This page is a compilation of blog sections we have around this keyword. Each header is linked to the original blog. Each link in Italic is a link to another keyword. Since our content corner has now more than 4,500,000 articles, readers were asking for a feature that allows them to read/discover blogs that revolve around certain keywords.
The keyword typical range and sensitivity analysis has 3 sections. Narrow your search by selecting any of the keywords below:
- input variables (12)
- input variable (6)
- cost estimates (3)
- project costs (3)
- data tables (3)
- discount rate (3)
- output variable (3)
- revenue growth (3)
- cost drivers (3)
- expert judgment (3)
- cost estimation (2)
- operating costs (2)
1.Key Concepts and Terminology[Original Blog]
One of the most important aspects of any project management is cost estimation. Cost estimation is the process of predicting the resources and expenses required to complete a project within a defined scope, quality, and time frame. Cost estimation helps to plan, budget, and control the costs of a project, as well as to evaluate its feasibility, profitability, and risk. However, cost estimation is not a simple task, as it involves many variables, uncertainties, and assumptions that can affect the accuracy and reliability of the estimates. Therefore, it is essential to understand the key concepts and terminology of cost estimation, as well as to follow the best practices and guidelines for cost estimation. In this section, we will discuss the following topics:
1. What is a cost estimate and what are its types? A cost estimate is a quantitative assessment of the likely costs of a project, based on available information and data. A cost estimate can be classified into different types according to its purpose, scope, accuracy, and level of detail. Some of the common types of cost estimates are:
- Order of magnitude estimate: This is a rough and preliminary estimate that is based on limited or incomplete information, such as analogous or historical data. It is usually done at the early stages of a project, when the scope and requirements are not well defined. It provides a general idea of the magnitude and feasibility of the project, but it has a high degree of uncertainty and error. The typical range of accuracy for this type of estimate is -25% to +75%.
- Budget estimate: This is a more detailed and refined estimate that is based on more information and data, such as parametric or unit cost methods. It is usually done at the planning stage of a project, when the scope and requirements are better defined. It provides a reasonable basis for allocating and controlling the project budget, but it still has some degree of uncertainty and error. The typical range of accuracy for this type of estimate is -10% to +25%.
- Definitive estimate: This is the most accurate and reliable estimate that is based on the most information and data, such as bottom-up or detailed cost methods. It is usually done at the execution stage of a project, when the scope and requirements are fully defined and the design and specifications are complete. It provides a realistic and precise estimate of the project costs, but it also requires the most time and effort to prepare. The typical range of accuracy for this type of estimate is -5% to +10%.
2. What are the main components and categories of a cost estimate? A cost estimate consists of various components and categories that represent the different types and sources of costs involved in a project. Some of the main components and categories of a cost estimate are:
- Direct costs: These are the costs that are directly attributable and traceable to a specific project activity or work package, such as labor, materials, equipment, and subcontractors. Direct costs are usually variable and depend on the quantity and quality of the resources used for the project.
- Indirect costs: These are the costs that are not directly attributable or traceable to a specific project activity or work package, but are necessary to support the project as a whole, such as overhead, administration, supervision, and contingency. indirect costs are usually fixed or semi-fixed and depend on the duration and complexity of the project.
- Capital costs: These are the costs that are incurred to acquire or construct the physical assets or facilities of the project, such as land, buildings, machinery, and infrastructure. Capital costs are usually one-time and non-recurring costs that are amortized or depreciated over the useful life of the assets.
- Operating costs: These are the costs that are incurred to operate and maintain the physical assets or facilities of the project, such as utilities, repairs, maintenance, and taxes. Operating costs are usually recurring and ongoing costs that are incurred throughout the useful life of the assets.
3. What are the main methods and techniques of cost estimation? There are various methods and techniques of cost estimation that can be used to calculate the costs of a project, depending on the type, scope, and level of detail of the estimate. Some of the main methods and techniques of cost estimation are:
- Analogous or top-down estimation: This is a method of cost estimation that uses the actual costs or data from similar or comparable projects or activities as a basis for estimating the costs of the current project or activity. This method is useful when there is limited or no information or data available for the current project or activity, or when a quick and rough estimate is needed. However, this method also has some limitations, such as the difficulty of finding truly analogous or comparable projects or activities, the need to adjust for differences in size, scope, complexity, and location, and the lack of accuracy and reliability of the estimate.
- Parametric or unit cost estimation: This is a method of cost estimation that uses statistical or mathematical models or formulas to estimate the costs of a project or activity based on one or more parameters or variables that affect the costs, such as size, quantity, duration, or complexity. This method is useful when there is sufficient and reliable information or data available for the current project or activity, or when a more detailed and refined estimate is needed. However, this method also has some limitations, such as the difficulty of finding or developing appropriate and accurate models or formulas, the need to account for uncertainties and variations in the parameters or variables, and the possibility of errors or biases in the data or assumptions.
- Bottom-up or detailed cost estimation: This is a method of cost estimation that uses the actual costs or data from the individual components or elements of a project or activity as a basis for estimating the costs of the entire project or activity. This method is useful when there is complete and detailed information or data available for the current project or activity, or when the most accurate and reliable estimate is needed. However, this method also has some limitations, such as the high amount of time and effort required to prepare the estimate, the need to coordinate and integrate the costs from different sources and levels, and the possibility of errors or omissions in the data or calculations.
Some possible ways to continue the response are:
- Provide an example of each method or technique of cost estimation: For example, suppose we want to estimate the cost of building a new bridge. We can use the analogous or top-down estimation method by looking at the costs of similar or comparable bridges that have been built in the past and adjusting for differences in size, scope, complexity, and location. We can use the parametric or unit cost estimation method by applying a model or formula that relates the cost of a bridge to its length, width, height, or other parameters or variables. We can use the bottom-up or detailed cost estimation method by breaking down the bridge into its individual components or elements, such as foundations, piers, beams, decks, railings, etc., and estimating the costs of each component or element based on the actual costs or data from similar or comparable components or elements.
- Discuss the advantages and disadvantages of each method or technique of cost estimation: For example, the analogous or top-down estimation method has the advantage of being simple, fast, and easy to use, but it has the disadvantage of being inaccurate, unreliable, and subjective. The parametric or unit cost estimation method has the advantage of being more detailed, refined, and objective, but it has the disadvantage of being complex, time-consuming, and data-intensive. The bottom-up or detailed cost estimation method has the advantage of being the most accurate, reliable, and comprehensive, but it has the disadvantage of being the most costly, laborious, and tedious.
- provide some tips or best practices for applying each method or technique of cost estimation: For example, when using the analogous or top-down estimation method, we should select the most relevant and recent analogous or comparable projects or activities, adjust for inflation and exchange rates, and apply a suitable contingency factor to account for uncertainties and risks. When using the parametric or unit cost estimation method, we should validate and calibrate the models or formulas, use multiple parameters or variables to increase the accuracy and reliability, and perform a sensitivity analysis to test the impact of changes in the parameters or variables. When using the bottom-up or detailed cost estimation method, we should use a work breakdown structure (WBS) to organize and structure the components or elements, use consistent and standardized cost categories and units, and perform a quality control and verification of the data and calculations.
2.Best Practices for Implementing Sensitivity Analysis[Original Blog]
Sensitivity analysis is a powerful technique that allows you to test how different inputs and assumptions affect the output of a financial model. By performing sensitivity analysis, you can identify the key drivers of value, assess the risks and uncertainties, and evaluate the impact of alternative scenarios. In this section, we will discuss some of the best practices for implementing sensitivity analysis in your financial models. We will cover the following topics:
1. How to choose the input variables and the range of values to test
2. How to set up the data tables and charts to display the results of sensitivity analysis
3. How to interpret and communicate the findings of sensitivity analysis
4. How to use sensitivity analysis to improve your financial model
1. How to choose the input variables and the range of values to test
The first step in performing sensitivity analysis is to select the input variables that you want to test and the range of values that you want to vary them by. The input variables are the factors that affect the output of your financial model, such as revenue growth, cost of capital, operating margin, etc. The range of values is the minimum and maximum values that you want to apply to each input variable, such as +/- 10%, +/- 20%, etc.
The choice of input variables and the range of values depends on the purpose and scope of your sensitivity analysis. For example, if you are doing a valuation analysis, you might want to test the sensitivity of the enterprise value to changes in the discount rate, the terminal growth rate, and the free cash flow projections. If you are doing a project finance analysis, you might want to test the sensitivity of the net present value and the internal rate of return to changes in the capital expenditure, the operating expenses, and the revenue assumptions.
The number of input variables and the range of values that you test should be reasonable and realistic. You don't want to test too many input variables or too wide a range of values, as this will make your sensitivity analysis too complex and difficult to interpret. You also don't want to test too few input variables or too narrow a range of values, as this will make your sensitivity analysis too simplistic and irrelevant. You should aim for a balance between simplicity and comprehensiveness, and base your selection on the available data, the industry standards, and the business judgment.
For example, suppose you are doing a sensitivity analysis for a company that sells software as a service (SaaS). You might choose the following input variables and the range of values to test:
- Revenue growth: +/- 5%
- customer churn rate: +/- 2%
- customer acquisition cost: +/- 10%
- Operating margin: +/- 5%
- Discount rate: +/- 1%
These input variables and the range of values are reasonable and realistic, as they reflect the key drivers of value for a SaaS company, and they are within the typical range of variation for these factors.
2. How to set up the data tables and charts to display the results of sensitivity analysis
The second step in performing sensitivity analysis is to set up the data tables and charts to display the results of sensitivity analysis. The data tables and charts are the tools that allow you to visualize and compare the effects of changing the input variables on the output of your financial model. There are two main types of data tables and charts that you can use for sensitivity analysis: one-way data tables and charts, and two-way data tables and charts.
A one-way data table and chart shows the relationship between one input variable and one output variable, holding all other input variables constant. For example, you can use a one-way data table and chart to show how the enterprise value of a company changes as you vary the discount rate, keeping the terminal growth rate and the free cash flow projections constant. A one-way data table and chart is useful for analyzing the impact of a single input variable on the output of your financial model, and for identifying the break-even point or the optimal value for that input variable.
A two-way data table and chart shows the relationship between two input variables and one output variable, holding all other input variables constant. For example, you can use a two-way data table and chart to show how the net present value of a project changes as you vary the capital expenditure and the revenue assumptions, keeping the operating expenses and the discount rate constant. A two-way data table and chart is useful for analyzing the interaction and trade-off between two input variables on the output of your financial model, and for identifying the best-case and worst-case scenarios for that output variable.
To set up a one-way data table and chart in Excel, you need to follow these steps:
- Identify the input variable and the output variable that you want to test, and the range of values that you want to apply to the input variable.
- Create a row or a column with the range of values for the input variable, and a cell with the formula for the output variable.
- Select the range of cells that includes the input variable, the output variable, and the empty cells where you want to display the results of sensitivity analysis.
- Go to the Data tab, and click on What-If Analysis, and then on Data Table.
- In the Data Table dialog box, enter the cell reference for the input variable in the Row input cell or the Column input cell, depending on whether you created a row or a column with the range of values for the input variable. Leave the other input cell blank.
- Click OK, and the data table will be populated with the results of sensitivity analysis.
- To create a chart from the data table, select the range of cells that includes the input variable and the output variable, and go to the Insert tab, and click on the chart type that you want to use, such as a line chart or a column chart. Format the chart as you like, and add labels and titles as needed.
To set up a two-way data table and chart in Excel, you need to follow these steps:
- Identify the two input variables and the output variable that you want to test, and the range of values that you want to apply to each input variable.
- Create a row with the range of values for the first input variable, and a column with the range of values for the second input variable. In the top-left cell of the intersection of the row and the column, enter the cell reference for the output variable.
- Select the range of cells that includes the two input variables, the output variable, and the empty cells where you want to display the results of sensitivity analysis.
- Go to the Data tab, and click on What-If Analysis, and then on Data Table.
- In the Data Table dialog box, enter the cell reference for the first input variable in the Row input cell, and the cell reference for the second input variable in the Column input cell.
- Click OK, and the data table will be populated with the results of sensitivity analysis.
- To create a chart from the data table, select the range of cells that includes the two input variables and the output variable, and go to the Insert tab, and click on the chart type that you want to use, such as a surface chart or a contour chart. Format the chart as you like, and add labels and titles as needed.
3. How to interpret and communicate the findings of sensitivity analysis
The third step in performing sensitivity analysis is to interpret and communicate the findings of sensitivity analysis. The interpretation and communication of the findings of sensitivity analysis are crucial for making informed and effective decisions based on your financial model. You need to be able to explain what the results of sensitivity analysis mean, how they relate to the purpose and scope of your sensitivity analysis, and what are the implications and recommendations for action.
To interpret and communicate the findings of sensitivity analysis, you need to consider the following aspects:
- The direction and magnitude of the change in the output variable as you vary the input variables. This tells you how sensitive the output variable is to changes in the input variables, and how much the output variable changes as a result of those changes. For example, if the enterprise value of a company increases as you increase the revenue growth, and decreases as you increase the discount rate, this means that the enterprise value is positively sensitive to revenue growth and negatively sensitive to discount rate, and that the change in enterprise value is proportional to the change in those input variables.
- The relative importance and ranking of the input variables based on their impact on the output variable. This tells you which input variables are the most influential and critical for the output variable, and which input variables are the least relevant and insignificant for the output variable. For example, if the net present value of a project is more sensitive to changes in the capital expenditure than to changes in the revenue assumptions, this means that the capital expenditure is a more important and decisive factor for the net present value than the revenue assumptions, and that the revenue assumptions are a less important and negligible factor for the net present value.
- The range and distribution of the possible values for the output variable based on the variation of the input variables. This tells you the uncertainty and risk associated with the output variable, and the likelihood and probability of different outcomes for the output variable. For example, if the internal rate of return of a project has a wide range and a skewed distribution based on the changes in the operating expenses and the discount rate, this means that the internal rate of return is uncertain and risky, and that there is a higher chance of a low or negative internal rate of return than a high or positive internal rate of return.
To communicate the findings of sensitivity analysis, you need to use clear and concise language, and support your statements with evidence and examples. You also need to use appropriate and effective visual aids, such as data tables and charts, to illustrate and highlight the key points and trends of your sensitivity analysis. You should also provide a summary and a conclusion that capture the main takeaways and insights of your sensitivity analysis, and suggest any actions or recommendations that follow from your analysis.
For example, suppose you have performed a
3.Techniques for Cost-Driver Estimation in Expenditure Projects[Original Blog]
In the realm of expenditure projects, accurate cost estimation is crucial for effective budgeting and resource allocation. The process of cost-driver estimation plays a pivotal role in this regard, as it helps identify the factors that significantly influence project costs. By understanding these cost drivers, project managers can make informed decisions, optimize resource utilization, and ensure successful project outcomes. In this section, we will explore various techniques employed for cost-driver estimation in expenditure projects, offering insights from different perspectives to enhance your understanding.
One widely used technique for cost-driver estimation is analyzing historical data from similar projects. By examining past projects with comparable characteristics, such as scope, complexity, and industry, project managers can identify patterns and trends in cost drivers. For example, if previous projects in the construction industry have consistently experienced cost overruns due to delays in obtaining permits, it becomes evident that permit acquisition time is a significant cost driver. This technique allows project managers to leverage lessons learned from past experiences and adjust their cost estimates accordingly.
2. Expert Judgment:
Expert judgment involves seeking input from individuals with extensive experience and expertise in the domain of the project. These experts can provide valuable insights into the potential cost drivers based on their knowledge and understanding of the industry. Their input can help identify hidden or less obvious cost drivers that may not be apparent through other means. For instance, an expert in software development might suggest that the complexity of integrations with existing systems could be a significant cost driver in a particular project. Relying on expert judgment enhances the accuracy of cost-driver estimation by incorporating the wisdom and insights of seasoned professionals.
3. Parametric Modeling:
Parametric modeling is a technique that uses statistical relationships between project attributes and cost drivers to estimate project costs. It involves developing mathematical models that express the relationship between the cost drivers and the resulting costs. For example, in a construction project, the size of the building (measured in square footage) could be a cost driver for various components such as materials, labor, and equipment. By establishing a parametric model based on historical data, project managers can estimate costs by inputting the relevant attributes.
4. Bottom-Up Estimation:
Bottom-up estimation involves breaking down the project into smaller work packages or activities and estimating the costs associated with each one. This technique allows for a more granular analysis of cost drivers, as it considers the specific requirements and characteristics of individual tasks. For example, in a software development project, if one module requires extensive testing due to its complexity, the associated testing effort becomes a cost driver for that particular module. By estimating costs at this level of detail, project managers can identify and prioritize cost drivers effectively.
5. Sensitivity Analysis:
sensitivity analysis is a technique used to assess the impact of variations in cost drivers on overall project costs. It involves systematically changing the values of individual cost drivers while keeping other factors constant to observe the resulting changes in cost estimates. This analysis helps project managers understand which cost drivers have the most significant influence on project costs and enables them to focus their attention on managing those drivers. For instance, in a manufacturing project, sensitivity analysis might reveal that fluctuations in raw material prices have a substantial impact on overall costs. Armed with this knowledge, project managers can develop strategies to mitigate the risks associated with volatile cost drivers.
6. Benchmarking:
Benchmarking involves comparing the cost drivers of a current project with those of similar projects in the industry. By benchmarking against projects that have already been completed, project managers can gain insights into the typical range of cost drivers and identify areas where their project deviates from the norm. This technique helps in setting realistic expectations and identifying potential cost drivers that may have been overlooked. For example, if a marketing campaign for a new product launch has higher advertising costs compared to industry benchmarks, it indicates that advertising could be a significant cost driver in this specific project.
Accurate cost-driver estimation is crucial for successful expenditure projects. By employing techniques such as historical data analysis, expert judgment, parametric modeling, bottom-up estimation, sensitivity analysis, and benchmarking, project managers can identify and prioritize the factors that significantly influence project costs. These techniques provide valuable insights from different perspectives, allowing for informed decision-making and effective resource allocation.
Techniques for Cost Driver Estimation in Expenditure Projects - Cost Driver Estimation: Cost Driver Estimation and Selection for Expenditure Projects