SENSITIVITY ANALYSIS

Saturday Brain Storming Thought (205) 13/01/2024

SENSITIVITY ANALYSIS

Sensitivity Analysis is a financial modelling tool used to analyse how different values of an independent variable affect a particular dependent variable under a certain set of assumptions

It studies how various sources of uncertainty contribute to the forecasts overall uncertainty by posing what if questions

Sensitivity Analysis of Valuation Model

To perform sensitivity analysis

You must select the key inputs or assumptions that have the most uncertainty or influence on your valuation and define a range of values for each

Then, create a data table that illustrates how the value of your model changes with each combination of input values

What-if Analysis

Sensitivity analysis is also called as What-if Analysis

It is the assessment of the impact for an output of a system by changing its inputs

Uncertain variables in budgeting process

1) Inflation rates

2) Interest rates

3) Operating expenses

4) Future tax rates

5) Headcount

6) Other variables may not be known with great precision

If these variables deviate from expectations, what will the effect be (on the business, system, or whatever is being analyzed), and which variables are causing the largest deviations

Partial Sensitivity Analysis

In a partial Sensitivity Analysis, you select one variable, change its value while holding the values of other variables constant

Best-case and Worst-case scenarios

Best-case and Worst-case scenarios establish the upper (best-case) and lower (worst-case) boundaries of a cost-benefit study’s results

This type of sensitivity analysis shows howxa broad range of a program or policys possible outcomes affect the bottom line

To perform Best-case Analysis

Use all of the most favorable assumptions

To perform Worst-case Analysis

Use all of the least favorable assumptions

Break-even Analysis

If you are unable to estimate a policys most likely effects or cannot find comparable studies to help determine its best-case and worst-case scenarios

You can use Break-even analysis

Monte Carlo Analysis

You can use Monte Carlo Analysis to

Examine multiple variables simultaneously and simulate thousands of scenarios, resulting in a range of possible outcomes and the probabilities that they will occur

Advantages of Sensitivity Analysis

1) Simplicity

2) Directing Management efforts

3) Ease of being Automated

4) As a quality check

Disadvantages of Sensitivity Analysis

1) It does not provide clear cut results

2) Not a solution in standalone form

Sensitivity Analysis performance

1) Create a model

2) Write a set of requirements

3) Design a system

4) Make a decision

5) Do a tradeoff study

6) Originate a risk analysis

7) Want to discover the cost drivers

Sensitivity Analysis importance

1) Uncertainty in various parameters used in Simulation models, feedback loops, probability distribution etc

2) Values of these parameters cannot be estimated previously due to data availability or time constraints

3) Less reliable models – tested for their sensitivity to the changes in model components

4) Components, to which simulation results are sensitive, need more attention than other parts of the model

5) Parameter sensitivity of the model can be compared with the information from real system

History of Sensitivity Analysis

1) The genetics studies on the pea by Gregor Mendel, 1865

2) The statistics studies on the Irish hops crops by Gosset (reported under the pseudonym student), ca 1890

Sensitivity Analysis methods

1) Correlation and Screening method

2) Regression method

3) Statistical Analysis

Sensitivity Analysis in Supply Chain model

1) Supply chains are good examples of material delay formulations that are rigorously discussed in system dynamics literature

2) Supply chains consist of a stock and flow structure for the acquisition, storage and conversion of inputs into outputs and the decision rules, governing these flows

3) Include negative feedback loops that create corrective action once discrepancy arises between the stock and it’s desired level

4) The transformation process in each supply chain takes some amount of time, ie there is a time delay in every supply chain structure

5) Interaction between negative feedback loops and the time lag may yield oscillations