Monthly Archives: April 2023

2030 is too late for Center-Led Procurement!

Especially since 2020 was too late! And organizations should have been there by then since center-led procurement was being discussed as the next generation model in the mid-2000s and, more importantly, as the futurists were predicting that the future of work, and companies, was remote and distributed last decade, every company should be “center-led” by now.

(Note that we mean “center-led” and not “centralized” where one central office handles all major procurement projects globally. We mean center-led where a centralized function determines the best procurement path for each category — which could be centralized, distributed, multi-level, or mixed — and provides guidance to all of the global teams and makes sure they build the right procurement — and supply chain — models up front.)

In fact, by now, all organizations should be working off of a virtual center-led model where the “center” is the Procurement A-Team, where the members could literally be spread out over the 6 continents to “locally” absorb the situations in each geography before making decisions and to always have someone available to answer questions on not just a follow-the-sun but follow-the-local-business hours model.

And while virtual / remote / distributed work still seems to be an entirely new thing that most companies didn’t think of before the pandemic and that most companies are trying to eliminate entirely now that the pandemic has been declared over (even though the next pandemic is just around the corner and, yet again, no one is prepared for it), those of us in IT and Supply Chain have been doing it for two decades (and the doctor has been primarily been working remote for the past 19 years — the tech has been there, and has worked, for two decades … and now that high speed is in just about every urban area globally, there’s no reason a hybrid/virtual model cannot work and work well).

The reality is that the pandemic not only brought global supply chains crashing down but brought to light the high risk embedded in them a few of us saw a decade ago, which went beyond the obvious risks of “all your eggs in one basket” (even though Don Quixote was published in 1605) and “The Bermuda Triangle“*1, but also included the risks of relatively centralized procurement where one team in one part of the globe made the all-our-eggs-in-the-China-basket and managed the relationship with one team at one factory in another part of the globe; so if either team got completely locked down with little remote/virtual support (and we saw some countries limit people to 1KM from their homes and China lock down entire cities and not even let people leave their apartments), the entire chain was shut down even beyond the worst case that some of us were envisioning a decade ago (and made our definitions of bad — which was factory goes out of business, shipping lane closes, or ship sinks — look good by comparison because, at least then, you could still go to work and travel to find a new factory, organize a new lane, or spin up the factory 24/7 until you remade the order).

However, with virtual center-led, you not only have a team that knows how to work distributed and remote, and who knows how to use that setup to better mitigate operational risks, but who also has a risk-mitigation mindset that any supply base should also be distributed and different locations remote from each other (two factories in the same town is not risk-mitigation; an earthquake destroys the roads, the entire town gets quarantined, or political borders shut and its effectively one cut-off source of supply) and will help the different parts of the organization design more risk-adverse, or at least risk-aware, supply chains — tapping into local expertise in each part of the world to make the best decision and allowing the organization to move management of the chain around as needed and local teams (because you’re not sourcing your Canadian snow-plow and igloo building services from India, for example) to always have remote access to guidance and best practices in snow-removal services RFP construction (and know how from Norway and Japan).

In other words, center-led procurement (of which you can find a lot of guidance on in the archives here and over on Spend Matters, especially since, now retired, Peter Smith of Spend Matters UK was a guru on this as well as sustainability) of the virtual kind is what you need to be doing now if you want to last until 2030.

 

*1 which, while statistically no more dangerous than any other part of the oceans, exemplifies the fact that even the biggest ships, with an entire year of your inventory on board, can sink, especially when oceanographers have finally realized [even though mathematicians working with wave models understood this concept decades ago] that rogue waves are not a once a in decade occurrence, but a DAILY occurrence on this planet, it’s just that the ocean is so big that the fraction ever covered by ships is so microscopic that the chances of any ship encountering a rogue wave are infinitesimal on a ship-by-ship basis)

It’s Time for the Return of Purchasing Consortiums …

… but not the kind you think!

In the good ol’ days, before everyone had access to cheap and easy e-Auctions (when inflation was low, delivery guaranteed, and supply outstripped demand) or on-demand RFX sourcing platforms, the answer to better “purchasing” was consortiums that pooled demand and negotiated lower costs (hopefully lower landed costs, but you took what you could get). Except in a few industries (like healthcare, where product requirements are highly regulated, or utilities, where manufacturing requirements are exact), these have all but disappeared with the rapid rise in modern sourcing, procurement, and source-to-pay platforms over the past two decades.

While this may have appeared to be for the best, as you lost control over who you bought from, a third party controlled the relationship (and you couldn’t always go direct to get problems resolved), and you had to pay them a pretty golden penny for their problems, the pandemic has shown us that this is maybe not the case. Even though you want to control you purchasing as a buyer for your organization, you need reliable supply … and the pandemic has demonstrated (what many of us new, and blogged, about a decade ago; search the archives) that when you are outsourcing halfway around the world, reliability is a myth.

You need nearshore supply that you can easily get by truck and, preferably, train for large shipments (as modern trains can be more environmentally friendly from a GHG perspective), but every since  the Big (5/6/8/whatever) analyst companies that followed told you to go China, not only did you put most of your home-grown manufacturing plants out of business (which, I’m sad to say, wasn’t always as big of a loss as whiny politicians would have you think and definitely didn’t nail the coffin shut, but that’s another post), but you also put many near-shore manufacturing plants in Mexico (and other Central, Latin, and South American locations) out of business (which did!).

They needed to be resurrected the day pandemic restrictions started relaxing, and every day the need for their reactivation (and modernization) / replacement gets worse!

But unless you are a Fortune 100, you don’t have the spend on your own to convince anyone to even think about restarting a factory somewhere closer, more reliable, and safer. (And even then, the risk equation is not any better than continuing to outsource to China and hoping for the best!)

That’s why we need a return of the Purchasing Consortium, but with a new mandate to not only pool and guarantee enough demand to keep a new(ly) (revived/modernized) manufacturing operation sustainable and profitable but, in the absence of anyone in the target location willing to take the startup risk, manage a multi-shareholder investment on behalf of the Global 3000 parties that need such an operation and can afford to invest in one!

It’s a win-win regardless of whether or not anyone is willing to buy the operation once started. Either someone steps in and takes it off of the consortiums hands, giving the initial investors a return on their investment in addition to guaranteed supply, or the investors, who maintain control, can keep purchasing costs down (and the potential for profits up).

The question is, besides companies like Apple and Microsoft that can afford to build their own chip plants near shore (because what else are they going to do with the Billions they have in the bank?), who else is going to step up and bring it back to where it should be.

 

(Now, before you go bashing the grumpy old analyst for China bashing, this post is not about China bashing [although that’s a great rant topic], it’s about the insanity of going halfway around the world for something you can get [close to] home. If you’re selling in Asia, you should damn well be manufacturing in Asia, as it would be insane to manufacture something in Mexico and ship it to China if it’s easy to manufacture in China!)

AI: Applied Indirection, Artificial Idiocy, & Automated Incompetence … The April Fools Joke Vendors are Playing on You Year Round!

So on the one day of the year when they should be making the joke, I’m going to reveal it.

The vast majority of vendors who claim “AI”, where they want you to think “AI” stands for Artificial Intelligence, have no “AI” in that context, and many don’t even have anything close. A few may have “Assisted Intelligence” (Level 1) and even fewer still may have “Augmented Intelligence” (Level 2), but “Analytical (Cognitive) Intelligence” (Level 3)? Forget it! And as for, Level 4, “Autonomous Intelligence”, which is the baseline that must be met before you could even consider a system true “AI”, doesn’t exist (at least as far as we know). (ChatGPT would be a 3 on this scale, 3.5 if you’re dumb enough to use it to power a semi-autonomous application.) (For more details on the levels of “AI”, see the detailed Pro piece the doctor wrote over on Spend Matters on how Artificial intelligence levels show AI is not created equal. Do you know what the vendor is selling?.)

However, thanks to ChatGPT/OpenAI and other offerings, every vendor all of a sudden feels that their solution has to have “AI” to compete, and is now claiming they have AI when, at best, they’ve implemented some third party “library” into their analytics module, which itself may or may not be AI, or, at worst, they just have classical rule-based automation and statistical-based predictive analytics (i.e. trend analysis) but have called it “AI” because, just like a classic decision-tree expert system from three decades ago, it can make a “recommendation”. Woo hoo.

Not that this is nothing new, three years ago a study by London Venture Capital Firm MMC found that 40% of European startups that are classified as “AI” don’t actually use AI in a way that is “material” to their business. MMC studied 2,830 “AI” startups across 13 EU countries, and in 40% of cases, [they] could find no mention of evidence of AI. (See the great summary in The Verge.) And even that statistic is a bit misleading, because I’m willing to bet that the “evidence” they did find was technology that didn’t necessarily mandate “AI” and could be implemented with “classical” techniques because, as a longtime blogger, analyst, due diligence professional and, most importantly, a PhD in theoretical computer science (read: advanced applied mathematics), I have found that most claims of “AI” weren’t really AI — in most cases they were just using a combination of automation and/or configurable rules and/or advanced statistics and/or machine learning and just had some of the foundations, but no real “AI”.

In our space, real “AI”, and by that I mean strong Level 2 / weak Level 3 (which is the best you can get) is quite rate and specific use cases are few and far between, and most AI is simply semi-unsupervised machine learning for transaction/categorical classification (spend analysis) or clause identification (contract analytics).

The problem is that, when no one really understands what “AI” is, and given that less than 1/10 Americans have the mathematical competency to even begin the university studies to try and garner an understanding [Level 4 on the PIAAC], it’s really easy form them to try and pull a fast one on you. This is especially true when the solution is able to automate certain tasks or recommend best practices in the majority of situations faster and more consistently than the average buyer (who, let’s face it, is under-educated — thanks to limited supply chain / operations management programs and almost no real Procurement training in Colleges and Universities, under experienced, and not an expert in modern technology), and the solution can be made to look “smart” (but, in reality, is dumber than a doorknob and definitely dumber than Maxwell Smart). But it’s not smart. Not at all.  And don’t be fooled.

The good news is the marketing manager using Applied Indirection to push a false AI solution at you probably doesn’t have a clue what they have anyway, and a few smart questions asked by someone who understands what AI is, and isn’t, can probably get pretty close to the truth pretty fast. For example:

1) “We have advanced AI data auto-class. It’s the most intelligent, and accurate, classification in the space.”

‘How does it work?’

“It uses a multi-level neural net that has been trained on tens of millions of records across over a hundred clients in the indirect space.”

‘Great, so basically it categorizes transactions based on similarity to other transactions in a slowly evolving manner, and I’m guessing for a new client in the indirect space, out of the box, you’re around 85% to 90% accuracy out of the box and you approach 95% with semi-supervised retraining over time — and that’s the upper bound and it will never be perfect.’

“Uhm, … well, … more or less … “

‘Got it!’ At this point you know it’s “AI” level for classification is augmented (as it learns and evolves over time), and barely, but it’s not “the best” mapping in the space as platforms that use AI to suggest rules (upon implementation and then for unmapped transactions) and do mapping and categorization based on the user selected and verified rules can produce 100% accurate mappings, always outperforming an “AI” solution that uses neural nets that are good (but not perfect).

‘Do you use AI anywhere else?’

“Uhm, what, why? It’s great where, and as, it is.

And now you know that there is no real AI in the analytics part of the platform, and there’s no reason to choose it over any other.

2) “We use AI for OTD prediction and risk in delivery prediction.”

‘Cool. What algorithm do you use?’

“Huh, what do you mean?”

‘How does the application compute the OTD and/or risk associated with the delivery.’

>Wait for the hand off to their “data scientist” …< “We use a blended least-squares method to produce a prediction function where, if there is enough data for the product, carrier, and lane, we’ll primarily use that data for the function, but if there’s not enough, we’ll use the most similar (using a mathematical distance function) product, carrier, and/or lane data … “

Is that AI, well, if there’s some sort of learning involved in the selection of “similar data” or recommendations as to parameter tuning IF parameters can be tuned, maybe, but this is just classical statistical trend analysis and not really any different than classical ARIMA based forecasting from the 70s, and did they have ANY AI then?!? (The answer is “NO”!)

3) “We use AI for our supplier recommendation process?’

‘Sounds promising … please explain!’

“We compute a relevance score taking into account a large number of factors including product base, geographic location, diversity, risk, etc.”

‘OK … how … ‘

>Cue the Eventual Hand Off to “Data Science” Team<

“Product Base is computed as a percentage of the category they can likely cover, geographic location as an average distance function, diversity as an estimate of diversity employment if there is no diversity ownership data (in which case it’s just 50%), the risk score from our risk model, etc. “

‘So, in other words, it’s just a formula … ‘

“A very sophisticated multi-level formula with conditionals and nesting that computes … “

‘Got it thanks!’ NO AI! Not even a hint there of as it’s just a functional risk score that could be built in ANY application with a formula builder.

This isn’t to say that a solution without AI isn’t right for you! (In fact, it probably is!) It’s all about solving your business problem, and many problems have been solved in our space just fine for the last decade or so with rules-based workflow and automation, optimization, and statistical modelling and trend projection. When guidance is needed, decision trees/matrices tied to expert curated best-practices (the modern equivalent of a classic “expert system”) often work better than one could imagine. In other words, it’s not AI, it’s not the hype, it’s what solves your problem, reliably and predictably time-after-time.

So don’t fall for the false hype and be the April fool.