Operationalizing the Pocket Cube for Exact Purchasing Part I

A few weeks ago, we not only told you that Exact Purchasing is a Pocket Cube, but we broke it down and defined each octant for you, as well as indicating which categories of goods and services were most likely to fall in each octant (with the disclaimer that there is variation between industry and sometimes even companies in the same industry based on size and focus).

This was a great start, but once you understand the breakdown, the next step is understanding how you go about sourcing and procuring the categories in each octant. In this follow-up series we dive in and define the core technologies you will use for each octant as well as their focus.

Today we start with the Transaction focussed-octants.

Low Complexity, Low Risk, Low Impact: Transaction Capture

This is the most “non-critical” of all of the categories in the pocket cube … the true lower left. The impact is minimal if a purchase is delivered late, or has to be replaced with another order. It’s so unimportant compared to literally every other category that, with the right technology, you can literally automate all of it without any worry whatsoever — because the worst case is an ASN/delivery doesn’t materialize and you re-order from someone else, a shipment doesn’t meet spec and you return it and reorder (from someone else), or a service isn’t up to snuff and you don’t (fully) pay for it.

The core technologies are the following:

  • (Strategic) Sourcing: (Deterministic) Autonomous Sourcing
  • Supplier Management: AVLs (Approved Vendor Lists)
  • Catalog Management: APLs (Available Product Lists)
  • Contract Management: Auto Creation and Auto-Sign
  • Procurement (Channel)*: Goods PO (Item Master), Service PO (Fixed Cost Service), PCard Purchase (One Time)
  • Monitoring: ACK, ASN, and Receipt in the Procurement System

Basically, once you define what the categories are, what the product requirements are in each category, and which vendors you have vetted as being real and “safe” enough to source with, you automate the entire sourcing process end-to-end. (You can even use experimental AI here if you want — the vast majority of the time worst case is that a wrong order is made, and you will have to inform the system of its error, return it, and order again. Unless, of course you ask for 100 10g pot for your nursery, and it interprets that as you needing 100 bags of 10gs of pot and orders 1,000 grams of marijuana for you off the dark web in a state where that’s still illegal … but that is rather statistically unlikely so you’re probably safe.) Once you have your AVLs and starting APLs that capture the specs, as well as your standard RFP/RFQ templates, classic robotic process automation can do the entire event from trigger (stock falls before a certain level, an approved buyer request comes in) to final payment on final delivery on final receipt. You step in if a human detects an issue, and otherwise, you just let (for what is typically tail spend) the process flow.

Low Complexity, Low Risk, High Impact: Continual Transaction Monitoring

The difference between this category and the last is that while the products are simple, commodity, and very low risk, you need them to keep operating day to day and you can’t be without them for too long. However, the lack of complexity and risk means that this is another category you heavily (heavily) automate and only step in to review the award recommendation to make sure the specs are met. You set up additional monitoring, and the system kicks off another event or PO (to a backup supplier) the minute an ACK or shipment from the primary is too late (and even sends a cancellation to the primary for breach of terms), again involving you only to verify an award (if the award is not one that has been previously verified, since a re-sourcing/re-order should be automatic).

This can again be handled mostly by classic RPA, but some AI will be used to monitor for new products from the existing supply base that can be used (even if the supplier hasn’t supplied the category/product before), because the human award review will ensure that new products get human approved before they are purchased.

  • (Strategic) Sourcing: (Deterministic) Autonomous Sourcing with Award Review
  • Supplier Management: AVLs and Performance Monitoring
  • Catalog Management: APLs and regular review and approval of new product options
  • Contract Management: Auto-Creation and Auto-Sign
  • Procurement (Channel)*: Goods PO (Catalog), Service PO (Fixed Cost), Contract Invoice (Fixed Payment Schedule), Blanket PO (Fixed Delivery Schedule)
  • Monitoring: ACK, ASN, Receipt in the Procurement System; Lead Time, Delivery, Quality Trends in the Inventory Management System;

* Unless the Channel-Master Joël Collin-Demers says otherwise.

The Proliferation of AI-Generated Content Guised As Research is Damaging Our Space!

Real Research Requires Real Human Intelligence and Effort

(I’m not here to be nice. I’m here to educated and inform. Something most sites, including LinkedIn, are doing very little of lately!)

Joël Collin-Demers recently made the understatement of the year when he said 15 functionalities comparing ZIP to Jaggaer isn’t analysis/comparison, it’s pattern-matching by an LLM with no domain context. At best it’s unhelpful. At worst it points procurement leaders toward the wrong tools entirely in response to, with no due respect, a complete crock of AI sh!t published by TEEM.Finance (and reported by a TEEM member who claims instant supplier sourcing & portfolio analysis, with AI# in the tagline, which is another crock of AI sh!t that I must also address).

First of all, at best someone selects an inferior product, wastes a lot of time and money, and ends up in a situation where they are still limping along trying to get basic tasks done with yet another platform that doesn’t come close to delivering on its promise while doing nothing to deliver an increased return on the large amount of money spent on SaaS supposed to solve the organization’s Procurement pain.

At worst, it points the buyer to a product that costs five times as much, doesn’t even accomplish core use cases (if the product works at all outside the demo lab), and results in an absolute disaster upon implementation (with next to zero adoption and more bypass than the organization has ever seen due to the lack of core capability) that results in the organization having to issue another RFP and go through the whole process again with a jaded and angry employee base who expects nothing good will come of it.

The danger of a poor Procurement product pick cannot be understated or underestimated. Nothing will cripple an overworked and under-resourced Procurement department faster than a bad platform (and doubly so if it contains [Autonomous] Gen-AI)!

So, with so many bad product comparisons and maps out there (including Gartner’s and Forrester’s), which I have tackled repeatedly on Sourcing Innovation, why the need to target this one? Because while Gartner and Forrester can be relied on to give you the generally best bet from among their customers which have been confirmed to have relatively equal core functionality,

  1. a random comparison between two different players based on a mere 15 data points that are randomly selected and called “use cases” only guarantees they both exist in the Source-to-Pay space,
  2. any use of AI is flawed from the get-go,
  3. and any comparison that scores Zip 94% and Jaggaer 100% is obviously a complete and utter crock of AI generated sh!t

Let’s revisit Joel’s comment where he calls out Solution Map (which Hackett will hopefully keep).

  • Over 500 clearly defined functions are scored on a scale of technical progression (from 0 to 5). Not 50. And definitely not 15!
  • A 100% based on TODAY’S known Best-In-Class functionality would require a Solution Map score of 4.0. Most suites averaged in the 2.5 to 3 range (average to slightly above). Jaggaer is no exception (and Zip is still far from a suite, it’s I2O slowly adding baseline procurement capabilities, not S2P). (Remember, I DESIGNED the core Sourcing, Supplier Management, Analytics, and Contract Management [this one joint with Pierre Mitchell] maps and DESIGNED the common core across all the maps for Solution Map 2.0. And I scored them for 7 years.)
  • They DO NOT cover everything … there’s always innovation, and always edge cases we ignored (as the goal was to produce a useful map for the majority).
  • They were TECH and CUSTOMER SATISFACTION only. And you need to assess more than that to select a vendor (as per our Successful Vendor Selection series). (And, sometimes, you have to figure out what you should even be looking at, which is why I penned a 39 part series to walk you though the thought process (and Joel, stop complaining about having to write an 8,500 word series on P2P functional requirements … you’re just getting started).
  • And they compared apples-to-apples. This report compares apple-to-oranges, as it’s conclusions are “choose JAGGAER ONE if your organization manages direct materials, manufactures products, or operates in a heavily regulated sector” or “choose ZipHQ if your procurement team needs to configure complex approval workflows across IT, Legal, and Finance without technical resources“, which effectively boils down to “choose Jaggaer if you need Source-to-Pay, and “choose Zip if you need Intake to Orchestration” which is a recommendation that DOES NOT require you to read a report to figure out. All you need to know is
    1. Jaggaer is Source to Pay.
    2. Zip is Intake to Orchestration

    and the answer becomes pretty f*ck!ng obvious!

In order to be useful, at a bare minimum, this is what a comparison needs to do. Define the product domain being compared. Identify the extent of core, should have, and nice-to-have functions required by a product to support the product domain (based on standard functionality and domain use cases). Create a maturity definition for each function. And then use HUMAN INTELLIGENCE to score each product selected for inclusion (on actual demos from the vendor or willing partners and/or current customers). Not bullsh!t Gen-AI that can be fooled by bullcr@p marketing!

Anything less is not a meaningful product comparison. It’s simply an exploration against a few points of interest.

Now, if that’s human led, that can be useful as supplementary material in a decision. After all, the Solution Map will merely grade functionality like flexible workflow configuration on a standard scale but won’t track specifics of how it’s done, how user friendly vs. partner friendly vs. vendor friendly the configuration is, actual customer use cases where the workflows had to intersect 3 or more departments and average customer sentiment on that feature, or provide any other color that might help you make a decision when two solutions look acceptable from a technical and customer satisfaction perspective.

So, if TEEM.finance or someone else wanted to hand pick the most common / relevant use cases, dive in, do a human review, and present their analysis as key points to consider — that would be awesome, and a great excuse to keep writing (so long as said writing is NOT turned over to [Gen-]AI)!

After all, I’m not going to do it (because, frankly, I’m not interested in seeing the same old functionality over and over [as I already saw, and wrote, about it all multiple times — and you should be able to access that if you have a Hackett Membership] as most of the suites have done little to upgrade anything in the last few years as they have switched private equity ownership and bled key talent), and neither are most analysts (who have to cover more vendors than most can handle — remember, there are over 700 vendors in our space, and if you don’t believe me, I again refer you to the mega-map of 666 vendors SI compiled for you).

But it has to be a real review, based on a real demo and/or real discussions with customers, and not AI in any way, shape or form. Otherwise, at best, it’s sl0p. At worst, it’s the written word equivalent of toxic waste. And let’s NOT forget that and continue to fight against the use of AI where AI should NOT be used!

Now, as to the other crock of sh!t, namely instant supplier sourcing & portfolio analysis, with AI. There’s no instant. Yes, there are some great tools out there that can identify a list of potentially relevant suppliers in seconds, compared to the weeks of manual searching you might have had to do in the past, and there are tools out there that can automate sourcing ONCE you have identified your precise item needs, your price tolerances, and your pre-vetted supply base … but, guess what, AI CAN NOT DO all the stuff in between, especially if the product (or category) is high-risk, high-complexity, or high-impact (under the Busch-Lamoureux Exact Purchasing Framework).*

You have to vet the supplier. You have to make sure it’s still operating, the license certificates, registrations, and insurance are both real and current, that the products are still offered, that they are real (by getting a sample), that they will suit your needs, and that the supplier is capable of producing the quantity you need in the time-frame you need it in. You then have to qualify the risks and impact, sign off on them, and enter the supplier (and approvals) in the system. Then you have to define the sourcing project, your tolerance, and your conditions for bid acceptance. YOU! Not BS AI!

In other words, there’s nothing instant about it … and for a highly complex product, or category, that could be days or weeks of manual human work even after all the tactical drudgery is automated for you. So, while a tagline that said faster supplier sourcing and portfolio analysis, with AI, would be 100% true, a tagline that says instant is inherently false. (Unless, of course, your risk tolerance is sky high and you don’t care if the worst case scenario hits and destroys your business … so if you’re looking to be the next Eddie Lampert and dismantle a 100+ Billion company [in today’s dollars] in record time, go for it!)

# name and image hidden as I’m not entirely sure it’s not a bot auto-publishing AI slop

* to be totally honest, you can’t even expect AI to be reliable for low-risk, low-complexity, and low-impact products/categories either, but since the impact of the mistakes it’s going to make will probably require less manual effort to clean up than dealing with all of those products manually, you can potentially live with it

AI CANNOT TELL YOU WHAT TO DO!

And I’m so glad I’m not the only one saying it!

The (Strategic Sourcing Decision Optimization [SSDO]) Grand Master himself Paul Martyn recently wrote a great post on LinkedIn that made this exceptionally clear and how the real problem is knowing what to do.

Paul starts off with three critical statements:

  1. AI can tell you what’s happening
  2. AI can’t tell you what to do
  3. In sourcing (procurement) the hard part isn’t visibility, it’s choice.

More specifically, it’s making a decision when every decision has tradeoffs, constraints, and (sometimes dire) consequences.

Unless you have an operating model to make those decisions, powered by technology that can actually help you adhere to the constraints, make the tradeoffs, and understand the consequences, the best case with AI is you get overwhelmed with the complexity of what’s happening.

So if you want to be buried in data and complexity and pretend you know what you are doing, there are dozens of BS AI players ready to help you.

But if you want the ability to make good decision, understand tradeoffs, restrict your inquiries to scenarios that adhere to constraints, and model the potential consequences when things go wrong, you need decision optimization with multi-objective capability. That’s Coupa (Trade Extensions). Or Jaggaer (Bravo Solution). Or Keelvar (just Keelvar). Not some BS AI startup offering nothing more than a clod or chat, j’ai pété LLM wrapper.

And if you want to know how to build the right operating model backed up by the right multi-objective optimization model(s) (and save millions while reducing risk and increasing quality), you contact Paul Martyn. He’s saved Billions. (Whereas in 94% of companies, AI has effectively saved 0.)

Now for those who don’t know, not only am I one of the last original (independent) analysts standing in our space (20 years doing SI next month), but I am likely the last original strategic sourcing decision optimization model builder left standing too. (Mindflow [acquired by Emptoris], 2000. First multi-line item model. Before CombineNet [acquired by SciQuest, renamed Jaggaer]. Before Emptoris [acquired by IBM and sunset]. Before all of them. Twelve years before Keelvar. First model to do more: Trade Extensions, acquired by Coupa.)

So unless Thomas Sandholm or Arne Andersson want to come out of retirement and recommend someone better — it’s Paul Martyn. No one still active in our space goes as far back or has worked with as many platforms as he has. (And I helped a PM/Consultant who worked at 2 different optimization providers get hired at 3 others over the past 20 years, and even that doesn’t match Paul’s resume!)

The Dark Ages Were Bad …

… and, after most of western society was likely still recovering from the long term devastating effects of the volcanic winter of 536, that probably set us back 1,000 years in the grand scheme of societal development and civilization advancement.

… but that’s a minor setback compared to what’s in store for the Age of Retardation that is coming!

But let’s back up. Consider this recent article on LinkedIn by Karl Waldman on this Medieval Lesson: Cutting Skilled Workers Hurts Long-Term Growth where Karl discussed why the age of great cathedrals came to an end.

It had nothing to do with lack of wealth — there’s always been wealth, all that changes is who controls it — or a lack of interest — the Christian religion has consistently held more than its fair share of dominance through Europe from the building of the first great cathedral until the present day (and whenever it loses control in one country it finds a new one to take over). It was lack of skill.

As per the post, the European cathedral builders developed an ornamental tradition so specialized it took decades of guild training to master. When the Black Death killed a third of Europe’s population, the skilled tradesmen disappeared because the training pipeline that produced it had been destroyed.

Now think about what we’re doing today.

We’re pretending AI can do the work of experienced professionals and cutting them left, right, and centre. We’re pretending we don’t need junior workers (because they do the tasks that AI seems to do okay) and not hiring. We’re walking all of our institutional knowledge out the door, as well as our ability to react and fix exceptional situations with creativity (that will break AI when they arise), while ensuring there’s no one around to absorb even a morsel of that knowledge and skill.

We’re not only replicating the end results of the black plague at a rate that’s even faster than the black death spread across Europe (it took about 7 years with the first 4 being the worst) — and not only are we destroying all of our capability to build tomorrow’s businesses, but we are throwing away all of our capability to even maintain today’s businesses if something goes wrong! After all, our current staffing levels are minimal, and most of the people we have left are in cognitive decline thanks to the AI they are being forced to use for “productivity” reasons.

When the next unstoppable pandemic hits, and wipes out all of our silver haired experts with no skilled talent to replace them, we will enter the Age of Retardation and our global society will collapse faster than the Aztec Empire. (And if you don’t know how fast one of the greatest civilizations in Central America fell, maybe you should brush up on your history!)

If Instead of Trying to Replace, You Redeployed People — What Could You Accomplish?

The big push for AI is not to help you, but to achieve every executive’s dream of a perfect utopia where they have 24/7/365 robotic workers they don’t have to pay, feed, or even provide safe working conditions for. Where they have endless slave labour, workers with no rights, and only have to worry about counting the virtual dollars in their endlessly increasing bank accounts.

But anyone with a working brain, who doesn’t live in a fantasy world, who hasn’t given into the cognitive surrender brought on by excessive use of Gen-AI, knows that reality is far, far, away. The algorithms are dumber than doorknobs, hallucinate to various degrees on almost every response, and are only good at sounding right, NOT being right. Intelligent humans are still needed, more than ever (as AI has NOT changed the fundamentals of Procurement. It HAS Only Strengthened Them.)

While there is very little Gen-AI can do, there is a lot traditional AI, and even more that (A)RPA (the real agentic technology) can do if properly defined, constrained, and deployed — and in many back office functions, a lot of the data analysis and processing (still) done by humans can be done by machines (and could be done by machines for at least a decade — if not two). In Procurement, we’ve had invoice technology that could automate invoice processing error free 95% to 98% of the time for over a decade, auto-reorder technology based on stock levels, forecast changes, or production schedules for over two decades, technology for automatic contract creation based on clause templates and clause libraries for almost as long, and sourcing automation since the first major sourcing platforms hit the market.

If this was properly done, and 80% of the tactical bit-pushing time that, with fire-fighting, constitutes about 90% of a Procurement professional’s time, was eliminated — imagine what could happen. All high impact and high risk categories could be strategically sourced. All complex categories could be examined in detail, BoMs and production technologies optimized, and supplier relationships (and thus supply assurance) strengthened. And that’s just the start.

Procurement would have time to examine, shape, and even divert (and eliminate) demand. From the classic example of negating the need for more printers, paper, and printer ink by just ensuring every employee had a second monitor at their desk and a tablet for mobile document receipt and review to a more modern example of elimination of expensive cell phones for non-sales on-demand employees by Whatsapp (and cheap subscription) mandates or elimination of expensive office leases in areas where most employees are/work remote most of the time and only a few hot-swap desks at a work-sharing centers (and the ability to book / rent meeting rooms for occasional meetings) is acceptable (as they all use laptops anyway), demand shaping can result in major organizational cost savings.

Moreover, Procurement could even go beyond demand shaping and reduction to true value identification by helping the departments they serve define, and redefine, what value actually is and how best to achieve that value when going to market.

A great example of this is how IKEA approached its use of AI in customer service. As per this great summary on LinkedIn by Alberto, when IKEA’s AI bot deflected 47% of calls, instead of calling it a win, firing half it’s staff, and moving on, IKEA did two things.

  1. They asked what the AI bot wasn’t helping with and what concerns still had to be handled by the customer support team.
  2. They retrained and redeployed over half of their customer support team to handle the most common inquiry, and built a ONE BILLION DOLLAR business around it. (So Far! It’s IKEA. And they’re just getting started.)

To clarify, many (potential) customers weren’t calling just about missing parts or issues understanding the assembly instructions. They were calling to ask what they should buy to meet their needs. “What works in a small living room.”

They needed basic interior design advice. So IKEA trained a significant portion of their customer service workforce as interior designers, and generated over €1 billion in additional business in the first year simply by spending the time to figure out what customers needed before they could make a purchase decision (interior design advice and the identification of products IKEA offered that would meet the design criteria) and giving them exactly what they needed.

Imagine how much value Procurement could add to the business if, instead of reducing staff with automation, the C-Suite retrained (or, if the existing staff doesn’t have the education/experience, replaced that staff with an equal amount of more senior personnel) and redeployed this suddenly freed up staff to act as an internal value identification consultancy that brings Procurement (cost management, risk mitigation, and supply assurance) best practice to the rest of the business.

Think about that before you try to replace real intelligent talent with unintelligent talentless AI (and find yourself in the bog of eternal stench that results from your lack of foresight).