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 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 measured), we’re going to talk about all the advanced features enabled by Assisted and Augmented Intelligence that are (or soon will be) in development (now) and you will see in leading best of breed platforms over the next few years.
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 emerging, and 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 is in development “today” (and expected to be in development by now when the first series was penned five years ago) and will soon be a staple in best of breed platforms (and may be found emerging in development beta versions of some platforms). (This article sort of corresponds with AI in Sourcing The Day After Tomorrow that was published in January, 2019 on Spend Matters.)
TOMORROW
Automatic Strategic Sourcing Events
Just like tomorrow’s Procurement platforms will automatically identify products/services and (sub) categories that should be pulled out of the tail and inventory/catalog/one-time req buying and pulled into a strategic sourcing event, tomorrow’s sourcing platforms will create automatic events from them. Furthermore, tomorrow’s sourcing platforms will automatically create the entire event using the default category strategy (possibly adjusted to the current market conditions, see the next forthcoming capability), automatically pull in the (organizationally approved) suppliers, automatically pull in any questionnaires or documents that need to be completed by the bidders, automatically pull in supplier profile information and current prices (where available), and, if you set the flag for “no review prior to event initiation”, automatically send out the RFX, which could be the first in a series of RFXs/e-Auctions in a multi-round event. If the event is multi-round, after each round it can analyze the responses and any supplier who provides all of the necessary information (and makes the cut price/quality/risk/carbon/etc. cut) makes the next round. It will auto-execute the next round and keep going until the event has been completed and an award recommendation is made. Then, depending on the setting (auto-award, human review), it will either compute a recommended award and notify a buyer to approve, modify, or reject the award, or automatically send the award to to the suppliers for acceptance (with a contract for high-value or strategic products/services or a PO for lower value, more tactical offerings).
From a tech perspective, all this needs is the ability to analyze spend patterns and demand trends (trend analysis) to identify categories ripe for sourcing, product classifications to match to the category strategy, and product-supplier pairings to pull in the suppliers (and associated data), with current and preferred suppliers getting priority if there are too many. The rest is just workflow automation until the initial responses are returned. Then, it’s just analyzing the data with respect to expectations and tolerances, and either recommending an award based on the strategy, organizational priorities, and organizational constraints, or sending out the next round requests (deeper RFIs, price updates, etc.) to those suppliers who provided complete, satisfactory, answers according to business rules. This is just analytics, optimization, and good ol’ math coded with human intelligence (HI!).
Market-Based Sourcing Strategy Identification
Today, the best platforms support category-based sourcing strategy identification where the platform can identify the standard, best-practice, strategy based on the category and items, determine whether or not the strategy is likely to be relevant given available market data (supply availability, historical price variants, current market prices, etc.), and make a go-no recommendation to the buyer. Tomorrow, these platforms will be able to first analyze all of the market information, supplier information, product information, carbon information, risk information, and compare that to current company performance an demand and identify the right sourcing strategy for the event, making sure to dynamically align the category (which can include adding or dropping items and services) as required.
From a tech perspective, all this needs is access to extensive market data feeds, a large history of sourcing event and results with associated market data (relative to the supply vs. demand imbalance, price trends, demand trends, major risk factors, etc.), pattern analysis that correlates successful events (with results < market price) with market conditions (supply > demand, prices steady or falling, low market risk in the supply base –> e-Auction; supply >= demand, prices rising with inflation, low to moderate risk –> RFX; supply projected <= demand, prices rising above inflation, moderate risk –> renegotiate with the incumbent(s) before the contracts expire), pattern analysis of the current market conditions compared to historical patterns of success, and the selection of the best match. All trend analysis, correlation/(k-)means analysis, tolerances, and, you guessed it, math! Then you just kick off the category-attuned sourcing event as above.
Real-Time Strategy Alignment in (Automatic) Strategic Sourcing Events
However, tomorrow’s AI-based sourcing capabilities won’t stop there. The platform will monitor all relevant market (related) conditions as the event progresses, compare all of the responses to those that were predicted/expected, and if, at any point during the (automatic) event something is too far off, it will automatically pause the event and either, depending on system configuration, alert the buyer that a shift in strategy is required (and what the new strategy it should be) or simply shift the event as appropriate (if possible; in the public sector, not always possible, but in the private sector, usually possible).
From a tech perspective, all this needs is trend and outlier analysis, pattern matching, and, you guessed it, math.
SKU Recommendation and Replacement
Tomorrow’s platforms will get better at identifying replacement SKUs not just in indirect (paper with similar thickness, weight, and gloss when the differences are inconsequential from a business point of view), but direct as well (compatible processors, with the same form factor, number of connections, compatible clock rate, and sufficient L1 cache). This is difficult because you need a lot of specification data, and most applications need it appropriately structured in a format no other application supports in order to process it. But, despite the focus on the Gen-AI bullcr@p, semantic processing is continuing to advance and as more and more validated database are built on each product and service type, and more specifications are added to each product and service type. As a result, these applications are getting better and better at helping to identify acceptable alternates with slightly different, but compatible, specs that can help Procurement and engineers find more cost-effective alternatives, including new tech that will have a longer shelf life.
As this tech continues to improve, it will be able to not just look at SKUs, but subassemblies, such as processor-controller board-memory combinations, that can be switched out to provide more cost effective alternatives with better reliability, risk span, or quality. This will be the result of not only a better understanding of each subcomponent, but the interaction requirements and overall processing power capable of handling the combinatorial explosion needed to automatically identify new potential subsystems, and not just components, automatically.
EOL Recommendation
Many niche PLM systems will already do this, but tomorrow’s sourcing systems will do this not just from a traditional “tech curve” perspective, but also from a Procurement and Supply Chain perspective, balancing life-span with price trends, material supply, market risk, and carbon impact. If a current product requires a large concentration of a rare earth mineral or metal (in short supply) or an ingredient that can only be grown in a few places in the world, and a new product comes along that requires less (or none) but still provides the same use (or at least a suitable alternative for consumption in the latter case), then it makes sense to switch over as soon as the cost is appropriate. Similarly, if one product is only available from a risky supplier or a risky country (with rising political or market instability) or has an unnecessarily high carbon cost, switching out could also be a priority.
Using trend analysis on demand and (future) cost, risk projections, and carbon costs, tomorrow’s sourcing systems will find the optimal inflection points (using analytics and optimization) for switch over and make early end-of-life recommendations so Procurement and Engineering can plan early for the switch-over and schedule the appropriate sourcing events for the appropriate timeframes (and ensure contract lengths are optimal). And, again, no Gen-AI needed!
SUMMARY
Now, we realize some of these descriptions are dense, but that’s because our primary goal is to demonstrate that one can use the more advanced ML technologies that already exist, harmonized with market and corporate data, to create even smarter Sourcing applications than most people (and last generation suites) realize, without any need (or use) for Gen-AI, that the organization can rely upon to reduce time, tactical data processing, spend, and risk while increasing output and overall organizational performance. It just requires smart vendors who hire very smart people who use their human intelligence (HI!) to full potential to create brilliant Sourcing applications that buyers can rely on with confidence no matter what category or organization size, always knowing that the application will know when a human has to be involved, and why!
