Estimation of time and resources has always been important for collaborative work. How did project estimation methods work in prehistoric communities?
Inaccurate project estimation stands proudly among the main reasons of major project failures. Its consequences range from missed deadlines to total inability to deliver the project. That’s why careful analysis of available time, budget and skill resources requires special attention.
Earlier, we’ve published an article about how project estimation worked in the prehistoric world (long story short – basically the same as now). But jokes aside, resource estimation is an essential part of project planning. It’s a prerequisite for properly planning work to be done, allocating it, getting timelines and budgets approved, and much more.
As there are various techniques for resource estimation, it’s not always obvious which one would work best for a specific project. In this article, we’ll take a look at the most common techniques and see where and when they can be used.
This estimation approach involves using the expertise of experienced specialists. Sometimes, it requires collection and analysis of relevant data – the expert’s role is interpretation of the result. Sometimes, it is based on the specialist’s opinion. Experts engaged in an estimation process based on this method should not necessarily belong to the project team: external specialists are often the case.
The judgment itself (whether it’s just an opinion or an interpretation of data analysis) usually represents the summary of the expert’s previous experience and includes theoretical knowledge within any specific paradigm, data and observations obtained from practice, and a set of criteria.
- Pros: the ability to take unique factors into account; the ability to use external opinions and expertise of experienced specialists
- Cons: this approach tends to involve personal opinions and therefore the result is subject to human bias
- When and where to use: complex projects where just qualitative estimation is not sufficient, and projects that match previous experience to a significant extent
Analogous estimation is based on comparing the progress and the results of previously delivered similar projects for estimating the resources for the upcoming one. Both positive and negative outcomes count: if the project was a success, it can be used as a model for estimating and planning the new project. If it was a failure, the experience received from it can be used for necessary adjustments in resource planning, risk forecasting, prevention of possible issues, work scope management, etc.
- Pros: one of the fastest ways to estimate resources
- Cons: low accuracy; high risk of erroneous conclusions
- When and where to use: this method works best for typical projects with similar work scope. It is often used at the early stages of a project to get a ballpark estimate of how much resources would be required.
The top-down resource estimation method is based on breaking project work down into high-level blocks, estimating them, and summarizing the estimates. Later, high-level chunks of project work can be decomposed into smaller parts as soon as more detailed requirements are available, and estimated separately. This estimation method is used in Agile projects where fast result matters.
- Pros: a fast way to estimate resources and assess project viability
- Cons: low accuracy; the resulting amount of resources can significantly vary from this estimate
- When and where to use: this method works best at the initial stages of a project when a rough and quick estimate is required.
Unlike the previous technique, bottom-up estimation uses estimates from small tasks included in the project scope. Summarized, these estimates provide the full picture of how much resources would the project consume. Logically, it is used at a step where all necessary details about possible project work breakdown are available.
- Pros: high accuracy of the result, relatively low variance of estimated vs. actual resources
- Cons: a significant level of detail and amount of time are required for using this technique
- When and where to use: this method is used at the steps where all details about detailed task breakdown are available. It provides a relatively accurate picture of resources necessary for completing a project
Parametric model estimating
Parametric models estimation uses variables for calculating estimates for upcoming projects. In this method, measurable variables are used to forecast and estimate upcoming work. This technique is more science-based than most other approaches, which allows to rely on its data as an accurate basis for planning project works.
- Pros: the method ensures maximum accuracy of the resulting estimate
- Cons: complex data collection and processing are an indispensable part of using this method
- When and where to use: this method is considered universally applicable, but works best in fields where project parameters can mostly be calculated in advance (construction, architecture etc.) and accurate data is crucial at early stages.
This estimation method originated from PERT (Program Evaluation and Review Technique) and uses three estimates for calculating the resulting estimate: optimistic, most likely, and pessimistic. The resulting figure is calculated as a weighted average of the three original estimates.
- Pros: the method allows to take into account estimates and opinions of various participants, allows automation, and can provide fast results
- Cons: significant data amounts and detail levels are required
- When and where to use: the method is used in project where planning is based on PERT, and in projects where uncertainty is reduced by using three-point estimates as a part of risk management.
Choosing the right estimation method depends on the project’s specifics, general practices in the company or team, field where the project is performed, and much more. These simple guidelines on various estimation methods are intended to give a quick overview of what may work for your specific project and provide an insight on how and where they work best.