Parametric estimation is one of the four primary methods that project companies use to produce estimates for the cost, duration and effort of a project. For parametric estimation, the person in charge of the estimates will model (or describe) the project using a set of algorithms. For instance: let’s say that your project includes carrying out a survey of 300 people. Each interview contains 20 multiple-choice questions, and past experience has shown that they take 10 minutes to administer. According to parametric estimation, the total effort for this task will be: E = nb of interviews × 10 minutes = 3000 minutes = 5 hours

1. Parametric Estimation in a Nutshell
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2. What is Parametric Estimation?
• Parametric Estimation is a method for estimating project cost and
duration / effort, in which the project is modelled (described) using
predefined algorithms
• Parametric Estimation is often perceived as one of the more accurate
and reliable estimation methods, because it uses actual historical data
or industry standards, and credible units to account for the work to be
performed.
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PX2

3. Let’s look
at an example
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4. The project
• Our team must prepare the cost estimates for a small scale
wind farm project. The team leader has opted for using
parametric estimation techniques.
• Specifications for this project include:
– Purchase and installation of 3 commercial grade wind turbines with
an output of 2MW per turbine
– Interconnection with the existing power system and associated
upgrades
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5. Parametric Estimation Process
Step 1 - Identify the equations that model our project
Step 2 – Evaluate the equations
Step 3 – Create (or update) the output
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6. Step 1 – Identify the equations applicable
• First, we need to break down the project into sub-components,
according to our library of equations.
• Our wind farm project is composed of three major components:
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1. The project site, which includes:
• Site assessment for the proposed project site
• Land lease
• Permits and associated studies
(archaeological, environmental impact, …)
2. The wind turbines, which include:
• The turbines and their tower
• Installation costs for each turbine
3. Interconnection with the existing
power system, which includes:
• Connectivity to the power system
• System upgrades to mitigate the
impact of this project on the power
system
To simplify this example a bit,
let’s focus now on the three elements in bold

7. Step 1 – Identify the equations applicable
The Land Lease (a simple equation)
• The windiest spot in the area – and the site earmarked for our
project – carries a lease negotiated at 3,000 USD per MW and
per year.
• The equation for the Land Lease is therefore:
𝐶𝑜𝑠𝑡 𝑜𝑓 𝐿𝑒𝑎𝑠𝑒 = 𝑛𝑏 𝑀𝑊 × 3000 𝑈𝑆𝐷
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8. Step 1 – Identify the equations applicable
Purchase cost of the turbines (equation using a lookup table)
• The price for the wind turbines by the preferred manufacturer
depends on the purchase volume:
• The equation for the cost of the turbines is therefore:
𝐶𝑜𝑠𝑡 𝑜𝑓 𝑇𝑢𝑟𝑏𝑖𝑛𝑒𝑠 = 𝑛𝑏 𝑡𝑢𝑟𝑏𝑖𝑛𝑒𝑠 × 𝐿𝑜𝑜𝑘𝑢𝑝𝑃𝑟𝑖𝑐𝑒(𝑛𝑏 𝑡𝑢𝑟𝑏𝑖𝑛𝑒𝑠)
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Nb of units
purchased
Price per turbine
1 3 million USD
2 to 4 2.8 million USD
5 to 9 2.5 million USD
Our project includes the installation
of 3 wind turbines.

9. Step 1 – Identify the equations applicable
Connectivity to the power system (equation with activation condition)
• The cost of connecting the wind turbines to the power system depends on
whether there is available capacity on the transmission lines near the project
site:
– If there is, then we can build feeder lines from the wind farm to the turbines. The cost is
80,000 USD per mile of feeder line + the cost of one step-up transformer
@ 35,000 USD per turbine.
– If there isn’t, we will have to build transmission lines from scratch, at a cost of
300,000 USD per mile
• The equation for the cost of connectivity is therefore:
– 𝐼𝑓 ∃𝑐𝑎𝑝𝑎𝑐𝑖𝑡𝑦 𝑡ℎ𝑒𝑛 𝐶𝑜𝑠𝑡 𝑜𝑓 𝑐𝑜𝑛𝑛𝑒𝑐𝑡𝑖𝑣𝑖𝑡𝑦 = 𝑑𝑖𝑠𝑡𝑎𝑛𝑐𝑒 × 𝑐𝑜𝑠𝑡 𝑓𝑒𝑒𝑑𝑒𝑟 𝑙𝑖𝑛𝑒𝑠 + 𝑛𝑏 𝑡𝑢𝑟𝑏𝑖𝑛𝑒𝑠 ×
𝑐𝑜𝑠𝑡 𝑠𝑡𝑒𝑝𝑢𝑝 𝑡𝑟𝑎𝑛𝑠𝑓𝑜𝑟𝑚𝑒𝑟
– 𝐼𝑓 ∄𝑐𝑎𝑝𝑎𝑐𝑖𝑡𝑦 𝑡ℎ𝑒𝑛 𝐶𝑜𝑠𝑡 𝑜𝑓 𝑐𝑜𝑛𝑛𝑒𝑐𝑡𝑖𝑣𝑖𝑡𝑦 = 𝑑𝑖𝑠𝑡𝑎𝑛𝑐𝑒 × 𝑐𝑜𝑠𝑡 𝑡𝑟𝑎𝑛𝑠𝑚𝑖𝑠𝑠𝑖𝑜𝑛 𝑙𝑖𝑛𝑒𝑠
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10. Step 2 – Evaluate the equation
• Once we’ve matched each subcomponent of our project with the right
equation, we need to evaluate them
• Let’s return to our example:
– Cost of the lease: the equation is simple and straightforward:
𝐶𝑜𝑠𝑡 𝑜𝑓 𝐿𝑒𝑎𝑠𝑒 = 𝑛𝑏 𝑀𝑊 × 3000 = 3 × 2 × 3000 = 18,000 𝑈𝑆𝐷 𝑝𝑒𝑟 𝑦𝑒𝑎𝑟
– Cost of the turbines: our project is for 3 turbines, so the lookup function will return
the price for the 2nd bracket (2.8 million USD)
𝐶𝑜𝑠𝑡 𝑜𝑓 𝑡𝑢𝑟𝑏𝑖𝑛𝑒𝑠 = 3 × 2.8 𝑚𝑖𝑙𝑙𝑖𝑜𝑛 = 8.4 𝑚𝑖𝑙𝑙𝑖𝑜𝑛 𝑈𝑆𝐷
– Cost of connectivity: the transmission lines near the site of our project have
sufficient available capacity. Therefore, the first equation is activated:
𝐶𝑜𝑠𝑡 𝑜𝑓 𝑐𝑜𝑛𝑛𝑒𝑐𝑡𝑖𝑣𝑖𝑡𝑦 = 𝑑𝑖𝑠𝑡𝑎𝑛𝑐𝑒 × 80,000 + 𝑛𝑏 𝑡𝑢𝑟𝑏𝑖𝑛𝑒𝑠 × 35,000
= 5 × 80,000 + 3 × 35,000 = 505,000 𝑈𝑆𝐷
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11. Step 3 – Create (or update) the output
• Once we’ve evaluated all the equations, we can consolidate
them in a cost plan
• A simple cost plan for
our project could
look like this:
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Project Task Estimated Cost
01 - Project Site
… …
B – Land Lease 18,000 USD per year
… …
02 – Wind Turbines
A - Purchase of the Turbines 8.4 million USD
… …
03 - Interconnection
A - Connectivity to the power system 505,000 USD
… …

12. Considerations for
implementing
Parametric Estimation
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13. When to use Parametric Estimation
• Parametric estimation is particularly useful in the early stages
of a project, as it does not require as much information as
other estimation techniques, and provides more accurate and
reliable estimates.
• Parametric estimation works best for tasks that are:
– Often repeated, with little variability (too much variability means that
a single equation to model the task will be difficult to design)
– With tangible deliverables
• For this reason, it is mainly used in the Life Science,
Engineering and Construction industries.
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14. When to use Parametric Estimation
• Parametric Estimation can be used in conjunction with
Analogous Estimation (which uses actuals from previous
projects to calculate estimates) when historical data is not
available for a specific task
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15. Benefits of Parametric Estimation
• Parametric estimation is believed to be more accurate than
other methods of estimation because the equations are based
on experience that takes into account a larger number of
factors.
• Effort to produce the first set of estimates and keep them up-
to-date is minimal:
– When parametric estimates are generated through a solid software
(usually PPM) solution, a new set of estimates can be generated on-
demand in minutes
– Similarly, the portfolio can be automatically updated via the batch
update mode
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16. Benefits of Parametric Estimation
• Parametric Estimation provides a centralised and standard
definition of how estimates should be produced:
– Because the algorithms used are used across projects, the forecast
“template” will be the same regardless of the type of project (or
template project)
– Likewise, the calculation method will be the same across all projects
of the portfolio
– This means that calculations are easily repeatable, and of the same
level of quality
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17. However…
• Parametric estimation requires a high effort upfront to build the
algorithm. It requires an analysis of each element that
contribute to the forecast, and all possible combinations, so as
to make available the right equations
• By definition, an equation for an activity can only be
determined once there is a large base of experience in that
activity
• If market conditions change, the equations will have to be
updated too
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18. Scoring Parametric Estimation
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Criteria Score Comments
Intended Forecast Horizon Medium / Long Too generic for accurate short term forecasting
Accuracy 4
Accuracy improves over time, with algorithm
calibration
Consistency 5 Consistent output for a given set of conditions
Transparency 2 Transparency depends on algorithm complexity
Ease of conducting initial forecast 4 Dependent on number of criteria to define
Ease of running alternative
scenarios
5 Most flexible approach for scenario analysis
Ease of implementation 1
Requires identification of all major criteria and
the magnitude of their impact

19. Where do the algorithms come from?
• There are two primary sources:
– Historical data, derived from an analysis of past projects, their
drivers, and other contributing factors
– Parametric rates published by specialised organisations
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20. Key success factors
• When starting to build a parametric estimation model:
– Start with the easiest role, in order to get familiar with the process of
analysing the data and building the algorithm
– Set a realistic / achievable accuracy goal
When setting up parametric estimation for a portfolio, aim for
portfolio accuracy, not project accuracy
– Limit the number of drivers per algorithm
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21. To learn more about Estimations and tools to
power them, visit our glossary:
www.planisware.com/glossary/earned-value-
management
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