Daily Archives: June 26, 2019

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.