enter data via a portal , how can you possibly scale this ?”
This , she says , is where the importance of dynamic supplier data comes in . The traditional way to collect such data was to use specialised services to clean , categorise , and supplement it . LaPierre points out that the trouble with this approach is the data tends to “ decay as soon as you receive it ”.
Another way to use dynamic data is to set up a portal in which suppliers can enter and update any relevant information . “ The problem is , many suppliers simply won ’ t do this at the speed or scale you need ,” LaPierre says .
She goes on to explain that the main strength of data harvested dynamically using AI and ML is that it “ enriches data in an automated way and gives you complete , quality data across 100 % of your suppliers ”.
She adds : “ Automated dynamic data reduces the dependency on services and on humans to maintain information across software solutions , and reduces the dependency on suppliers to visit multiple portals .
“ To achieve digital procurement success , organisations absolutely need quality and complete supplier information at the speed , scale and visibility that meet these requirements . Tier one suppliers are already a challenge , but now we ' re talking about tiers two and three , as well as Scope 3 emissions .
“ Once you turn the light onto your master data , you can see duplications , categories where too many suppliers are very similar , and where you should be driving consolidation and compliance among existing suppliers .
supplychaindigital . com 107