a proven approach, for a detailed , very accurate cost modelling (Telecom environent)

1. Cost Management
Forecasting the future

2. Forecasting the Future
Executive Management needs to have concise budget estimates, enabling them to
compose thee right portfolio with the best outcome for the Organisation.
The more accurate the projection is, the more confident the Executive Management is
concerning the investment, outcome and financial position of the Organisation
Very important is to identify all –major, or significant- metrics having an impact
(influence) on the cost (variance).
Number of devices, or components, labour cost to install the component, costs linked to
geographical area, and other components with impact on the budget.
Why?

3. ranking portfolio candidates
Sort the List by
Sort Method
project
budget (k€)
cash flow
(k€)
capex (k€) opex (k€) risk nbr mandays resources(# FTE)
project 28 2193 74 754 281 65% 75 000 29,64
project 20 1583 55 811 299 50% 75 000 28,77
project 11 5309 327 1109 414 85% 75 000 16,23
project 26 3003 137 833 285 85% 50 000 21,92
project 18 2037 81 811 286 15% 50 000 25,15
project 9 3081 124 980 366 10% 50 000 24,85
project 29 10551 287 688 256 2% 25 000 36,76
project 21 3468 114 734 268 12% 25 000 30,42
project 12 3853 139 868 317 50% 25 000 27,72
project 23 2836 103 775 275 45% 20 000 27,53
project 15 3946 188 1005 389 45% 20 000 20,99
project 6 3604 99 591 112 5% 20 000 36,40
project 2 3110 101 772 265 15% 20 000 30,80
project 13 2231 91 946 339 12% 17 500 24,52
project 22 3588 186 939 358 5% 15 000 19,29
project 14 5310 164 802 276 5% 15 000 32,38
project 5 4338 184 810 273 12% 15 000 23,58
project 1 2356 90 878 300 10% 15 000 26,18
portfolio
klein naargroot groot naar laag
The above is part of a tool to enable the ‘desired’ portfolio composition;

4. project 28
project 5
project 12
project 7
project 3
project 18
project 11
project 16
0
1
2
3
4
5
6
7
8
9
10
0 2 4 6 8 10
RISK
OPPORTUNITY
investment area matrix
profit strategic
business growth market share

5. Parametric models in which the project characteristics are mathematically represented.
Estimates can be refined when more information becomes available during the course of
a project. Eventually this results in a detailed unit cost estimate with a high accuracy.
Remaining uncertainties in estimates that will likely result in additional cost can be
covered by reserving cost (e.g. using escalation and contingencies).
Cost projection is important to enable the Executive Management to invest.
The more accurate the projection is, the more confident the Executive Management is
concerning the investment, outcome and financial position of the Organisation
Very important is to identify all –major, or significant- metrics having an impact
(influence) on the cost (variance).
Number of devices, or components, labour cost to install the component, costs linked to
geographical area,
Design of a performant cost model

6. Input & preset output
Set
• Constellation <node project>
• Scope project (SoW)
Specific local issues
Streams:
Design, build, audit, small
enhancements, & other
supplier labour &
services (€ per
‘unit’)
Materials (€)supplier materials
(€ per unit)
metrics
High level planning,
Contract exceptions
<blackbox>
Calculation
system, MS
Excel based
Kost berekenen van:
• Typisch node project
• Typisch node kost per
subCo
• Project kost (node) per
SubCo
• Kost van node per ‘locatie’
budget FC berekenen van:
• Budget per maand (HL
planning)
• Budget per SubCo
• Budget per jaar
• Budget E2E
• Earned value,
• gap analyse
• Simulations (what if)
Kost (en aantallen)
berekenen van:
• Materialen forecast
(maand, jaar, E2E,
estimation at completion);
Quick budget estimatie (en
aantallen) berekenen van:
• Nieuwe (similaire) project
budget ramingen opstellen
(SFR)
Cost model components (hl)

7. Input & preset output
Set
• Constellation <node project>
• Scope project (SoW)
Specifieke lokale issues
SubCo (SoW)
• Design
• Preparatory Work
• Construction
• Audit
• Corrective Work
Leverancier labour&services
• Design: D-SubCo_1-2
• Audit: A-SubCo_1-2
• construction:
L-SubCo_1-6
• Corrective work:C-
SubCo_1-5
• Groundworks:GW-
SubCo_1-4
Materialen
• Component_1
• Component_2
• Component_X
Leverancier materialen
• Leverancier_1
• Leverancier_2
• Leverancier_Y
Set
• metrics
Set
• HL planning, per
locatie, per maand
Set
• HL planning per SubCo
Specifieke contract issues
• Uplift: % , task,
“Leverancier_1”
<blackbox>
Calculation system,
MS Excel based
Kost berekenen van:
• Typisch node project
• Typisch node kost per
subCo
• Project kost (node) per
SubCo
• Kost van node per ‘locatie’
budget FC berekenen van:
• Budget per maand (HL
planning)
• Budget per SubCo
• Budget per jaar
• Budget E2E
• Earned value,
• gap analyse
• Simulations (what if)
Kost (en aantallen)
berekenen van:
• Materialen forecast
(maand, jaar, E2E,
estimation at completion);
Quick budget estimatie (en
aantallen) berekenen van:
• Nieuwe (similaire) project
budget ramingen opstellen
Cost mode components

8. <blackbox>
Contractmanagement
Supplier
design
Supplier
build
Supplier
material_
1
Supplier
material_
2
Supplier
material_
3
Supplier
service
scope
High level planning
Detailed planning
Project (scope) characteristics
metrics
labour materialstopology
Budget(monthly,yearly,E-2-E)
Cost(monthly,yearly,E-2-E)
Forecast(monthly,yearly,E-
2-E)
Gap analysis
monthly
Blackbox (MS Exel):
cost model engine,
processing input data.
Cost model scheme
‘WBS’ per
project

9. Approach: Parametric estimating
Parametric estimating entails the analysis of cost, programmatic and
technical data to identify cost drivers and develop cost models. The
approach essentially correlates cost and manpower information with
parameters describing the item to be costed.
This process results in sets of formulae known as “Cost Estimation
Relationships” (CERS), which are applied to produce cost outputs for
different elements of an estimate.
Parametric Method generally involves the use of a regression analysis
(linear and nonlinear) to determine the best algorithms for a model.

10. Approach: Parametric estimating
• Prediction, mathematical approach
• Applicable formulas:
• Regression analysis
• Stochastic model
• Monte Carlo modelling
• Probability modelling

11. Cost model, elements
• Predictive modeling is a technique that uses mathematical
and computational methods to predict an outcome. A
mathematical approach uses an equation-based model that
describes the phenomenon under consideration.
• The model is used to forecast an outcome at some future
state or time based upon changes to the model inputs.
• For this reason, metrics are important, as well as collected
data of the first steps of the project; number of materials
consumed, number of devices installed, meters of cabling per
installation, labour cost (per installation, hour,…), ABC – time
driven,…

12. Forecast approach:‘rolling average’
• In statistics, a moving average (rolling average or running average) is a calculation to analyze data points by creating series of averages of different
subsets of the full data set. It is also called a moving mean (MM) or rolling mean and is a type of finite impulse response filter. Variations include:
simple, and cumulative, or weighted forms.
• Given a series of numbers and a fixed subset size, the first element of the moving average is obtained by taking the average of the initial fixed subset
of the number series. Then the subset is modified by "shifting forward"; that is, excluding the first number of the series and including the next value
in the subset.
• A moving average is commonly used with time series data to smooth out short-term fluctuations and highlight longer-term trends or cycles.
The overall average of the ‘main’ component is known data, but programme started
with the ‘smaller’-projects first, tackling the larger later on. Challenge was to forecast
the remaining projects (main-component driven)
Project closed
Project E-2-E (known data)
Project todate
Project todate
Moving average

13. Forecast approach: Monte Carlo method
• Monte Carlo methods (or Monte Carlo experiments) are a broad class of computational algorithms that rely on repeated random sampling to obtain
numerical results. Their essential idea is using randomness to solve problems that might be deterministic in principle. They are often used in physical
and mathematical problems and are most useful when it is difficult or impossible to use other approaches. Monte Carlo methods are mainly used in
three problem classes:[1] optimization, numerical integration, and generating draws from a probability distribution.
Most probable project cost outcome is between 84,2 to 84,9 m€ [ the final result of the project had
a deviation of € k210, (0,25%) of Mont Carlo’s projection], and this for a 3 years investment programme.

14. Monte Carlo method
• To obtain this accurate outcome projection, you still need a very accurate,
meticulous cost model, a in-depth understanding of the project, , scope, WBS, related
costs, and ‘big data’ KPI, metrics, contracts of suppliers, tariffs & rates.

15. Forecasting the Future
• Knowing the future is helpful for Executive Management taking correct decisions;
• A performant cost prediction is part of ‘Forecasting the Future’;

16. • How sure are you of the data?
• Cost management is often dependent of handed sets of data, massive data, hoping
that the data is correctly gathered.
• ‘Hope’ is not in line with ‘accuracy’, nor trustworthy.
• Being sure, that the data is trustworthy
• And a test exists: ‘Benford’ analysis
• In this example, there might be doubts about the correctness of the data [ too many
“1”, and deviating “8” and “9”]
Benford's law, also called Newcomb-Benford's law, law of anomalous numbers, and first-digit law, is an observation about the frequency distribution of leading
digits in many real-life sets of numerical data. The law states that in many naturally occurring collections of numbers, the leading significant digit is likely to be
small.[1] For example, in sets that obey the law, the number 1 appears as the most significant digit about 30% of the time, while 9 appears as the most
significant digit less than 5% of the time. If the digits were distributed uniformly, they would each occur about 11.1% of the time.[2] Benford's law also makes
predictions about the distribution of second digits, third digits, digit combinations, and so on
nr samle rate benford
1 37,7% 30,1%
2 17,0% 17,6%
3 11,3% 12,5%
4 11,3% 9,7%
5 7,5% 7,9%
6 5,7% 6,7%
7 5,7% 5,8%
8 1,9% 5,1%
9 1,9% 4,6%
Data testing: Benford’s Law
0.00%
5.00%
10.00%
15.00%
20.00%
25.00%
30.00%
35.00%
40.00%
1 2 3 4 5 6 7 8 9
samle rate
benford
?
?
?