Category Archives: Best Practices

Forget Best in Class, Hype, or Futurism — If You Want To Improve, Mature!

As you know, and as we’ve written about repeatedly, the hype cycles for orchestration and Gen-AI are in full swing (even though both should be declining, they are both picking up steam, likely due to the ridiculous amount of money spent on marketing — which includes vendors buying analyst studies and reports that focus on areas where they look good).

Consultancies are not only trying to promote and sell you these technologies as a panacea for all your technology ills, but also trying to tell you that it’s what the best-in-class do and, by the way, that if you want to be best-in-class, you have to upgrade all of your processes (with their help) to those that the best-in-class use (whatever that means).

Furthermore, both are trying to tell you what the Future of Procurement is in 2030, 2035, 2040, etc.

And the reality is that NONE of this helps you. Not one bit.

As we have repeatedly pointed out, most of the currently hyped technology is still in experimental/beta stages. This is not technology that will help you mature. In fact, if you are not an industry leader, and mature in your processes, it may actually hold you back because you need to be a mature industry leader with your Procurement organization running smoothly to have the time and experience to properly evaluate these technologies and where they might fit in your organization.

Furthermore, every organization is different. As a result, what is a best practice for one organization may not be a best process for another. In fact, it might not even be relevant. While you will need to improve your processes, and streamline them for digitization, there is no set of fixed processes you can just plug and play and succeed.

And, don’t pardon my French, why the fuck would you care about what Procurement will be like in 5, 10, 15, 25 years. That does NOT solve your problem today. You care about what a better organization would like today and how to get there. That’s it. Just like the journey of a thousand miles begins with a single step (and possibly a single kick in the ass), the path to success is continual improvement, and, simply put, doing better tomorrow than you are doing today.

This means that the key to success is good old maturity levels, current state assessments, and simple step-by-step plans to get from one level to another. Nothing fancy. Nothing tech-centric. And definitely nothing hyped!

While the doctor admits he did get a little tired of the plethora of these maturity maps that appeared in rapid succession in the late 2000s and early 2010s, including the one he did, it was much preferable to today where the dearth of these, and simple advice, is deafening. The help that is desperately needed is not there — replaced by (Gen-AI generated) (Gen-)AI and orchestration hype, not how they can (and cannot) support the solutions you need.

[Plus, let’s not forget that analyst firms and consultancies tend to ignore government regulations and industry compliance (except in country-specific studies), day-to-day pain points (because they aren’t sexy and won’t sell the hype), and, unless they can make a quick-buck (or get a major uptick in eyeballs), changing global conditions that require (temporary) supply chain pivots.]

So, if you truly want to improve, find a maturity model that walks you through the process and knowledge improvements you need to

  1. get to where you should have been when you started Procurement
  2. get to where you should be today
  3. prepare for the next 3 to 5 years (since no one looks beyond that anymore)
  4. slowly build out a foundation that will take you beyond that (without another massive investment)

That’s it. That’s how you make progress. And how you do it without flushing Millions of Dollars down the (Big X) consulting toilet.

Need a starting point? You can still download the classic paper the doctor wrote back in 2012, that was sponsored by BravoSolution (acquired by Jaggaer), on Taking the First Step on Your Next Level Supply Management Journey which describes the levels of maturity from standardization and complexity reduction (which is typically the first step an organization takes on its journey), to operational excellence (which is typically the second step an organization takes on its journey), to strategic business enablement (which is when it typically becomes best in class).

If you do a web search, you will find others from the big consultancies, but this gives you an idea of what to look for in a model that you can build a progress plan on. Where do you start, where will go next, and where do you want to end up. Note that a good model is tech free. Tech should support your growth, not the other way around. (In other words, it’s never Tech-First or AI-First, it’s solution first, and then you identify the right tech.)

And if you need help with a current state assessment, or flushing out a roadmap from one level to the next, or where you are now to standardization and complexity reduction, hire a niche consultancy who will take a no-nonsense approach to get you there at a reasonable cost. (This shouldn’t cost millions of dollars in a transformation project. Depending on your organizational size and complexity, somewhere in the low six figures should typically be enough to get your started, or mid to high five figures if you want to just focus on a few core areas at a time. But definitely NOT seven figures. That comes during the transformation process once you have identified the tech you need, and NOT the tech everyone is trying to shove down the proverbial throat.)

Optimization CAN NOT Be Automated!

Not long ago, THE PROPHET said that the future of optimization is self-adjusting autonomous systems that just “do it”.

And while future systems should:

  • automatically aggregate, verify, and enrich data from multiple sources
  • adapt constraint and model recommendations based on organizational and market trends
  • continuously monitor environments and suggest the next events based upon the opportunity
  • suggest categorization and framework refinements that would allow for more successful events
  • consider volatility and risk in its models and recommendations

These models should not:

  • autonomously seek out and integrate data without human validation
  • autonomously change constraints and models
  • automatically run events for categories still under contract
    (on the probabilistic expectation the savings will exceed the penalty)
  • change your categorization and framework without approval
  • replace deterministic models with probabilistic ones with unknown weightings on volatility and risk

and these models should definitely not run fully autonomously in the background and make commitments without human approval and intervention.

Going back to basics, which THE PROPHET says he knows well, there’s a very simple reason you need a human in the loop for sourcing, and the simple way to explain it is this. To a machine, a 3.5″ lid is a 3.5″ lid, especially when it’s not!

Apply this next generation fully autonomous optimization platform concept to a global fast food chain, and the first thing it’s going to identify is that the human is following a “hidden constraint” by always buying matching cup and lid sizes from the same vendor, and doing away with this arbitrary constraint will save a global operation millions a year.

The new junior buyer, upon seeing this, will jump and down and tell the platform to “Lock the order and output the savings report so I can demonstrate this new AI optimization tool saved millions”.

But that “hidden constraint” is a real constraint because 3.5″ is not 3.5″ across manufacturers who are still running on decades old production technology as the process to create the cups and lids for those fountain drinks hasn’t changed since we were kids, there were no standards then, and the measurements were always off a bit.

If you’ve ever wondered why sometimes the lid just stopped fitting when the “serve yourself” trend started, this is why — someone broke the unwritten rule — and the chain tried to pretend the problem didn’t exist.

Why did they try to pretend that the problem didn’t exist? That’s because the “fix” is to order the matching inventory from the same supplier, sit on double inventory, and send costs through the roof.

In other words, this twenty five year old hidden constraint that the doctor personally saw sourcing optimization consultants overlook (when they were told by the client that you couldn’t use manufacturer’s X lids with manufacturer’s Y cups and that constraint should, obviously, be part of the model) is still a valid constraint today. And other examples abound across categories. The specs seem the same on the spec sheet, but only the engineers and buyers know when they are not and apply “unnecessary” or “hidden” constraints to account for these situations.

Moreover, going back to the suggestions of THE PROPHET:

  • machines don’t know truth from lies, so if someone publishes false data, they will use that false data in enrichment, and there goes your model!
  • as we just demonstrated, sometimes AI will remove necessary constraints or not detect “hidden” constraints that need to be included
  • you don’t break a contract on a hunch — you break it when it’s not working out; if you find a better product or lower cost, you start switching over as soon as you can or by diverting as much as you can from an un-contracted/contractually satisfied supplier to that new supplier
  • you don’t completely change categorization and upend the financial reporting and other dependent processes because it suits the optimization module
  • you use the probabilistic assessments, you don’t replace your deterministic model, where you can compute optimality and confidence, with them

When it comes to optimization, you want Augmented Intelligence and a system that, with input and verification at the right points, does all of the tactical drudgery and thunking that the machines are great at (and we are not). You don’t want it autonomously making strategic decisions it doesn’t understand.

We’ll Say It Again. Analyst Firm 2*2s Are NOT Appropriate for Tech Selection!

Last year, while ranting about the plethora of utterly useless logo maps (which includes the Mega Map the doctor created to demonstrate the extreme futility of these maps), we also did a dive into why analyst firm 2*2s are NOT appropriate for tech selection. This is coming up again as a certain firm is really pushing All AI all-the-time and you can tell it’s about to infuse all their maps. Plus, the biggest firms are really pushing their quadrants, waves, and marketscapes, and most of these are showing the same solutions they showed last year and the year before that and the year before that and so on (going back a decade in some cases).

That, and a number of people are lamenting their lack of usefulness on LinkedIn, with one person even creating yet another logo map to highlight the “significant solutions that matter” (but we’ll save that rant for another day), so it’s time to make it clear that these maps are not appropriate (on their own) for tech selection. For example, in a discussion on my post on how your standard sourcing doesn’t work for direct, Thomas Audibert correctly states that static quadrants, in any form, do not work. (And then went on to correctly note that if you say there are, for instance, 80 sourcing solutions, it means that there are at least 20 niche (geographic, industry, customer size, …) categories of interest and that, unless they are catered within 20 different quadrants, this makes no sense to me.

And it doesn’t, because all a map can do, in the best situation, is give you a set of more-or-less comparable solutions that each serve a specific function (so you don’t end up trying to compare a Strategic Sourcing to a catalog-based e-Procurement to an Accounts Payable solution which, of course, serve three completely different functions). If it’s a good map, and by that I mean focussed on two things max, like Spend Matters Solution Map that only scores tech (on one axis) and only presents tech vs average customer scores (on the other axis), then you can use it to verify that one or two of your key requirements are met (such as the tech is solid and the customers are generally happy), but that’s it. (But if it’s a map that squishes 16 different scores into 2 dimensions, that’s useless … you don’t know what is contributing to the scores. What’s most important to you could be the lowest score in that score mish-mash number that looks above average.)

Moreover, at the end of the day, all an analyst can do that is useful is rate a vendor on one or more business independent objective dimensions that can be scored easily and, more importantly, give a customer comfort that the vendor does well on this dimension and they don’t have to worry about it in their evaluation. (For example, if a vendor does well in Spend Matters Solution Map, you know you don’t have to evaluate the underlying technical foundations, which is something most companies aren’t good at.) However, that’s not enough for a selection.

When it comes to tech, it’s important that:

  1. it’s solid
  2. it fills the need you are searching for
  3. it is easy to use by the majority of the users for the functions they will be doing the majority of the time

And, guess what, an analyst can only verify the first requirement. Why? An analyst doesn’t know your needs, you do. Moreover, they don’t know the TQ (technical quotient) of your users, the functions they do daily, or the processes they follow. You do. So, how can you expect an analyst to produce a map that tells you that.

But, if you’ve been paying attention, the solution to your problem is not tech. It’s process. And until you nail that, and then select the tech that matches that process, tech alone will NEVER solve your problem. NEVER.

And since analysts don’t know your business, or your

  • business size, Procurement department size, maturity
  • culture
  • risk tolerance
  • innovation level/comfort
  • current processes / required processes
  • customer service needs
  • etc. etc. etc.

or even how these slide on a scale across different companies of different sizes across industries, there’s no way they can produce a map that tells you all of this. Or even a fraction of this.

That’s why you need an analyst or independent consultant that truly understands the solution space you are searching in, what those solutions should do, and how to help you identify the subset that is not only technically solid but is also likely to meet your business requirements. (And remember, It’s the Analyst, not the analyst firm. If the analyst hasn’t reviewed dozens of vendors in the space you are searching in that offer the type of solution you are searching for, doesn’t know the must vs. should vs. nice to have requirements, and, most importantly, doesn’t have the technical chops to validate the solution technically (which is the weakness of every non-IT / non-Engineering business department), he’s not the analyst for you!

What Are the Biggest Organizational Cost Saving Levers?

Every year there is a new survey or research report that will name one to three levers as the biggest cost savings levers in an organization, but it’s really not that simple. For example, the SCMR last year reported on a BCG study and the Hackett Group 2024 Procurement Key Issues Report and said, in Managing Procurement in a Price-Sensitive Environment, that:

  • supply chain costs and
  • manufacturing costs

are the biggest levers for cost savings. And while generally true if more than 50% of revenue is being spent outside the global organization’s many four-wall structures, it’s not true if most of the spend is internal (on headcount, property, etc.).

And it’s not true at all in the current environment in America where now tariffs are increasing costs by up to 145% (and there’s no solution, beyond BTCHaaS) and everything is unpredictable.

Moreover, supply chain is generic — is the cost inefficiency in the manufacturer (and if so, is it in their material and component supply chain or in their operation), the distributor, the logistics partners, or the organizational warehousing and inventory management. And if its manufacturing costs, is the bulk of the costs raw materials governed by commodity markets or in the production process? If the former, you can’t do much. If the latter, the assembly line is your oyster.

And then, even if you find the lever, where is it located? Who has access? Do they have the strength and permission to pull it? It’s tough!

Let’s look across the spend (ignoring tariffs because they are beyond your control):

  • products: low quantity, no lever; high quantity, sourcing if the market conditions are in your favour (or about to not be in your favour, so you lock a contract in early for a small hit); if the product was never sourced before, it’s tail spend which typically sees 15% to 30% overpsend
  • services: low quantity, tiny lever; high quantity, across a nation or the globe, if you take a multi-level view, are willing to work with multiple providers, and apply SSDO (Strategic Sourcing Decision Optimization), 30% to 40% can be shaved off with no detriment in service level
  • logistics: mode matters; intermediate storage matters; FTZs matter; source and sinks matter (if you’re selling in multiple countries, you might want to consider producing from multiple countries); easy to take 10% off just with a better network design, sometimes 20% off with a better network design, smarter load distribution across carriers, more cross-docking (and less intermediate storage), and the most appropriate (mixed-modal) transport plan
  • taxes and tariffs: source and sink matters! and, in some countries, so does minority/diversity/etc.; you can cut these in half (or even eliminate them) with better planning; when tariffs can be 20% or more, this matters
  • warehousing: major cities and hubs are expensive, secondary locations can be a fraction of the cost; and if smartly located, can cut your “local” distribution costs to your “local” stores, plants, offices, and/or customers; for years all the studies said inventory cost can be as high as 25% of product cost; better management (not just JIT, that can lead to more stock-outs and losses than a few extra percentage points) can halve this while reducing stock-out rates
  • facilities: if you’re willing to consider a balance between on-site and remote, shared spaces (and designated lockers), locale of choice, costs (and savings) can vary wildly; millions can be saved here in larger companies;
  • personnel: you pay the best people the best rates and you keep them as the best deliver an ROI multiple that is many times an average Joe; but that doesn’t mean you have to overpay for benefits (and with good negotiation, you can get great benefit plans at below market average rates); this can be hundreds of thousands to tens of millions

There are many levers, and the savings potential differs by industry, company size, organizational Procurement maturity, and individual company.

In other words, don’t just look at the top two or three levers, look at all of them and focus on the ones with the most potential, even if they are on the bottom of the “expert lists”.

Follow the Money to Find Future Opportunity — Which Will NOT Be Fully Found With Autonomous Sourcing!

Spend Matters has thrown caution to the wind and followed Gartner’s lead jumping onto the AI Hype Bus (with no steering and no brakes) that is still heading straight for the cliff and are wheeling out webinars on AI faster than a prairie fire with a tailwind. (Needless to say Sourcing Innovation does not think this is a good thing. There are valid uses for AI and automated processing, but fully handing over financial decisions is like wheeling in the Trojan Horse and leaving it unguarded in the server room with unrestricted access to your bank integration.)

Recently, The Maverick advertised yet another Spend Matters webinar on Autonomous and AI Sourcing where he said we should “follow the money”. Which we should, but there are a few things we need to clarify first.

1. No Money Changes Hands In Sourcing

It changes hands in Procurement … and it’s because most companies don’t follow the money after the contract is signed that 30 to 40 cents of negotiated savings never materialize in many companies, which The Maverick should remember from his AMR and Hackett days, as it was laid clear in Mickey North Rizza‘s famous 2009 “Reaching Sourcing Excellence” series, which we know is in his archives.

2. “Speed” is NOT a strategic edge if you don’t get it right!

If you don’t go out with the right strategy, don’t know the current market price, don’t know the reason for the current market price, and don’t have the knowledge to project if the trend is going to continue, stabilize or reverse, going to market is not a good decision … and it’s an even worse decision to automate the sourcing project and secure an award as fast as possible if you don’t know if it’s the best you could have done or the worst you could have done.

3. “Pecunia non olet”, but yet these vendors are asking you to treat it like it does!

They want you to automate spend analysis, sourcing, contracts, purchases, and everything else that involves money by turning over everything to their Agentric AI because, apparently, money stinks and you don’t want to touch it. (But they are quite happy to not only spend yours for you but takes as much of it as they can for their services.)

But here’s what they don’t tell you.

  • AI is NOT Intelligent.
    The level of intelligence in their “AI” is equivalent to the level of intelligence in a carpenter’s hammer. The level of effectiveness is entirely dependent on how skilled the person “training” the system and how skilled the person “using” the system is, just like the effectiveness of a hammer is dependent on how well the carpenter was trained and how experienced he is in it’s use.
  • AI Does Not Know What it Does Not Know.
    If the data is incomplete, the recommendation is very likely incorrect.
  • AI Cannot Do Better than the Best A Human Has Ever Done in Decision Making.
    So, if none of the situations it was trained on led to great results, neither will what it recommends for you.

You need to remember how Gen-AI does its work (or should we say does not work). It is large document search and summarization and chain of compute. Now, the more advanced players are trying to embed knowledge graphs into this, but these are not perfect either. With good training examples, and a very similar situation, the probability it will work well is very good, but it’s still only a probability. As a result, nothing should ever be fully automated where money is concerned. The tools should be used for their recommendations, and if the recommendations are good, and the risk is low, most of the tactical data processing and event management should be automated, but the decisions should ALWAYS be made by a human, who should be involved at every decision point. Even if that decision is verifying the system recommendation. It only takes one miscalculation due to an incomplete data source to project a wrong trend, rush an auction, lock in a price 3X what you are paying now, only for it to fall in a month later when a factory (which went offline temporarily due to a manmade or natural disaster) comes back online and the supply-demand balance returns to normal. And while you may have stocked out for two weeks, those losses will be orders of magnitude less than paying 3X at a contract you have to honour (unless you want to get dragged into court).

Now, if you really want to make money, forget all this Autonomous and Agentric AI BS, look for Augmented Intelligence solutions that make your staff two, three, five, and even ten times more efficient, purchase those, and, remembering that the US infrastructure is crumbling fast (and not going to get renewed under a Republican administration that is more interest in trickle-on economic tax cuts for its billionaires than ensuring you have running water), it’s time to remember how the smart made money in ancient Rome — public bathhouses and latrines. Time to invest in your own desalination facilities and be ready when the public wells run dry. After all, “Pecunia Non Olet“.