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 Supplier Discovery 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 Supplier Discovery The Day After Tomorrow that was published in March, 2019 on Spend Matters.)

TOMORROW

Intelligent Supplier Discovery

How is this different than deep capability match that is available today? Because it sure sounds like the ability to match to a very detailed request is “intelligent”. Compared to most search capabilities in most platforms, it is. But there’s more to selecting a supplier, especially one with whom you need a long term relationship, than just tech specs and certification checks. There are also performance considerations, innovation ability (hard to measure), culture, and other, softer factors.

First of all, you need a platform that can predict the ability of a given supplier to innovate and, more importantly, innovate for you based upon your specific needs. To do this, you need to chart the “innovation history” of a supplier (how many innovations per year, typical gap between innovations), compare the “innovation history” to other suppliers in the industry and category, use a predictive curve fitting or other ML algorithm to predict it’s rate (vs. the average). This is a lot of semantic processing to identify innovations and approximate dates, a lot of trend analysis to find the right predictive algorithms, and a lot of calculation. And then you need the ability to refine the innovation rate by category for a multi-category supplier so the trend line matches your need, and not your competitor’s.

Secondly, you need to be able to parse the “reviews” not just for sentiment, but positive or negative interpretations of specific, relevant “soft factors” like communication, working culture, etc. and compute appropriate ratios or bands that can be compared and be considered in super search / match criteria that is relevant to your organization. Next generation targeted sentiment analysis on factors identified on deep semantic analysis. No Gen-AI needed, just domain specific refinements of traditional approaches (trained on highly vetted, validated data sets).

Predictive Smart Search

For a company in direct manufacturing, electronics, pharma, or another industry where advanced innovation at a fairly rapid pace is required not just for growth, but continued market share retention, identifying the right suppliers is critical. This requires a very deep search, and for specific projects, potentially dozens of requirements and validations that need to be done before a supplier can be invited to an event.

So many in fact that, even if a buyer could identify all of these up front, building the search criteria to capture them all could be difficult. Next generation platforms will learn from each search entered into a platform for a product, category, supplier, etc. and extract the typical criteria, the frequency, and the preferences by organization and user.

Based on this data, when a buyer, new product development specialist, etc. starts a search for a new supplier in a category and/or for a product, the platform will predict which factors are relevant to the user, recommend those factors and factors, and intelligently build the right search and tolerances for the user. And then retrieve the best suppliers, ranked with match percentages.

None of this requires Gen-AI. Frequency is just frequency mapping by product, category, and supplier. Matches are matches as per deep search. Auto query creation is rules based automation. Soft factors are identified by semantic and sentiment analysis. And so on. It just requires a lot of Human Intelligence (HI!) to put it all together.

Is That All, Folks?

Probably not. The more data that is collected, the more analysis that can be done, and the more matching and prediction that can be done across people, products, services, and solutions. And the more “intelligence” (which CAN NOT be generated by Gen-AI) that can be put forward beyond your search before you invite a supplier to an event. But it’s the next step, and we’re going to stop here because we are going to refresh our series on Supplier Management as well.

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 and AI technologies that already exist, harmonized with corporate, market and community data, to create even smarter Supplier Discovery 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, and risk while increasing supplier intelligence 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 Supplier Discovery 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!

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 Supplier Discovery.

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 Supplier Discovery 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 Supplier Discovery success. (This article sort of corresponds with AI in Supplier Discovery Tomorrow that was published in March, 2019 on Spend Matters.)

TODAY

Deep Capability Match

As noted in our original posting, if you want a custom produced FPGA, an industrial strength power converter that can handle feeds from your wind farms and water wheels, or a new state-of-the-art surround sound system, you don’t want just any supplier. This is especially true if all they do is produce a fixed set of products, use production technology that is not appropriate for the design you want, have a record of sourcing inferior raw materials, or don’t have the right quality processes in place.

So, when we last tackled this subject five years ago, the new/leading supplier discovery platforms were working on deep capability match that could take a set of requirements for a product, or even a bill of materials, and find matching suppliers for the parts.

Especially since all this needed was deep capability identification and tagging across categories, products, and services that included production process, certifications, materials, etc. Which means that deep capability match was essentially just a super smart search capability across not just a few, but dozens of requirements — as long as the data was properly structured and indexed.

This requires the ability to crawl websites and extract all text and documents, OCR those documents to text, and then semantically process for the relevant information along the recorded dimensions. This just required classical semantic processing which uses ontologies, semantic networks, and custom trained (neural) networks for POS/concept identification when classical processing is not sure. Tech that has now been around and ready for production use for over 15 years. The big challenge was the magnitude of data that needed to be processed and indexed, which is not a problem anymore given the processing power of racks, the size of modern data centres (which require 10X to 100X the processing power for the Gen-AI trainwrecks that don’t deliver), and modern distributed processing algorithms and technology.

And, of course the ability to do rapid semantically aware reg-ex (across similar key words / phrases) for anything not indexed, or indexable in a standard taxonomy.

Resource Capability Match

Sometimes you need very specialized services. As we noted five years ago, for new product design, you need an engineering resource who has designed similar products and is familiar with the new production technologies and components that are on the market. For software implementation, you need a team who has installed the current software in a similar environment that has the same ERPs, OSs, data sources, etc. For utility installation, you need engineers with the right skills and certifications. And so on.

This is essentially just a variant of deep capability match, except you are matching on the services capabilities and the individual’s resumes. Getting here was just determining everything that was relevant for a service, processing large amounts of data, tagging and indexing it appropriately, and supporting very deep multi-faceted searches, using the same semantic technology as described above, but tuned for different service (instead of product) domains.

That’s All For Now, Folks!

Again, focus on supplier discovery was, and still is, limited as there were, and still are, only a few vendors doing it. The good news is that we’re starting to see the technology predicted for “tomorrow” five years ago starting to emerge in these platforms as well.

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” Supplier Discovery tech before Gen-AI hit the scene, and all of these capabilities are pretty straight-forward to understand. And, if you want to dive deeper, the baseline requirements for most of these capabilities were described in depth in the doctor‘s March 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 and AI methodologies could be implemented in unison with human intelligence (HI!) to create smarter Supplier Discovery applications that buyers could rely on with confidence.

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 application, 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 (as we don’t really have true appercipient [cognitive] intelligence or autonomous intelligence, and we’d need at least autonomous intelligence to really call a system artificially intelligent — the doctor described the levels in a 2020 Spend Matters article on how Artificial intelligence levels show AI is not created equal. Do you know what the vendor is selling?) that have been available for years (if you looked for, and found, the right best-of-breed systems [many of which are the hidden gems in the Mega Map]). And we’re going to continue with Supplier Discovery. (Find our series on Advanced Procurement — No Gen-AI Needed! Yesterday, Today, and Tomorrow and our series on Advanced Sourcing — No Gen-AI Needed! Yesterday, Today, and Tomorrow through the embedded links.)

Unlike prior series, we’re going to mention some of the traditional, 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, might not match one-to-one with what the doctor chronicled five years ago because, like time, tech marches on.)

Today we move on to AI-Enhanced Supplier Discovery that was available yesterday (and, in fact, for at least the past 5 years if you go back and read the doctor’s original series, which will provide a lot more detail on each capability we’re discussing). (This article sort of corresponds with AI in Supplier Discovery Today that was published in March, 2019.)

YESTERDAY

Smart Search

As penned in the original, while this is not really AI in any sense of the definition, extremely powerful searching and faceted filtering can really help an organization find the information, or in this case, the suppliers they are looking for. In the early days, searches were super simple — suppliers for product X in this category. If you wanted something like “suppliers in eastern Europe which supply widgets and sprockets with a third party financial risk score of 3 or less that is ISO UVWXY certified with a maximum carbon output per unit of Y”, forget it. You’d get a starting list of all suppliers in all of Europe that supplied widgets or sprockets (and not necessarily both) and have to vet them one by one.

But, thanks to advances in processing and database tech, traditional semantic processing, and tagging, you can now do multi-faceted searches across multiple dimensions on million record plus databases in less than a second, and do regex processing of associated descriptions for key words or phrases for specific requirements not tagged or indexed. And all of the semantic indexing and tagging can be done with traditional semantic analysis and custom trained last gen neural nets (and done with very high accuracy).

Community Intelligence

Like searching, while most of this technically doesn’t require ML/AI, community intelligence that spans ratings, capability verifications, (past) inter/intra organization relationships, and buyer sentiment can be quite useful to a buyer. It’s not just a group of suppliers that seem to meet your requirements of “suppliers in eastern Europe which supply widgets and sprockets with a third party financial risk score of 3 or less that is ISO UVWXY certified with a maximum carbon output per unit of Y”, it’s a group that will actually meet your needs, and the best way to zero in on that group is to use community intelligence from other buyers who have used the supplier and can provide valuable feedback on their capabilities and performance.

Most of this doesn’t require any ML/AI at all as it just requires ratings, feedback on various dimensions, recording of products and services used, etc. Only the sentiment analysis requires the AI domain, and it’s just building on semantic context analysis, which uses semantic processing and customized neural nets to predict sentiment (to detect things like sarcasm, etc.).

That Was It, Folks!

In the early days, Supplier Discovery was overlooked when it came to ML/AI, because it was not seen to be as important as sourcing, procurement or supplier management (because you knew who the suppliers were, you just needed to manage them better). However, as the leaders realized that the best opportunity for innovation was often in the supply chain, focus switched to supplier discovery and real ML/AI worked it’s way in.

SUMMARY

Now, we realize this was very brief, but again, that’s because this is not new tech, that was available long before Gen-AI, which should be native in the majority (if not the entirety) to any true best-of-breed Supplier Discovery platform, that is easy to understand — and that was described in detail in the doctor’s 2019 article for those who wish to dive deeper. The whole point was to explain how traditional ML methods enable all of this, with ease, it just takes human intelligence (HI!) to define and code it.

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!

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.