So far we have looked at scenarios, sensitivities and templates in isolation. Suppose we want to combine these techniques? Consider a case where we want to compare two different demand forecasts for 3G services, and also wish to independently examine an alternative technology solution which offers greater bandwidth capacity in the basic carrier. We would set up two independent dimensions:
Figure 1: Initial two-dimensional scenario space
The demand parameters will be varied independently of the technology, resulting in four distinct but consistent scenarios. But now suppose we want to replicate this part of the model structure on a geographical basis, using a template:
Figure 2: Replicating the structure using a template
STEM can combine the two examples above because the scenario variant data for the capacity and cost of the
Carrier, and for the penetration of the 3G services, will be first copied into the master template, before being copied identically to all replicas in parallel with the separate template variant data for the customer base and number of sites, which vary with geotype. In general, STEM can combine templates and scenarios so long as the scenario parameters are either:
- completely separate from any template; or
- invariant within the scope of any template (i.e., the scenario parameter is not also a template parameter).
In the example above, our original scenario variant data for Capacity and Capital Cost for the
Carrier, and Penetration for 3G services, are first copied into the master template. These scenario data are then copied identically to all replicas in parallel with the separate template variant data for Customer Base and Sites. The non-overlapping scenario and template variant data simply mesh together.
Figure 3: Combining scenario and template variant data
Overlapping parameters
However, if in the original template we added scenarios directly for the number of subscribers, then these data would be overwritten when the template was replicated and each separate geo-type customised with the template Variant Data.
In order to preserve the scenario data, it is necessary to create scenarios for each of the template Variant Data, i.e., one row in the scenario Variant Data for each column in the template Variant Data.
Figure 4: Scenarios for individual template variant data
From version 7.1, STEM automates this process so that when you ask to create scenarios for a field which is already a template parameter, STEM offers to create scenarios for each of the template Variant Data associated with that field.
Sensitivity analysis
Sensitivities can be directly superimposed on any combination of scenario parameters. However, sensitivities for template parameters must be applied to the corresponding template variant data, as per scenarios.
Sequence of applying variant data
In order to make effective use of scenarios, geographical variants and sensitivities when combined in the same model, it is essential to know how the underlying mechanisms interact. The following table summarises the logic of how STEM processes scenarios, sensitivities and geographical variants, and the types of files generated.
Stage |
Action |
Generated files |
Generate scenarios
|
The appropriate Variant Data for each dimension parameter are applied to separate copies of the working model – the scenarios |
[Model.dtl] model.scn\1.dtm model.scn\2.dtm …
|
Generate sensitivities
|
The requested sensitivities are applied to copies of the working model and each scenario to generate the delta models |
model.scn\0.1.1.+1.dtm model.scn\1.1.1.+1.dtm model.scn\2.1.1.+1.dtm …
|
Replicate
|
The working model and each scenario and delta model are separately expanded to replace template elements with replicated copies for each template variant – the expanded models
|
model.exp.dtm model.scn\1.exp.dtm model.scn\2.exp.dtm …
|
Generate results
|
All generated models are run in sequence to calculate the requested results
|
*.smr |
Figure 5: Sequence of applying variant data