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Sensitivity analysis From Wikipedia, the free encyclopedia Sensitivity analysis is the study of how the variation in the output of a model (numerical or otherwise) can be apportioned, qualitatively or quantitatively, to different sources of variation. In a decision problem, the analyst may want to identify cost drivers as well as other quantities for which we need to acquire better knowledge in order to make an informed decision. On the other hand, some quantities have no influence on the predictions, so that we can save resources at no loss in accuracy by relaxing some of the conditions. Business Context Sensitivity analysis can help in a variety of other circumstances which can be handled by the settings illustrated below: to identify critical assumptions or compare alternative model structures guide future data collections detect important criteria optimize the tolerance of manufactured parts in terms of the uncertainty in the parameters optimize resources allocation model simplification or model lumping, etc. However there are also some problems associated with sensitivity analysis in the business context: Variables are often interdependent, which makes examining them each individually unrealistic. eg: changing one factor such as sales volume, will most likely affect other factors such as the selling price. Often the assumptions upon which the analysis is based are made by using past experience/data which may not hold in the future. Assigning a maximum and minimum (or optimistic and pessimistic) value is open to subjective interpretation. For instance one persons 'optimistic' forecast may be more conservative than that of another person performing a different part of the analysis. This sort of subjectivity can adversely affect the accuracy and overall objectivity of the analysis.
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