Sensitivity Analysis for Decision Models
1. Sensitivity Analysis for Decision Models
Hello! This video is about Sensitivity Analysis in decision modeling.2. Sensitivity Analysis for Decision Models
Sensitivity analysis examines how changes in input variables impact the output of the decision model. It allows scenario testings by adjusting inputs to evaluate how outcomes behaves. It supports decision-making by clarifying the dependencies between variables.3. Exploring Sensitivity Analysis
Sensitivity Analysis identifies which inputs have the most influence on the decision outputs. Next, let's explore sensitivity analysis. The initial step identifies the inputs and outputs involved in the decision.4. Exploring Sensitivity Analysis
Step two defines the ranges, determining possible values for each input.5. Exploring Sensitivity Analysis
With ranges defined, analyze the impact of each input on the output variables. This involves changing one input while keeping the others constant to observe how the output behaves.6. Exploring Sensitivity Analysis
Finally, generate insights on how changes in inputs impacts the outputs. Now, let's apply sensitivity analysis to a decision problem.7. Applying Sensitivity Analysis
Two executives at the Mattress King are texting about the sales strategy: The CEO believes that if they increase prices, sales may decrease but the overall profit will be greater.8. Applying Sensitivity Analysis
Contrarily, the CTO believes that if they decrease the prices, sales will increase and so will the profit!9. Applying Sensitivity Analysis
Clearly, they are uncertain. Let's use sensitivity analysis to help them make this decision.10. Identifying inputs and outputs
Firstly, identify inputs and outputs. The influence model shows Profit, an output influenced by inputs Costs, Sales, and Prices.11. Identifying inputs and outputs
Additionally, it shows costs influenced by costs per units, Store Rent and Payroll.12. Classifying variables
We'll now classify the inputs and outputs as fixed or floating variables. Fixed variables have static values. In this case, Payroll, Store rent, and Cost per mattress are fixed variables.13. Classifying variables
Floating variables have changing values. Here, Mattress price and units of Mattress sold are floating variables.14. Classifying variables
Now, we tie everything in one equation. Profit, the output, is the "price of the mattress multiplied by units sold" minus the: "Payroll, plus store rent plus the cost per mattress times the number of units sold."15. Defining ranges of variables
Let's now define value ranges for the floating variables. Currently, the Mattress King sells 1000 mattresses for the price of $150.16. Defining ranges of variables
Given our equation, their current profit is $40,000.17. Defining ranges of variables
Let's now extend the ranges for the floating variables: We'll simulate prices varying from $75 to $200 and units of Mattresses sold varying between 600 and 1400.18. Calculating the impact of variables
The concluded table shows the calculated profit for each combination of Price, and Units sold.19. Generating conclusions and insights
Given this sensitivity analysis, the executives can make an informed decision about profit. Here are some insights: If price is increased to $175, they would need to sell at least 800 mattresses to reach the current profit.20. Generating conclusions and insights
If price is increased to $200, they would need to sell approximately 700 mattresses to match the current profit.21. Generating conclusions and insights
Alternatively, if price is decreased to $125, they would need to sell approximately 1400 mattresses to match the current profit.22. Generating conclusions and insights
At $100, even by selling 1400 mattresses, profit would reduce to $10,000, a 75% reduction.23. Let's practice!
Let's practice more of this!Create Your Free Account
or
By continuing, you accept our Terms of Use, our Privacy Policy and that your data is stored in the USA.