Today’s post is from Ryan Melowic, Assistant Vice President, Procurement COE at SafeSourcing.
My experenience with eProcurement RFQ reporting is that procurement team’s eyes are often bigger than their stomachs. I say that because there are lots of numbers gathered during an eProcurement RFQ. That data can be sliced and diced many different ways in order to show many different scenarios. What procurements teams need from an eProcurment RFQ is just a few options.
One option is to look at all Low Qoutes. What this identifies is how many vendors would need to be awarded in order to relize maximum savings. AS an example when a company has 81 current suppliers and they can now award 14 companies in order to maximize savings it makes good sense to explore this oppritunity because 14 suppliers represents an 83% reduction in sources of supply and the administrative improvements associated with that reduction..
A second option to consider is that of Low Company. This provides a single source of supply option. In some cases it’s just plane scarey to put all of a companies eggs in one basket. In most cases this option is a reduction in savings compared to the all low quote scenario.
A third option to consider is a hybread award scenario. This takes into consideration low quotes, overall low companies and potential another option like by division or number of vendors. Typically, the top preformers are defined globally and then an analisys is conducted by location. This data shows which of the top companies show the most savings by location.
A final option might be to identify what saving looks like when vendors are selected by division and a split award is considered. For instance if a company has 10 locations 3 locations are awarded to one vendor and the remaining to another. When you do this type of scenario analisys savings numbers are typically reduced.
When you apply the Law of Diminishing return to eProcurement RFQ reporting please keep in mind anything outside of what procurement teams need to make the award descion is an over abundance of data.
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