Author Archives: thedoctor

Theoretical Procurement Models Are Cool – But Not Always Recipes for Success

For this piece, I’m going to pick on a recent post, and graphic, from James Meads. Not because he got the models wrong, they’re perfect, but because neither are appropriate for today’s Procurement. We’ll first discuss what, at a high level, the right model actually is and then discuss what ALL models miss that is key for Procurement success.

In short, it’s a blend. It’s a model that shoots for the ultimate expression of what Procurement should be in a perfect world, while accepting the reality that the world is not perfect (and there ain’t no living in a perfect world anyway) and you have to function accordingly. To explain this, we’ll tackle each of the six dimensions and explain why entrepreneurial procurement, the perfect model for a perfect world, is not always the recipe for success and why, occasionally, it’s no better than the old-school technocratic procurement most procurement departments are still stuck in (which, while successful yesterday, is no longer successful today).

Let’s start with goals. It’s obvious that yesterday’s technocratic goals of cost savings and compliance are not a recipe for success, as the emphasis they place overlooks risk and resilience and sees Procurement being involved too late in the process to have any significant impact on total cost. On the flip-side, it’s not all innovation and value-creation — innovation can take years to from ideation to reality and value-creation usually involves new service offerings which rely a lot on suppliers who will need to be developed to get there. That’s why success is a balance between the original goal of Procurement — supply assurance (because No Sale, No Store) — and value generation. It’s conceited and absurd to think Procurement is the source of all value creation in an organization. It’s not. But it is the source of all value realization and generation — because it’s up to Procurement to acquire the required products and services in perfect orders where, when, and at the right TCO that is required for the value realization, and, preferably, at lower cost and higher quality than budgeted for to generate additional value beyond what was expected. It’s a fine balance between aspiration and cold, harsh, reality.

Now we’ll move on to process. The process should be engineered in a layered fashion that builds on must-have, should-have, and nice-to-include steps that, when layered and strung together completely defines a well engineered, almost rigid, process that a junior buyer new to the category can follow and be guaranteed success in typical market circumstances; that an intermediate buyer can strip down to the should-haves, adapt slightly to current market conditions and time-constraints, and use their sourcing experience to take advantage of the specific market conditions and/or an expanded supplier pool; and that a senior buyer with category expertise can strip down to the absolute must haves (engineering part verification, mandatory compliance requirements, etc.) and execute rapidly in an emergency situation. Not all categories are created equal, and the degree of agility and flexibility that can be supported is highly dependent on the category, the market conditions, the buyer’s experience, and urgency of the need. For example, you can’t be 1mm off in (electronics) hardware acquisition. But if the paper/posters are off 1mm from specs, who cares!

Mindset needs to be balanced between the reality that can crush you (and even bankrupt your organization on a bad, experimental, sourcing decision) and the future state you hope to some day achieve. While being too risk-averse can close off the discovery of great new suppliers with great new production methodologies, or great new software technologies to accelerate your Procurement (capability), being too experimental and open-minded can lead to decisions that ignore emerging risks that can result in supply lines suddenly disappearing, production lines going down, and losses in the tens of millions. In an age where geopolitical tensions are at an all time high, tariffs are materializing daily, sanctions are one retaliation away, shipping lanes are being cut off by terrorist activity (Red Sea) and lack of rainfall (Panama Canal), and your entire rare earths supply is one dictatorial decision away from disappearing, you have to be risk-centric in all your decisions. For critical products, you can’t increase risk if they are (primarily) sole-sourced (or primarily dependent on a sole-source somewhere downstream).

Technology is not about UX, because that ultimately comes down to UI for the majority of users, and that results in the prettiest system being selected. But you have to remember, it’s not about pretty, it’s about function, and if you want success, remember what the Northern Pikes told us 36 years ago when you’re selecting tech and being sold a flashy UI: she ain’t pretty, she just looks that way. You want something easy to use, but first and foremost it has to do what you need done. It HAS to support the process. UI/UX ONLY comes into play once the baseline functionality has been established. (And if someone won’t learn the necessary processes and systems required to ensure success, they should be replaced. That includes Chat-GPT addicts who prefer cognitive decline to actually trying to learn and improve!) Furthermore, the technology must be more than just configurable (using adaptive rules), but it must also be agentic so that, once appropriate rules are defined [and exception cases identified], it can run automatically so that, over time, the buyers spend less and less time on tactical tasks and more and more on strategic decision making, supplier development, and value generation and realization. Finally, it must not just be “connected” but be “concentric” and be built to be connectivity-first so that it can sit at the center of all of the organization systems that contain the data Procurement needs to do a proper analysis and make the right decisions.

Supplier Focus should definitely lean towards partnership and growth, but “partnership” and “growth” are nebulous and subjective and fuzzie-wuzzies don’t guarantee that the relationship is valuable to the buyer and definitely don’t provide a foundation for joint value growth for both parties over time. While there must be a joint commitment to improve, the focus needs to start with 360 performance measurement which become the foundation for jointly created and agreed upon development plans that will increase value, and it must continue with a constant eye out for risks and the development of mitigation and remediation plans should the risks become significant. It’s not about touchie-feelies, it’s about true value realization over time.

Finally, while the organization wants to be seen as an indispensable business partner, there’s no way that’s ever going to happen if it’s not seen as a source of value. And even if it is, considering Procurement is always going to come with process, overhead, and forced evaluation before a “preferred choice” can be selected, organizations like Marketing, IT, etc. are never going to see it as an IBP. But as long as the C-Suite sees it as a core source of value, it’s utilization is always going to be mandated!

In short, while theoretical perfect-world models are great in theory, and do a great job of giving us something we should want to strive for, success requires not forgetting the reality that surrounds us and in an age where free trade is crumbling, supply sources are at risk, and supply lines are crumbling. We have to be reality first to ensure supply, which has again become Procurement’s most critical function. Nothing else matters if there are no products or services to sell because critical supply sources/lines disappeared and Procurement wasn’t ready to replace them.

Logistics is in BIGGER Trouble.

There’s been a truck driver shortage for almost two decades. I remember writing on the estimated shortage of 240K drivers back in 2013.

Moreover, with so many drivers being immigrants or cross-border drivers from Mexico, and the immigration crackdown in the US, it’s only become much worse, as chronicled yet again in the latest #HFSResearch piece.

However, I don’t think their answer of autonomous fleets in the answer. The tech isn’t there yet (as even Tesla can’t deliver fully reliable and safe autonomous vehicles yet, and it’s been working on them the longest in North America), half the states don’t even support testing of such vehicles yet, and, as always with new tech, we’re one bad accident away (as a result of rushed trials) from a major backlash that will stall progress for a decade.

I think it’s time we look back and take lessons from history (which I know most of my American colleagues have forgotten, or you wouldn’t be so enamoured with your current administration that is looking to the 1930s for its administrative policy and looking to the 1880s for its industrial policy), and remember the beginnings of trade. It was horse and carriage (well, mule-and-wagon or donkey-and-wagon) until we got the first cargo ship, which could move mass cargo by sea. Great for port cities, not so great for inland cities. Then the train was invented, and that revolutionized transport (and then travel). Locomotives quickly became more and more powerful, standardized tracks allowed them to run coast to coast, and up to 200 cars of cargo and people could be carried at once, especially if multiple locomotives are used. TWO HUNDRED RAIL CARS.

A flatbed rail car can be up to 89′ in length and 10′ wide.

A standard cargo container, used on ships, is 20′ x ‘8 or 40′ x 8’. A properly engineered flatbed rail car can hold two long or four short containers.

A typical long haul transport truck today is 53′ x 8’6″ (x 13’6″ high). No reason the trailer can’t be replaced with a specially designed 42′ x 8’6″ flatbed that could lock and load a standard 40′ container or that automated systems to lock and unlock couldn’t be designed to easily allow movement between both ships and rail cars AND between both rail cars and trucks. This would considerably shorten the distance that 400 containers (200 flatbeds x 2 containers each) would need to be transported across American roads, and significantly free up the availability of 400 drivers per train (and corresponding lane).

An average long-haul route in the US is 500 miles+! (With many routes up to 800 miles, or more).

An average short-haul route in the US is closer to 150 miles.

Long haul trucking could be reduced by 2/3 if rail was used more and all routes were short haul! Considering long-haul trucking accounts for about 200 Billion miles a year in the US, that’s 120 Billion miles that can be freed up, which greatly reduces the driver need! (If a driver drove 60 miles/hour for 50 weeks a year, that’s 120K miles.) In fact, it reduces the need by almost 100K drivers! It might not solve the entire problem, but it would be a huge dent!

It’s time we start looking back as well as forward if we want to solve the problems of today!

The reality is that over 500 BILLION miles of annual trucking is just too much! Almost 73% of freight by weight should NOT be moving by inefficient truck transport! Trucking.org has some good, and scary, statistics.

This post first appeared in a slightly abbreviated form on LinkedIn.

There are MANY reasons you are NOT ready for AI!

A few weeks ago, we told you that if you think you’re ready for AI, you’re not ready for AI because, even though the vast majority of you are chasing AI, only a minority of you are ready to even investigate it. And we mean investigate, not use. That depends on whether or not there are any relevant AI solutions for you needs — and despite the repeated BS claims by the big AI vendors, there may not yet be any!

And it’s not just because you haven’t

  • admitted you’re only chasing AI because of FOMO and FUD
  • assessed where you are
  • realized you are generations of tech behind
  • determined you just don’t have the right resources

But it goes beyond that.

In order to have any hope of succeeding with AI:

You need great data and great Master Data Management
… but you don’t even know where your data is! You have no governance policies, no management processes to ensure data is kept up to date (or even backed up unless you have already suffered a data loss and determined losing that specific data would be disastrous), and no clue about what that entails. And even if you realize that you need (master) data management, you won’t get the C-Suite to sign off on it, even if you call it E-MDMA and tell them they’re getting free samples!
You need a good IT infrastructure, with context-based integration and workflow capability
… but you have no central strategy for data integration, system orchestration, or enterprise workflows, and your IT infrastructure is whatever cloud your ERP runs on. AI, especially Gen-AI, requires massive data and massive compute and, guess what, that requires massively powerful, solid, infrastructure — and yours is probably held together with spit, glue, and duct tape!
You need an in-depth understanding of not only the problem you want to solve, but what AI algorithm will actually work reliably and with measurable confidence
… but guess what? In order to properly evaluate AI, you need an advanced understanding of the technology, which usually requires an advanced, graduate level, understanding of the underlying mathematics as well as deep understanding of the problem and how to mathematically model it.
You need a strong technical quotient (TQ) to implement, train, and verify those AI algorithms
… and that’s more than just a single expert who can evaluate, but a strong bench of architects and developers to make it work — you can’t rely solely on the vendor as they can go away, their bench can leave, or they can get pressured by their investors to just sell, sell, sell (and pretend you don’t exist once they get the cheque) and that leaves you to your own skillsets.
You need domain experts on hand to verify the results
… and this goes double for critical results. If you are using an augmented intelligence to help with sourcing, market analysis, strategy recommendations, etc. you can’t let an agentic system execute on a computation without verifying it. No system ever has all the data, no system ever knows all of the options, and no system has the soft information (and how you might be able to work a sales rep to your advantage). And if someone messed up the data, considering just one wrong number can entirely throw off a hundred thousand variable model, you’re in deep doo-doo if the system executes an order without your verification.
You need to redesign your processes to optimally take advantage of AI
… because your processes come from the time before office machines existed, so obviously they weren’t designed for modern technology. And while traditional workflow / RPA can easily automate what you have (even though it shouldn’t), since AI requires good data, good structure, properly designed models, etc. — it’s not going to work with whatever Guilded Age process you’re using now.

And so on. The reality is, despite what all the big vendors, big consultancies, and big analyst firms tell you — you’re just not ready for AI. (And definitely NOT ready for big bang projects that will end in big busts!) It’s just the latest silicon snake oil panecea — like all purpose predictive analytics, the fluffy magic cloud, SaaS, and the World Wide Web and every other panacea that has come before. (Just remember the last time silicon snake oil was hyped this much, it resulted in the dot com bust!)

Dangerous Procurement Predictions Part IV

As per our first three posts, if you read my predictions post, you know SI hates predictions posts. It fully despises them because the vast majority of these posts are pure optimistic fantasy and help no one. Why are the posts like this? Because no one wants to hear the sobering reality off of the bat in the new year and the influencers care more about clicks than actually helping you.

But the predictions are not only bad, they’re dangerous if you believe them. So we are continuing to lay bare the reality of the situation to make sure you understand that this year isn’t much different than last year, no miracles are coming, and only hard work and the application of your human intelligence are going to get you anywhere. Today we tackle the next set, and we hope we’re at the end of the series, but if we stumble across more bad predictions, we’ll have to do a Part V. But we hope not!

11. Negotiation gets productized.

Here’s the thing, in a few niche industries like electronics, we have a few niche players like Levadata that bundle “should-cost” + playbooks + concession sequencing for experienced buyers to help them leverage the state of the market for the best results possible. But they’re hardly used relative to the total electronic market size, as they are used mainly by component buyers / manufacturers, not consumers of such tech (to understand the manufacturer’s margins).

Similar offerings don’t exist across most industries. And even if they did, most buyers are not sophisticated enough to do this. Most struggle with a multi-round RFX, yet alone detailed should-cost/target cost models, negotiation playbooks (which have to cover all standard market conditions and unique situations), and the concept of BATNA, especially relative to offers and counter-offers in a structured concession sequence.

Without these domain relevant niche offerings and career negotiations trained in deep tech, which are both few and far between, this is not going to happen. And Artificial Idiocy certainly isn’t going to fill the gap!

12. AI As a “Governance” Engine.

The claim: When you design them well, agents encode judgment, compliance and brand values into every transaction. Uhm, no! At least not if they are Gen-AI agents that can’t judge (as they can’t even reason), may or may not execute compliant with regulations, and will happily screw a supplier (by refusing to pay an invoice) or customer (by refusing to honour a claim) if it thinks that’s what it needs to do to make you happy or stay turned on (because it was told to find savings of 500K and it’s calculations determine that paying certain invoices or honouring certain claims will not allow that savings goal to be met, if it was even possible when the AI told you it was as it may have arbitrarily multiplied a calculation by -1 just to make the math work).

Governance, by definition, requires the act of governing. And governing, by definition, requires the wisdom as well as the authority to conduct the affairs of the organization. And only truly intelligent beings (i.e. HUMANS) can acquire wisdom over time.

13. There will be no more “X” employees because AI will replace them all!

First of all, how many times do we have to repeat that there are NO AI Employees, you shouldn’t believe the degrading, demeaning, and, frankly, dehumanizing claims, and that you definitely DO NOT want Agentic Buying through fake AI Employees. Secondly, it can’t even do the basic tasks that even the dumbest drunken plagiarist intern can do on a daily basis. But let’s not digress too far before giving you the major examples.

Claim #1: Contract Administrator / Staff Attorney

THE PROPHET has been trying to Kill ALL the Lawyers for quite some time now, and it seems he’s not alone.

But here’s the thing. While AI systems are pretty good (and as good as the drunken plagiarist interns) at spotting grammar errors, redlining against standard clauses, pointing out missing clauses in most organizational contracts, etc., they aren’t good at everything. They can’t identify unaddressed risks without being told what those risks are, they can’t judge the full extent of liability without understanding what those liabilities could be, and they can’t judge the supply geo-political and supply chain risks without broader context.

Plus, they can’t always back up their suggestions; often make up case law, case decisions, and authors; and can’t always judge the requirements of potentially relevant regulations. And we’ve seen many times what happens when even trained lawyers use AI — they get lazy, fall for the slop, get reprimanded and fined by judges tired of the laziness (with a recent example happening in November in Mata v. Avianca, Inc). The previous link also lists three other notable cases where lawyers (and their firms) were fined and sanctioned, but, by now, there are dozens!

But hey, go ahead and replace your lawyer, write bad contracts, make decisions on fake case law, and risk your entire business if you want to. (If you want to, it’s probably safe to go ahead and get rid of the intern who does the redlining and the clerk that does the filing, the AI is probably just as good at that, but do not ever, ever replace a real qualified lawyer with a piece of sh!t “AI”.)

Claim #2: Spend Analyst

Sure you can buy auto-classification that might get to 95%, auto-cubing that can build any cube you can imagine, auto-analytics that can run the entire slate of standard analytics and compute past, current, and projected costs against past current, and projected market data based upon current buying patterns and suggest items, categories, and/or suppliers to (re) source, switch from/to, and possibly (re)shape demand.

But this doesn’t mean that it’s the right items or categories to chase, the right suppliers to use, or even the right area to focus your efforts. It’s based on math, and an assumption of consistent, stable, market conditions, but those don’t exist anymore. If you’re not also considering geo-politics, natural disaster risk, uncertain logistics when the panama canal reaches historic lows for much of the year, terrorists block the Red Sea, and unpredictable weather make sailing around the capes more dangerous than other, and sourcing for resiliency and not just cost, your “spend” analytics are useless. You need an analyst with a good understanding of economics (and access to an economist), geo politics (and access to local experts), and resiliency, not just total cost of ownership buying. (Now, the junior data pushers are probably all dead and gone, but not the real experts!)

Claim #3: Sourcing Event Manager

Now, transactional buyers are gonna get replaced by autonomous systems that use next generation (advanced) robotic process automation enhances with machine learning in Agentic systems, because ordering off of contracts, ordering from catalogs, and doing low-cost non-strategic buys through quick-quote RFPs doesn’t take any brainpower whatsoever (making it perfect for AI that has none).

But strategic sourcing requires more than just buying off of contracts, ordering from catalogs, and issuing quick-quote RFPs! It requires defining key criteria (that go beyond what engineering, marketing, or maintenance provides), identifying validated suppliers (or identifying suppliers that can be easily validated), holistically analyzing the market conditions, determining the best event type, determining the negotiation strategy, etc. The tools might be able to help with initial supplier identification, collecting numerical (commodity) market data, letting you know what event types were run in the past, compiling fact-based playbooks, and, of course, automating each extent of the process, but they can’t do real strategic sourcing that requires real human intelligence. And with today’s geo-political uncertainty, that human intelligence is needed more than ever which means that expert sourcing professionals are needed more than ever. (But dumb buyers will join the dodos.)

There are more ridiculous claims, but you get the point. Skilled jobs are not going away. (But bit pushers are.)

14. New standards for Ethical and Sustainable Supply Chains.

In some countries, current standards aren’t even being met. Good luck getting new standards introduced, since there aren’t a lot of global internationals (with those headquartered in the US in particular) that want even more rigour, especially if it will cost money! As long as laws are being minimally met, or reasonably-sized “facilitation payments” can make problems go away, this is not a priority, especially if going beyond would cost more money!

15. The “AI Singularity” is coming faster than we can process.

It’s not, because the models can’t get bigger, there is no more data, and no one has yet come up with a model that has any hope of even getting close to the actual intelligence of a pond snail.

Plus, if it ever did happen, considering a “singularity” is actually a black hole, it would rapidly consume (i.e. destroy) the Earth, and we wouldn’t have to worry about it. This is just more nonsense from the A.S.S.H.O.L.E.