Forum Discussion
Analyzing PO Performance
Hi Nate,
Which text are you referring to? All I've done so far is merge the tables using Excel Power Query and removed all the non-relevant columns for the sake of displaying here.
Rob
- Anonymous6 years agoNot applicable
Sorry, I mean the text from Excel, instead of a screenshot. Then I can copy and paste that as a source in Power BI.
- Anonymous56 years agoRegular Visitor
Nate,
Here you go. I've never posted an entire table before so let me know if it's not what you're after. Below is the first 30 rows of the table.
PO_NUMBERRFQ.CREATED_DATERFQ.APPROVED_DATEFISCAL_EFFECTIVE_DATELAST_RECEIPT_DATELAST_DELIVERY_DATE
1118979 12/20/2018 12/28/2018 1/1/2019 2/26/2019 3/24/2019 1118980 12/31/2018 12/31/2018 1/1/2019 1/16/2019 1/26/2019 1118989 12/31/2018 1/1/2019 1/1/2019 1/9/2019 1/19/2019 1119003 11/19/2018 1/2/2019 1/2/2019 1/23/2019 2/20/2019 1118878 12/12/2018 12/20/2018 1/2/2019 3/28/2019 6/17/2019 1118877 12/19/2018 12/19/2018 1/2/2019 1/17/2019 1/30/2019 1118990 12/26/2018 1/1/2019 1/2/2019 4/10/2019 4/28/2019 1118996 12/26/2018 12/31/2018 1/2/2019 2/14/2019 2/23/2019 1118997 1/2/2019 1/2/2019 1/2/2019 1/10/2019 1/16/2019 1119025 4/3/2018 12/30/2018 1/3/2019 1/29/2019 2/26/2019 1119012 10/4/2018 1/2/2019 1/3/2019 5/21/2019 5/21/2019 1119005 11/14/2018 1/2/2019 1/3/2019 4/1/2019 4/30/2019 1119022 12/17/2018 1/1/2019 1/3/2019 2/25/2019 10/9/2019 1119013 12/19/2018 1/2/2019 1/3/2019 1/22/2019 3/6/2019 1119014 12/20/2018 2/18/2019 1/3/2019 1119021 12/21/2018 1/1/2019 1/3/2019 1/18/2019 1/20/2019 1119029 12/24/2018 1/2/2019 1/3/2019 1/9/2019 1/15/2019 1119024 12/24/2018 1/2/2019 1/3/2019 1/11/2019 1/15/2019 1119030 12/24/2018 1/2/2019 1/3/2019 1/10/2019 1/16/2019 1119010 12/26/2018 1/2/2019 1/3/2019 1/9/2019 1/16/2019 1119023 12/28/2018 1/1/2019 1/3/2019 1/18/2019 1/20/2019 1118992 12/31/2018 1/2/2019 1/3/2019 1/31/2019 2/19/2019 1119006 12/31/2018 1/2/2019 1/3/2019 2/7/2019 2/21/2019 1119006 12/31/2018 1/2/2019 1/3/2019 2/7/2019 2/21/2019 1119028 12/31/2018 1/1/2019 1/3/2019 1/14/2019 1/23/2019 1119027 12/31/2018 1/1/2019 1/3/2019 1/10/2019 1/23/2019 1119027 12/31/2018 1/1/2019 1/3/2019 1/10/2019 1/23/2019 1119027 12/31/2018 1/1/2019 1/3/2019 1/10/2019 1/23/2019 1119019 12/31/2018 1/3/2019 1/3/2019 1/9/2019 1/28/2019 - Anonymous6 years agoNot applicable
Anonymous5 - You could use something like the attached pbix file. There are several steps to consider with this solution:
- Create a Date table with an M (Power Query) script.
- Use a lookup table to identify the previous event.
- In the PO table:
- Unpivot the dates - this will allow you to easily compare values within a single measure.
- Find the next event date by looking up the previous event, and merging table with itself.
- Do the same for "days since create date".
- Calculate the number of days between event and previous event, and created date to event.
- Create a parameters table to allow user to choose between analyzing
- Days from creation to event or days since previous event.
- Which event's date (for instance, quarter) is being analyzed - create, previous event, or event.
- Create an Event lookup table, with the purpose of ordering the events.
- Create relationships between the Date table and PO table, and between the Event table and the PO table.
- Create measures which take into account the selections from the Parameters table.
- Create the visualizations
Note: The parameters table could be eliminated and the measures simplified, if only a single type of analysis makes sense.
