Linear Programmers are Sensitive

I am have been slowly making my way through the book The Science of Decision Making and also using my old Operations Management textbook to gain a deeper understanding of Linear Programming. While I have performed the graphical method of Linear Programing and have used the Solver in Excel to find an answer, I had not used the "Sensitivity Report" before.

The "Sensitivity Report" is feedback for the data model. Using it a data modeler can can compare the "shadow price" vs. the real price of an extra unit of production. If the real price is greater than the shadow price then the results of the model given is considered optimal. If the shadow price is higher than the real price of an extra unit of production then adjustments can be made to the model. Another bit of feedback the "Sensitivity Report" gives you is the range of units for each constraint before the answer would change. Hence the amount of change required to change the answer can be considered along with the shadow price to determine if the model should change and if so in which direction.

As I gain greater insight to how linear programming works, I hope to post some examples with real data. Also at this time I am using Excel as that is what both books use for examples. At some point I plan to try the same thing with SciPy's optimizer.