Category Archives: Technology

Advanced Procurement 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 Procurement 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. (This article sort of corresponds with AI in Procurement The Day After Tomorrow that was published in November, 2018 on Spend Matters.)

TOMORROW

AUTOMATIC CATEGORY IDENTIFICATION

Building on the above, there’s no reason it can’t look at common product / service characteristics from BOMs (bills of materials) and descriptions, find commonalities, and suggest new sourcing/procurement categories that would maximize opportunity and leverage. This is just building on last-gen tech with more encoded human intelligence (HI!), RPA, and (gasp!) math. This is especially useful for identifying when tail-spend should go to 3-bids-and-a-buy tactical sourcing and when mid-tier tactical categories are large enough for full blown strategic sourcing with strategy identification, in-depth market research, multi-round bids and negotiations, etc.

AUTOMATIC PROCUREMENT METHOD IDENTIFICATION

When we are talking about mid-tier tactical sourcing, when a category (currently in the tail) goes beyond a simple catalog / e-comm-like site buy, determining whether it should be a 3-bids-and-a-buy RFQ, auction, or negotiation with an incumbent (whom you have a relationship with in another category or who is currently getting most of the business off-contract) can be automated based on an assessment of current market conditions (supply vs. demand, price trends, category risk, etc.) and encoded Human Intelligence (HI!) on best-practice (and the conditions that tilt one method in the favour of another baed on past savings against similar market conditions). While it won’t be perfect, it will better than most buyers in most organizations will be able to do without deep category expertise and/or a lot of experience in strategy selection and implementation — and more than good enough for an average mid-market enterprise for the majority of their mid-tier spend.

ELIMINATION OF UNMANAGED TAIL SPEND

Tail Spend can be 30% to 40% of spend in some organizations, and overspend (as determined by a variance analysis, market prices across marketplaces, and/or average savings from a 3-bids-and-a-buy RFP or even just a bulk discount on standard catalog pricing) in the 15% to 30% range.

(That’s why so many laggards are getting bamboozled by the new generation of fake-take [better known as intake] procurement applications that make it easy to process requisitions and do one-time buys, because they often see a 10% savings on spend out of the gate and think they are doing fantastic, even when they aren’t. First of all, they are only getting market-price [because they aren’t doing real procurement, which requires a basic level of strategy, and definitely not doing strategic sourcing], which means they are leaving money on the table. Secondly, by not identifying items that should be bundled across requisitions from the week OR managed as MRO / commodity inventory [which can be managed automatically], they are wasting time (and thus money) processing essentially the same requisition over and over [and over]. And so on.)

However, given that we have made great advances in trend analysis, community intelligence, market price intelligence, demand management, market dynamics classification, etc., there’s no reason that, for any tail spend item, the system can’t, with high probability, identify the appropriate methodology for any requisition, which, for tail spend, should include:

  • fulfill from inventory (and auto manage / order the inventory)
  • fulfill from catalog (from contract / preferred suppliers)
  • combine requisitions and fulfill via RFQ
  • combine requisitions and fulfill via e-Auction
  • fulfill as standalone RFQ
  • fulfill as standalone e-Auction
  • promote to a tactical sourcing / strategic procurement category

PERFORMANCE IMPROVEMENT

Procurement is always overworked and under-resourced from a people, capital, and technological perspective, so performance is critical. A great system will increase performance not just along the “cost savings” dimension (as that’s a given with Procurement, whoever said “I have been tasked to spend more” in Procurement), but also along the time, risk, and sustainability measurements.

A great system will monitor utilization and not only allow itself to be configured to minimize steps and effort for everyday tasks through built in configuration capabilities in the dashboards, workflows, rules, etc., but will suggest to the admin changes to configuration, process, or policy over time as the metrics indicate that changes would reduce process time. Process analysis systems already exist, it’s just a matter of integrating them into procurement systems and integrating the analytics necessary to do the suggestions and linking them to the workflow.

But procurement systems aren’t limited to identifying savings opportunities across money and time, they can also identify opportunity across risk if appropriate risk metrics are incorporated (and suggest strategies, suppliers, or products with lower risk) using trend and comparison analytics.

Similarly, they can integrate carbon models and carbon data and identify the (expected) carbon cost of every product or service being considered (depending on whether the data comes from an industry data base, country database, supplier measurement, or third party auditor, will determine how accurate the carbon value is), and identify suppliers or products that would reduce carbon, as well as the cost decrease and/or risk increase of any carbon improvements.

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 Procurement 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 Procurement 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!

Data, Technology, and Insight.

Data is just data.

Tech, even “AI”, is just tech.

Insights only come from Human Intelligence, and, specifically, from the people with the Knowledge of the Data and the Wisdom to apply the right technology that uses the right methods to extract the right Insights.

That’s how it’s always been, that’s how it is, and until we have real AI*, that’s how it will be.

the doctor has written about aspects of this many times here on Sourcing Innovation and on LinkedIn.

However, if you haven’t yet, today he wants you to read the words of wisdom from THE REVELATOR. It’s important you understand what data democratization, data visualization, and Ozempic have in common.

* (which will likely bring about our destruction very shortly after it is created, but that’s a different article)

Advanced Procurement 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 Procurement.

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 Procurement 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 Procurement success. (This article sort of corresponds with AI in Procurement Tomorrow Part I, AI in Procurement Tomorrow Part II, and AI in Procurement Tomorrow Part III that were published in November, 2018 on Spend Matters.)

TODAY

OVERSPEND PREVENTION

By integrating trend analysis on demand and price, the platform can easily predict the date the budget will be exhausted, and if that’s before the end of the year, it can proactively pause an order for budgetary review EVEN IF it would be automatically approved in a last-gen system because it was still within budget and from an approved supplier.

POLICY IDENTIFICATION and ENFORCEMENT

One reason fake-take (better known as intake) solutions are so popular, besides the fact they make tail spend procurement easy (which we’ll discuss in more detail in our next part), is that they make it easy to identify and follow organizational procurement policies, especially since they will even guide a user through the correct process once the product / service need is identified.

At the end of the day, this is just guided buying with integrated access rules (who can request / buy something), budget rules (what budgets do they have or have access to), approval rules (who needs to approve and when), as summarized in, and extracted from, policy handbooks (which can be done with traditional semantic processing and human verification).

AUTOMATIC INVISIBLE BUYING

In last-gen platforms you had to define items you wanted on auto-reorder, define specific rules for each, and manually maintain this list, and associated rules, on an ongoing basis. But, at the end of the day, for example, MRO is MRO is MRO and commodity stock is commodity stock is commodity stock and there’s no reason that you shouldn’t be able to turn over the entire category to the platform. After all, if you’re ordering the item regularly, as we described in Yesterday’s Smart Automatic Reordering, you have enough data to compute demand trends, price trends, delivery times, and EOQs (economic order quantities) and, as long as everything is within a threshold of predictability, the system should just re-order for you — and if something appears to be going off the rails, pause automatic re-order and alert a buyer to examine the situation and either do a manual re-order (which could include accepting the system suggestion), change the rules or thresholds for automatic reorders, or redefine the category / reassign the product or service.

AUTOMATIC OPPORTUNITY IDENTIFICATION

As noted in “AI In Procurement Tomorrow: Part II“, a high-performing organization tackles at most 1/3 of spend strategically on an annual basis, due to lack of manpower and time. The fact of the matter is that, unless you have a true best-of-breed spend analysis system and the experience to use it efficiently and effectively (as well as sufficiently cleansed and complete data to work on), it’s a significant effort just to do the spend analysis required to identify and fully qualify the market opportunity and shape it into an appropriate market event.

But there’s no reason that the platform couldn’t encode all of the standard analytic workflows used by best-practice consultants, identify the top product/services/categories with the most spend not under contract/management, look at the spend variability, look at current market prices and trends, look at average historical community savings data (from community, consultancy, and GPO intelligence), and evaluate and rank opportunities. And the best platforms do. (Are the rankings 100%? No — no platform has complete market data or complete knowledge of every variance to a market situation, but 90% is more than enough as that will free the buyers up to keep up with market dynamics and do real exploratory analysis that is not easily automated.)

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 developments and discussed as “the future of” Procurement 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 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 November 2018 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 to create smarter Procurement applications that buyers could rely on with confidence.

What SHOULD Procurement Officials Learn from CrowdStrike?

A recent article over on on GovTech titled What Can Procurement Officials Learn from CrowdStrike caught my eye because I wondered if it contained the most important lesson.

The article, which sub-headlined on how CrowdStrike is a useful lesson for officials who draw up government IT contracts, pushing them to ask the question of how future contracts can prepare for any unplanned outages, hit on five important point(s) of modern SaaS / Cloud-powered technology.

  • additional safeguards are needed in IT contracts
  • even with safeguards, there is still the possibility of a cyberattack, so there must be an immediately actionable disaster response and recovery plan (which vendors must be able to live up to)
  • there should be alternate backup/failover options, even if non-preferred, and that can include paper in the worst case (as far as the doctor is concerned, it’s absurd when a store shuts down in broad daylight because they lost power or internet connectivity to the bank — that’s why we have cash and credit card imprint machines)
  • one should consider specifying liquidated damages up front, to prevent long drawn out lawsuits and delayed response time from the third party (who will want to avoid those damages)
  • consider cyber insurance, either on the vendor side or your side

Which is all good advice, but misses the most important point:

NEVER ALLOW A CRITICAL SYSTEM TO BE AUTOMATICALLY UPDATED (en masse)

Now, there’s a reason the military will exactly configure a system designed for single use and LOCK IT DOWN. That’s so it can’t accidentally go down from an unplanned / uncontrolled update when it’s needed most.

For example, there’s no way any update, no matter how minor, should be pushed out to a core airline operations terminal without an administrator monitoring the update (which could be on the vendor side IF the vendor maintains a [virtual] configuration that is the exact same as the customer’s configuration) and ensuring everything works perfectly after the update. And then the updates should be propogated to the rest of the terminals in a staged fashion. (Unless you’re dealing with a critical zero-day exploit that could expose financial or personal information, there’s no need for rapid updates; and even then, there should be techs on standby after that test update is complete just in case something goes wrong and a system has to be immediately rolled back or rebooted.)

Modern operating system installations, like Windows 11, can have up to 100,000,000 (that’s one hundreds million) lines of code and since you never know where the bugs are, there is no such thing as a low-risk update. Any update has the chance of taking down the OS or the application you are updating that is integrated with the OS.

But this is not the only critical lesson to takeaway. The next is:

For critical systems, your provider must maintain backup hot-swap redundant systems!

Once a configuration is confirmed to be bug-fee, it must be propagated to the backup, which must have a backup redundant data store with all transactions replicated in real-time (so that you’d never lose more than a minute or two of updates with an unexpected failure) that can be hot-swapped through a simple IP redirection should something catastrophic happen that takes down the entire primary system. This backup redundant system must have enough power to run all critical core operations (but not necessarily optional ones like reporting, or tasks that only need to be run every two weeks, like payroll, etc.) until the primary system can be brought back online. A catastrophic event like a rolling failure from a security or OS update or cyberattack should be recoverable in minutes simply by re-routing to the failover instance and rebooting all the local machines and/or restarting all the browser sessions.

Those are the lessons. If a system is so critical you cannot operate at all without it, you must have redundancy and a failover plan that can bring you back online with an hour, max.

Advanced Procurement 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, 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 start with Procurement.

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 start with AI-Enhanced Procurement 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 Procurement Today Part I and AI in Procurement Today Part II published in November, 2018 on Spend Matters.)

YESTERDAY

TRUE AUTOMATION

Not sorry to burst the Gen-AI believers’ bubble, but true automation has existed in leading Procurement technology for almost two decades, using tried-and-true rules-based RPA that supports advanced rule construction using the full breadth of boolean logic, mathematical formulae construction, and flexible (regex, clustering, etc.) pattern matching.

SMART AUTO RE-ORDER

Threshold re-order points, adaptive trend analysis (based on sales data for quantity, expected delivery time and economic order quantity for interval and volume determination), and contract/preferred suppliers can handle this better than most stock clerks for MRO / commodity stock items.

GUIDED BUYING

All you need to do this amazingly well is RPA, rules based on contract/preferred/budget, and semantically aware keyword/phrase matching, and, if you want a NLI (Natural Language Interface), traditional semantic processing to extract the key-words/phrases that are the appropriate nouns (and items of interest).

SMART (ADAPTIVE) AUTOMATIC APPROVALS

This is just RPA using a rules based workflow, thresholds, and exception-based decision pattern analysis to allow the thresholds to be adjusted within a range based on an approval and/or the platform to infer the thresholds/rules actually being applied by the approver using pattern identification (based on significant factor analysis or fingerprinting) across exceptions to suggest the necessary rule modifications.

ERROR PREVENTION

This just requires valid pattern definition, context-based range analysis, and outlier detection (using clustering, curve fitting, or trend analysis). Anything that can’t be done with the right mix of these methods can’t be done reliably.

M-WAY MATCH

Anything you can’t do with RPA using rules-based workflow, identifier matching, and confidence-based pattern matching and suggestion SHOULD NOT BE DONE. Moreover, anything that can’t be matched with certainty should be flipped back to the supplier for correction/completion (if key identifiers were missing), possibly with a suggestion/question (for e.g. does this invoice correspond to PO 123XYZ?).

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 Procurement platform, that is easy to understand — and that was described in detail in the doctor‘s 2019 articles 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.