Spreadsheet Models for Managers
Getting Access to Spreadsheet Models for Managers
If 
you use Excel to model businesses, business processes, or
business transactions, this course will change your life. You’ll learn how to create tools for yourself that will amaze
even you. Unrestricted use of this material is available in two ways.
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Spreadsheet Models for Managers
| Using the LCA Approach | 10/8 Session Links |
This page discusses design strategy. The recommended strategy produces a design with only one convolution per lease type. This approach is superior because convolution, whether performed by table or by macro, is an expensive proposition — much computation is involved. In models that rely heavily on convolution, recalculation time can be significant. So it’s good to acquire a habit of combining before convolving, even if most of the models you create make only light use of convolution.
Last Modified: Wednesday, 27-Apr-2016 04:15:26 EDT
The Lease Characteristic Array is an example of a construct that makes more effective the modeling of a wide class of problems involving leasing. Certainly, it can be generalized, and certainly there are other possible constructs that could also be helpful with these kinds of problems. But let’s not stop there.
Certainly there are other problems that are susceptible to similar approaches. That is, we can create spreadsheet “idioms” that have applicability to problems more general than the problem immediately at hand. In a field as rich and complex as spreadsheet modeling, it is extremely unlikely that you will find all such techniques described in books, course, or Web sites. From time to time, you’ll have to invent them yourself.
You’ll do best if you can figure out how to produce useful idioms that can help solve wide classes of problems.
Many models are linear with respect to some of their input parameters and input streams. This means, among other things, that when we multiply the input by a constant K, the output also increases by a factor of K. If you know that your model is supposed to be linear with respect to some input parameter or input stream, you can use this fact to check your model: doubling the input should double the output.
You can build these checks into your model from the beginning by defining parameter multiplication factors. For instance, suppose you know that the model’s personal computer expenses should be proportional to the number of employees. By introducing a scaling parameter (normally 1.0), which multiplies the initial number of employees, and multiplies the hiring streams, before they’re used in the rest of the model, you can easily investigate the effects of changing the number of employees, to verify proportionality.