Order of Magnitude Estimate

Test Your Knowledge

Quiz: The Art of the Rough Estimate

Instructions: Choose the best answer for each question.

1. What is the primary purpose of an Order of Magnitude (O.M.) Estimate?

a) To provide a precise and detailed cost breakdown. b) To obtain an accurate cost figure for project bidding. c) To offer a quick and rough approximation of project costs. d) To replace detailed cost analysis in all project phases.

2. Which of the following is NOT a typical benefit of using O.M. Estimates?

a) Early stage decision making. b) Resource allocation. c) Ensuring accurate cost predictions for final budgeting. d) Project prioritization.

3. How accurate are O.M. Estimates typically expected to be?

a) Within +/- 5% b) Within +/- 10% c) Within +/- 25% to +75% d) Within +/- 100%

4. Which of the following is NOT a common method for creating an O.M. Estimate?

a) Cost Capacity Curves b) Scale Up/Down Factors c) Detailed budget analysis with precise cost breakdowns d) Approximate Cost Capacity Ratios

5. What does the term "SWAG" refer to in the context of O.M. Estimates?

a) A scientifically validated and rigorous cost estimate. b) A sophisticated and highly accurate cost forecasting method. c) A lighthearted term acknowledging the rough nature of an O.M. Estimate. d) A specific type of O.M. Estimate used for large-scale projects.

Exercise: The New Website Project

You're tasked with creating a rough estimate for the cost of developing a new website for your company. You have some historical data from a previous website project:

• Previous Website Cost: $50,000
• Previous Website Size: 10 pages

The new website will be much larger, with approximately 50 pages. Use the "Scale Up/Down Factors" method to create a quick O.M. Estimate for the new website project.

Techniques

Chapter 1: Techniques for Order of Magnitude Estimation

Order of Magnitude (O.M.) estimation relies on several techniques to generate a quick, approximate cost figure. These techniques are often used in combination, depending on the available data and the project's complexity. Key techniques include:

1. Analogy or Comparative Estimating: This involves identifying similar past projects and using their costs as a basis for the estimate. Adjustments are made to account for differences in scope, complexity, and inflation. This method relies heavily on the availability of relevant historical data.

2. Parametric Estimating: This technique uses statistical relationships between project parameters (e.g., size, weight, duration) and cost. These relationships are often derived from historical data or industry benchmarks. For example, a construction project's cost might be estimated based on its square footage using a cost per square foot figure.

3. Top-Down Estimating: This approach starts with a broad overview of the project and breaks it down into major components. Costs are assigned to each component based on experience and judgment. This method is useful in early stages when detailed information is limited.

4. Bottom-Up Estimating (with simplification): While traditionally a detailed method, bottom-up estimating can be adapted for O.M. purposes. Instead of meticulously estimating the cost of every task, this simplified approach groups tasks into broader categories and applies approximate costs. This approach requires more detailed knowledge of the project than top-down.

5. Expert Judgment: This technique relies on the expertise and experience of individuals familiar with similar projects. While subjective, it can be surprisingly accurate when used by experienced professionals. Often, a consensus approach from multiple experts is employed.

6. Scale-Up/Down Factors: If a cost estimate exists for a similar but smaller (or larger) project, this method scales the cost up or down proportionally based on the difference in size or scope. This method is simple but can be inaccurate if the scaling relationship is not linear.

7. Cost Capacity Curves: These curves graphically represent the relationship between project size (e.g., capacity, number of units) and its cost. These are particularly useful for projects with established cost-size relationships.

The choice of technique depends on the project, the availability of data, and the desired level of accuracy. Often, a combination of techniques is used to arrive at a more robust estimate.

Chapter 2: Models for Order of Magnitude Estimation

Several models can be used to structure and formalize the O.M. estimation process. These models help ensure consistency and transparency in the estimation process. Key models include:

1. Simple Mathematical Models: These models use straightforward mathematical relationships to estimate costs. For example, a linear model might relate project cost to project size (e.g., Cost = a + b*Size), where 'a' and 'b' are constants determined from historical data or expert judgment.

2. Statistical Models: These models utilize statistical techniques to analyze historical data and predict future costs. Regression analysis is a common technique used to identify relationships between cost and various project parameters.

3. Expert Systems: These systems utilize the knowledge of experts to develop rules and algorithms for estimating costs. They often incorporate heuristic rules and decision trees to handle complex situations.

The selection of a model depends on factors such as the complexity of the project, the availability of historical data, and the level of sophistication desired in the estimation process. Often, a simple model is sufficient for an O.M. estimate, while more complex models may be required for higher accuracy.

It's crucial to remember that no model perfectly captures the complexities of a project; these models serve as frameworks for structuring the estimation process.

Chapter 3: Software for Order of Magnitude Estimation

While dedicated O.M. estimation software is less common than software for detailed cost estimating, various tools can assist in the process:

1. Spreadsheets (e.g., Microsoft Excel, Google Sheets): Spreadsheets are widely used for organizing data, performing calculations, and creating simple models. They are particularly useful for applying parametric methods or top-down approaches. Functions like SUM, AVERAGE, and various statistical functions can aid in the estimation.

2. Project Management Software (e.g., MS Project, Asana, Jira): While primarily for managing tasks and timelines, these tools can offer features to track budget allocation and provide a framework for high-level cost estimates. They often allow for simple cost assignment to tasks or milestones.

3. Cost Estimating Software (with simplified application): Software designed for detailed cost estimating can be adapted for O.M. purposes. The user may focus on high-level cost categories and utilize simplified input to achieve a rough estimate. This is useful if you already own such software.

4. Custom Scripts or Programs: For organizations with standardized estimation methods or large volumes of data, custom scripts or programs can automate the estimation process and ensure consistency. This would involve programming languages such as Python or R.

The choice of software depends on the organization's resources, technical capabilities, and the complexity of the project. For many O.M. estimates, a simple spreadsheet may suffice.

Chapter 4: Best Practices for Order of Magnitude Estimation

To ensure the most effective O.M. estimates, several best practices should be followed:

1. Define Scope Clearly: A clear and concise project definition is paramount. Ambiguity in scope leads to inaccurate estimates. Focus on high-level objectives and deliverables.

2. Identify Appropriate Technique: Select the estimation technique(s) best suited for the project and the available data. Consider the strengths and weaknesses of each technique before making a decision.

3. Use Multiple Techniques (Triangulation): Employing multiple techniques and comparing results helps reduce bias and improve accuracy. This "triangulation" approach offers a more robust estimate than relying on a single method.

4. Document Assumptions and Justifications: Clearly document all assumptions made during the estimation process. Explain the reasoning behind cost allocations and any adjustments made. This transparency is crucial for review and future improvements.

5. Consider Risk: Account for potential risks and uncertainties that may impact the project cost. Add contingency buffers to the estimate to reflect these uncertainties.

6. Involve Experienced Estimators: Utilize the expertise of individuals with relevant experience in similar projects. Their judgment and intuition can significantly improve the accuracy of the estimate.

7. Regularly Review and Update: As more information becomes available during the project lifecycle, review and update the O.M. estimate to reflect the evolving understanding of costs.

8. Communicate Effectively: Clearly communicate the purpose, limitations, and accuracy range of the O.M. estimate to stakeholders. Manage expectations by emphasizing that this is a preliminary estimate.

Following these best practices enhances the reliability and usefulness of the O.M. estimate, making it a valuable tool for early-stage project planning.

Chapter 5: Case Studies in Order of Magnitude Estimation

Several examples illustrate the practical application of O.M. estimation techniques across various industries:

Case Study 1: Software Development Project: A software company needs to estimate the cost of developing a new mobile application. Using a parametric approach, they might use historical data to establish a cost per line of code, multiply this by the estimated lines of code for the new app, and add costs for design, testing, and deployment. This provides a preliminary cost estimate.

Case Study 2: Construction Project: A construction company needs to estimate the cost of building a new office building. They use a combination of analogy (comparing costs of similar buildings) and top-down estimating (breaking down the project into major components like foundation, structure, and finishes) to develop an O.M. estimate.

Case Study 3: Research and Development Project: A pharmaceutical company wants to estimate the cost of developing a new drug. They employ expert judgment, drawing on the experience of scientists and project managers familiar with similar drug development projects. Uncertainty is explicitly acknowledged in the resulting estimate.

These case studies illustrate how O.M. estimation is applicable across different project types. The specific techniques and models used will vary depending on the industry, project complexity, and available data. The common thread is the focus on speed and feasibility assessment in the early stages of project planning. The limitations of the O.M. estimate are always acknowledged and communicated appropriately.