How can you use sensitivity and risk analysis together?

1 What is sensitivity analysis?

Sensitivity analysis is a method of testing how sensitive your model is to variations in the input parameters, such as costs, demands, capacities, or probabilities. It tells you how much the optimal solution or the objective value changes when you change one or more parameters within a certain range. Sensitivity analysis can help you identify the most critical parameters, the trade-offs between different objectives, and the robustness of your solution.

4 What are the benefits of using sensitivity and risk analysis together?

Using sensitivity and risk analysis together can provide several advantages to your decision making. It allows you to factor in both deterministic and stochastic elements that have an effect on your model and decision. You can also evaluate the trade-offs between efficiency and reliability, or between profit and risk, so as to pick the solution that best meets your objectives. Furthermore, you can identify the sources of uncertainty and variability in your model and decision, and take actions to minimize them or handle them. Additionally, you can test the robustness and flexibility of your solution, making necessary changes if needed. Finally, you can use visual and numerical tools to communicate your results and suggestions more effectively, demonstrating the impact of sensitivity and risk analysis.

5 What are the challenges of using sensitivity and risk analysis together?

Using sensitivity and risk analysis together can also present some challenges that you should be mindful of. Reliable and relevant data and information must be gathered, and appropriate methods and tools must be selected in order to perform both types of analysis. Additionally, the results of both types of analysis should be interpreted and presented with caution in order to avoid confusion, bias, or oversimplification. Lastly, a balance between complexity and simplicity must be found when constructing the model and making decisions, so as to avoid overconfidence or paralysis by analysis.

6 How to learn more about sensitivity and risk analysis?

If you want to learn more about sensitivity and risk analysis, and how to use them together in operations research, you can check out some of the following resources:

- Sensitivity Analysis in Linear Programming: Introduction and Application by N. V. Sahinidis (2017), a book that provides a comprehensive and practical guide to sensitivity analysis in linear programming models.

- Risk Analysis in Operations Research by E. Zio (2013), a book that covers the main concepts and methods of risk analysis in operations research, with applications to engineering, management, and policy.

- A Tutorial on Sensitivity and Risk Analysis in Spreadsheet Models by A. Saltelli and S. Tarantola (2002), a paper that explains how to perform sensitivity and risk analysis in spreadsheet models, using examples and software tools.

7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?