Forum Discussion
Anonymous5
6 years agoRegular Visitor
Analyzing PO Performance
Hi all, I’m new to the community and new to Power BI. I am truly excited about the capabilities of this program for our company. My first attempt at using PBI will be to help our Procurement gro...
Anonymous5
6 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 |
Anonymous
6 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.