A company should supply 2 products namely A and B. The company can buy A and B in advance or in real-time, however at different prices. The demand is uncertain and prices are considered to be deterministic. Therefore, I developed a stochastic model in which uncertain demand and prices are the input and its revenue for selling the products is the output.
LA : The price of product A when buying before the delivery time. LB : The price of product B when buying before the delivery time. CA: The price for selling A to the customer CB: The price for selling B to the customer LAA : The price of product A when buying at the delivery time. LBB : The price of product B when buying at the delivery time. R : Revenue of the company
Now I want to apply a sensitivity analysis based on market prices. My assumption is to gradually increase each price while keeping the others constant and see it's the effect on the revenue. Therefore, I will get 6 tables regarding the relationship between the increase in each price wrt the revenue.
For example: Let say LA = 30 while R = 50. Thus I change LA to 31 and feed it to the model. Let say the R' will be 48. I continue this routine until LA = 40. Then I can see the relationship between LA and R for a range of prices.
However, this method only considers the impact of the change in one input variable on the output. Therefore, it doesn't consider the combined variation of inputs (LA, LB). How can I perform a valid sensitivity analysis for this problem where a subset of the inputs are changed while the others are fixed? Moreover, the prices in the model are somehow correlated in reality.