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FTTH Council Europe, February 2016
Handling uncertainties in the planning process
Marlies Van der Wee
Jeroen Goossens
Peter Bonne
James Wheatley
3 Sessions
13:00 – 14:45 Eir’s experiences
Key Learnings from FTTH deployment in Ireland
15:00 – 16:30 New Zealand’s UFB deployment
Answers to important FTTH deployment barriers
16:45 – 17:30 Handling Business case uncertainties
Exploring ways to improve your business case
2
Speakers
3
Marlies Van Der Wee
iMinds
James Wheatley
GE
Lomme Devriendt
Orbit
Jeroen Goossens
FiberPlanIT
Handling uncertainties in the business case
Marlies Van der Wee
4
Uncertainties in the entire process…
5
Model
Evaluate
Refine
Scope
Subdivideproblem
Collectinput
Processinput
Infrastructure
Processes
Investmentanalysis
Sensitivityanalysis
Value network analysis
Game theory
Realoptions
Revenues
A lot of uncertain parameters
6
Geographic / demographic / economic Area type
Population density
Level of education
Income
Legal Right of Way and licenses
Regulation
Infrastructure Existing networks / equipment
Reuse of locations (poles, buildings)
Market Competition
Adoption
Scope
How to handle uncertainty?
7
Model
Evaluate
Refine
Scope
Subdivideproblem
Collectinput
Processinput
Infrastructure
Processes
Investmentanalysis
Sensitivityanalysis
Value network analysis
Game theory
Realoptions
Revenues
Sensitivity analysissimulates impact of input variation
8
2.5
3
3.5
4
4.5
5
5.5
6
6.5
7
7.5
90% 92% 94% 96% 98% 100% 102% 104% 106% 108% 110%
NPV(millionEUR)
Parametervariation
Adoption
Trenching
Fiber
0.90 0.94 0.98 1.02 1.06 1.10
Pro
ba
bilit
y
0.69 0.82 0.94 1.07 1.19 1.31
Pro
ba
bilit
y
0.90 0.94 0.98 1.02 1.06 1.10
Pro
ba
bilit
y
Real optionsallow to value flexibility to react to uncertainty
Weak aspect of NPV evaluation Assumes strict planning, with no flexibility
Real projects Anticipate on changing market circumstances
Solution: “real options thinking” principle
9S = value share on exercise date
va
lue
ca
ll o
n e
xe
rcis
e d
ate
X
Scale up/down
Study/Start
Shut down
Upfront choice between big and small cabinetbased on flexibility in later extension (given uncertain uptake)
10
Options reduce negative outcomeUpfront most flexible (small cab) is chosen
10% risk: NPV < €6,000,000 8.3% risk: NPV < €6,000,000
Install small
cabinets
11
Game theory
12
Game theory is a discipline aimed at
modeling situations in which decision
makers have to make specific actions
that have mutual, possibly conflicting,
consequences.
Prisoners’ dilemmaNon-Pareto Optimal Nash Equilibrium
Ref: J. Nash, 1950, Equilibrium points in n-person games, Proc. of the National Academy of Sciences 36(1), 48-49.
Pareto
optimum
Nash
Equilibrium
1 year 1 year 3 years 0 years
0 years 3 years 2 years 2 years
(betray)
DEFECT(stay silent)
COOPERATE
(be
tray)
DE
FE
CT
(sta
y s
ile
nt)
CO
OP
ER
AT
E
13
Competition has big impactas shown by game theory
14
expected pay-off
vs.
actual pay-off in case of
competition
Competition has big impactas shown by game theory
15
Pareto optimum
Nash
Equilibrium
Competition has big impactas shown by game theory
16
React to competition and uncertaintyusing options, games and sensitivity
17
Refine
Sensitivityanalysis
Game theory
Realoptions
0.90 0.94 0.98 1.02 1.06 1.10
Pro
ba
bili
ty
0.69 0.82 0.94 1.07 1.19 1.31
Pro
ba
bili
ty
0.90 0.94 0.98 1.02 1.06 1.10
Pro
ba
bili
ty
Uncertainty on the value
of input parameters
SENSITIVITY ANALYSIS REAL OPTIONS GAME THEORY
Effect of timing on
decision making
Impact of competition on
the business case
1,0 1,2 0,1
0,3 0,1 2,0
A1 A2 A3
B1
B2
How data quality affects planning and design
Jeroen Goossens
18
Quality of input data for network design
Geo-referenced data (GIS) is essential
(streets, demand points... of area)
Accurate unit costs (labour, material)
Geo-marketing data
ROW (rights of way)
…
19
Input GIS data examples
How many apartments
(homes) in the building?
Re-use street poles and
existing conduits?
Which type of trenching for
specific roads?
…
20
Garbage in, Garbage out
Bad input data
Inaccurate network design
More costly deployment
21
Strategic planning
1. Extrapolation
Design small area manually
Taken from previous projects
2. Excel model
3. Full designs (automated calculations)
Different levels of input data required
Different levels of output accuracy expected
22
Different quality at different stages of project
Strategic planning stage
Small errors allowed
Ballpark cost estimations
Network design
Generate to build plan
High accuracy input/output required
23
Impact of bad data quality
Bad volume and cost estimations
Equipment (BOQ), labour
Design errors
E.g. Insufficient fibre capacity in cables
Inefficient (costly) design
Cost savings possible through smart planning and design
Longer planning and deployment time
Verification, corrections of to build plan
Total budget overrun
24
Using Mobile Mapping to collect data
Lomme Devriendt
25
Introduction to Mobile Mapping
Mobile Mapping brings the full representation of 3D reality onto the
desktop, using sensors mounted on a mobile vehicle (car, train,
boat, bike, even a person).
26
Introduction to Mobile Mapping
Result : Full 3D View in 360° imagery and laser-pointcloud
With exaction positioning and measurement capabilities
27
What can we do with it ?
Reduces field trips
Extraction of road infrastructure
Inventory of assets
Visuals checking and judgement
Placement checks
Evaluate trenching options (ground works)
3D Analysis (Line of Sight)
And much more
28
From within the office !
Use Case 1 : Base map
Base maps are traditionally generated by
Photogrammetry (aerial)
Surveying (terrestrial)
3D Mapping allows ad hoc addition, correction, completion
29
Use Case 2 : Managing Assets
Inventory of Assets
Display
Check
Add
Build
Planning checks
Position
Surroundings
Ground
Connectivity
30
Some more examples (short movies)
Roadside Pole
Catenaries / Height above ground
Line of Sight
31
Verification of the To-Build plan
Check locations
Wall-mount
Ground situation
Asset positioning
Update planning
32
Cost Effectiveness
Typical case: Single Collection = Multiple Use
Basic Mapping uses
Use for Road infrastructure update
Use for Asset Inventory creation / update / verification
FTTx planning uses
Check planning
Prep operations
Further use
Continuous availability of 3D view in day-to-day operations
33
Companies using Mobile Mapping today
34
What happens post construction?
James Wheatley
35
Data, data, data everywhere…..
Low level design requires good quality input
data to realise efficient design
Subsequent operations & maintenance
processes also require good quality data
about the network to be held in the inventory
Processes dependent upon physical
inventory data include
Service fulfilment
Service assurance
36
Why the focus on data quality?
Network never built as designed despite
best efforts
Important to quickly and efficiently capture as-
built network changes
Reduce service fulfilment issues by digitising
as-built update process
Faster as-built returns mean up to date data in
inventory
Electronic returns improve data quality by
reducing errors
37
Field-based updates
Enable field teams to capture as-built changes
there and then in the field
Build into process flow defined in work
management solution
Service fulfilment
Less fall outs – data available when request is made
Service assurance
Accurate location of faults on new or changed network
Network design
Subsequent designs based on current state of the
network
38
Session Wrap-up
Jeroen Vanhaverbeke
39
Session Summary
Looked at how to manage uncertainties in the planning process
Explored concepts such as game theory and sensitivity analysis
Highlighted how data quality will impact the design results
Investigated how Mobile Mapping can be used to provide higher quality
data
Finally described how can improve data quality on the as-built network
held in the GIS-based network inventory through field-based as-built
updates
40
FTTH Council Europe, February 2016
Thank you for your attention!
Any questions?
www.technoeconomics.ugent.be