Top Down Estimating

Test Your Knowledge

Top-Down Estimating Quiz

Instructions: Choose the best answer for each question.

1. What is another name for top-down estimating?

a) Detailed estimating b) Bottom-up estimating c) Analogous estimating

2. Which of the following is NOT an advantage of top-down estimating?

a) Quick and efficient b) Provides detailed cost breakdowns c) Helps establish a budget baseline

3. What is the primary source of data used in top-down estimating?

a) Project specifications b) Expert opinions c) Historical data from similar projects

4. Which of the following is a limitation of top-down estimating?

a) It requires extensive resources. b) It can be inaccurate when significant differences exist between the current and historical projects. c) It doesn't allow for budget adjustments.

5. What method is often used to refine the initial cost estimates derived from top-down estimating?

a) Bottom-up estimating b) Parametric cost estimating c) Both a) and b)

Top-Down Estimating Exercise

Scenario: You are the project manager for a new software development project. You need to estimate the initial project cost using top-down estimating.

Task:

1. Identify three similar software development projects from your company's history.
2. Collect the total cost, labor hours, material expenses, and overhead for each project.
3. Analyze the differences between the current project and the historical projects (e.g., complexity, size, technology).
4. Adjust the historical data to account for these differences.
5. Apply the adjusted historical data to estimate the initial cost range for the current project.

Remember to consider the following:

• Inflation: Adjust the historical data for inflation using a relevant inflation rate.
• Technological advancements: Account for any changes in technology or tools since the historical projects.
• Project-specific requirements: Consider any unique features or requirements of the current project.

Exercice Correction:

Techniques

Top-Down Estimating: A Detailed Exploration

Here's a breakdown of the provided text into separate chapters, expanding on the information and adding depth to each section.

Chapter 1: Techniques

Top-down estimating relies on several core techniques to derive cost estimates from historical data. The primary technique is analogous estimating, which directly compares the current project to similar past projects. This involves identifying projects with comparable scope, size, and complexity. Key considerations include:

• Project Selection: The accuracy of the estimate heavily depends on selecting truly analogous projects. Factors to consider include the technology used, the team's experience, the project's environment, and the overall risk profile. Care must be taken to avoid selecting projects that are too dissimilar.
• Data Normalization: Raw historical cost data needs to be adjusted for inflation, changes in labor rates, material costs, and any technological advancements since the completion of the comparable project. This normalization is crucial for achieving a realistic estimate.
• Scaling Adjustments: Simple scaling (e.g., adjusting cost proportionally to project size) might not always be appropriate. Complex projects don't always scale linearly. Experience and judgment are required to make accurate adjustments.
• Expert Judgment: While data-driven, analogous estimating often relies on the expertise of experienced project managers and cost estimators to interpret historical data, identify comparable projects, and make adjustments for unique factors. This subjective element introduces some uncertainty but can be essential for successful estimation.
• Regression Analysis: A more sophisticated technique utilizes regression analysis to statistically model the relationship between project parameters (size, complexity, duration) and cost. This allows for more accurate predictions, especially when dealing with many past projects.

Beyond analogous estimating, parametric estimating is a closely related technique. This method uses statistical relationships, often derived from historical data, to model the cost based on measurable project attributes. This provides a more refined approach, particularly if a significant amount of data is available. Parameters can include things like lines of code, square footage, or the number of features.

Chapter 2: Models

Several models underpin top-down estimating techniques. The simplest is a proportional scaling model, which directly scales historical project costs based on a measurable difference in project size or scope. For example, if a previous project of 10,000 lines of code cost $100,000 and the current project has 15,000 lines of code, a naive proportional model would estimate a cost of $150,000.

More sophisticated models include:

• Regression Models: These statistical models use historical data to establish a mathematical relationship between project attributes (independent variables) and cost (dependent variable). Linear regression is common but more complex models (e.g., polynomial regression) might be necessary to account for non-linear relationships.
• Cost Estimation Algorithms: Some sophisticated software packages utilize proprietary algorithms that incorporate various factors and historical data to predict costs. These algorithms often combine regression techniques with other statistical methods.

The choice of model depends on the data available, the project's complexity, and the desired level of accuracy.

Chapter 3: Software

Several software tools facilitate top-down estimating. These tools often incorporate parametric models and statistical analysis capabilities:

• Spreadsheet Software (Excel): Excel can be used for simpler top-down estimations, particularly for basic proportional scaling and regression analysis using built-in functions.
• Project Management Software (MS Project, Primavera P6): Many project management tools offer cost estimation modules that support various techniques, including analogous and parametric estimating. These often include features for tracking historical data and analyzing cost performance.
• Dedicated Cost Estimation Software: Specialized software applications focus on cost estimating, providing advanced capabilities for data management, statistical modeling, risk assessment, and reporting. These tools can significantly improve the accuracy and efficiency of top-down estimations.

Chapter 4: Best Practices

Effective top-down estimating requires careful planning and execution:

• Thorough Data Collection: Ensure accurate and complete historical cost data from comparable projects. This includes labor costs, material costs, overhead, and any other relevant expenses.
• Careful Project Selection: Choose analogous projects that are truly comparable in scope, complexity, and other relevant factors.
• Appropriate Model Selection: Select the most suitable estimation model based on the data available and the project's characteristics.
• Sensitivity Analysis: Conduct a sensitivity analysis to understand how changes in key parameters affect the estimated cost. This helps identify potential risks and uncertainties.
• Documentation: Thoroughly document the estimating process, including the chosen model, assumptions made, and justifications for adjustments.
• Expert Review: Have experienced cost estimators and project managers review the estimate to identify potential errors or biases.
• Iterative Refinement: Top-down estimates should be refined as more detailed information becomes available throughout the project lifecycle. Combine top-down with bottom-up estimates for a more comprehensive view.

Chapter 5: Case Studies

(This section would require real-world examples. The following are hypothetical examples to illustrate the concept.)

• Case Study 1: Software Development: A software development company uses historical data from similar projects to estimate the cost of a new application. They use a parametric model based on lines of code, features, and technology complexity, achieving a reasonably accurate estimate within 10% of the actual cost.

• Case Study 2: Construction: A construction company uses analogous estimating to determine the cost of a new building project by comparing it to previously completed projects with similar size and design complexity. They adjust for inflation and material cost changes, resulting in a preliminary estimate that serves as a basis for further refinement using a bottom-up approach.

• Case Study 3: Infrastructure Project: A large-scale infrastructure project, like a bridge, uses both parametric and analogous estimating combined with expert judgment. Parametric models predict costs based on length and materials, while analogous data from similar bridge projects adjusts for local conditions and labor costs. This combination yields a robust estimate. These case studies would highlight the successes and challenges associated with implementing different approaches.

This expanded structure provides a more comprehensive treatment of top-down estimating, covering its core techniques, underlying models, supportive software, best practices, and illustrative case studies. Remember to replace the hypothetical case studies with real-world examples for a more impactful presentation.