Life Cycle Costing (LCC)
Sensitivity Analysis in Excel using a VBA Macro PluginUsage and Reference
The "Sensitivity Analysis Knoll" Add-In for Microsoft Excel is a handy tool that was specifically developed for life cycle cost assessments (LCC / LCCA) in Excel spread sheets. The basic idea of varying model parameters in input cells of the spread sheet and to observe / draw the resulting output changes similarly to the "What-If" functions already provided within Excel has been extended in this Add-In macro for further convenience.
In the documentation of your work:
"The work has been carried out using the "Sensitivity Analysis Knoll" Add-In for Microsoft Excel ."
In the reference section:
 Knoll, Thomas M.; "Sensitivity Analysis Add-In for Microsoft Excel"; URL: https://www.life-cycle-costing.de/sensitivity_analysis/
Although the Add-In is provided free of charge, it is not meant to be published on any other web page for downloading.
Download Add-In (Save the downloaded file first to a folder, where it will remain for long (not being moved or deleted) and then double click the file at this chosen location)
No Guarantee / No Liability
The Add-In is provided free of charge to be used on your own risk. No liability is taken for instance - but not limited - for data loss or damage.
Installation and Documentation
Please see the Installation and Documentation document.
Use of the Sensitivity Add-In on Excel sheets with user created macros
In order to guarantee the correct operation of sensitivity calculations in Excel sheets with user created macros, please mark the respective input cells of the user created macro to autmatically update themself.
Sensitivity Analysis in STEM (Strategic Telecoms Evaluation Model)
The modelled demand and cost parameters of that LTE roll-out business model are based on assumptions and forecast values. This naturally includes uncertainty and could potentially lead to wrong modelling results. In order to challenge the model with varying parameters, the sensitivity analysis is used to determine the impact of input parameter changes onto output results. This way, the most influential parameters can be derived and in turn modelled in more rigorous accuracy. Exemplarily, the parameters for eNodeB capital and maintenance expenditures are varied by +/- 10% of uncertainty, which yields the output as shown in the tornado and time series graph below.
As a second example, a +/- 10% uncertainty in the market size of all roll-out city types varied independently results in the following figures for the input market size and the resulting Net Present Value (NPV).
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