C: D3M Use Guide
252
D3M was ully de eloped by Sys em dynamics modelling using Ja a codes and hos ed by AnyLogic 8
P o essional 8.7.11.
This documen se es as a guide o he bes p ac ice o D3M. The in o ma ion in his documen does
no ully eplace a ca e ul unde s anding o LPS and BIM me hods.
Figu e III-4 -- Use inpu window.
Figu e III-5 (a) illus a es he op ions o p ese inpu s o he scena ios o ex eme alue simula ion,
comp ising six bu ons. Figu e III-5 (b) is an example o code used o se all LPS- ela ed pa ame e s in
scena ios A o 5 as he maximum alue o he Like scale used h oughou he simula ion and ollowing
SEM esul s.
(a)
(b)
Figu e III-5 -- p ese bu ons o ex eme alue es s.
III.Appendix C: D3M Use Guide
253
Ano he op ion o allow use s o es a ia ions o he s udied pa ame e s is a ailable h ough slide s, as
shown in Figu e III-6.
Figu e III-6 -- Slide con olle s o se pa ame e alues.
D3M esea ch was unded by he Po uguese Founda ion o Science and Technology (FCT), wi h g an
numbe 2021.04751.BD
III.Appendix C: D3M Use Guide
254
III.2. D3M olde
The D3M olde mus no be downloaded o he compu e i he use is no using Anylogic so wa e. In
case he use holds an Anylogic license, he ollowing iles a e included:
•Execu able D3M ile (*.alp)
•Welcome message (*.png)
•D3M Excell sp eadshee (*.xlsx)
•D3M da abase olde (db.p ope ies) and (db. sc ip )
•D3M Use Guide (*.pd )
III.3. D3M guidelines and ou pu s
To s a using D3M, a mas e plan is equi ed. The dynamic model can be edi ed o modi ied in AnyLogic
8 P o essional 8.7.11 o la e .
III.3.1. D3M guidelines
The sys em dynamic model should be un h ough some use inpu da a ha mus include he ollowing:
•Es ima ed inish da e o each s age o miles one.
•Es ima ed du a ion in mon hs.
•Es ima ed numbe o esou ces used.
•Scale o LPS-BIM pa ame e s 1 o 5.
III.3.2. D3M ou pu
The mos i al ea u e o D3M is no only he simula ion o s ock and low diag ams bu also eal- ime
in o ma ion p esen ed in he con ol dashboa d ha shows he s o ed alues o cons ain s, making do
inciden s and was es in he (Tasks) uni (As gi en in Figu e III-7).
Figu e III-7 -- D3M dashboa d.

III. Appendix C: D3M Use Guide
255
Figu e III-8 (a) ep esen s how many s ages a e planned acco ding o he mas e schedule. (b)
showcases he numbe o asks in WIP s ock and (c) he numbe o disco e ed cons ain s. (d) The
p og ess o inished wo ks is ca ego ized in o subs ages: (e) he numbe o asks ha eme ged was es
and ( ) he numbe o MD inciden s ca ego ized.
(a)
(b)
(c)
(d)
(e)
( )
Figu e III-8 -- Con ol cha s o D3M.
The i s page o s ock and low diag ams, p esen ed in Figu e III-9, shows he wo k p og ess loca ed.
The panning cu so / will be ac i a ed by p essing on he sc olling oll. (a) ep esen s he mas e
schedule in o ma ion, (b) he in e ac ion be ween WIP, inished wo ks and cons ain s and (c) includes
he socks o making do and was es. Each s ock in his igu e should be wo king and i is necessa y o
III.Appendix C: D3M Use Guide
256
iden i y he a ea o cons uc ion, miles ones da es and expec ed inish da es o each subs age o
cons ain he model in o eal-wo ld beha iou .
Figu e III-9 -- Wo k p og ess and MD subsys ems.
Figu e III-10 illus a es he space managemen sys em; he essen ial alue is he wo kspaceA ailable
a iable because his a iable will be used o calcula e p oduc i i y.
Figu e III-10 -- Loca ion subsys em.
The igu e below desc ibes he impac o esou ces on p oduc i i y and spaces,
TimeToInc easeWo k o ce and esou ceAdjus men Time > 0; i mus be mo e han 0 when needed.
LPS, BIM and COO pa ame e s ha e an impac on esou ce E iciency. Pa (a) ep esen s he impac
o wo k low on esou ces; RR is he con e sion be ween uni s ( asks) o (pe son); (b) his sec ion
(a)
(b)
(c)
III.Appendix C: D3M Use Guide
257
ep esen s he co e pa o he esou ces subsys em; (c) is he mos i al pa whe e i connec s he
ela ionship om esou ces o he p oduc i i y a e.
Figu e III-11 -- Resou ces subsys em
Figu e III-12 showcases how schedule p essu e a ec s p oduc i i y (p od), o e ime, wo k low, space
limi a ion and MD decisions a e included in p oduc i i y calcula ion.
Figu e III-12 -- P oduc i i y subsys em
(a)
(b)
(c)
IV.Appendix D: D3M Componen s
258
IV. Appendix D: D3M Componen s
IV.1. E alua ed LPS-BIM Pa ame e s
Table IV-1 explains he pa ame e s o LPS-BIM, which a e s a egies o mi iga e MD and inc ease he
adop ion o LPS-BIM.
Table IV-1 -- Pa ame e s explana ion and scale
ID Fac o Explana ion Ra ing Scale
VA1
Handling con lic s
among a ious
s akeholde s
This pa ame e measu es how he
eam can
add ess con lic s among di e se s akeholde s in
esol ing dispu es abou esponsibili y o
cons ain s and he mechanisms employed o hei
emo al.
Like 1-5
VA2 Coo dina ion le el
This inpu speci ies he pe cen age o coo dina ion
implemen ed du ing Las Planne Sys em (LPS)
sessions.
Like 1-5
VA3 Enabling discussions
This pa ame e speci ies he le el o opening
discussions, how p ojec managemen encou ages
dialogue among ele an pa ies du ing he planning
phase and
he esolu ion o hei asks wi hin
lookahead and make- eady planning sessions.
Like 1-5
VA4 Local adjus men s o
o ganisa ions
This pa ame e encompasses
he modi ica ions
made o accommoda e he Las Planne Sys em in
alignmen wi h he o ganisa ion's cul u e, esou ces,
p ojec s, p io i ies, goals and isions.
Like 1-5
VA4 Engagemen in
cons ain s analysis
This pa ame e enables use s o
designa e he
deg ee o in ol emen o each ade in he
cons ain s analysis p ocess. A heigh ened le el o
engagemen can esul in mo e eliable plans and a
educ ion in MD p ac ices when cons ain s a e
emo ed co ec ly and collabo a i ely.
Like 1-5
IV.Appendix D: D3M Componen s
265
DE15 cons ain sRa e[CONS
TRAINTS]
LPSF_S <=60? zidz(cons ain s[CONSTRAINTS],
ime ode ec Cons ain s[CONSTRAINTS]):
zidz(zidz(cons ain s[CONSTRAINTS]
, ime ode ec Cons ain s[CONSTRAINTS]),
impac O COOonCons ain s *
Impac o LPSonCons ain s(LPSF_S))
Task/ ime
DE16 ime ode ec Cons ain
s
e ec OFSceduleP essu eOnCons ain sRemo al
(comple ionF ac ion.a e age()) ime
DE17 sumcons s[CATEGORI
ES] cons ain s[CONSTRAINTS] Task
DE18 manH sNeeded zidz(WIP_ alue.a e age(),maxResou ces.a e age
()) Task/Pe son
DE19 TaskPe Pe sonPe Mon
h
zidz(manH sNeeded, emainingTime.a e age()) Task /
(Pe son *
ime)
DE20 impac s[CATEGORIES]
COO + MDK < 592?
MDCAT1(sumcons s[CATEGORIES])
:MDCAT1(sumcons s[CATEGORIES]) /1.5
Task/ ime
DE21 MD oImpac Ra e
[CATEGORIES]
LPSF_S <= 100?
MD[CATEGORIES]*pe cen ageOFnega i eMD[CA
TEGORIES]:
MD[CATEGORIES]*pe cen ageOFnega i eMD[CA
TEGORIES]
Task/ ime
DE22 pe cen ageOFnega i e
MD MDK<1? 0.9: 0.5 1/ ime
DE23 inno a i eMD
[CATEGORIES]
MD[CATEGORIES]*(1-
pe cen ageOFnega i eMD[CATEGORIES]) Task/ ime
DE24 comple ionF ac ion
[SUBSTAGES]
zidz( inishedWo k[SUBSTAGES],WIP_ alue[SUBS
TAGES]) uni less
DE25 p ojec IsDone
[SUBSTAGES]
comple ionF ac ion[SUBSTAGES]>=1?1:0 uni less

IV.Appendix D: D3M Componen s
266
DE26 equi edWo k low
[SUBSTAGES]
p ojec IsDone[SUBSTAGES] != 0 &&
WIP[SUBSTAGES] < 0 ? 0 :
max(xidz(WIP[SUBSTAGES],
emainingTime[SUBSTAGES],
maxWo k low[SUBSTAGES]) ,
maxWo k low[SUBSTAGES])
Task/ ime
DE27 emainingTime
[SUBSTAGES] max( 0, comple ionDa e[SUBSTAGES] - ime() ) ime
DE28 comple ionRa e[SUBST
AGES]
p ojec IsDone[SUBSTAGES] ==1 ?0 :
delay(min( equi edWo k low[SUBSTAGES] ,
p od[SUBSTAGES] *
esou cesP oduc i i yPe SS[SUBSTAGES]),s a M
on h[SUBSTAGES])
Task/ ime
DE29 imeToDisco e MDs[S
UBSTAGES]
e ec OFSceduleP essu eOnCons ain sRemo al
(comple ionF ac ion[SUBSTAGES]) ime
DE30 MD_Inciden sRa e[CAT
EGORIES]
(0.061*MD[CAT1]+0.04*MD[CAT4])/ imeToDisc
o e MDs[SS1] Task/ ime
DE31 MD_Inciden sRa e
[SUBSTAGES]
MD[CATEGORIES]/ imeToDisco e MDs[SUBSTA
GES] Task/ ime
DE32 esou cesComing[RES
OURCES]
esou cesGap[RESOURCES] >
TOT_Resou ces[RESOURCES]?
( esou cesGap[RESOURCES]-
TOT_Resou ces[RESOURCES])
/TimeToInc easeWo k o ce:0
Pe son/ ime
DE33
c owdingE ec OnCons
uc ionE eciency
[RESOURCES]
zidz( equi edResou ces[RESOURCES],
esou ces[RESOURCES])
ime
DE34 esou ces[RESOURCE
S]
changeInResou ces[RESOURCES] -
dismissals[RESOURCES]* zidz(
esou ces[RESOURCES],
TOT_Resou ces[RESOURCES] )
Pe son
IV.Appendix D: D3M Componen s
267
DE35 esou cesE iciency
[RESOURCES]
c owdingE ec OnCons uc ionE eciency[RESOU
RCES]*BIME ec OnCommunica ion*plannedP o
duc i i y *LPSE ec OnE iciency
Pe son
DE36 Re iciency_To_SS[SU
BSTAGES] Reg ession Equa ion Pe son
DE37 plannedP oduc i i y uni o m_disc (20,40) Pe son
DE38 TOT_Resou ces
[RESOURCES]
esou ces[RESOURCES]+newWo kFo ce[RESOU
RCES] Pe son
DE39 dismissals
[RESOURCES]
esou cesGap[RESOURCES] <
TOT_[RESOURCES] ?
( TOT_Resou ces[RESOURCES] -
esou cesGap[RESOURCES] )/
dismissalTime[RESOURCES] : 0
Pe son/ ime
DE40 scheduleP essu e[SUB
STAGES]
emainingTime[SUBSTAGES] <= 0 &&
!(p ojec IsDone[SUBSTAGES] != 0) ?
maxScheduleP essu e :
zidz( equi edWo k low[SUBSTAGES],
no malWo kFlow[SUBSTAGES] )
uni less
DE41 no malWo kFlow
[SUBSTAGES] RG_TO_SS[SUBSTAGES]*no malP oduc i i y Task/ ime
DE42 o e ime [SUBSTAGES] o e imeTBFTN(scheduleP essu e[SUBSTAGES]) ime
DE43 e Fa igueP oduc i i y
[SUBSTAGES] =
a igueE P oduc i i yTBFN(a e ageO e ime[SUB
STAGES]) 1/ ime
DE44 p od [SUBSTAGES]
e Fa igueP oduc i i y[SUBSTAGES]*
no malP oduc i i y
* o e ime[SUBSTAGES]*
impac O Wo kSpaceLimi a ion[SUBSTAGES]+(M
DSS[SUBSTAGES])
Task / ( ime *
Pe son)
DE45 wo kspacePe C ewA ai
lable[SUBSTAGES]
wo kspaceA ailable* esou cesP oduc i i yPe SS[
SUBSTAGES]
Pe son *
space
DE46 impac O Wo kSpaceLi
mi a ion
impac O Wo kSpaceLimi a ions(wo kspacePe C
ewA ailable[SUBSTAGES])
IV.Appendix D: D3M Componen s
268
DE47 chanegeInLoca ions
comple ionF ac ion.a e age() <1 &&
loca ionsDesign >0 ? 1 : 0 space/ ime
DE48 uncompensableDelays
p ojec Du a ion*0.85 <= ime() &&
comple ionF ac ion.a e age() <
0.80 ?abs(p ojec Du a ion - ime()) : 0
ime
DE49 cons uc ionResou ces
Cos
a e ageO e ime.a e age()>1?
newWo kFo ce.a e age()*
cons uc ionResou ceUni Cos Pe cen age *
a e ageO e ime.a e age()*o e imeUni Cos :
newWo kFo ce.a e age() *
cons uc ionResou ceUni Cos Pe cen age
R$/ ime
DE50 cons uc ionResou ceU
ni Cos Pe cen age 7.5 1/ (Pe son *
ime)
DE51 o e imeUni Cos 1.2 $/ ime
DE52 WIP[SUBSTAGES]
MD_Inciden sRa e[SUBSTAGES] -
comple ionRa e[SUBSTAGES] +
cons ain sRa e[Cons ain s]
Task
DE53 was eRa e[SUBSTAGE
S]
LPSF_S + BIMF_S >=50 ?
was eGene a ionRa e(comple ionF ac ion[SUBST
AGES]) *comple ionRa e[SUBSTAGES]*0.7 :
was eGene a ionRa e(comple ionF ac ion[SUBST
AGES])
Task/ ime
DE54 eplanningRa e[CONST
RAINTS]
zidz(Was e[IMPACTS],
ime oDe ec Cons ain s[CONSTRAINTS]) Task/ ime
DE55 d(Was e[IMPACTS])/d MD oImpac Ra e[CATEGORIES]+was eRa e[SUB
STAGES]- eplanningRa e[CONSTRAINTS] Task
DE56 spaceChange
zidz( du a ionInH s * esou cesGap.a e age()
, inishedWo k.a e age()) 1/ ime
DE57 loca ionsU ilisa ionRa e
occupiedLoca ions *spaceChange >=
ini ialLoca ions ? 0
:occupiedLoca ions *spaceChange
space/ ime
IV.Appendix D: D3M Componen s
269
DE58 wo kspaceA ailable ini ialLoca ions- inishedLoca ions-
occupiedLoca ions space
DE59 ini ialLoca ions 50 space
DE60 LPSE ec OnE iciency
LPSF_S >= 50 ?
1.5*impac O Commi emen Planning :
1*impac O Commi emen Planning
uni less
DE61 p ojec Cos
indi ec Cos +
zidz(uncompensableDelaysCos ,uncompensable
Delays)* ime() +
$
DE62 uncompensableDelays
Cos 1.25 $
DE63 indi ec Cos indi ec Cos P ecen age * Was e.a e age() $
DE64 indi ec Cos P ecen age 0.6 $/ Task
DE65 impac O Commi emen
Planning COO>=50? 1.5: 1 uni less
DE66 MDSS[SUBSTAGE] MDSS_SUBSTAGE(inno a i eMD.a e age()) Task
DE67 cons ain sRa e[CONS
TRAINTS]
LPSF_S <=60? zidz(cons ain s[CONSTRAINTS],
ime ode ec Cons ain s[CONSTRAINTS]):
zidz(zidz(cons ain s[CONSTRAINTS]
, ime ode ec Cons ain s[CONSTRAINTS]),
impac O COOonCons ain s *
Impac o LPSonCons ain s(LPSF_S))
Task/ ime
DE68 comple ionRa e
p ojec IsDone[SUBSTAGES] ==1 ?0 :
delay(min( equi edWo k low[SUBSTAGES] ,
p od[SUBSTAGES]
* esou cesP oduc i i yPe SS[SUBSTAGES]),s a
Mon h[SUBSTAGES])
Task/ ime
DE69 d(newWo kFo ce
[RESOURCES])/d
esou cesComing[RESOURCES] -
changeInResou ces[RESOURCES]
- dismissals[RESOURCES] *
zidz( newWo kFo ce[RESOURCES],
TOT_Resou ces[RESOURCES] )
Pe son
IV.Appendix D: D3M Componen s
270
DE70 changeInResou ces
[RESOURCES]
newWo kFo ce[RESOURCES]/ esou ceAdjus em
en Time Pe son/ ime
DE71 esou ceAdjus emen Ti
me 2 ime
DE72 MD oImpac Ra e
[CATEGORIES]
LPSF_S <= 100?
MD[CATEGORIES]*pe cen ageOFnega i eMD[CA
TEGORIES]:
MD[CATEGORIES]*pe cen ageOFnega i eMD[CA
TEGORIES]
Task/ ime
IV.4. Table Func ions
Table IV-4 p esen s he able unc ions, p ese non-linea unc ions ha a e no ma hema ical and
ep esen ela ionships be ween di e en a iables; some o hese ables a e obse ed om collec ed
da a and li e a u e.
Table IV-4 -- Table unc ions in D3M
Table Func ion Da a Uni
T1
e ec OFSceduleP essu e
OnCons ain sRemo al
{(0 , 5 ),( 0.1 , 4.5),( 0.2 , 4) , (0.3 , 3 ),( 0.4
, 2) , (0.5,1 ), (0.6,0.9) , (0.7 , 0.8) , (0.8 ,
0.7) , ( 0.9 , 0.6) , ( 1 , 0.5) }
ime
T2
esou cesAlloca ionTBFT
N {(0 , 1 ),( 0.5 , 0.8),( 0.8 , 0.2) , (1 , 0 ) } uni less
T3 MDCAT1
{(0 , 0 ),( 1 , 0.5),( 2 , 5) , (5 , 12 ),( 10 , 13)
, (15,14 ), (30,15) } Task/ ime
T4 MDCAT2
{(0 , 0 ),( 10 , 20),( 20 , 50) , (30 , 100 ),( 40
, 110) , (50,115 ), (60,120) , (70 , 125) , (80
, 130) , ( 90 , 135) , ( 100 , 140) } Task/ ime
T5 MDCAT3
{(0, 0 ),( 5, 3),( 20, 5), (30, 8 ),( 40, 9),
(50,10 ), (60,15), (70, 17) } Task/ ime
T6 MDCAT4
{(0, 0 ),( 2, 3),( 3, 30), (4, 35 ),( 5, 40),
(6,61.5 ), (9,62), (11, 63), (15, 64) }
Task/ ime

IV.Appendix D: D3M Componen s
271
T7 MDCAT5
{(0 , 0 ),( 3 , 4),( 8 , 6) , (25 , 8 ),( 35 , 12) ,
(45,20 ), (50,21) , (60 , 21.5) , (70 , 22) , (
80 , 22.5) , ( 90 , 23) } Task/ ime
T8 o e imeTBFTN
{(0 , 0.7 ),( 1 , 1),( 1.2 , 1.2) , (1.5 , 1.4 ),( 2
, 1.45) , (5,1.5 ), (10,1.55) , (20 , 1.6) }
ime
T9
a igueE P oduc i i yTB
FN
{(0 , 1 ),( 0.1 , 0.95),( 0.2 , 0.9) , (0.3 , 0.87
),( 0.4 , 0.83) , (0.5,0.8 ), (0.6,0.75) , (0.7 ,
0.7) , (0.8 , 0.68) , ( 0.9 , 0.65) , ( 1 , 0.6) }
1/ ime
T11
impac O ApplyingBIMT
echnology
{(0 , 0.1 ),( 0.5 , 0.15),( 1 , 0.35) , (1.5 , 0.55
),( 2 , 0.725) , (2.5,1.1 ), (3,1.5) , (3.5 ,
2.025) , (4 , 2.825) , ( 4.5 , 3.7) , ( 5 , 5) }
uni less
T12 was eGene a ionRa e
{(0 , 0.05 ),( 0.1 , 0.09),( 0.2 , 0.25) , (0.3 ,
0.4 ),( 0.4 , 0.45) , (0.5,0.55 ), (0.6,0.65) ,
(0.7 , 0.55) , (0.8 , 0.45) , ( 0.9 , 0.35) , ( 1 ,
0.2) }
uni less
T13
scheduleP essu eE ec T
BFTN
{(0 , 0.4 ),( 0.25 , 0.5),( 0.5 , 0.6) , (0.75 , 0.8
),( 1 , 0.9) , (1.25,1 ), (1.5,1.2) , (1.75 , 0.9)
, (2 , 0.6) , ( 0.9 , 0.35) , ( 1 , 0.2) }
uni less
T14
impac O Wo kSpaceLimi
a ions
{(5 , 0.65 ),( 10 , 0.68),( 15 , 0.7) , (20 , 0.76
),( 25 , 0.8) , (30,0.93 ), (35,0.95) , (40 ,
0.97) , (45 , 0.98) , ( 50 , 0.99) }
uni less
T15 imeToDisco e Was e
{(0 , 5 ),( 0.1 , 4.5),( 0.2 , 4) , (0.3 , 3 ),( 0.4
, 2) , (0.5,1 ), (0.6,0.9) , (0.7 , 0.8) , (0.8 ,
0.7) , ( 0.9 , 0.6) , ( 1 , 0.5) }
ime
T16
Impac o LPSonCons ain
s
{(0 , 0 ),( 20 , 0.1),( 40 , 0.22) , (50 , 0.5 ),(
60 , 0.75) , (100,0.88 ), (140,0.91) , (180 ,
0.94) , (200 , 0.96) , ( 250 , 0.98) , ( 260 ,
0.99) }
uni less
T17
Impac O LPSonWas eRe
duc ion
{(0 , 0.05 ),( 20 , 0.09),( 40 , 0.25) , (50 , 0.4
),( 60 , 0.45) , (100,0.55 ), (140,0.65) , (150
, 0.75) , (160 , 0.8) , ( 170 , 0.9) }
uni less
272
T18 MDSS1
{(1 , 0.04685 ),( 2 , 0.12186118),( 5 ,
0.23425) , (10 , 0.4685 ),( 15 , 0.70275)}
Task/ ime
T19 MDSS2
{(1 , 0.14344 ),( 2 , 0.37309476),( 5 ,
0.7172) , (10 , 1.4344 ),( 15 , 2.1516)}
Task/ ime
T20 MDSS3
{(1 , 0.33001 ),( 2 , 0.85805652),( 5 ,
1.65005) , (10 , 3.3001 ),( 15 , 4.95015)}
Task/ ime
T21 MDSS4
{(1 , 0.06306 ),( 2 , 0.1640118),( 5 , 0.3153)
, (10 , 0.6306 ),( 15 , 0.9459) }
Task/ ime
T22 MDSS5
{(1 , 0.09973 ),( 2 , 0.2593535),( 5 ,
0.49865) , (10 , 0.9973 ),( 15 , 1.49595) }
Task/ ime
T23 MDSS6
{(1 , 0.062061 ),( 2 , 0.16138018),( 5 ,
0.310305) , (10 , 0.62061 ),( 15 , 0.930915)
}
Task/ ime
T24 MDSS7
{(1 , 0.06463 ),( 2 , 0.16815084),( 5 ,
0.32315) , (10 , 0.6463 ),( 15 , 0.96945) } Task/ ime
T25 MDSS8
{(1 , 0.01873 ),( 2 , 0.0487316),( 5 ,
0.09365) , (10 , 0.1873 ),( 15 , 0.28095) } Task/ ime
T26 MDSS9
{(1 , 0.25275 ),( 2 , 0.65722892),( 5 ,
1.26375) , (10 , 2.5275 ),( 15 , 3.79125)} Task/ ime
T27 MDSS10
{(1 , 0.22201 ),( 2 , 0.5774409),( 5 ,
1.11005) , (10 , 2.2201 ),( 15 , 3.33015)} Task/ ime
T28 MDSS11
{(1 , 0.07174 ),( 2 , 0.18663684),( 5 ,
0.3587) , (10 , 0.7174 ),( 15 , 1.0761)} Task/ ime