View source for Calculating the Production Focus score using data exports

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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  ==
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.

== Process/Steps to calculate production focus score ==
To calculate the production focus score, we need the following values: 
# '''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)
# '''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

[[File:Production focus score screenshot 1.jpg|border|1200x1200px]]

===== Calculate '''Expected production hours (Pe)''' =====
'''Mathematical Formula -'''

'''Expected production hours (Pe)''' = '''Total''' '''Expected hours''' * '''Production %'''

'''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). 

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

'''Pa''' can be calculated by summing the production time of the user for a specific day.

The formula to calculate the score is '''=IF(Pa>(1.2*Pe),10,IF(Pa<Pe,(Pa/Pe)*8,10*Pa/Pe-2))'''

'''Steps to calculate the score using data export–'''

The following provides the steps to calculate score using the weekly data export of the user mentioned in above screenshot. ''' '''The ex

1.     Create a new pivot in a new sheet.

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 –

== Results ==
What do you expect the user to see or be able to do after they complete this design pattern?