As we said yesterday, hopefully we have made it clear by now that most of the time you hear AI you should think “Applied Indirection” and not “Any form of Intelligence” because most solutions claiming to be AI are really just dumb systems with RPA (robotic process automation) and classic statistical models from the 90’s (which were available in SAS in the 90’s as well, you just didn’t have enough memory on your PC to run all the data you wanted to run).

But since we want to make it abundantly clear that most of the “AI”, even in our space, is not “AI” at all, we are going to continue to take the major areas of SPT (Strategic Procurement Technology) and highlight some areas where AI is commonly claimed, but rarely found, continuing with Sourcing.

This doesn’t meant that there aren’t vendors with true AI, especially when you classify it as Assisted Intelligence (and sometimes even Augmented Intelligence), in the space, just that, as the buzz-acronym reaches new heights, there will be many more vendors claiming AI than those that actually have AI and you will need to do your homework to find out which is which.

Example #1 of Applied Indirection in Sourcing: Automated Auctions

Some of the shiny new sourcing platforms are really slick and can run an automated auction and do everything from the time you do a product/service selection all the way to final award recommendation. Now, I’m sure you’re thinking such a platform must be at least on the order of augmented intelligence, approaching cognitive, to do all this, but the reality is that you can do all this with simple RPA and a rules-driven workflow. Supplier selection? Just select the past suppliers and pre-approved suppliers from the last sourcing event. RFIs? Use the template with the standard terms and conditions. Ceiling prices? Use current price or current market price if the price has been relatively flat for the past year or falling. Floor prices? Pull in the should-cost model, marked up by a fair margin, if it is available, or use the lowest of the lowest paid historical price and lowest market advertised minus the typical savings percentage in the category. Minimum Decrement? 1%, rounded to three significant digits. Pre-populated bids? Current prices or last bids or advertised market price. Duration? Standard auction duration for the category. And so on. It’s literally just a set of rules, with tolerances, and RPA.

There’s only AI if the platform can run a sophisticated market and category analysis on internal, external, and automatically identified market data, identify a prime set of products in a category for an automated auction, determine the appropriate contract length (and projected demand) to result, and basically take a bunch of the analysis off of your plate for non-strategic / low-value, regular, purchases.

Example #2 of Applied Indirection in Sourcing: RFX Auto-Fill

One of the most time-consuming parts of the Sourcing process is waiting for the suppliers to fill out the RFIs with not just the bids but all of the other information you require. However, there’s no reason that a lot of this information can’t come from their profile, their catalog, and previous RFI responses (where you asked the same questions).

It doesn’t take AI to encode meta-data mappings between profile fields and standard RFI fields, catalog fields and standard RFI fields, and re-use of the same question across RFIs (and previous responses) to pre-fill the majority of an RFI and simply require the supplier to confirm and enter new, additional, or changed data (and, of course, check the boxes that say they accept the T’s & C’s, verify the data, and confirm their bid).

Unless the platform can seek out additional data on the supplier’s web-site, third party directories, third party audit sites, and process semi-structured and un-structured data using natural language processing and identify and extract new data and new information relevant to the RFI (and the sourcing event), it likely doesn’t have anything close to AI (even in AI’s weakest form).

Example #3 of Applied Indirection in Sourcing: Outlier Identification

Here’s another capability that doesn’t require anything close to AI. Statistical algorithms to identify outliers have existed for decades and decades. Many even pre-date modern computers. Run a simple mean/modal comparison, do a simple clustering maybe even use a regression. Easy-Peasy. Even Kitty can do it!

The trick is to identify outliers that aren’t easily identifiable mathematical outliers. A bid in range that is not sustainable for a supplier because the bids were supposed to be landed cost bids (and included shipping as the buyer wasn’t taking possession until the goods hit the warehouse) and one supplier’s bids clearly didn’t, when you think about it, is the type of outlier we want the system to detect for us.

If four of the suppliers are near-shore, and the cost of shipping for them on a unit basis is typically 10% of unit cost, but a fifth supplier is offshore, and the cost of shipping for that supplier by unit is typically 30% of unit cost, even if the bid looks okay, it might not be. For example, if the bids for all the nearshore suppliers are between $100 and $120, their actual unit prices are about $90 to $110. If the off-shore supplier, with a shipping cost around $30, comes in at $100, a mathematical outlier algorithm won’t detect that it’s underlying unit bid is roughly $20 less than the lowest bid, and if the driving costs are expensive raw materials, and the should cost model for that supplier indicates a production cost of $80 (even though their production and overhead costs are significantly less), then a bid of $100 is unsustainable and an outlier (or will not be honoured as the supplier misunderstood and expected to bill shipping separately). If the platform truly has assisted intelligence, it should detect this, even when a human doesn’t. (While you always want the lowest cost, you want the lowest sustainable cost — it doesn’t do you any good if the supplier goes bankrupt halfway through the contract and you have to scramble to find another.)

Note that SI is not saying that systems with the non-AI abilities discussed above are not valuable, as any system that automates tactical processes and minimizes non-strategic busy work is valuable. We are just saying you shouldn’t pay for what you’re not getting, or overpay for what you are. Buy what you need, and pay accordingly.