The model formulation process.

The model formulation process includes four features that help derive a model:
I. Decision,
II. Outcome,
III. Structure
IV. Data
The model formulation process is a framework of the type’s variables (decisions) as input to a model and the possible outcomes derived from those decisions. The formulation process also defines structure. Structure is the logic and math that ties the various parts of a model together. Lastly there is data; data can be observed information (AKA raw or empirical) such as actual counted items produced in production or it can be mathematical assumptions - this information that is input to our model.
Here is an example:
Decision:
Increase production of widgets in anticipation of expected sales.
Outcome:
Production is increased and the amount widgets on store shelves are doubled. A possible outcome can be a shortfall of sales and lower than projected income from sales. Costs associated with increased manufacturing production are not recouped. Unanticipated loss of popularity of widgets could be one possible cause. Another possible outcome is widget popularity has increased and the production estimates have erred on the low side. The net effect is that widgets have flown off the shelves and public demand has stores clamoring for more widgets. While all costs associated with increased widget production are recouped and profit expectations where met; company analysts misjudged market conditions and a window to satisfy public demand is missed as unhappy would be widget owners lose interest. However; all is not lost as there a significant widgets on back-order.
Structure:
These could be simple mathematical production formulas with input variables such cost of labor, how many widgets per hour produced, cost of materials, facility overhead etc..
Data:
I. Raw/Empirical
a. Widget owner opinion survey results.
b. Public opinion surveys regarding widgets
II. Estimates
a. Projected sales