Hi All
Hope you all are well
I have 2 sets of data where agents are scheduled to do an activity and another set of data of what agents actually did
I have listed a set of start and stop times for a paticular agent on that day
Agents are assigned to a group so there could be 10 agents in group 1 and 10 agents in group 2 etc
What im trying to check is to see if the 80% of the group have logged on the time they should have done and 80% of the group have logged off when they should have done but im struggling with a formula that gives me that information
The other problem i have is that agents some time could leave themselves logged on ie should have logged at 16:00 but system logged them out out at midnight. On those scenarios i want to highlight how many times in that group they left them selves logged in but in terms of whether the group had logged off - i want it to go against them as they should have logged off and this will bring the right behaviours
Can you please help me with a formula that gets me this out put
Here is an example of my data set
Schedule
Date ID start stop Time Group StartShift EndShift
09/04/2018 1117779 09:00 14:00 5 Group 1 09:00
09/04/2018 1117779 14:00 14:30 0.5 Group 1
09/04/2018 1117779 14:30 17:00 2.5 Group 1 17:00:00
10/04/2018 1117779 09:00 14:00 5 Group 1 09:00
10/04/2018 1117779 14:00 14:30 0.5 Group 1
10/04/2018 1117779 14:30 17:00 2.5 Group 1 17:00:00
11/04/2018 1117779 09:00 14:00 5 Group 1 09:00
11/04/2018 1117779 14:00 14:30 0.5 Group 1
11/04/2018 1117779 14:30 17:00 2.5 Group 1 17:00:00
12/04/2018 1117779 09:00 14:00 5 Group 1 09:00
12/04/2018 1117779 14:00 14:30 0.5 Group 1
12/04/2018 1117779 14:30 17:00 2.5 Group 1 17:00:00
13/04/2018 1117779 09:00 14:00 5 Group 1 09:00
13/04/2018 1117779 14:00 14:30 0.5 Group 1
13/04/2018 1117779 14:30 16:30 2 Group 1 16:30:00
16/04/2018 1117779 09:00 14:00 5 Group 2 09:00
16/04/2018 1117779 14:00 14:30 0.5 Group 2
16/04/2018 1117779 14:30 17:00 2.5 Group 2 17:00:00
17/04/2018 1117779 09:00 14:00 5 Group 2 09:00
17/04/2018 1117779 14:00 14:30 0.5 Group 2
17/04/2018 1117779 14:30 17:00 2.5 Group 2 17:00:00
Actual
Date ID start stop Time Group StartShift EndShift
04/05/2018 1117779 11:14 15:52 4.633333333 Group 2 11:14 15:52
09/05/2018 1117779 09:59 10:10 0.183333333 Group 1 09:59
09/05/2018 1117779 10:10 12:17 2.116666667 Group 1
09/05/2018 1117779 12:17 12:23 0.1 Group 1
09/05/2018 1117779 12:23 12:26 0.05 Group 1
09/05/2018 1117779 12:26 12:29 0.05 Group 1
09/05/2018 1117779 12:29 12:33 0.066666667 Group 1
09/05/2018 1117779 12:33 13:20 0.783333333 Group 1 13:20
09/05/2018 1117780 13:28 13:34 0.1 Group 2 13:28
09/05/2018 1117781 13:34 13:37 0.05 Group 2
09/05/2018 1117782 13:37 13:43 0.1 Group 2
09/05/2018 1117783 13:43 13:46 0.05 Group 2
09/05/2018 1117784 13:46 13:50 0.066666667 Group 2
09/05/2018 1117785 13:50 13:54 0.066666667 Group 2
09/05/2018 1117786 13:54 13:56 0.033333333 Group 2
09/05/2018 1117787 13:56 13:59 0.05 Group 2
09/05/2018 1117788 13:59 14:00 0.016666667 Group 2
09/05/2018 1117789 14:00 14:30 0.5 Group 2
09/05/2018 1117790 14:30 14:31 0.016666667 Group 2 14:31
Hope you all are well
I have 2 sets of data where agents are scheduled to do an activity and another set of data of what agents actually did
I have listed a set of start and stop times for a paticular agent on that day
Agents are assigned to a group so there could be 10 agents in group 1 and 10 agents in group 2 etc
What im trying to check is to see if the 80% of the group have logged on the time they should have done and 80% of the group have logged off when they should have done but im struggling with a formula that gives me that information
The other problem i have is that agents some time could leave themselves logged on ie should have logged at 16:00 but system logged them out out at midnight. On those scenarios i want to highlight how many times in that group they left them selves logged in but in terms of whether the group had logged off - i want it to go against them as they should have logged off and this will bring the right behaviours
Can you please help me with a formula that gets me this out put
Here is an example of my data set
Schedule
Date ID start stop Time Group StartShift EndShift
09/04/2018 1117779 09:00 14:00 5 Group 1 09:00
09/04/2018 1117779 14:00 14:30 0.5 Group 1
09/04/2018 1117779 14:30 17:00 2.5 Group 1 17:00:00
10/04/2018 1117779 09:00 14:00 5 Group 1 09:00
10/04/2018 1117779 14:00 14:30 0.5 Group 1
10/04/2018 1117779 14:30 17:00 2.5 Group 1 17:00:00
11/04/2018 1117779 09:00 14:00 5 Group 1 09:00
11/04/2018 1117779 14:00 14:30 0.5 Group 1
11/04/2018 1117779 14:30 17:00 2.5 Group 1 17:00:00
12/04/2018 1117779 09:00 14:00 5 Group 1 09:00
12/04/2018 1117779 14:00 14:30 0.5 Group 1
12/04/2018 1117779 14:30 17:00 2.5 Group 1 17:00:00
13/04/2018 1117779 09:00 14:00 5 Group 1 09:00
13/04/2018 1117779 14:00 14:30 0.5 Group 1
13/04/2018 1117779 14:30 16:30 2 Group 1 16:30:00
16/04/2018 1117779 09:00 14:00 5 Group 2 09:00
16/04/2018 1117779 14:00 14:30 0.5 Group 2
16/04/2018 1117779 14:30 17:00 2.5 Group 2 17:00:00
17/04/2018 1117779 09:00 14:00 5 Group 2 09:00
17/04/2018 1117779 14:00 14:30 0.5 Group 2
17/04/2018 1117779 14:30 17:00 2.5 Group 2 17:00:00
Actual
Date ID start stop Time Group StartShift EndShift
04/05/2018 1117779 11:14 15:52 4.633333333 Group 2 11:14 15:52
09/05/2018 1117779 09:59 10:10 0.183333333 Group 1 09:59
09/05/2018 1117779 10:10 12:17 2.116666667 Group 1
09/05/2018 1117779 12:17 12:23 0.1 Group 1
09/05/2018 1117779 12:23 12:26 0.05 Group 1
09/05/2018 1117779 12:26 12:29 0.05 Group 1
09/05/2018 1117779 12:29 12:33 0.066666667 Group 1
09/05/2018 1117779 12:33 13:20 0.783333333 Group 1 13:20
09/05/2018 1117780 13:28 13:34 0.1 Group 2 13:28
09/05/2018 1117781 13:34 13:37 0.05 Group 2
09/05/2018 1117782 13:37 13:43 0.1 Group 2
09/05/2018 1117783 13:43 13:46 0.05 Group 2
09/05/2018 1117784 13:46 13:50 0.066666667 Group 2
09/05/2018 1117785 13:50 13:54 0.066666667 Group 2
09/05/2018 1117786 13:54 13:56 0.033333333 Group 2
09/05/2018 1117787 13:56 13:59 0.05 Group 2
09/05/2018 1117788 13:59 14:00 0.016666667 Group 2
09/05/2018 1117789 14:00 14:30 0.5 Group 2
09/05/2018 1117790 14:30 14:31 0.016666667 Group 2 14:31