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Socio-organo Complexity and Project Performance
cxcx
Antoniadis D.N.(Project Manager)
Edum-Fotwe F.T.(Loughborough University)
Thorpe A.(Loughborough University)
DAnton-progmDAnton-progm
The speaker
D.N. Antoniadis
Working for a major Construction Company in UK.
Completed a part time PhD in Project Management
and Complexity at Loughborough University, with
over 25 years experience in Construction Project and
Programme Management, having worked at various
management levels, for Clients, Contractors, and
Consultants.
Why this subject
The selection of this subject has not been accidental.
Complexity is a subject for which we - practitioners
and academia - require to innovate and deliver much
more if we are to develop further Project Management
as a profession and improve the Project Management
Outcome and Project performance.
The background
•Lack of implementation
of processes
•Non-linearity of project
management
•Interfaces /
interconnections
•Formation of Boundaries
•Importance of Soft issues
Knowns
• Effects of Complexity on
project performance
• Tools to manage
Complexity
• Complexity of
interconnections
• Characteristics mirrored
in project management
Unknowns
Methodology - 1Five case studies were conducted, on construction projects covering all stages of the project life cycle, as part of a multi-methodology implemented to investigate complexity and its effects on project performance.
The investigation focused on the effects of complexity on project performance through the processes of selecting team members and structuring the teams, as well as the management style adopted.
The PMs had a minimum of 10 years experience.
Chemical removal plantCommissioningG1.2
Building – 28 luxury flatsConstructionG2.1
Redevelopment of Airport
Terminal Lounge
ConstructionG1.1.4
Airport Terminal Forecourt extension
Design / Early Construction
G1.1.2
Waste Water Treatment
works
FeasibilityG1.3
DescriptionProject StageCase Study
Methodology - 2
Case Study G2.1
Stage: Construction
Extract from a typical
case study programme
R17 - Clarity of communication / instruction cause confusion to the team which took some time
to react to the misunderstanding
Instability - structureC210
R16 - Communication between team and othersCo-evolution – structureD110
R15 - Problem with designUnpredictabilityB101
R14 - Problem from initial stages of project re-surfaced and caused delayUnpredictabilityB100
R13 - Line of command needed clarificationDownward causation – managementD420
R12 - Definition of work structure needed clarificationDownward causation – structureD410
R11 - Team selection could have been better in terms of tackling the taskDownward causation - team
selection
D400
R10 - Authoritative approach caused problem in teamNon-linear – managementC420
R09 - Structure of team required improvementNon-linear – structureC402
R08 - No expertise within the team – external input requestedAttractors / Non-equilibriumC500,
C300
R07 - Extensive time taken to resolve a problemAutonomous agentsC100
R06 - Lack of Flexibility within the team Co-evolution – teamD100
R05 - Team was not prepared (trained appropriately) to accept influences from the project
environment
Undefined values – teamB310
R04 - Inter-team issue (please elaborate)Undefined values – structureB300
R03 - Team cohesionEmergenceD700
R02 - Lack of appropriate level of trainingNon-linear, Self-reproductionC400,
D301
R01 - Lack of appropriate level of inductionSelf-reproductionD300
ReasonCharacteristicCode cxcx
Extract from list of ‘Reasons for activity delay’
Results
0%
26%22%24%23%
32%
29%
9%
26%
0
4
6 6
8 8 8
6
8
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
wk1 wk2 wk3 wk4 wk5 wk6 wk7 wk8 wk9
% D
rop in P
erf
orm
ance
0
2
4
6
8
10
Num
. of C
hara
cte
ristics
Drop in Performance
Num of Complexity Characteristics
Case study G1.3, % drop in performance against number of complexity characteristics
that affected performance
Modeling Performance
0
20
40
60
80
100
120
140
160
180
w k0 w k1 w k2 w k3 w k4 w k5 w k6 w k7 w k8 w k9
To
tal D
Us
Cum Plnd DUs
Cum Dus Achieved
Case study G1.3 modelling of performance based on Total Duration achieved
against time taken. Only 74% of the planned activity time was achieved
Case Study G1.3
Stage: Feasibility
Frequency of coded reasons for delay
0
1
2
3
4
5
6
7
8
9
R03 R04 R06 R11 R16 R18 R26 R27 R28 R29 R30
Nu
m.
of
Co
de
d R
ea
so
ns
fo
r d
ela
y
From this
Histogram of frequency of occurrence
of coded reasons for delay
Number of reasons causing delay
0
2
4
6
8
10
12
14
B3 C2 C3 C4 D1 D4 D7
Nu
m.
of
Co
mp
lex
ity
Re
as
on
s
cxcx
Frequency of complexity characteristics causing delay
To that
Case Study G1.3
Stage: Feasibility
Number of Complexity characteristics & % Drop in Performance
0%
43%
55%58%
56%50%
49%
52%
55%
50%
0
6
13
10
1110
11
13 13
12
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Wk0 w k1 Wk2 w k3 w k4 w k5 w k6 w k7 w k8 w k9
% D
rop in P
erform
ance
0
2
4
6
8
10
12
14
Num
. of chara
cte
ristics
Drop in Performance
Num ofComplexity characteristics
Case study G1.1.2, % drop in performance
against number of Complexity characteristics
that affected performance
Modeling Performance
654
373
0
100
200
300
400
500
600
700
Wk0 wk1 Wk2 wk3 wk4 wk5 wk6 wk7 wk8 wk9
To
tal
Du
s
Cum. Planned Du
Cum Achieved Du
Case study G1.1.2, modelling of performance based on Total Duration
achieved against Time taken. Only 57% achieved
Case Study G1.1.2
Stage: Design/Early
Construction
Number of Complexity characteristics & % Drop in Performance
0%
41%
23%20%
29%
34% 35%
36%
41%42%
0
4
14
14 1414
12
11
5
4
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Wk0 wk1 Wk2 wk3 wk4 wk5 wk6 wk7 wk8 wk9
% D
rop in P
erf
orm
ance
0
2
4
6
8
10
12
14
16
18
20
Num
of
chara
cte
ristics
Drop in Performance
Num of Complexity characteristics
Case study G1.1.4, % drop in performance
against number of complexity characteristics
that affected performance
Modeling Performance
679
401
0
100
200
300
400
500
600
700
800
Wk0 wk1 Wk2 wk3 wk4 wk5 wk6 wk7 wk8 wk9
To
tal D
us
Cum. Planned Du
Cum Achieved Du
Case study G1.1.4, modelling of performance based on Total Duration
achieved against time taken. Only 59% achieved
Case Study G1.1.4
Stage: Construction
Case study G2.1, % drop in performance against
number of complexity characteristics that affected performance.
Modelling Performance
590
252
0
100
200
300
400
500
600
700
Wk0 wk1 Wk2 wk3 wk4 wk5 wk6 wk7 wk8 wk9
To
tal
Du
s
Cum. Planned Du
Cum Achieved Du
Case study G2.1, modelling of performance based on Total Duration achieved
against time taken. Only 42% achieved
Case Study G2.1
Stage: Construction
Number of Complexity characteristics & % Drop in Performance
0%
57%58%58%59%
65%
61%67%
72%
62%
0
9
5
77
7
5
5 55
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Wk0 wk1 Wk2 wk3 wk4 wk5 wk6 wk7 wk8 wk9
% D
rop in P
erform
ance
0
2
4
6
8
10
Num
. of re
asons
Drop in Performance
Num of Complexity characteristics
Number of Complexity characteristics & % Drop in Performance
0%
53%
27%
21%19% 19% 18% 17% 17%
40%
0
6
7
7 7 7 7
6 6 6
0%
20%
40%
60%
80%
100%
wk0 wk1 wk2 wk3 wk4 wk5 wk6 wk7 wk8 wk9
% D
rop in P
erf
orm
ance
0
2
4
6
8
10
Num
. of
Chara
cte
ristics
Drop in Performance
Num. of Complexity characteristics
Case study G1.2, % drop in performance
against number of complexity characteristics
that affected performance
Modeling Performance
154
128
0
20
40
60
80
100
120
140
160
180
wk0 wk1 wk2 wk3 wk4 wk5 wk6 wk7 wk8 wk9
To
tal
Du
s
Cum. Planned Du
Cum Achieved Du
Case study G1.2, modelling of performance based on Total Duration achieved
against time taken. Only 83% achieved
Case Study G1.2
Stage: Commissioning
Case Study Performance comparison
CS G2.1 - Cnstr
CS G1.1.2 - Cnstr
CS G1.3 - Dsgn
CS G1.2 - Cmsng
CS G1.1.4 - Cnstr
0%
20%
40%
60%
80%
100%
w k0 w k1 w k2 w k3 w k4 w k5 w k6 w k7 w k8 w k9
% D
rop in P
erf
orm
ance
Case studies summary; drop in performance due to the effects of complexity
% Drop in Performance - Only Construction Case Studies
0%
50%
56%58%
57%58%55%
59%56%
64%
0%
20%
40%
60%
80%
100%
w k0 w k1 w k2 w k3 w k4 w k5 w k6 w k7 w k8 w k9
% D
rop
in P
erf
orm
an
ce
Case Study G2.1 Case Study G1.1.2
Cnstr Average
Construction only performance drop curves, including average
Outcome
% Drop in Performance - Average of all Case Studies
0%
40%37%
40%
39%40%
39%
40% 39% 37%
0%
20%
40%
60%
80%
100%
w k0 w k1 w k2 w k3 w k4 w k5 w k6 w k7 w k8 w k9
% D
rop
in
Pe
rform
ac
e
Average % performance drop for all case studies
Harmonic oscillator with damping.
Case Studies Theory
Comparing Case study results to relevant theory
∆t
t
AMinimise drop in performance and reduce the spread of response
∴∴∴∴minimise ∆t by adjusting behaviours, fast enough response,
reducing wasted effort, faster implementation and acceptance of
change.
Therefore, and using the formula as a guide, a solution would be to:
a) Minimise the circular frequency element [sin(ω(√(1-ζ2t))+a)],
b) Identify the values of the damping ratio (ζ) and natural frequency (ω), which then c) Will make the exponential decay function to equal A.
Thus: Ae(-ζωt) = AIn project management this idealistically is interpreted as performance at 100%, or
otherwise 0% drop in performance.
Formula of motion:
x = [Ae(-ζωt)][sin(ω(√(1-ζ2t)) + a)]
Extrapolating from known theory
ConclusionsThe effect of complexity on project performance has similarities to the performance of underdamped systems
The results confirm once more the non-linearity of project management.
Current techniques do not address the effects of complexity of interconnections and very few actions are taken to manage these effects.
A complex environment requires implementation of a framework for the management of the effects of complexity.cxcx
cxcx
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Contact Details: Dimitris AntoniadisTel. No.: (++)44 7754 522 049
Email: [email protected]
Website: www.danton-progm.co.uk
Thank youThank you
DAnton-progmDAnton-progm