Reference class forecasting
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The RCFM consists of three steps approach. Then based on the degree of tolerance for the risk we decide what quantity of uplift would be needed to account for bias in cost and time estimates | The RCFM consists of three steps approach. Then based on the degree of tolerance for the risk we decide what quantity of uplift would be needed to account for bias in cost and time estimates | ||
| − | + | * Identify a reference class that has similar attributes to the project on hand. There is no role of thumbs when choosing a reference class. However, the reference class can not be narrow to get a reliable result if the categories were too small. The reference class can not be too wide either. Furthermore, an analysis of the risk of overrun project cost and duration should be revealed. | |
| − | + | * Determine a probability of distribution for the chosen reference classes. This requires trustful historical data from several projects within the reference class. The result of the probability distribution of the historical data will be used to estimate the level of uncertainty. From a statistical perspective, regression models are an essential tool in deriving the probability distribution | |
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| + | * Comparing the project on hand with the reference class distribution in order to determine the desired outcome such as budget and project duration | ||
| + | The RCF should be implemented prior to initiating the project to get a unique opportunity on reflecting on the budget before completion and planned duration. | ||
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== Limitation== | == Limitation== | ||
The RCF method is more useful in cases where errors are due to non-random events such as cognitive/human bias in decision making with uncertain future events’ | The RCF method is more useful in cases where errors are due to non-random events such as cognitive/human bias in decision making with uncertain future events’ | ||
Revision as of 03:35, 21 February 2021
Contents |
Abstract
The definition of project success according to the standard published by the project management institute is meeting customers' expectations without exceeding the desired requirement such as cost, duration, and scope. [1] However, executing projects on time following a planned framework and budget is a challenging aspect of project management. Reference class forecasting is a method that studies the overall view of certain projects by forecasting similar projects rather than focusing solely on the considered project. This method allows a project manager to avoid errors due to human judgment by basing the forecast on similar projects. It also assists to take decisions under uncertainties through assessing the risk of the planned project. [1] In this article, the RCFM method developed by Kahneman and Tversky will be presented. That will be followed with clear guidance on how to use the method. Then its application and limitations.
Big Idea
Optimism bias is a term coined by Daniel Kahnemann means that people tend to see the world in a more positive light. Optimism bias is the foundation of the RCF method, the method states that human judgment is biased, as it tends to be more optimistic than realistic due to overconfidence which leads to underestimating cost, completion times, and risks of planned actions. they tend to overestimate the benefits of those same actions. Such error is caused by actors taking an "inside view" Research made on a sample of 250 large projects executed over the last 7 decades shows that 90% of these projects exceeded the original budget and duration planes. According to re-searcher, almost all projects do not meet their goals which emphasizes the need of applying the RCFM. RCFM is recommended by the American Planning Association which “encourages planners to use reference class forecasting in addition to traditional methods as a way to improve accuracy. The RCF attempts to fit a certain event into a probability of distribution of comparable class reference. Furthermore, this method of enhancing decision-making in light of un-certainties has proved to be effective. It allows for adjustments to be made in the original cost-benefit analysis (CBA) so the plan includes margin errors. The reference class forecast provided an external point of view and act as an enabler of better planning based on historical data of projects that have similar attributes. By doing so, the project managers can reduce bias that is caused due to assessing available information “inside views” and neglecting unknown unknowns or other considerations “outside views”.
Implementation
RCFM requires a large amount of work The RCFM consists of three steps approach. Then based on the degree of tolerance for the risk we decide what quantity of uplift would be needed to account for bias in cost and time estimates
- Identify a reference class that has similar attributes to the project on hand. There is no role of thumbs when choosing a reference class. However, the reference class can not be narrow to get a reliable result if the categories were too small. The reference class can not be too wide either. Furthermore, an analysis of the risk of overrun project cost and duration should be revealed.
- Determine a probability of distribution for the chosen reference classes. This requires trustful historical data from several projects within the reference class. The result of the probability distribution of the historical data will be used to estimate the level of uncertainty. From a statistical perspective, regression models are an essential tool in deriving the probability distribution
- Comparing the project on hand with the reference class distribution in order to determine the desired outcome such as budget and project duration
The RCF should be implemented prior to initiating the project to get a unique opportunity on reflecting on the budget before completion and planned duration.
Limitation
The RCF method is more useful in cases where errors are due to non-random events such as cognitive/human bias in decision making with uncertain future events’ RCF is not a new tool within the decision-making framework for large infrastructure investments. However, its application to energy projects is rare
Annotated Bibliography
References
- ↑ 1.0 1.1 "https://app-knovel-com.proxy.findit.dtu.dk/web/toc.v/cid:kpGPMBKP02/viewerType:toc/root_slug:viewerType%3Atoc/url_slug:root_slug%3Aguide-project-management?kpromoter=federation/A Guide to the PROJECT MANAGEMENT BODY OF KNOWLEDGE"
