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Calculating the Production Focus score using data exports
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{{Design pattern|Title=Calculate Production Focus score using weekly/monthly data exports|Description=How to calculate Production Focus score using weekly/monthly data exports|Version=8.5|Applications=Pega Workforce Intelligence|Capability Area=Workforce Intelligence|Owner=Agrawal, anurag}}
  
|Request to Publish=
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Workforce Intelligence compares the actual production hours accumulated by a department or team or associates to their expected hours, to calculate a Production Focus score.
  
|Curator Assigned=
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Clients like to combine and compare weekly or monthly aggregate data exports with additional (external) metrics generated by third-party systems. For example, they would like to compare Production Focus score with Claims productivity metrics. Using the weekly or monthly aggregate data exports, they can calculate the Workforce Intelligence Production Focus score and use/compare it with additional (external) metrics.
|Description=Calculate production focus score using weekly/monthly data exports
 
|Applications=Workforce Intelligence
 
|Capability Area=Custom reporting
 
|Version=8.5
 
 
 
}}
 
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== Description ==
 
Workforce Intelligence compares the actual production hours accumulated by a department or team or associates to its expected hours, to calculate a production focus score.
 
 
 
== Use case examples ==
 
Clients like to combine and compare weekly or monthly aggregate data exports with additional (external) metrics generated by third party systems. For example, they would like to compare Production Focus score with Claims productivity metrics. Using the weekly or monthly aggregate data exports, they can calculate the WFI production focus score and use/compare it with additional (external) metrics.
 
  
 
== Before you begin  ==
 
== Before you begin  ==
It is important to download and save the weekly or monthly aggregate data export files. The file can be accessed either from the exports page '''(Analysis -> Exports)''' or by leveraging export API to download them. The clients should have access to either MS Excel or other BI tools to calculate the production focus score. The production focus score in this article is calculated using MS Excel.
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It is important to download and save the weekly or monthly aggregate data export files. The file can be accessed either from the exports page '''(Analysis -> Exports)''' or by leveraging the export API to download them. The clients should have access to either MS Excel or other BI tools to calculate the Production Focus score. The Production Focus score in this article is calculated using MS Excel.
  
== Process/Steps to calculate production focus score ==
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=== Understand the terms required for Production Focus calculation ===
To calculate the production focus score, we need the following values:  
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To calculate the Production Focus score, we need the following values:
 
# '''Pa''' – Actual production hours (recorded by Pega Robot Runtime)
 
# '''Pa''' – Actual production hours (recorded by Pega Robot Runtime)
 
# '''Pe''' – Expected production hours (calculated by Workforce Intelligence based on the expected hours and production percentage target)
 
# '''Pe''' – Expected production hours (calculated by Workforce Intelligence based on the expected hours and production percentage target)
 
# '''Production %''' - The percentage of time spent in production applications and websites during the expected hours or shift.
 
# '''Production %''' - The percentage of time spent in production applications and websites during the expected hours or shift.
The data export provides the Total Expected Hours value of a user for a given calendar day
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'''Note:''' ''The data export provides the Total Expected Hours value of a user for a given calendar day.''
  
[[File:Production focus score screenshot 1.jpg|border|1200x1200px]]
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Sample Screenshot from the Summary page highlighting the values required for calculation:
  
===== Calculate '''Expected production hours (Pe)''' =====
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[[File:Production focus score screenshot 1 alt.jpg|border|frameless|1200x1200px]]
'''Mathematical Formula -'''
 
  
'''Expected production hours (Pe)''' = '''Total''' '''Expected hours''' * '''Production %'''
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===== Calculate Expected production hours (Pe) =====
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'''Mathematical Formula -''' '''Total''' '''Expected hours''' * '''Production % = Expected production hours (Pe)'''
  
 
'''Example -'''  
 
'''Example -'''  
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In the above screenshot, the total expected hours is '''9.5h.''' This value should be multiplied by '''production %''' which is set to '''0.5''' (by Workforce Intelligence administrator).  
 
In the above screenshot, the total expected hours is '''9.5h.''' This value should be multiplied by '''production %''' which is set to '''0.5''' (by Workforce Intelligence administrator).  
  
After multiplying these two values, the expected production hours ('''Pe)''' is '''4.8h'''
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'''Expected production hours (Pe) = 9.5''' * '''0.5'''  
  
'''Pa''' can be calculated by summing the production time of the user for a specific day.
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After multiplying the two values, the expected production hours ('''Pe)''' is '''4.8h'''
  
The formula to calculate the score is '''=IF(Pa>(1.2*Pe),10,IF(Pa<Pe,(Pa/Pe)*8,10*Pa/Pe-2))'''
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Actual production hours ('''Pa''') can be calculated by summing the production category time of the user for a specific day.
  
'''Steps to calculate the score using data export–'''
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===== Calculate Production Focus Score =====
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'''Mathematical Formula = IF(Pa>(1.2*Pe),10,IF(Pa<Pe,(Pa/Pe)*8,10*Pa/Pe-2))'''
  
The following provides the steps to calculate score using the weekly data export of the user mentioned in above screenshot. ''' '''The ex
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== Prepare the data export file to calculate the Production Focus score ==
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Use the following steps to calculate the score using the weekly data export of the user mentioned in above screenshot:
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# Select the data and create a new pivot in a new sub-sheet.
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#* Add '''shift_date,''' '''user_name''' and '''category''' fields under the Rows section of the pivot.
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#* In the pivots, value column, add the following fields -
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#** Sum of '''time_seconds''' field
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#*** This will provide the total time for a specific day, for a specific user and by different categories
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#*** Rename the column to '''Total Time (secs)'''
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#** Max Value of '''expected_time_seconds''' field
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#*** The data export provides the total expected time, which is same for specific day, specific user and for every category.
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#*** Rename the column to '''Total Expected Time (secs)'''
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# Store the '''Total Expected Time (secs)''' value in a new cell.
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#* You can convert this value from secs to hrs. in a new cell.
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# Calculate the '''Expected Production Hours (Pe)''' by multiplying the '''Total Expected Time (secs)''' value with '''production %''' (production % is 0.5 in this example)
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#* You can convert this value from secs to hrs. in a new cell.
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# Store the '''Actual Production hours (Pa)''' and '''Expected Production Hours (Pe)''' in new cells.
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# Use the '''Production Focus Score''' formula to calculate the production score.
  
1.     Create a new pivot in a new sheet.
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== Results ==
 
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The whole setup should look like this:
a.     Add shift_date, user_name and category columns in the Rows section
 
 
 
b.     In the values column –
 
 
 
                                                            i.     Summation of time_seconds – This will provide the total time for a specific day, for a specific user and by different categories. Rename the column to Total Time (Secs)
 
 
 
                                                           ii.     Max Value of expected_time_seconds (The data export provides the total expected time which is same for specific day, specific user and for every category). Rename the column to Total Expected Time (secs)
 
 
 
2.     Store the Total Expected Time value in a new cell.
 
 
 
3.     Calculate the Expected Production Hours (Pe) by multiplying the value with production percentage target (Production percentage target is 0.6 in this case)
 
 
 
4.     Store the Production Total Time (Pa) and Expected Production Hours (Pe) in new cells.
 
 
 
5.     Use the above formula to calculate the score.
 
  
The whole setup should look like this –
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[[File:Production focus score screenshot.jpg|border|frameless|1350x1350px]]
  
== Results ==
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== Additional resources ==
What do you expect the user to see or be able to do after they complete this design pattern?
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For more information, see
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* [http://help.openspan.com/wi30/concepts/understanding_scoring.htm?rhsearch=scores&rhhlterm=scoring%20scores%20score Production Scoring]
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* [http://help.openspan.com/wi30/concepts/understanding_expected_hours.htm?rhsearch=expected%20hours&rhhlterm=expected%20hours Expected Work Hours]

Latest revision as of 17:48, 7 June 2021

Calculate Production Focus score using weekly/monthly data exports

Description How to calculate Production Focus score using weekly/monthly data exports
Version as of 8.5
Application Pega Workforce Intelligence
Capability/Industry Area Workforce Intelligence



Workforce Intelligence compares the actual production hours accumulated by a department or team or associates to their expected hours, to calculate a Production Focus score.

Clients like to combine and compare weekly or monthly aggregate data exports with additional (external) metrics generated by third-party systems. For example, they would like to compare Production Focus score with Claims productivity metrics. Using the weekly or monthly aggregate data exports, they can calculate the Workforce Intelligence Production Focus score and use/compare it with additional (external) metrics.

Before you begin

It is important to download and save the weekly or monthly aggregate data export files. The file can be accessed either from the exports page (Analysis -> Exports) or by leveraging the export API to download them. The clients should have access to either MS Excel or other BI tools to calculate the Production Focus score. The Production Focus score in this article is calculated using MS Excel.

Understand the terms required for Production Focus calculation

To calculate the Production Focus score, we need the following values:

  1. Pa – Actual production hours (recorded by Pega Robot Runtime)
  2. Pe – Expected production hours (calculated by Workforce Intelligence based on the expected hours and production percentage target)
  3. Production % - The percentage of time spent in production applications and websites during the expected hours or shift.

Note: The data export provides the Total Expected Hours value of a user for a given calendar day.

Sample Screenshot from the Summary page highlighting the values required for calculation:

Production focus score screenshot 1 alt.jpg

Calculate Expected production hours (Pe)

Mathematical Formula - Total Expected hours * Production % = Expected production hours (Pe)

Example -

In the above screenshot, the total expected hours is 9.5h. This value should be multiplied by production % which is set to 0.5 (by Workforce Intelligence administrator).

Expected production hours (Pe) = 9.5 * 0.5

After multiplying the two values, the expected production hours (Pe) is 4.8h

Actual production hours (Pa) can be calculated by summing the production category time of the user for a specific day.

Calculate Production Focus Score

Mathematical Formula = IF(Pa>(1.2*Pe),10,IF(Pa<Pe,(Pa/Pe)*8,10*Pa/Pe-2))

Prepare the data export file to calculate the Production Focus score

Use the following steps to calculate the score using the weekly data export of the user mentioned in above screenshot:

  1. Select the data and create a new pivot in a new sub-sheet.
    • Add shift_date, user_name and category fields under the Rows section of the pivot.
    • In the pivots, value column, add the following fields -
      • Sum of time_seconds field
        • This will provide the total time for a specific day, for a specific user and by different categories
        • Rename the column to Total Time (secs)
      • Max Value of expected_time_seconds field
        • The data export provides the total expected time, which is same for specific day, specific user and for every category.
        • Rename the column to Total Expected Time (secs)
  2. Store the Total Expected Time (secs) value in a new cell.
    • You can convert this value from secs to hrs. in a new cell.
  3. Calculate the Expected Production Hours (Pe) by multiplying the Total Expected Time (secs) value with production % (production % is 0.5 in this example)
    • You can convert this value from secs to hrs. in a new cell.
  4. Store the Actual Production hours (Pa) and Expected Production Hours (Pe) in new cells.
  5. Use the Production Focus Score formula to calculate the production score.

Results

The whole setup should look like this:

Production focus score screenshot.jpg

Additional resources

For more information, see