Category Archives: Technology

DO NOT CONFUSE THE ILLUSION OF UNDERSTANDING WITH ACTUAL UNDERSTANDING!

Because if you do, you will believe AI is Actually Intelligent when, in fact, as we have pointed out again and again and again, it is Artificial Idiocy, and the best modern technology only uses AI for thunking, not thinking, as thinking needs to remain the domain of us humans (before X robs us of our ability to use actual words).

Not only is there no AI, but when you type a command, there isn’t even any understanding by the algorithm of what you are asking for when you type a query into an AI tool. NONE. It’s all based on a statistical algorithm that uses pre-computed similarity probabilities to infer what you are asking. That’s not understanding. Not even close.

The Guardian recently published a long read article on Weizenbaum’s nightmares: how the inventor of the first chatbot turned against AI that anyone who is even mildly contemplating an AI tool needs to read. Slowly and carefully. Three times.

Weizenbaum, who was a mathematician, computer scientist, and a student of psychoanalysis, was one of the founders of modern artificial intelligence who not only invented the first chatbot (Eliza), but also built early (mainframe) computers (back when they used vacuum tubes and took up entire rooms) for the University he was studying at, General Electric, and the Navy. In the 1960s, he was part of Project MAC at MIT, a Pentagon program for “machine aided cognition” that perfected time-sharing, created in-system messaging (like instant messaging or early email), and created new tools for word processing.

He was also one of the first to think about the implications of Artificial Intelligence years, if not decades, before anyone else and one of the founders of computer ethics. He was a genius, and when he said that Artificial Intelligence is an “index of the insanity of our world“, he was totally right — and he was right five decades before AI became the buzz-acronym-du-jour. Few people effectively saw that far ahead in technology, so maybe we should sit back and listen. Carefully.

So please take the time to read Weizenbaum’s nightmares: how the inventor of the first chatbot turned against AI and realize that AI is not the answer. Deterministic algorithms developed by smart people that have studied the problem, tested their assumptions, and been consistently proven reliable are the answer. They may be based on machine learning, but machine learning that is expertly selected, tuned, and monitored by validation code that detects when the algorithm is not performing to expectation and interjects a human into the process. Not a multi-layered pseudo-random statistical algorithm that randomly predicts the next seven days worth of orders, starting on Monday, are 210, 198, 307, 250, 185, 250, and 3095 and thinks everything is A-OK even though the store is closed on Sunday.

That’s Right, You Do NOT need AI for Automation!

In our last article, we stated that our space was full of Overpriced “AI” you don’t need in source-to-pay, and one of our three examples was “Sourcing Automation” in Sourcing. To be clear, we’re not saying you don’t need automation — the whole point of software has always been efficiency through automation — we’re saying you don’t need “AI” automation.

The reason we’re doubling down into this topic is that we know there are a number of vendors pushing AI Automation and while automation is very good, AI is just not needed. But we know you’re going to get pushback if you echo the doctor‘s viewpoint here, so we’re going to double down into the details and explain why no AI is needed for great automation.

In our last post, we noted that, at its simplest, it’s the ability to auto-source a (set of) product(s) or service(s) once the need has been identified or the request approved. It’s useful, but you don’t need AI to accomplish this, just good-old rule-based (workflow) automation. After all, it’s just

  1. instantiating a new RFP (which can be done if you have a template tied to the product/service types)
  2. distributing it to known, approved suppliers (which is easily done if you have supplier management that tracks approval status and associated products/services)
  3. collecting the bids (automated submission management through a portal or provided spreadsheet for upload)
  4. selecting the lowest bids and marking it as an approved award (simple analytics)
  5. assembling the contracts (with templates, it’s just sucking in the supplier details, product details, and bids using tag-based search and replace)
  6. push it into the e-Signature portal (via the API)
  7. alert the buyer when the contract is ready for signature (via alerting)

1 You just need templates, and good providers have had those for a long time. And “AI” is not going to invent one you can trust.*1 It’s not too hard to tag your (provider’s) existing templates to all of the products and services you buy, and you only have to do it once.

2 When you onboard a supplier, you should tag it as approved, associate it with the products and services it is approved for, look up its risk and environmental scores, and track its performance over time. If it’s performance drops, it can automatically be suspended from consideration for new projects using old-fashioned business rules that will prevent it from being included in events it shouldn’t be. Thus, approved supplier management isn’t that hard to do and simple saved searches find all the suppliers that should be automatically invited to an event.

3 RFP and e-Auction software has been around for 25 years, so don’t let anyone ever tell you that you need AI.

4 If you’re trying to administer an award subject to constraints or goals, that’s good old fashioned strategic sourcing decision optimization. That’s not AI. MILP using classic tableau and interior point algorithms works just fine in predefined scenarios that suck in the organizational constraints … that leading SSDO (Strategic Sourcing Decision Optimization) providers were building over two decades ago.

5 Contract templates should be prescribed by Legal Counsel, not by software flipping random bits using layered statistical algorithms in combinations no one truly understands. The vendor will provide you with templates, but you should be the one reviewing them to make sure they are too your liking. This includes the standard clauses and variation by geography, industry, or risk you want to address.

6 Software integration happened for decades before AI.

7 Alerts have been standard software capability for decades, no AI needed.

If the right data is captured, and the right rules are written, standard workflow-driven software systems can be fully automated without any AI. The only thing preventing them from going from one step to the next is the human verification checkbox being completed. You can turn that off and they will work just fine. So, again, don’t be fooled that you need AI for Sourcing Automation, because you don’t. And with rules-based systems, you’re guaranteed you won’t get the odd, unpredictable result, every 10th sourcing project (because AI is only statistically effective, which means, eventually, it will always fail).

*1 Sure “Generative AI” can generate one. But there’s no guarantee it won’t be hot garbage.

Yes Mid-Markets, 120K is More Than Enough for Source-to-Pay!

the doctor is sure that by now you have certain (mega-)suite vendors whispering in your ear that you really need their full 1 Million+ (annual subscription) S2P solution to maximize efficiency and savings (and that the doctor was crazy*0 when he told you that you should be able to get a sufficient Source-to-Pay solution for 120K a year), which, while possibly true stated that way, you don’t need to spend nearly that much to maximize your ROI.

But how do you maximize ROI without necessarily maximizing savings and/or efficiency? Simple! The same way you optimize profit by optimizing COGS vs. increasing volume. Just like every $1 of savings goes straight to the bottom line vs only $0.10 of revenue, every dollar you don’t spend on a technology solution goes straight to the bottom line vs. only squeezing out an extra 1% on savings.*1

But the best way to see this is to, gasp, do some math! Let’s take three mid-markets at 250M, 500M, and 750M. We’ll use industry averages for COGS (with 33% salaries & contractors; 2% utilities; 5% rental; and 20% amortization/depreciation) and assume 40% external spend. Depending on the industry, external costs can go to 50% or more, but not much in the Mid-Market (MM). We’ll assume an average 5% savings potential and 80% spend addressability over 3 years (as some existing contracts will be long term and not addressable in the short term, and some tail spend will just be too small / one time to ever bother with). We’ll assume that a base solution can achieve 80% of that savings potential, or 4% over three years (if there is sufficient manpower to address all the relevant categories [semi]-strategically).

 

Size 250M 500M 750M
Addressability (80% of 40%) 80M 160M 240M
Savings Potential @ 4% 3.2M 6.4M 9.6M
3 Year Cost 360K 360K 360K
ROI 8.8 17.6 26.4
Savings Potential @ 5% 4M 8M 12M
3 Year Cost 3M 3M 3M
ROI 1.4 2.7 4.0

 

Now, what type of ROI would you like to see if you are a 250M MM? A 1.4X ROI or a 8.8X ROI? the doctor knows what type of ROI he’d like to see! Also, if the mega-suite provider cuts the price in half, it only doubles the ROI to 3.2X. Barely acceptable, and you need the manpower to identify the full savings potential and everything to go perfectly to realize it. (What’s the probability that this will hold true continuously for three [3] years? Zero Percent. 0%)

Unless you have a (very) large category over 10M (where the savings potential on that category is 500K), the reality is that the 80% solution you will get by an average across-the-board solution / self-assembled platform-powered BoB suite will provide you an ROI that far outshines what the oversized, overpriced solutions will do for you as a mid-sized business. (Those suites are only needed for 1B+ enterprises where there are 50M to 100M+ categories where an extra 1% makes a huge difference.)

the doctor loves sourcing optimization, but it typically won’t find that much savings beyond what you can find with good spend analysis on RFP data in a category < 5M. (It might take a few hours of spend analysis, but you will get 80% of the savings with intelligence. If the vendor includes an affordable optimization module (2K/month; likely with model size caps), then you should use it on every category, if just to get a baseline, as you will get a good ROI from the module with continuous use, but if they want 10K/month and you are a 250M business, you likely won’t get enough of a return, especially since most of your categories aren’t that large or complex. Note that if you are a 1B+ multi-national enterprise, the story is the exact opposite. You absolutely need it and in your well managed categories, you won’t identify enough savings without it.)

For most categories, all you need to do in sourcing is 3-5 bids, side by side unit cost and total landed cost (TLC) comparisons, supplier award selection with RFP (spend) analysis, contract cutting to capture the price, configured POs in the eProcurement system to capture the contracted price, and line-item match on the invoice to the PO to make sure you’re paying what you should be. This is two-decade old tech now, but more than sufficient, when properly implemented and enforced, to capture 80% of the “savings” (or cost avoidance) in a category. Procurement savings come more from the proper implementation of a process than from technology that enables that process. What technology does is make it easy to do the process efficiently and effectively because it can guide you through the process, prevent you from missing steps or making mistakes, provide you the insight you need to make the best decisions, and even train you on best practices you aren’t familiar with. And allow you to repeat the process many more times on many more categories in a much shorter timeframe than if you were trying to do it all by hand.

Plus, the technology will allow you to do more with less, so you can minimize the need to expand the Procurement team as the company grows. Remember, good people cost $$$. In fact, a fully burdened high-end resource will cost as much as you pay for the tech, if you are paying the right price. This means that the tech will not only provide you an ROI on measurable cost reductions, but a measurable cost avoidance as you grow as you will not need to add as many people to a Procurement department that will become more efficient over time (as more and more tactical tasks get automated, freeing up the team to focus on value-add tasks). (Remember, tech never replaces the people you need, it just makes them many times more efficient so that you only need one or two high performing individuals for a function vs ten for one that is poorly managed; allowing you to add those ten resources elsewhere to produce more product or grow the business further. However, remember that Procurement does more than one function, so you may still need those 10 people for contract management, supplier development, additional strategic sourcing events, etc. but you won’t need them processing paperwork.)

So don’t overpay for S2P tech. You absolutely need S2P tech, but overpriced tech won’t get you the ROI!

*0 they may be right, I may be crazy … but it just may be a lunatic you’re looking for …

*1 An extra savings of 10% on a maximum savings of 10% leads to a maximum additional savings of 1% overall on a single category. In inflationary times, which we are now back to, you’ll never find more than 10% slack in the TCO of any category. In fact, you’ll do good to find 5%, which means going from average capability to advanced capability will only shave an extra 0.5% off of the total category spend on average.

Don’t think that these inflationary times are going away anytime soon. Supply chains are at their shakiest thanks to both the pandemic and the repercussions thereof, the rapid increase in climate change which has led to a rapid increase in natural disasters, the increased geopolitical destabilization around the globe, and the rebelling workforce, many of whom have gone from living barely above the actual poverty line (relative to where they live) to below it. Now add that to the flat and recessionary economic conditions in most major GDP players, and we won’t be seeing good times ahead for quite a while.

SupplHi – A Best of Breed Supplier Management Platform for Industrial MRO

In a recent article, we noted that It Does Not Matter Where You Start, You End with BoB in SXM, and if you in the business of industrial MRO, it’s likely that your BoB will be SupplHi.

SupplHi is one of the broadest, and deepest, solutions we’ve seen for Industrial MRO (and Direct in general, but the fact that they have 90% of the supply base in certain MRO categories makes them extremely suited for that categories, as well as the fact that they have the deepest out-of-the-box categorization for MRO which includes 2,600 categories across 250 families in 45 groups of supply [request download] makes them extremely well suited to MRO), covering (at least) baseline functionality across (at least) 7 of the 10 core areas and information tracking in 2 more (Quality and Performance), a claim that only a select few vendors can make in Direct and, as far as we know, none can make in MRO (Maintenance, Repair and Operation) [for both equipment and services).

SupplHi can be summed out as the Closed Loop Supplier Management Hub for your Industrial MRO Supply Base, which not only allows you to centralize all of the data (if not manage it natively) to support all of the supplier related activities, but also gain visibility through multiple levels of the supply chain while evaluating potential (Tier 1) suppliers for risk, compliance, and performance.

If you tried to read that, you’ll realize that’s a mouthful and tightly packed with impressive claims, so let’s talk about how SupplHi supports the Industrial MRO/Classic Direct lifecycle, and then quickly overview the main features.

  • Discovery: a network of over 100K suppliers that is growing daily focused on Industrial MRO
  • Onboarding: a plethora of features and apps to make it easy to onboard suppliers
  • Evaluation: in addition to collecting information on products AND capabilities, collect and store public/shared information on risk, sustainability, certifications, perform due diligence, etc.
  • Monitoring : track all relevant quality, compliance, sustainability, risk, and performance data
  • Management/Development : performance evaluation, sustainability monitoring (including Scope 3), non-conformity management, and development campaigns
  • OffBoarding : status marking, performance evaluations, (de)qualification, etc.

… and if a supplier corrects an issue (lack of certification), adds a capability (factory upgrade), address a major risk, etc., then the cycle can begin again with (re-)onboarding. It’s truly closed loop — and the (pre-defined) master data management capability is among the most extensive data models we’ve ever seen.

The SupplHi site markets a large number of capabilities (which it calls apps, of which there appear to be 25+, in addition to integration services, ad-hoc services, etc.), but six key capabilities that make SupplHi stand out are:

  • DEEP EXTENSIBLE PROFILE: it’s MDM capability allows it to track any and all data you need to track on the supplier, including products, capabilities, certifications, sustainability ratings, quality (metrics), performance metrics, sub-tier supplier linkages, etc.
  • DOCUMENT MANAGEMENT: all product specs, certifications, (insurance) certificates, contracts, assessments, etc.
  • CERTIFICATION AND BANK ACCOUNT VALIDATION: in the platform, no reliance on a buyer NOT fat-fingering a critical piece of info.
  • MULTI-TIER VISIBILITY: few platforms have this, but due to their deep knowledge of the Industrial MRO space and extensible Master Data Management approach that allows suppliers to identify their suppliers, they can map, and visualize, a typical supply chain to the source suppliers even during the Scouting/Discovery phase
  • SUPPLIER CAPABILITY TRACKING: it can track the types of engineer specialties, the machinery available, international codes/standards supported, sub-tier suppliers by category (down to level 3 in the category tree), policies, energy efficiency, and other data required for a proper assessment of an equipment and/or services Industrial MRO supplier
  • ACTION MANAGEMENT: simple information requests, quality issues, development projects (as part of a campaign), etc. all fall under actions that the platform can manage

In other words, as we said before, it’s broad, it’s deep, it has direct capabilities that only a few competitors posses, and it’s built-in category framework and extensive supplier network make it unparalleled in Industrial MRO.

You don’t have to just take our word for it. You can also see:

Don’t Trust an Analyst Firm to Score UX and Implementation Time!

A post late last month on LinkedIn started off as follows:

“If you’ve ever read any research papers or solution maps on procurement tech, you’ve probably figured out a couple of things.

1. It’s confusing and overly complex
2. It doesn’t cover the basic, most obvious-of-the-obvious fundamentals that everyone needs to consider.

These are:

– User interface and user experience (UI/UX)
– Ease and speed of implementation

Why don’t they do this?

Honestly, I don’t know the answer.

The cynic in me says it’s because their biggest paymasters have a horrible UI/UX and require a very complex and lengthy implementation.”

This really bothered me, not because UX and implementation time aren’t super important, they are, and they are among the biggest determinants of adoption (which is critical to success), but because anyone would think an analyst firm should address this.

The reality is that no proper analyst will attempt to score these because they are completely subjective! As a result:

  1. There is no objective, function-based/capability-based scale that could be scored consistently by any knowledgeable analyst on the subject and
  2. What is a great experience to one person, with a certain expectation of tech based upon prior experience and knowledge of their function, can be complete CR@P to another person.

Now, some firms do bury such subjective evaluations on UX and implementation time in their 2*2s where they squish an average of 6 subjective ratings into a dimension, but that is why those maps are complete garbage! (See: Dear Analyst Firms: Please stop mangling maps, inventing award categories, and evaluating what you don’t understand!) So no self-respecting analyst should do it. As an example, one analyst might like solutions with absolute minimalist design, with everything hidden and everything automated against pre-built rules (that may, or may not, be right for your organization and may result in an automated sourcing solution placing a Million dollar order with payment up front for a significant early payment discount to a supplier that subsequently files for bankruptcy and doesn’t deliver your goods) while a second might like full user control through a multi-screen multi-step interface for what could be a one-screen and one-step function and a third might like to see as much capability and information as possible squished into every screen and long for the days of text-based green-screens where you weren’t distracted by graphics and animations and design. Each of these analyst would score the same UX completely different! On a 10 point scale, for a given UX design, three analysts in the same firm could give scores of 1, 5, and 10, averaged to 5 … and how is that useful? It’s not!

(And while analysts can define scales of maturity for the technology the UX is based on, just because a vendor is using the latest technology, that doesn’t mean their UX is any good. New technology can be just as horrendously misused as old technology.)

The same goes for implementation time. An analyst that mainly focuses on simple sourcing/procurement where you should just be able to flick a SaaS switch and go would think that an implementation time of more than a week is abysmal, but an analyst that primarily analyzes CLM and SMDM would call BS on anything less than six weeks and expect three months for an implementation time. This is because, for CLM, you have to find all the contracts, feed them in, run them through AI for automated meta-data extraction, do manual review, and set up new processes while for SMDM you have to integrate half a dozen systems, do data integrations, cleansing, and enrichment through cross-referencing with third party sources, create golden records, do manual spot-check reviews, and push the data back . Implementation time is dependent on the solution, the architecture, what it does, what data it needs, what systems it needs to be integrated with, what support there is for data extraction and loading in those legacy systems, etc. Implementation time needs to be judged against the minimum amount of time to do it effectively, which is also customer dependent. Expecting an analyst to understand all the potential client situations is ridiculous. Expecting them to craft an “average customer situation”, base an implementation time on this, and score a set of random vendors accordingly is even more ridiculous.

The factors ARE absolutely vital, but they need to be judged by the buying organization as part of the review cycle, AFTER they’ve verified that the vendor can offer a solution that will meet

  • their current, most pressing, needs as an organization,
  • their evolving needs as they will need to get other problems under control, and
  • do so with a solution that is technically sound and complete with respect to the two requirements above while also being capable of scaling up and evolving over time (as well as capable of being plugged into an appropriate platform-based ecosystem through a fully Open API).

A good analyst an guide you on ways to judge this and what you might want to consider, but that’s it … you have to be the final judge, not them.

That’s why, when the doctor co-designed Solution Map when he was a Consulting Analyst for Spend Matters, the Solution Map focussed on scoring the technological foundations, which could be judged on an objective scale based on the evolution of underlying technology over the past two-plus decades and/or the evolution of functionality to address a specific problem over the past two-plus decades. It’s up to you whether you like it or not, think the implementation time frames are good or not, believe the vendor is innovative or not, and are satisfied with the vendor size and maturity, not the analyst. Those are business viewpoints that are business dependent. Analysts should score capabilities and foundations, particularly where buyers are ill-equipped to do so (and this also means that analysts scoring technology MUST be trained technologists with a formal, educational, background in technology — computer science, engineering, etc. — and experience in Software Development or Implementation –and yes, the doctor realizes this is not always the case, and that’s probably why most of the analyst maps are squished dimensions across half-a-dozen subjective factors [as they are not capable of properly evaluating what they are claiming to be subject matter experts in; as a comparison, when you have a journalist or historian or accountant rating modern SaaS platforms that’s the equivalent of having a plumber certify your electrical wiring or a landscaper judging the strength of the framing in your new house — sure, they’re trade pros, but do you really want to judge their opinion that the wiring is NOT going to start an electrical fire and burn your house down or the frame is strong enough for the 3,000 pounds of appliances you intend to put on the 2nd floor? the doctor would hope not!).

The cynic might say they don’t want to embarrass their sponsors, but the realist will realize the analysts can’t effectively judge vendors on this and the smart analysts won’t even try (but will instead guide you on the factors you should consider and look for when evaluating potential solutions on the shortlist they can help you build by giving you a list of vendors that provide the right type of solution and are technically sound, vs. three random vendors from a Google search that don’t even offer the same type of solution).