In the world of project management, accurate estimation is a crucial factor in determining project success. Estimating the time, cost, and resources required for a project can be challenging, but it is essential for effective planning and decision-making. One method that has gained popularity in recent years for project business is parametric estimating. This article aims to demystify parametric estimating, explain its benefits, and explore how it can enhance project management accuracy.
What is Parametric Estimating in Project Management?
Parametric estimating in project management is a technique that uses historical data and statistical analysis to estimate project parameters and costs. It involves identifying key project variables, such as duration, cost, or resource requirements, and establishing mathematical relationships between these variables and relevant project characteristics. By analyzing historical data and utilizing these relationships, parametric estimating enables project managers to make more accurate and informed estimates for new projects.
Parametric Estimating Using Project Modeling
Parametric Estimating Example
In the simplest form, parametric estimating involves creating formulas to calculate the estimates of the new project based on previous project information. The formula for such an estimation might look like this:
E = A-prev / D-prev x D-curr
E = parametric estimate
A-previous = historical amount of cost or time
D-previous = historical value of the parameter
D-current = value of the parameter in the current project
Using the above formula, a simple example might be:
A previous project for the construction of a new suspension bridge required 20,000 linear feet of braided steel cabling and at a cost of $1,00,000 for product and installation. A new project similar in nature to the previous project will require 30,000 linear feet of braided steel cabling. The parametric formula to estimate this part of the project looks like this:
E = $1,000,000/20,000 x 30,000
E = $1,500,000
Using the algorithmic approach enables project managers to estimate different parts of a new project quickly and accurately.
Analogous vs Parametric Estimation
Analogous estimating and parametric estimating are two commonly used techniques in project management for estimating project parameters. While they share similarities in their goal of estimation, they differ in their approach and the level of detail involved.
Analogous estimating (see more at PMI), also known as top-down estimating or expert judgment, relies on historical data from similar past projects to estimate the duration, cost, or resource requirements of a current project. It involves using the experience and expertise of project managers or subject matter experts to make educated guesses based on similarities between projects. Analogous estimating is often used in the early stages of a project when limited information is available. It provides a quick and relatively simple estimation method, but it can be less accurate than other techniques since it relies on subjective judgment and assumes that the current project will follow a similar pattern as past projects.
In contrast, parametric estimating is a technique that uses statistical analysis and mathematical models to estimate project parameters. It involves identifying key variables and establishing mathematical relationships between these variables and relevant project characteristics. Parametric estimating relies on historical data and statistical techniques such as regression analysis to develop mathematical models. These models can then be used to generate estimates based on input values. Parametric estimating is a more systematic and data-driven approach, providing a higher level of accuracy compared to analogous estimating. It requires a more extensive collection of historical data and relies on the assumption that the relationships identified in the data will hold true for the current project.
You can also think of the difference between analogous and parametric estimating as the difference between using project templates vs project models. Templates use past projects as static estimates for new projects, while project models use formula driven components of projects to build accurate estimates based on the current projects particular parameters.
Benefits of Parametric Estimating
Parametric estimating leverages historical data and statistical models, allowing project managers to make more accurate estimates. By relying on real-world data, it minimizes the risks associated with subjective or overly optimistic estimates. It provides a systematic approach to estimation, resulting in greater precision and improved project planning.
Traditional estimation methods often require significant time and effort, especially for complex projects. Parametric estimating reduces the time spent on estimation by utilizing pre-existing data and mathematical models. This enables project managers to quickly generate estimates and allocate resources efficiently.
Parametric estimating ensures consistency in estimation across projects. It eliminates the bias and variations that can arise from relying solely on expert judgment or personal experiences. By using standardized models and historical data, organizations can establish a consistent estimation approach, leading to better decision-making and resource allocation.
Parametric estimating is particularly valuable when dealing with large-scale projects or multiple similar projects. The ability to apply established models and historical data to new projects allows for scalability and facilitates estimation across a portfolio of projects. This approach enables organizations to identify patterns, trends, and potential risks across projects and make informed decisions based on past experiences.
Project Modeling as a Parametric Estimating Method
Project Modeling is a tool used to create parametric project estimates. Similar to a product configurator for manufacturing, a project modeler builds the components (i.e. the tasks and costs) of a project from a predefined set of models. Project Modeling creates a streamlined planning and estimating process that enables project-based companies to scale and automate best practices from project sales through execution.
Download the Project Modeling Whitepaper to learn how to create a streamlined and controlled parametric estimation process for your company.
Any type of project can be modeled, whether it’s predominately labor, such as consulting and professional services, or involves a complex supply chain and production equipment, such as construction and ETO manufacturing. Regardless of what the output of the project is or the content of its structure, any type of project that a company performs repeatedly is a candidate for project modeling.
Implementing Parametric Estimating
Historical Data Collection
The first step in implementing parametric estimating is to gather reliable historical data from completed projects. This data should include project attributes, such as scope, duration, cost, and resource utilization. The more data available, the more accurate the parametric estimation will be.
Identify Key Variables
Determine the project parameters that need to be estimated, such as duration or cost. Identify the variables that are most likely to influence these parameters. For example, in software development, lines of code, complexity, and team size might be crucial variables for estimating project duration.
Perform statistical analysis on the historical data to identify patterns and relationships between project characteristics and the key variables. Regression analysis, correlation analysis, or other statistical techniques can help establish mathematical models that capture these relationships.
Develop parametric models based on the statistical analysis. These models should incorporate the identified variables and their corresponding relationships. The models may take the form of equations, algorithms, or mathematical formulas that estimate the project parameters based on input values.
Validation and Calibration
Validate the accuracy of the parametric models by comparing their estimates with actual data from past projects. If necessary, calibrate the models to refine their accuracy. Continuously updating and improving the models with new data will enhance their reliability over time.
Parametric estimating offers project managers a systematic and data-driven approach to estimation, resulting in greater accuracy and efficiency. By leveraging historical data and statistical models, organizations can make informed decisions, allocate resources effectively, and improve project planning. However, it is crucial to gather reliable historical data and validate the accuracy of the parametric models to ensure their effectiveness. With the growing availability of data and advancements in statistical analysis tools, parametric estimating is becoming an increasingly valuable tool for project management professionals seeking to enhance their estimation capabilities.
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