Specifically, start with auto-buy on your tail-spend and non-strategic spend.

Seriously. If you’re a relatively mature organization using SSDO (Strategic Sourcing Decision Optimization) on your higher dollar or strategic categories, using auctions and RFX for mid-dollar and somewhat strategic categories, and GPOs or catalogs for significant spend categories, there’s still one category of spend that’s costing your organization a small fortune. That spend is tail spend. Up to 30% in some organizations, the average overspend is typically 15% or more (and can be up to 20% or 30%). Do the math in the typical case. Fifteen percent of thirty percent is 4.5%. If you’ve tackled your strategic sourcing categories two or three times now, chances are you’re trying to eek out 6% savings on the top 33% of spend. That’s about 2% savings.

What’s costing you more? If you’re an advanced or leading organization – the tail spend. But it’s not something you can do much about — it’s tail spend because you don’t have the manpower to deal with it. Yes, you can put a GPO or catalog in place, but it only works if you can force buyer to not only use it, but always select the right product when there are multiple options — not something most platforms can do (well). Especially if the preferred option is temporarily out of stock and something is needed tomorrow. (And the what’s the second preferred option? The third? And if it’s common, shouldn’t it be in inventory?)

And, more importantly, since most tail spend consists of individual requests, some of which should be aggregated, if the requests are directed to different buyers, how will they ever know if there are requests that should be aggregated? (They won’t. And that’s how it is.)

So why are your people even trying to manage parts of the tail-spend when, in fact, a modern AI platform can do it much better. It can amalgamate all similar requests, analyze usage trends, gather market prices, scour and compare options in your catalog and your GPO’s master contract, identify third party options available on the network, analyze usage and feedback reviews and data, determine the options that best meet your users’ needs, and select the one that offers the best value (lowest cost against reliability against organizational need) at a cost that doesn’t exceed market cost. So even if it doesn’t get the best deal, it at least ensures you don’t pay more than market price across your tail spend, which is 15% better than you are doing today.

So now that we have systems — including, but not limited to, Dhatim, LevaData, and Xeeva — that can auto-buy, it’s time to find one that works for you and get the tail spend under control. (And use them to recommend options for higher-value and more-strategic buys that you might not come up with on your own.)