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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

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- 1. Parametric Estimation in a Nutshell © 2017 Planisware
- 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. © 2017 Planisware 2 PX2
- 3. Let’s look at an example © 2017 Planisware 3
- 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 © 2017 Planisware 4
- 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 © 2017 Planisware 5
- 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: © 2017 Planisware 6 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 © 2017 Planisware 7
- 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) © 2017 Planisware 8 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 © 2017 Planisware 9
- 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 © 2017 Planisware 10
- 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: © 2017 Planisware 11 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 © 2017 Planisware 12
- 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. © 2017 Planisware 13
- 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 © 2017 Planisware 14
- 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 © 2017 Planisware 15
- 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 © 2017 Planisware 16
- 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 © 2017 Planisware 17
- 18. Scoring Parametric Estimation © 2017 Planisware 18 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 © 2017 Planisware 19
- 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 © 2017 Planisware 21
- 21. To learn more about Estimations and tools to power them, visit our glossary: www.planisware.com/glossary/earned-value- management © 2014 Planisware 22