Category Archives: rants

How Can Indirect Spend *NOT* Be Well Managed in 2023?

the doctor gets a lot of press releases. Some of them contain a lot of BS (which is good, he writes best when he’s on an angry rant), others contain a lot of “findings” that, if true (and the findings usually are for the right for the right subset of the market), are simultaneously scary and ridiculous. In this particular case, as the doctor writes this, he received a press release that said the research finds that 82%+ of procurement leaders say their indirect spend is not well managed, leaving substantial cost savings on the table.

The question is, how is that number so high? We’ve had source-to-pay suites for a decade (which were originally designed to source indirect products and services, create catalogs of those sourced selections, support purchase orders only for items in the catalogs, and ensure invoices matched the item prices in the catalog. And for those willing to do custom integration, it was possible to integrate a best of breed sourcing solution and a best of breed catalog management solution and a best of breed e-invoicing solution and achieve this in the late 2000s.

Now, in a mixed solution, there was no guarantee that the sourcing event would choose the best mix (since early solutions generally didn’t support optimization or advanced analytics), that the catalog would force the lowest cost (or even preferred) selection when there were multiple options, or that the invoice management could detect when shipping costs were too high or handling fees shouldn’t be there, but there was still management and any overruns were not substantial (at least compared to pre-solution overages in indirect; an organization could easily cut out 80% of the fat, which could be as high as 30% in some categories; so if the overage went from 30% to 6%, that was well managed — and solutions have only become better over time).

What’s even worse is when the expected reality is put into hard numbers. According to the press release “two-thirds of suppliers (68%) report increased demand for their offerings compared to the past year and nearly half (43%) are planning to increase prices in 2023“. Thanks to global inflation, prices are going up as demand does (which is still pent-up post-pandemic), and we know it, but knowing costs will be uncontrollable to an extent is a tough one.

Of course the press release says that the key to cutting cost is to implement (autonomous) technology that saves on day one, which you should know by now, but the question is why have so many companies not yet implemented basic S2P functionality, either as a suite or as BoB integrations, as such technology would have ensured indirect was well under control, and reduced a likely organizational overspend by (85% of 15% of 35% =) 5% (est. realization * avg. savings * avg. indirect spend) of total spend, which would go straight to the bottom line! No autonomous tech needed!

For those interested, the press release came from a third party PR firm and was based on Globality’s “2023 Research Insights for CFOs”.

Where’s the Procurement Management Platform?

Where’s the Procurement Management Platform?

When we started out in the very, very, very late nineties, it was all about Procurement and/or Strategic Sourcing, which, in the beginning was all about RFPs and on-line auctions. The focus was on taking many organizations from fax and spreadsheets to integrated bids and on-line analysis and reporting (even if utterly simplistic).

Then, in the early naughts, we had the introduction of spend analysis, CLM, S(R)M, and invoice management and by mid-decade vendors were building mini-suites for upstream (Source-to-Contract) and downstream (Procure-to-Pay, which included Catalog Management, etc.) Sourcing and Procurement. By the time the teens came upon us, the big suite vendors were taken steps to merge upstream and downstream and you had the mega S2P suites start appearing in the early to mid-teams, some through over a decade of development and others through acquisition (mania). They third generation of these products/suites were heralded as the one platform solution (which ERP vendors like SAP and Oracle were hailing themselves as back in the eighties), but …

1) Even though the mega-trend in the 2010s of the Source-to-Pay mega-suite was supposed to be the end of decades of advancement in S2P, we soon found out that even a suite that had the six-core applications of Sourcing, SRM, CLM, Spend Analytics, Procurement, and Invoice to Pay didn’t meet all of an organization’s needs as they needed supplier networks to engage with suppliers, data providers for discovery and diversity, CSR & GHG data providers for risk, custom sourcing tools for complex/niche categories, etc. etc. etc.

2) Most of these platforms had little to no project management, process management, or opportunity management

3) Most assumed that serving procurement meant serving buyers and that was it … but you have to serve reports and oversight up to management and pull purchasing needs in from across the organization. I.e. no (out-of-the-box) management / Finance reporting and projections or intake management (facilitating the need for further Excel usage, and not less)

4) Even those with great spend analysis didn’t always revolve around the spend, and when you think about how business measures its metrics, spend should be the foundation.

And, in summary, they didn’t, and still don’t, deliver an organization everything it needs to be successful (which is why the BoB vs Suite debate rages on today), because Procurement is not an island (even though it was once staffed like the Island of Misfit Toys), and instead is the front-end interface to the supply chain, which, for some companies can include 10,000 companies when you trace all of the product requirements down 3, 4, 5+ levels to the raw material source. (But that’s another topic for another day.)

Getting back to the topic at hand, if you had a proper Procurement Management Platform, which was designed to support data-centric end-point integrations for specific processes and organizational needs, then

1) it would be quite easy to augment and add in custom applications for niche processes or data collections for niche process and reporting management as needed

2) it would be built around sourcing and procurement centric project management and contain the extensible workflow capability required to add customized process and opportunity management as needed

3) it would allow for the creation or integration of intake applications and interfaces to gather needs and report on decisions and progress and to synthesize all relevant data for roll-up views and KPIs that finance and management needs on a regular basis

4) it could be built to use the organizational spend as the foundational data source …

and Procurement could build up, maintain, and evolve the solution it really needs to be successful over time — which is something it can’t do today because buyers can’t code low level APIs, app stores don’t ensure app connectivity, and today’s “networks” merely support data exchange and not overall process management.

So where do you get this when no single provider on the market has (historically) had this? Good question … and one that we’ll hopefully answer in the year ahead.

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.