Back in late 2018 and early 2019, before the GENizah Artificial Idiocy craze began, the doctor did a sequence of AI Series (totalling 22 articles) on Spend Matters on AI in X Today, Tomorrow, and The Day After Tomorrow for Procurement, Sourcing, Sourcing Optimization, Supplier Discovery, and Supplier Management. All of which was implemented, about to be implemented, capable of being implemented, and most definitely not doable with, Gen-AI.
To make it abundantly clear that you don’t need Gen-AI for any advanced enterprise back-office (fin)tech, and that, in fact, you should never even consider it for advanced tech in these categories (because it cannot reason, cannot guarantee consistency, and confidence on the quality of its outputs can’t even be measured), we’re going to talk about all the advanced features enabled by Assisted and Augmented Intelligence that were (about to be) in development five years ago and are now available in leading best of-breed systems. And we’re continuing with Sourcing.
Unlike prior series, we’re identifying the sound, ML/AI technologies that are, or can, be used to implement the advanced capabilities that are currently found, or will soon be found, in Source to Pay technologies that are truly AI-enhanced. (Which, FYI, may not match one-to-one with what the doctor chronicled five years ago because, like time, tech marches on.)
Today we continue with AI-Enhanced Sourcing that was in development “yesterday” when we wrote our first series five years ago but is now available in mature best of breed platforms for your Sourcing success. (This article sort of corresponds with AI in Sourcing Tomorrow Part I and AI in Sourcing Tomorrow Part II that were published in January, 2019 on Spend Matters.)
TODAY
Event-Based Category Alighnment
As per our Procurement series, a good AI based platform continuously analyzes (i.e. re-runs an analysis on a monthly basis) every product or service for inclusion against every organizational category and comes up with the most logical mix for the procurement organization based on likeness, current supply-base, spend-mix, and other existing parameters.
However, when it comes time for sourcing, the category should be appropriate for a sourcing event. This depends on volume, available supply base, and the category strategy (see the next item).
When it comes to sourcing, the AI will look at not only the product specifications, but also ensure there is a sufficiently large supply-base, with supply availability, spend-mix, and price trends. It will do this based on key material analysis (to identify additional suppliers in the market not yet supplying the organization), identification of market offers and volume disclosures from third party distributors vs. organizational need and overall percentages, analysis of spend vs. typical sourcing event sizes using simple (k-means) analysis, and price trends using basic curve fitting/projection. Nothing fancy.
Based upon the demand (volume), available supply base, supply availability, spend mix, price trends, and defacto templated sourcing strategy, the platform will recommend the event proceed using the standard strategy and template, proceed with modifications, or not proceed (alerting the buyer it’s not a good time, not a good event, or a new strategy is needed). It’s all traditional analytics, a smattering of machine learning, a sprinkling of pattern matching, tolerances, and confidence calculations. Nothing super fancy. The recommendation(s) will depend on a number of factors that revolve around the market conditions at the time. Current prices. Available supply base. Category dynamics in the consumer marketplace. Etc.
Category-Based Sourcing Strategy Identification
In our prior series, we indicated we’d have market-based sourcing strategy identification, and while that is in development, we’re not quite there yet. Market-based strategy identification requires a lot of data — market, supplier, marketplace, (anonymized) community intelligence, past event data, and past data from similar situations … the global marketplace has been so dynamic in recent years that we haven’t seen anything like it since pre-2000 … which was before the introduction of mass-market sourcing / procurement / modern supply chain software and we just don’t have the data.
That being said, for the majority of commodity categories, a number of leading firms have developed one or more standard sourcing strategies for the category and categorized the market conditions under which the strategies work. Modern sourcing platforms will run all the analytics against the specified demand ranges, supply vs. demand imbalance, historical price variances (since the last event), current market prices, check the thresholds, compute the match percentage and confidence, and then recommend go, go with changes/caution, don’t go — all using straight-forward trend analysis and mathematical calculations — no Gen-AI needed!
Real-Time Market vs. Response Monitoring and Automatic Pauses/Updates
As the responses come in, the application will not only track bids vs open market prices (and current prices), but compute the averages and if the bids coming in are worse than expected, alert the buyer. In a multi-round scenario, or RFQ-powered auction, the trends will be analyzed and if they are not as expected, the buyer will be alerted. In both cases, if something is off beyond a tolerance, which will adjust over time as buyer feedback on go-no go is collected, the event will automatically be paused if necessary. This just requires simple calculations against means and expectations. Good old math, a few business rules, and some workflow automation is all that is required.
Suggested Award Scenarios
Even if the platform doesn’t contain (true) strategic sourcing decision optimization [SSDO] (and see this recently updated article on Questions to Ask Your Optimization vendor for the requirements for a true SSDO solution), most modern platforms will recommend one or more award scenarios that take into account cost, business constraints, risk and carbon. It’s just a lot of combinatorial mathematical calculations and basic analytic verifications.
Carbon Impact Analysis
Using standard models for carbon production based on available data by industry, country, and when available, factory, modern platforms will use standard models and formulas to compute the carbon footprint by item, based on the supplier, the source location, and the location it is going to (and even take into account logistics based carbon production). It will do this for every item you’ve purchased, every item you’re considering, and show you the carbon impact of different award decisions vs. the status quo. No Gen-AI required! (Just a lot of formulae and data!)
SUMMARY
Now, we realize some of these descriptions, like yesterday’s, are also quite brief, but again, that’s because this is not entirely new tech, as the beginnings have been around for a few years, have been in development and discussed as “the future of” Sourcing tech before Gen-AI hit the scene, and all of these capabilities are pretty straight-forward to understand (especially with many of the fake-take and Gen-AI providers marketing these, or similar, claims, even though they are not entirely realizable within their platforms). And, if you want to dive deeper, the baseline requirements for most of these capabilities were described in depth in the doctor‘s January 2019 articles on Spend Matters. The primary purpose of this article, as with the last, was to explain how more sophisticated versions of traditional ML methodologies could be implemented in unison with human intelligence (HI!) to create smarter Sourcing applications that buyers could rely on with confidence.