Difference between simulation and sensitivity analysis
Answers
Step-by-step explanation:
Sensitive Analysis
A sensitivity analysis is an analysis that estimates how sensitive your dependent variable (in this case, amount of material sold) is to a change in an independent variable (for your casing composite, price). In some situations, you may have historical data to use when completing your sensitivity analysis, but in your case, you have to use as much information as you can find about how much work it is for mobile phone manufacturers to change their suppliers, how much they currently pay for casing material, and how much you might be able to help them be offering a truly innovative new feature on their phone.
In a sensitivity analysis, you are only looking at how sensitive the change in an outcome is based on changing a single variable. But, you can consider many more factors than just the outcome. For example, you may think a manufacturer is going to pay big bucks for your composite, but think about the implications. If they use your composite, they will lose a lot of the money they make by selling power adapters and cords. Also, will your composite work with the cases they already sell, or will they have to use a special case?
At the end of a sensitivity analysis, you want to be able to estimate that 'an X% increase in price will result in a Y% decrease in the amount of my product sold.' While that may be an ideal result for a sensitivity analysis, it doesn't have to be quantitative. You may simply want to know what factor, within your control, will have the most significant impact on sales. If you go through a logical, methodical process to identify the impact one variable has on our desired outcome, you are conducting a sensitivity analysis.