Filtered Statistics allows stratification of data collected in variables – that is, multiple variables collected simultaneously.
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Let’s use an example. A prominent support center stratifies customers based on Type (5 types), Priority (4 priorities), and Service Level (3 service levels). The combination of these descriptive elements equals sixty (5 * 4 * 3) potential statistics. Each variety has a separate service level agreement. A sub example might be Priority 1, Level 1, for customer type 1 requires that 95% of all calls resolve in less than 90 minutes.
When using variables to report service level agreements above, I would need 120 variables and 120 to track the information required. Using 240 elements to define and collect 60 statistics is not reasonable. Using Filtered Statistics, I can collect all the information to report on all groups, answering the same questions with only four variables. To accomplish this data collection task, the variables Time value, Priority, Type, and Service Level, need to be written simultaneously.
How to Use Filtered Stats
All data used in filtered stats must be collected using the same action statements at the same clock time. All Activities or Routes collecting stats would use the same action logic. An example is shown below:
v_Time = Clock( ) – CycleStart (If you were not counting off sift time, then the first line might look like v_Time = v_Clock – a_Cycle_Start where v_Clock is a is part of a model object reaching only on shift time).
v_Type = a_Type
v_Priority = a_Priority
v_Service_Level = a_Service_Level
From the Filtered Stas dialog, select the main variable to display. All variables collected at the same instant display as potential variables used to filter the main variable.
Clicking on the filter dropdown arrow provides methods to filter the main variable.