There is No One Optimal Team Structure for Procurement …

… not even if you get industry and size specific! But first, let’s backup.

Today I’m going to pick on Tom Mills because he’s well followed, a great practitioner, and gets a lot of stuff right (and I mean a lot of stuff right) … including key functions your “optimal” procurement team needs to support. We’re tackling this now because, in addition to prophetic prediction posts which are full of fantasy, the new year also brings the annual posts that tell you what the Procurement function is, what it’s primary tasks are, and what team you need to address it. And even the most well intentioned ones by the smartest consultants and practitioners don’t always get it right — at least to the extent they think they do.

There’s a couple of reasons for this, and they all relate to their Procurement world view which:

  • boils down to their (limited) experience, which is usually with a few companies in a single industry or related industries
  • typically consisted of sourcing primarily one or two of the six major types of Procurement (which are indirect, direct, services, tail, software, and strategic consulting / commissioning projects — all of which need to be approached differently and often need completely different solutions from different providers to tackle)
  • and usually revolved around a small set of systems and software offerings

Now, I’m not saying I can give you a perfect team model for your company, because I can’t. (In fact, without a deep analysis and evaluation of your company, no one can!) Not even if I created a starting one by industry, size, and geography — because every company is different, and those differences will create minor variations in optimal structure — which sometimes comes down to the talent you can get your hands on.

For example, in most companies product management and product marketing is usually two different functions because it’s rare that one person can do both. But someone who could do both would shift the organizational structure because a person who can do both would bring unique value — being able to design product and communicate the unique value to the market not only ensures all communication is accurate but all design is influenced by market need and reiterated to the market in a meaningful manner.

Now let’s review Tom’s proposal. As per our opening, it’s quite good. In fact, the elements are really good. You need business and category leads. You need sourcing and supplier value. You need operations and governance and someone definitely has to do that. And you need data and digital.
(And if it’s so close, why are we picking on Tom? Because to pick on someone who’s model is bad would require us to write a long multi-part essay or book chapter, and that’s just too much to make a single point.)

So, you need all of the people that are named (or at least the skillsets), but are they leads? Maybe. Maybe not. And is the model appropriate. Somewhat, but not really — not for a lot a of organizations (not being run by Tom or those with his Procurement world view).

But let’s start with the business and category lead and sourcing and supplier value lead. Maybe these are separate, maybe they are not. It all comes down to your philosophy on how you run Procurement. Are you event-based or category-based? If you are truly category-based, sourcing is part of category management, it’s not a separate function or activity — and your category leads know how to source. They will use analysts to help them understand the current market conditions; break down the cost structures; create should and target costs; identify the most likely suppliers; etc. But they will choose the strategy and own the sourcing event. There will be no “sourcing leads”, just “analyst leads” and “supplier development” leads.

Now let’s tackle the “data and digital lead” category. You’ll have a senior analyst lead who runs the team, which will consist of one or more spend and performance analysts and risk and resilience analysts, but the most critical member will be the Procurement Master Data Manager who will work with IT to ensure the necessary data is captured, maintained, enriched, and applied appropriately. Especially since any AI tool you use will blow up in your face without good data. (And if you’re using an LLM there’s no guarantee that it won’t blow up even with good data, but it’s much less likely to blow up with good data than with bad data.)

As for “digital and enablement specialist”, let’s start by clearly stating that any professional that isn’t digital 31 years after Nicholas Negroponte published Being Digital isn’t going to survive much longer in a world where everyone is chasing the AI Dream and trying to automate everything, even that which can’t be automated. Especially since those departments that lie and say it’s AI and adopt tech that works will be three, five, and even ten times more efficient than those that don’t. Every member will be responsible for digital enablement, not just a lead. The team may use expert consultants to help them pick the right tech and evaluate AI (to identify the hybrid or, better yet, old-school AI that actually works), but it shouldn’t be a separate lead in a modern organization.

Working back through the structure, let’s review the ops. An ops manager is critical — and a lot of departments miss this trying to be lean and mean. Someone has to ensure that all of the operations are aligned to support all of the category manager’s requirements from analysis through sourcing support though supplier development through compliance and risk management. And you probably will need a policy and compliance specialist, but should buying channel leads be separate from category management? And if so, is it a channel manager or a technology manager you need? You’re either buying off of contract, usually through an auto-reorder or catalog; from a marketplace; or through a sourcing event. Are those channels? (We’re not talking sales.) But you probably need an internal catalog manager and a marketplace expert.

Finally, the commercial advisory specialist and the contract and commercial manager should probably be on the same team in many organizations (i.e. the commercial advisory team).

In other words, the presented team structure is a great start for identifying key roles, but might not be the perfect org structure for you … or it might be. As noted above, it depends on whether or not you are category driven or not, tech centric or tech supported, and how much support the different roles need.

But most importantly, it depends on what industry you are in and what you are primarily purchasing. If you are in manufacturing, and are primarily purchasing direct, you will need a category manager for each major category as well as a liaison in the appropriate R&D and Manufacturing production teams for each major category. And since, in some categories, the supply will be limited it will be more about negotiation and target costs than open strategic sourcing, you will need engineering experts for target costs; risk experts to identify potential regional, natural, and economic risks related to a supplier; negotiation experts who understand BATNA who can balance supply assurance, quality, and cost; etc.

But if you are a retailer and just need finished goods, you barely even need a category manager. And you certainly don’t need to have a category expert embedded in another department. You just need to source, source, source. And there’s not a lot of risk analysis that needs to be done. It’s finished goods. If one supplier doesn’t supply, you go to another. Unless the retailer is a luxury retailer, it doesn’t care too much what the brands are as long as it can supply products that will satisfy its customers’ needs. And it will be the one organization that latches onto the digital and AI specialist as it will need tech constantly scouring for new suppliers, distributors, and marketplaces that can enhance supply certainty, quality, and/or cost effectiveness — because achievine any two of its three desires ain’t bad!

In other words, the optimal team depends on what the organization actually needs to succeed based on its industry, size, and maturity. It can start with a great template, but it will need to customize based upon its specific circumstances, processes, and maturity. And it might need help to define what that is.

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!)