Published on February 24, 2016
1. FTTH Council Europe, February 2016 Handling uncertainties in the planning process Marlies Van der Wee Jeroen Goossens Peter Bonne James Wheatley
2. 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
3. Speakers 3 Marlies Van Der Wee iMinds James Wheatley GE Lomme Devriendt Orbit Jeroen Goossens FiberPlanIT
4. Handling uncertainties in the business case Marlies Van der Wee 4
5. Uncertainties in the entire process… 5 Model Evaluate Refine Scope Subdivide problem Collect input Process input Infrastructure Processes Investment analysis Sensitivity analysis Value network analysis Game theory Real options Revenues
6. 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
7. How to handle uncertainty? 7 Evaluate Refine ScopeCollect input Pr in Sensitivity analysis Game theory Real options
8. Sensitivity analysis simulates 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) Parameter variation Adoption Trenching Fiber 0.90 0.94 0.98 1.02 1.06 1.10 Probability 0.69 0.82 0.94 1.07 1.19 1.31 Probability 0.90 0.94 0.98 1.02 1.06 1.10 Probability
9. Real options allow 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 valuecallonexercisedate X Scale up/down Study/Start Shut down
10. Upfront choice between big and small cabinet based on flexibility in later extension (given uncertain uptake) 10
11. Options reduce negative outcome Upfront most flexible (small cab) is chosen 10% risk: NPV < €6,000,000 8.3% risk: NPV < €6,000,000 Install small cabinets 11
12. 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.
13. Prisoners’ dilemma Non-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 (betray) DEFECT (staysilent) COOPERATE 13
14. Competition has big impact as shown by game theory 14
15. expected pay-off vs. actual pay-off in case of competition Competition has big impact as shown by game theory 15
16. Pareto optimum Nash Equilibrium Competition has big impact as shown by game theory 16
17. React to competition and uncertainty using options, games and sensitivity 17 Refine Sensitivity analysis Game theory Real options 0.90 0.94 0.98 1.02 1.06 1.10 Probability 0.69 0.82 0.94 1.07 1.19 1.31 Probability 0.90 0.94 0.98 1.02 1.06 1.10 Probability 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
18. How data quality affects planning and design Jeroen Goossens 18
19. 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
20. 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
21. Garbage in, Garbage out Bad input data Inaccurate network design More costly deployment 21
22. 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
23. 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
24. 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
25. Using Mobile Mapping to collect data Lomme Devriendt 25
26. 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
27. Introduction to Mobile Mapping Result : Full 3D View in 360° imagery and laser-pointcloud With exaction positioning and measurement capabilities 27
28. 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 !
29. Use Case 1 : Base map Base maps are traditionally generated by Photogrammetry (aerial) Surveying (terrestrial) 3D Mapping allows ad hoc addition, correction, completion 29
30. Use Case 2 : Managing Assets Inventory of Assets Display Check Add Build Planning checks Position Surroundings Ground Connectivity 30
31. Some more examples (short movies) Roadside Pole Catenaries / Height above ground Line of Sight 31
32. Verification of the To-Build plan Check locations Wall-mount Ground situation Asset positioning Update planning 32
33. 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
34. Companies using Mobile Mapping today 34
35. What happens post construction? James Wheatley 35
36. 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
37. 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
38. 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
39. Session Wrap-up Jeroen Vanhaverbeke 39
40. 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
41. FTTH Council Europe, February 2016 Thank you for your attention! Any questions? www.technoeconomics.ugent.be
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