AI: Applied Indirection in Supplier Discovery & Management

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 we want to make it abundantly clear that most of the “AI”, even in our space, is not “AI” at all. So, to do this, we’re going to take the major areas of SPT (Strategic Procurement Technology) and highlight some areas where AI is commonly claimed, but rarely found, starting with supplier discovery and management.

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: New Supplier Identification

A true assisted intelligence system will scour a database, network, etc. and identify potential suppliers based on common product categories, like production or service capabilities, and community profiles and use some fuzzy logic* and adaptive modelling to make recommendations that you might not even thought about.

In contrast, many systems that claim to be AI will simply use SKU, key word, or strict sub-category meta-data matching to suggest the same suppliers over and over again, most of which you’ll already know as these will be the ones coded in the database or network to meet a particular demand. That’s not AI, that’s just multi-faceted search.

Example #2 of Applied Indirection: Auto-Profile Completion

Many systems that claim to be AI will simply use meta-data to map profiles from one system to another where a mapping between the field names and types exist (in a canned profile) and the data types are compatible. That is just ETL that has existed for over two decades, with good RPA behind it to identify the right mapping file and deal with exceptions appropriately.

In contrast, a true assisted intelligence system will be able to automatically construct mapping profiles from a new supplier record in a new system to the current system based on meta-meta data, automatically identify missing data, and automatically identify that data in semi-structured / un-structured text in a supplier description or overview from the supplier’s page on a directory or their “about-us” site using state-of-the-art NLP (natural language processing) technology, only asking a human to intervene and approve this automatically identified data if the probability of accuracy is not sufficiently high or to manually enter data (or contact the supplier for such data) when such data is not easily found.

Example #3 of Applied Indirection: Auto-Issue Identification

Many modern supplier management systems can automatically identify supplier-related issues, notify you with an alert, and even kick off a corrective action management process with a single click. But in most systems, this is not AI by any stretch of the imagination, not even close. It’s just RPA and classical statistical trend analysis in the best case, and simple rules and workflow in the average case. After all, you can detect an issue if a defect rate in the latest shipment is above a tolerance, if an invoice is for five times the number of units, or the satisfaction survey is less than 80% with a simple arithmetical rule. And in a slightly more advanced system, if the OTD rate is on a downward trend that will drop below a minimally acceptable level within three shipments, and so on, a simple trend analysis will suffice. And kicking off a corrective action management process is just automatically starting a workflow. No AI, by any stretch of the imagination, is needed.

In comparison, if there was true assisted intelligence, the system would go beyond simple rules, trend analysis, and notice early deviations in typical performance by looking across standard metrics and surveys to spot outliers that might indicate a trend, augment this with sentiment analysis on recent buyer feedback, and see if there is any external data that could indicate a potential downward trend is coming (such as a lot of recent negative sentiment directed to the supplier’s twitter feed or an article indicating a natural disaster in the immediate vicinity of the supplier’s plant). A true assisted intelligence system will give you early warning of a potential issue so that an account manager can investigate, and if there is a potential issue, take action before it materializes, or at least mitigate the issue (such as a supply disruption) if it can’t be prevented.

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.

*Fuzzy Logic is a recognized area or discipline of mathematical study. The name of the domain was first proposed by Lotfi Zadeh in 1965, but it is actually an extension of infinite-valued logic that has been studied since the 1920s, by Lukasiewicz and Tarski, among others. And while such systems might not use this particular technique, they will use similar techniques that can use vague, or incomplete, or only partially matching data to derive conclusions and make recommendations with reasonable statistical probability.

AI: Applied Indirection Part III.B

Again, since we are in the situation where most claims of AI are just Applied Indirection to the lack of new technology being offered by the platform which is wrapping up old tech in a new UX with a little bit of RPA and, hopefully, better canned reporting and analytics, we are diving into the different levels of analytics to help you understand where AI might be and, more importantly, where it definitely isn’t. Because you don’t want to shell out six or seven figures (or more) for a “modern” solution that is actually only “modern” in the literary sense of the word (which defines the modernist period that started around 1900 and ended around 1965). And we’re not exaggerating here … some of the core statistical algorithms that form the foundation for a few of the bigger name analytic systems on the market date back to the 60s (and even 50s). (In other words, even the old grey beards who remember working on the last of the mainframes forty years ago wouldn’t have thought these techniques new back then.)

Yesterday we covered the first two levels of analytics. The next three are:

Level 3: Predictive

This is what most of the “advanced” analytic solutions on the market offer, predictive analytics, which, when you unwrap the messaging and peel off the fancy packaging, are simply statistical trend fitting and classic trend analysis algorithms that have existed in ERP for 20+ years and MRP for 30+ years. If the price is more-or-less going up according to a slight nonlinear curve, then the price is going to be predicted against the best non-linear curve the box-of-statistical-tricks can fit the data too. And so on. Again, not even a hint of AI here.

Level 4: Prescriptive

This is where AI in its weakest form MIGHT creep into the picture. The keyword here is MIGHT. You see, a prescriptive software application takes the results of a predictive analysis and makes recommendations on what you should do to improve the situation. However, there are two categories of recommendations here. The first category, which most of the applications are based on, is canned recommendations. For example, if the organization is currently spending over market price, prices are projected to go up, but demand still exceeds supply, the canned response will be an auction that invites the suppliers used in the past and highly rated alternative suppliers on the supplier network, as identified by community peers. No real intelligence, or even computation, there. The second category is dynamically computed recommendations, which may be based on a large set of rules or may actually use machine learning and dynamic computation and fall into assisted intelligence and actually make atypical recommendations when situations outside of the norm are detected due to unusual trend patterns or externally identified data (as per our example of web scraping in Part II).

Level 5: Permissive

A permissive system is a system that automatically executes a recommendation on your behalf but, contrary to manic marketing, is not autonomously intelligent. These systems are really just slick RPA (robotic process automation) systems that use a large rule base to drive workflows based upon whether or not recommendations are above a certain confidence interval, costs are within a certain bound, timelines are within reason, and so on (as configured by the vendor and the client on system implementation). More advanced systems will use analysis designed by experts to determine whether or not a certain recommendation can be automated, and then automate it with RPA if it can, and the most advanced — and these are extremely few and far between — will use Machine Learning that will record what a user does and then learn when a user is more than likely to take a certain response (based on past behavior) and when it can just begin to automate an action based on past behavior (and, in effect, define and modify it’s own automation rules). But the vast majority of systems still have no AI here whatsoever.

So, at the end of the day, while many vendors have sold their auto-classification, visibility, and prediction systems as AI — there was actually no AI under the hood and all the AI was applied indirection in the marketing organization. So, again, before buying such a system, be sure to apply a bit of logic and a sniff test. And if all you can smell is parfum de mouffette, you can be pretty sure there’s nothing there.

The Devil Went Down to Vegas …

The devil went down to Vegas
He was lookin’ for some souls to steal
He was in a bind
‘Cause he was way behind
And he was willin’ to make a deal

When he came upon this young man
Speaking on the big stage and workin’ the crowd
And the devil jumped
Up on a podium
And said, “boy, let me tell you what

I guess you didn’t know it
But I’m a keynote speaker too
And if you’d care to take a dare, I’ll make a bet with you

Now you work the grand stand like magic, boy
But give the devil his due
I’ll bet a statue of gold
Against your soul
‘Cause I think I’m better than you

The boy said, “my name’s Robert
And it might be a sin
But I’ll take your bet
And you’re gonna regret
‘Cause I’m the best there’s ever been

Robert, step up your game and work your magic hard
‘Cause hell’s broke loose in Vegas and the devil deals the cards
And if you win, you get this shiny statue made of gold
But if you lose, the devil gets your soul

The devil straightened up his tie
And he said “I’ll start this charade
And sparks flew from his fiery eyes
As he angled off his shades

Then he breathed his first into the mic
And it made an evil hiss
And a band of demons cheered him on
As the mark he never missed

When the devil finished
Robert said, “well, you’re pretty good, old son
But sit down in that chair right there
And let me show you how it’s done

He worked the crowd into hysteria
The devil’s in the house of the rising sun
When he was done, everyone was floating on air
Robert, will you save us? Yes, child, yes!

The devil bowed his head
Because he knew that he’d been beat
And he laid that golden statue
On the ground at Robert’s feet

Robert said, “Devil, just come on back
If you ever want to try again
I done told you once you son of a bitch
I’m the best that’s ever been

He worked the crowd into hysteria
The devil’s in the house of the rising sun
When he was done, everyone was floating on air
Robert, will you save us? Yes, child, yes!

 

And if you haven’t figured it out yet, Coupa Inspire ’19 starts today at the Cosmopolitan of Las Vegas. (And it won’t be long until their next dance video is out. For reference, here’s ’13, ’14, ’15, ’16, ’17, and ’18.)

Purchasing Blues (Repost)

It’s the first day of summer, so:

Click Here to sing along!

Well, it’s time to raise a fuss
and it’s time to raise a holler
About diminishing returns
from the corporate dollar
I just heard from my boss
who governs me
If I don’t save the cash
he’s gonna fire me

Sometimes I wonder
What I’m gonna do
If there ain’t no cure
For the purchasing blues

The buyer he told me to
go beat on the supplier
That his margins must be high
with ours under the wire
So I talked to the supplier
he said that costs were elevated
He was losing all his money
at the rates we had created

Sometimes I wonder
What I’m gonna do
If there ain’t no cure
For the purchasing blues

So I found a consultant
told her ’bout my problems
She discovered that
the supplier was just stalling
Material costs were falling
and the exchange rate was fair
I had wasted all my time
just pulling out my hair

Next time I have a problem
I’ll find me a solution
I’ll find a sourcing expert
and get my retribution

No more will I wonder
What I’m a-gonna do
I’ll find me a cure
For the purchasing blues

AI: Applied Indirection Part III

By now you probably get the point that most claims of AI are just Applied Indirection to the lack of new technology being offered by the platform which is wrapping up old tech in a new UX with a little bit of RPA and, hopefully, better canned reporting and analytics — but certainly not intelligence by any stretch of the imagination. (When you get right down to it, the bean dealer who sold the beans to Jack Spriggins was more honest when he said they were magic because the fact that seeds can sprout and grow into monstrously sized plants and trees over time that seemingly reach the clouds [and do if they grow on mountain tops] is pretty magical when you think about it.)

We also gave you a bit of a sniff test yesterday when we told you to think about it because common sense tells us there is no true artificial intelligence (autonomous or otherwise), that true cases of augmented intelligence technology (that can come up with what human experts can’t) is rare, but that assisted technology is more likely (but, again, it has to come up with what we would, not just automate dumb tasks — that’s just RPA [robotic process automation] driven by a rules-based workflow).

Since most of the “AI” that is being sold today revolves around analytics, in order to help you conduct better sniff tests (since if you can’t smell what The Rock is cooking, you know there’s nothing there), we’re going to discuss the five levels of analytics (and tell you right now there is no hint of AI even in it’s weakest form unless the analytics offered is at least level 4).

The first two levels are:

Level 1: Descriptive

This is classical reporting and the level of analytics that the majority of (leading) solutions offer. Even it contains a bundled report builder, if all that report builder does is let you produce custom reports on base and derived fields, that’s just same-old same-old descriptive reporting in a new packaging.

Level 2: Classificative

This is what most modern spend analysis systems offer you — the ability to (auto) classify transactions to a taxonomy for reporting purposes in the bundled descriptive report builder. And while most will tell you this is AI, in most cases, it’s anything but. Most of these systems are just using classic clustering, classic neural networks that are trained in (semi) supervised mode, and, if they are slightly more advanced, fingerprint techniques that extract the seemingly most differentiated details (which are usually identified by a human during training) and use those details for classification purposes in a neural network or n-dimensional kernal machine. But, at the end of the day, the classification is done using 90’s statistical techniques. Humans have to select the algorithms, the data elements in the transactions the algorithms will focus on, train the algorithms, and then implement the algorithms to work on a subset of the data.

Come back tomorrow for a description of the next three levels (and whether or not there is even a hint of AI under the hood).