Category Archives: Sourcing Innovation

Are the Seven Suites Sailing the Seven Seas Sans a Sextant?

Post Edit: The summary on LinkedIn has been removed. Note that this is an opinion piece and read why it was removed from LInkedIn in the Social Media Policy.  [Also note that this is about [marketing] direction, not about the actual platform, which we have covered, or advised the coverage on, here or on Sourcing Innovation!  We have recommended all of these suites and will continue to recommend them from a product and platform perspective where they are a great fit (even if we lament the [marketing] direction).]

You knew this was coming. It was foreshadowed in our top 10 ways to be labelled as a (Procure)Tech Noise / TroubleMaker article. (Where satisfaction was guaranteed if you followed the advice.)

Basically, in this new age where hype trumps straight-shooting, fake Gen-AI* trumps HI (Human Intelligence), intake and orchestration trumps the ability to actually do Proper Prudent Procurement, and carbon calculation trumps the ability to actually do anything about carbon reduction, the question becomes are the senior stalwart suites, who have an install base, good recurring revenue, and the ability to weather storms, staying the charted course or straying off towards the rocks and the siren’s call?

More specifically, are the seven suites that have ruled the enterprise Source-to-Pay Solution Maps since those maps were introduced still staying the course and slow and steady moving towards the next generation of real Source-to-Pay solutions that will solve real customer problems, or getting lured in by the siren’s call and sailing towards the rocks (and inevitably delaying the next great version of their platform)?

After noting we have links to previous in-depth coverage at the end of this article (so you can get the full picture), let’s take them one-by-one:

(SAP) Ariba

As far as we are concerned, SAP has been sailing without a direction since they acquired Fieldglass and Concur in 2014 (after acquiring Ariba in 2012) and formed the Intelligent Spend Group in 2015 (which has since seen numerous changes in leadership, direction, and even interpretation). They started off with a great idea (like Jaggaer who came up with “One” in 2018 after acquiring Pool4Tool and BravoSolution in 2017), but never really did anything significant with those acquisitions. (We can’t say why, but numerous leadership changes suggest disagreement on direction and priorities.)  They are still, more or less, three solutions, on three stacks, which aren’t deeply integrated … unless, of course, you buy their new brand-spanking new Spend Control Tower which will integrate all your Procurement, CWM, and T&E spend, as well as your HR/payroll spend and other un-captured spend if you have other SAP modules or modules that integrate with SAP.  (Or augment it with an orchestration interface that was built for SAP.)

Their top of page message about “automating spending processes and actively manage more spend for better control, greater value, and more savings” and “managing all sources of spend for increased control and business resilience” is pretty good, and one might think they are staying the course … still a decade behind the times in some ways (as Control Towers were so early 2010s), but staying the course. Unless, of course, you were paying attention to their most recent announcement pushing “SAP Business AI built into your procurement processes“. Even though they first announced an AI Co-pilot in 2018, it received pretty low fanfare, and was kept in the backroom, until this year, where it is now “super-charging” its AI Co-pilot with Gen-AI and other new capabilities!

While a bit behind the times (which makes sense for them as their enterprise customers don’t want to be suffering hit-and-miss innovation on the bleeding edge), everything was looking pretty good in our books until they started diving all in on the (Gen-)AI Co-pilot.

We’re especially saddened by the new, deep, focus on the (Gen)-AI Co-pilot because, and while this is as much on the SAP side as the Ariba side, they seemed to have been making some very good progress on the improvement and modernization of the direct side — catching up to the big specialists (namely Ivalua and Jaggaer) that were, through their DirectWorks and Pool4Tool acquisitions, respectively, working hard to build a better direct mousetrap and take SAP customers away).

Coupa

Well, you know our opinion here … the way things are going, this could be Coupa’s Third and Final Act. They replaced BSM with MMM, which they say stands for Make Margins Multiply, but what does that mean?

Even worse, they’ve apparently now gone all-in on AI, recently releasing 100+ AI powered innovations in their spend management platform and redoing their tagline to “optimizing your spend with the #1 AI total spend management platform“. Which is sad for us. It’s acquisition of Trade Extensions made it the #1 sourcing optimization platform and its acquisition of Llamasoft gave it the ability to optimize supply chain networks as well. It could literally optimize your spend across Souce-to-Pay and Supply Chain better than any other source to pay platform out there and yet it too has gone all in on AI, which does NOT optimize!

Ivalua

Ivalua, which, like Coupa, has been tracked by SI since the early days, was one of the platforms we felt was on the right track. Especially since it was one of the few platforms that was built up on one native, integrated, code base that allowed for true, integrated, end-to-end S2P processes that felt fragmented on other suites that built up their functionality by acquiring modular vendors and loosely integrating them.

At first glance, it seems like they are still on the right path. Off the top its “SIMPLIFY PROCUREMENT with a Unified Source-to-Pay platform” messaging is on course. Moving on to “complete transparency, seamless automation, and enhanced collaboration” elicits a hear, hear. “It will make you #LoveProcurement“. Doubtful in our books (unless you love it already because, if you love procurement, you love the process and not the tech). But you certainly won’t hate it with a good platform. “Make your spend matter with a complete, future-proof platform to manage all spend.” Not quite all spend, but certainly enough to make a big difference!

… and use “IVA to supercharge procurement with Gen-AI“. NOPE! It was a great start when they tried to be the first S2P vendor to create a true cross-platform search capability through a single search bar. Their early chat-bot interface which allowed for the execution of platform functions through simple statements, which could be learned and remembered, as they would be interpreted the exact same way every single time, was good for people who didn’t want to jump around through menus and modules and quickly access reports, documents, and simple system functions.

But when you introduce Gen-AI, you have unpredictability, the need to answer six or seven questions to explain, and get, what you hopefully want, and the opportunity for inappropriate (and if you hook it up to the web, even bad) data to be retrieved, which means bad decisions and bad results. In our view, they were so close, but now … we’re hoping they reverse course just a little bit.

Jaggaer

As we noted above, after the acquisition of Pool4Tool and BravoSolution in 2017, Jaggaer announced “ONE” in 2018, but under Accel KKR, they never achieved “ONE”, and, in fact, they didn’t even come close to true cross-platform data integration (level 1 on a 5 level hierarchy of integration) in our deep assessments (which included SolutionMap evaluations at the time), largely due to all the layoffs and operational cost reductions. It wasn’t until Cinven (who flipped them to Vista Equity earlier this year) acquired Jaggaer in 2019 that they started serious integration efforts (and, in the early 2020s, made very good progress).

Since then, they have continued to focus on:

1. “Harnessing the power of ONE intelligent S2P platform to turn procurement into a value-adding force with Jaggaer’s AI-powered, S2P solutions and supplier collaboration platform“, which is great.

2. “Unlocking the shared value in your procurement ecosystem to accelerate business outcomes, automate complexities, and manage spend with Jaggaer’s intelligent S2P and supplier collaboration platform“, which is greater.

3. “Accelerating your autonomous commerce journey to turn procurement into a value-adding force with Jaggaer’s AI-powered S2P solutions and supplier collaboration platform“, and there it is. Autonomous is okay when “autonomous” is just automating tactical tasks. But AI powered … especially when they are now going down the Gen-AI path, not good. Not good at all in our books. They have all the know how between old SciQuest (Indirect), Pool4Tool (Direct), and BravoSolution (Complex Services) to be one of the few suites that can handle any type of buy using best-in-class processes and capabilities. Continuing to bring that together, magnifying the opportunities, and continuing to introduce new capabilities and streamlined workflows and interfaces around that would not only be a huge differentiator, but one of the biggest in our book. Wasting talent on Gen-AI conversational interfaces which carry the risk of exacerbate as many complex events as they simplify, the exact opposite.  (As per our previous, grudging, admission, we believe Gen-AI has very few valid uses in Procurement, and believe that for most of those uses, Jaggaer already had better tech and approaches — and all that was needed was some streamlining and UX improvements.)

Edit: 2024-Nov-06: Jaggaer has reached out and indicated that while they believe in using Gen-AI wherever they think it has value, it is fully optional and they are more concerned with maintaining their focus on full platform integration and utilization of the right AI for automation where tactical processes can be automated.  Hopefully marketing can balance the messaging more going forward so people don’t make inferences to the contrary.

GEP

GEP used to be all about “SMART” Procurement. They named their suite, which was a complete re-write from scratch as a fine-tuned integrated suite, “SMART” back in 2013. They were playing a bit of catch-up, but it was a good, well oiled, integrated suite. Then they built NEXXE, so they could do supply chain, which, while not quite on the scale of a Big dedicated Supply Chain player (like Blue Yonder or Infor), given that they are also a full-service company, was more than good enough to support end-to-end S2P and Supply Chain for many of their G3000 clients.  (And if you knew who some of their clients were, you’d be amazed.  They support big names with very complex sourcing, procurement, and supply chain problems.)  By the end of the 2010s, the UX needed updating in both, but if you look at the Spend Matters Solution Map scores, it was more than enough to do the job. A few new advanced capabilities in a few areas and it was a great solution for their target market (G3000 who wanted end to end software and services across S2P and Supply Chain).

So where are they now? “GEP’s AI-First approach seamlessly integrates strategy, software and managed services, enabling enterprises to rapidly establish the infrastructure and capabilities necessary to build and run high-performance procurement and supply chain organizations.”

Why, why, why? First of all, in our experience, customers don’t buy GEP for AI-First, or even UX, they buy because they want the one-throat-to-choke solution-and-services model that works that GEP offers across source-to-pay and supply chain, which is something only SAP and Oracle can offer, and, more importantly, neither of them can offer it as a company that started as a S2P, and then Supply Chain, specialist (as SAP and Oracle both started as ERPs and also split focus across so many other areas — HR, CRM, etc.). Secondly, they want their their Procurement and Supply Chain managed, not run by a dumb bot who may or may not make random decisions on anything at any time. Thirdly they want predictable, repeatable results that they can bank on, as well as the ability to get to the root cause when something is screwed up. None of this says, or even implies, AI.  And definitely not Gen-AI!  Ugh.

Zycus

While much less visible in North America over the last few years, Zycus for a while was making a splash as the “affordable” solution that could be obtained in the six (and not seven) figures and do the majority of what most large mid-markets (LMM) and enterprises needed, especially in industries that didn’t require a lot of “direct”. It was a great solution for LMM multi-nationals on the rise to true global enterprises. And their messaging was straight to the point: the “Power of Procurement” through their Source-to-Pay Procurement Suite. It was clear what they did, what they offered, and why you wanted to use Zycus to go digital. Even the most novice of Procurement practitioners could understand it. Heck, they were one of the first to build a custom intake module (iRequest) for their entire platform a decade ago. (It should have been built in at the core, but not many suites would admit this oversight halfway through their journey and actively work to correct it.  Zycus deserves big props for this.)

Where are they now? “Make Procurement Intelligent: World’s first Generative AI powered S2P Platform that helps you achieve 10X speed and efficiency in procurement.” All-in on the Gen-AI hype train! (And given how many specialists launched on Gen-AI over the last couple of years, and how they are usually showing up after the party starts, following a best value approach, we will tell you that while they are one of the first, they are definitely NOT the world’s first.  However, in fairness, we will note that they were undeniably one of the first to investigate and deliver automated spend classification, and were so early in doing this they could have been the first full source-to-pay suite to have it, all depending on your definition of suite.)  Basically, more of our hopes and dreams that the big suites are resisting the Gen-AI hype are dashed.

Interlude

So where are we now? That’s six suites all in on AI, and, for the most part, Gen-AI, and, in our opinion, sailing the seas sans a sextant (at least with respect to their marketing direction)! (After all, despite the fact that it continues to perpetrate the Gen-AI hype, Gartner recently reported 85% failure rates in AI projects last year — and Bain is now reporting technology project failure rates of 88%, an all time tech failure high! This should be more than enough to turn away from Gen-AI, even without a discussion of all the problems Gen-AI comes with and all the risks Gen-AI entails. [Hallucinations, sleeper code, implanting false memories, etc. etc. etc.])

This is very sad in our view as these are six suites that grew and attained their status by attempting to, and then building, real solutions that solved real customer problems sufficiently enough to sign big customers, keep big customers, and grow big customer accounts. Since, with the possible exception of Ariba (that might have been added on by a SAP ERP customer), these aren’t ERP vendors with ERP lock in, you know they had to, and have to, be doing something right! Why risk that track record on unproven (and usually inappropriate) (Gen)-AI?

Now, as per our Coupa coverage, we hope we are dead wrong, that they all have a plan to continue building great solutions without Gen-AI (dominance), that they will continue to remain strong suites, and that we will be covering them for years to come (if they ever return our emails and messages and answer our demo requests).  We’ve invested almost two decades covering these solutions, and, more importantly, we have recommended and strongly recommended all of these solutions in the past and fully expect to keep doing so.  (Some clients need a suite, and we base our recommendations on current product capabilities, not how good the vendor is at marketing those capabilities.  That’s the advantage of having a deep understanding of technology!)

This just leaves us with:

Oracle

Specifically, their Fusion Cloud Procurement, which, to be honest, was the one suite we would almost never put on a shortlist for a company looking for a specialist S2P solution since they were usually less extensive and less feature-rich than the other suites that started as best-of-breed. (But definitely would for Oracle Shops that liked strong S2P integration with the ERP.)

However, when you go to their page, you see their messaging is all about asking if “your procurement suite can automate procure-to-pay, strategic sourcing and supplier management processes“? Then their messaging about how their “Fusion Cloud Procurement capabilities, built-in collaboration and analytic insights drive agility, manage risk and increase margins“. Moreover, “Oracle Fusion Cloud Procurement is an “integrated source-to-settle suite that automates business processes, enables strategic sourcing, improves supplier relationship management and simplifies buying resulting in lower risk, improved savings and greater profitability.” And, finally, it consists of modules for supplier management, sourcing, procurement contracts, purchasing, direct procurement, and procurement analytics.

Moreover, not a single mention of ANY AI on their main page. Just straight to the point messaging an average buyer and executive can understand. Moreover, searching for AI immediately takes you to Oracle AI for Fusion applications where you have a list of traditional AI for spend classification, predicted shipment and cycle times, dynamic discounting, supplier recommendations, demand sensing, anomaly detection, etc. No Gen-AI out of the box. If you dig deep, you find that you can have a Gen-AI based Procurement Tool with Natural Language Queries if you want it (and you are willing to custom build, configure, and train it), but they aren’t pushing it, you have to look for it, and ask for it. In other words, if you really want it, you can have it (because some customers will want it without researching it and they do give customers what they want), but they recognize you don’t need it for value, so they aren’t focussed on selling it (or even marketing it). (Just like they didn’t fall for The Cloud is a Crystal Ball hype, they ain’t falling for the Gen-AI Hype [yet].)

And that makes them the only suite that might not be sailing the seven seas sans a sextant (at least as far as their marketing direction is concerned). Now, we’re not saying they’re the best at using the sextant and charting a course, as they are typically behind most of the other suites in leading S2P functionality, but the simple fact they know that getting to your destination requires staying the course says something, and, in our book, that now makes them definite short-list material. Plus, like SAP, most of their customers are big enterprises that don’t want leading (and definitely don’t want bleeding) edge and instead want tested tried and true solutions.  And that’s why they’re going to be around for their 50th anniversary in 2027 and, if they so desire, might even be around in 2077.

In other words, they might be the tortoise, but we know in the end, if it stays the course, it eventually wins the race. (And this is something we never thought we’d write about Oracle in the early days. After all, Davie left Oracle to start the Coupa Factory in 2005 because he didn’t think they’d ever get where was needed in a timely fashion. How times have changed!)

Markets evolve, suites evolve, and messaging evolves.  Maybe when the Gen-AI hype dies down, we’ll see clearer messaging and be able to see the routes the suites are charting.  Only time will tell.


* Gen-AI stands for Generative Artificial Intelligence but should stand for Generative Artificial Idiocy as none of the generic Gen-AI LLM tools are intelligent and, moreover, can’t even do basic reasoning. Only the “generative” part is accurate, as generative literally means “make stuff up” and that’s what this hallucinatory technology does all the time!  (And that’s why SI is so scared when vendors start trying to rapidly incorporate it into their products.  As per previous coverage, their aren’t many valid uses cases with high reliability.)

Now, in full disclosure, SI hasn’t reviewed any of these solutions, except for two discrete modules in Coupa (Sourcing Optimization and Supply Chain Solutions), in the last 2 years, but this is not for lack of trying. SI has reached out (multiple times) to all but two of these companies, and, despite being one of the first sites to cover some these companies in the past (and do so in the early days when no one else would), has either been declined or completely ignored. (All the suites seem to care about now is Gartner and sometimes Spend Matters.)

First Coverage of:

  • Ariba: one of the few vendors that would not talk to SI in the early days, first covered by the doctor on Spend Matters in a 2-part piece in 2018 on Sourcing Decision Optimization Part 1 and Part 2#
  • Coupa was first covered in 2006 on the first Procurement Independence day
  • Ivalua was first covered in 2010 in a two-parter on end-to-end sourcing and procurement (Part 1 and Part 2)
  • Jaggaer is rebranded SciQuest, which was another vendor that wouldn’t talk to SI in the early days, even though the majority of its acquisitions, including AECSoft, Upside Software, Spend Radar, CombineNet, Pool4Tool, and BravoSolution, all did; the doctor did advise on Jaggaer coverage on Spend Matters as Lead Solution Map Analyst, but did not cover them directly;  he would advise checking out Spend Matter’s coverage if you have access: (S2C 1, S2C 2, and
    S2C 3 as well as P2P 1, P2P 2, and P2P 3)
  • GEP was not covered on SI, even though it acquired Enporion which was, as GEP was not highly relevant for SI’s market (focussed on companies who wanted insight into DIY platforms); the doctor did work with Xavier Olivera, Pierre Mitchell, and Jason Busch to do a deep dive in 2019 (Part 1, Part 2, Part 3, Part 4, Part 5, Part 6, Part 7) which was the 6th most popular PRO article of the year! (Source)
  • Zycus, unfortunately, was not covered on SI as they never made our radar until the doctor started contributing to Spend Matters, where he advised on coverage, but did not do the vendor coverage (which included the 2018 Vendor Analysis, which we recommend: Part 1, Part 2, and Part 3)
  • Oracle, another of the original vendors that would not talk to SI, was never covered on SI, and while the doctor did consult on their platform capability during Solution Map capabilities, never contributed to a write-up (but would recommend anything Xavier Olivera or Jason Busch ever wrote about them, including Cloud Surprise, Part 1, Part 2, Part 3, and Update 1)

# Note that the Spend Matters site migration of June 2023, in addition to removing all articles pre-2013 and many more pre-2020, also dropped co-authors on many articles as well. Most of what the doctor wrote in the early days was always co-authored with Jason Busch who usually received lead credit (and, thus, was the author who survived the migration).

Advanced Supplier Discovery Tomorrow — No Gen-AI Needed!

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!

Advanced Supplier Discovery Today — No Gen-AI Needed!

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.

Advanced Supplier Discovery Yesterday — No Gen-AI Needed!

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.

Advanced Sourcing Tomorrow — No Gen-AI Needed!

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!