Question

• Identify a specific data model (e.g., optimization, simulation, predictive, etc.). Name one example of that data model and one application (e.g., What if analysis, decision trees, sensitivity analysis, forecasting, etc. Elaborate on the data models in the discussion forum (e.g., provide a schematic diagram, discussion, or visual for a data model).

There is no data given.

Answer 1

Solution:

Capital budgeting is, by definition, forward looking. When dealing with expected resources and demands, uncertainty is a major factor. Sensitivity analysis is a statistical tool that determines how consequential deviations from the expected value occur. Sensitivity Analysis deals with finding out the amount by which we can change the input data for the output of our linear programming model to remain comparatively unchanged. This helps us in determining the sensitivity of the data we supply for the problem. It also helps to determine the optimal levels of each input.

Sensitivity analysis can be useful for a number of reasons, including:

• Support decision making or the development of recommendations for decision makers (e.g., testing the robustness of a result).
• Enhance communication from modelers to decision makers (e.g., by making recommendations more credible, understandable, compelling or persuasive).
• Increase understanding or quantification of the system (e.g., understanding relationships between input and output variables).
• Model development (e.g., searching for errors in the model).

In order to conduct a sensitivity analysis, all of the inputs and parameters are connected via an algorithm to produce the output. For example, a model of the inputs and parameters for a company interest in creating a new product may include information about expected availability of raw material, inflation rates, and number of employees working in R&D. The output would be the profit generated by the new product. The sensitivity analysis entails changing each variable and seeing how that changes the output. Generally, only one variable is changed at once, with all of the others fixed at their base value. This makes it easy to see how much a variable affects the output. However, not all of the inputs may be independent so changing inputs one at a time does not account for interaction between the inputs.

Sensitivity of a Variable: Sensitivity analysis determines how much an output is expected to change due to changes in a variable or parameter. In this case, the output (y-axis) decreases exponentially with an increase in the input (x-axis). This is mapped out for each input.

Scenario Analysis

Scenario analysis is a process of analyzing decisions by considering alternative possible outcomes.

LEARNING OBJECTIVES

Explain scenario analysis is used by investors

KEY TAKEAWAYS

Key Points

• Scenario analysis is designed to see the consequences of an action under different sets of factors. For example, it shows how an investment ‘s NPV would differ under high and low inflation.
• Scenarios should be feasible enough to provide an accurate picture of the outcomes. A “good” scenario for an investor should not include winning the lottery because, though good, it is neither probable nor realistic for analyzing possible results.
• Many scenario analyses use 3 scenarios: base case, worst case and best case. However, the number and conditions of the scenarios in each analysis can vary.

Key Terms

• scenario: A set of factors that can affect the consequences of an action. The environment.
• analysis: A process of dismantling or separating into constituent elements in order to study the nature, function, or meaning.
• scenario analysis: a process of analyzing possible future events by considering alternative possible outcomes

Scenario Analysis

Scenario analysis is a strategic process of analyzing decisions by considering alternative possible outcomes (sometimes called “alternative worlds”). It is not a predictive mechanism, but rather an analytic tool to manage uncertainty today..

Factor Normal case Best case Worst case Yield + 10% -20% Exchange rate Cedi/EUR +10% - 10% Transport cost - 5% + 20% Marketing cost - 5% + 20% Result cost price 0.86 0.75 0.99 Sales price 1.03 1.05 1.00 Profit/loss 0.17 0.30 0.01 Profit % 17% 29% 1%

Scenario Analysis: This scenario analysis shows how changes in factors like yield and transport cost can affect profits.

For example, a firm might use scenario analysis to determine the net present value (NPV) of a potential investment under high and low inflation scenarios.

In another example, a bank might attempt to forecast several possible scenarios for the economy (e.g. rapid vs. moderate vs. slow growth) or it might try to forecast financial market returns (for bonds, stocks and cash) in each of those scenarios. Perhaps, it might also consider sub-sets of each of the possibilities. It might further seek to determine correlations and assign probabilities to the scenarios (and sub-sets if any). By analyzing these various scenarios, the bank will be in a better position to consider how best to allocate its assets.

Many scenario analyses use three different scenarios: base case, worst case and best case. The base case is the expected scenario: if all things proceed normally, this is what the expected outcome will be. The worst and best cases are obviously scenarios with less and more favorable conditions, but they are still confined by a sense of feasibility. For example, an investor creating the worst case scenario would not be well served to have it include a meteor strike that destroys the company. While clearly a bad scenario, it is not realistic enough to be helpful.

The purpose of scenario analysis is not to identify the exact conditions of each scenario; it just needs to approximate them to provide a plausible idea of what might happen.