Capturing Data on Buyer Messages

I used an Add-In query to capture Invoice Messages data monthly at my previous company. I queried MAINVMSG and then compiled statistics by Buyer and the actions they took in regards to the messages.

We have used 2 tables the BUYER and the MAINVMSG to show by buyer code the time taken to resolve a buyer message by taking the difference between mainvmsg.create date and the respond date. You can get this information by the buyer code as well as the auth code and operator on the mainvmsg table.

I look at two sets of reports for my team.

I have a daily report that runs each morning to show me a snapshot of all open cost messages (my particular focus) and their age. We differentiate between Buyers and Agreement Administrators (PO25). So my report is a cross-tab showing Total Open Messages, Average Age and Maximum Age for each intersection of Buyer and Agreement Admin.

I also run a report to review how many messages were created on each day. This is a YTD look-back report. There is a caveat to this report...if AP deletes an invoice and rekeys the values correctly, it drops from my report. So, looking at a specific date, the numbers can drop a bit here and there over time, but they generally stabilize after 30 days.

Both reports run from a combination of seven tables, which I can detail if desired.

Good luck,
Red

I use MSA. Query MAINVMSG with criteria Create Date >12/31/2010 and Status = 9 (closed). Subtract the Create Date from the Respond Date to determine the number of days the message was open. Divide that by the total number of messages and derive the average number of days it takes to respond to the message. I good target is 5 days or less. A great target is 3 days or less. It's helpful to plot results on a line graph.

We do what everyone has posted here but break it out by a range of days- 0 to 3 days, 4 to 7 days, 8 to 14 days, 14 to 21 days, 22 to 28 days, and 29+ days. I graph the data in a stacked bar graph by month. The 0-3 day range gets a light shade of blue and the shade gets darker as the day range gets longer. We then get a visual of how quickly the buyers are answering cost messages. The lighter in color their graph is the faster they're answering.