Category Archives: AI

Pretty Fly That Gen-AI!

To the tune of Pretty Fly for a Rabbi by Weird Al.
[Which is to the tune of Pretty Fly (For a White Guy) by The Offspring.]

A I, A I!
(How ya doin’, Schulman?) A I, A I!
(How ya doin’, Schulman?) A I, A I!
And all my rabbis say it’s pretty fly that Gen-AI!

Meccha Gen A I, meccha technologica!

Our space has had a fair share of tech hype in the past
But most of it was nudnik and none of it would last
But our new tech’s real kosher, we think it’ll do the trick
I tell ya, it’s to die for, it really knows its shtick

So how’s by you? Have you seen this tool?
Reads the Reddit, does its own synopsis too
Working’ like a dog at a synagogue
It scans all day, it scans all day

Just type “Vay is mir!” and it will kick into gear
And bring you lots of fear and tell you the world’s end is near
Just grab your shelter kit and
Hey! Hey! Do that Prepper thing!

A I, A I!
(How ya doin’, Schulman?) A I, A I!
(How ya doin’, Schulman?) A I, A I!
And all my rabbis say it’s pretty fly that Gen-AI!

It learns from the forums, not just any will suffice
It has to find extremist views ’cause that’s what masses like
Doesn’t care if it’s a fact, as long as you come and play
Hallucinations plenty, just eat one rock a day!

People used to scoff, now they say “Mazel tov!”
It’s so attractive ’cause they think they can lay you off
It computes non-stop and outputs slop
What’s not to like? What’s not to like?

For endless days, it works without complaint
And never asks for pastrami on white bread with mayonnaise
Just grab your vacation thong and
Hey! Hey! Do that Celeb thing!

When it’s computing your new payroll, now that you shouldn’t miss
It’ll happily add a Billion when the dot it skips
It’s got a lot of chutzpah, and it really is quite daft
The coders pay the moyl and then you just get the shaft!

A I, A I!
(How ya doin’, Schulman?) A I, A I!
(How ya doin’, Schulman?) A I, A I!

Meccha Gen A I, meccha technologica!

It’s doin’ well, I gotta kvell
The yentas love it, even shicksas think it’s swell
For your mental health, it is top shelf
It don’t care if you go and off yourself

Yeah it calls the shots, we really bow down lots
Oy gevalt, I’m so ferklempt that I could plotz!
Grab your pasta strainer
Bow down to the sphaghetti god!

Put on your pasta strainer and
Hey! Hey! Join the FSM!

Dedicated to THE PROPHET.

You Don’t Need Gen-AI to Revolutionize Procurement and Supply Chain Management — Classic Analytics, Optimization, and Machine Learning that You Have Been Ignoring for Two Decades Will Do Just Fine!

This originally posted on March 22 (2024). It is being reposted because we need solutions, Gartner (who co-created the hype cycle) published a study which found that Gen-AI/technology implementations fail 85% of time, and its because we have abandoned the foundations — which work wonders in the hands of properly applied Human Intelligence (HI!). Gen-AI, like all technologies, has its place, and it’s not wherever the Vendor of the Week pushes it, but where it belongs. Please remember that.

Open Gen-AI technology may be about as reliable as a career politician managing your Nigerian bank account, but somehow it’s won the PR war (since there is longer any requirement to speak the truth or state actual facts in sales and marketing in most “first” world countries [where they believe Alternative Math is a real thing … and that’s why they can’t balance their budgets, FYI]) as every Big X, Mid-Sized Consultancy, and the majority of software vendors are pushing Open Gen-AI as the greatest revolution in technology since the abacus. the doctor shouldn’t be surprised, given that most of the turkeys on their rafters can’t even do basic math^* (but yet profess to deeply understand this technology) and thus believe the hype (and downplay the serious risks, which we summarized in this article, where we didn’t even mention the quality of the results when you unexpectedly get a result that doesn’t exhibit any of the six major issues).

The Power of Real Spend Analysis

If you have a real Spend Analysis tool, like Spendata (The Spend Analysis Power Tool), simple data exploration will find you a 10% or more savings opportunity in just a few days (well, maybe a few weeks, but that’s still just a matter of days). It’s one of only two technologies that has been demonstrated, when properly deployed and used, to identify returns of 10% or more, year after year after year, since the mid 2000s (when the technology wasn’t nearly as good as it is today), and it can be used by any Procurement or Finance Analyst that has a basic understanding of their data.

When you have a tool that will let you analyze data around any dimension of interest — supplier, category, product — restrict it to any subset of interest — timeframe, geographic location, off-contract spend — and roll-up, compare against, and drill down by variance — the opportunities you will find will be considerable. Even in the best sourced top spend categories, you’ll usually find 2% to 3%, in the mid-spend likely 5% or more, in the tail, likely 15% or more … and that’s before you identify unexpected opportunities by division (who aren’t adhering to the new contracts), geography (where a new local supplier can slash transportation costs), product line (where subtle shifts in pricing — and yes, real spend analysis can also handle sales and pricing data — lead to unexpected sales increases and greater savings when you bump your orders to the next discount level), and even in warranty costs (when you identify that a certain supplier location is continually delivering low quality goods compared to its peers).

And that’s just the Procurement spend … it can also handle the supply chain spend, logistics spend, warranty spend, utility and HR spend — and while you can’t control the HR spend, you can get a handle on your average cost by position by location and possibly restructure your hubs during expansion time to where resources are lower cost! Savings, savings, savings … you’ll find them ’round the clock … savings, savings, savings … analytics rocks!

The Power of Strategic Sourcing Decision Optimization

Decision optimization has been around in the Procurement space for almost 25 years, but it still has less than 10% penetration! This is utterly abysmal. It’s not only the only other technology that has been generating returns of 10% or more, in good times and bad, for any leading organization that consistently uses it, but the only technology that the doctor has seen that has consistently generated 20% to 30% savings opportunities on large multi-national complex categories that just can’t be solved with RFQ and a spreadsheet, no matter how hard you try. (But if you want to pay them, an expert consultant will still claim they can with the old college try if you pay their top analyst’s salary for a few months … and at, say, 5K a day, there goes three times any savings they identify.)

Examples where the doctor has repeatedly seen stellar results include:

national service provider contract optimization across national, regional, and local providers where rates, expected utilization, and all-in costs for remote resources are considered; With just an RFX solution, the usual solution is to go to all the relevant Big X and Mid-Sized Bodyshops and get their rate cards by role by location by base rate (with expenses picked up by the org) and all-in rate; calc. the expected local overhead rate by location; then, for each Big X / Mid-Size- role – location, determine if the Big X all-in rate or the Big X base rate plus their overhead is cheaper and select that as the final bid for analysis; then mark the lowest bid for each role-location and determine the three top providers; then distribute the award between the three “top” providers in the lowest cost fashion; and, in big companies using a lot of contract labour, leave millions on the table because 1) sometimes the cheapest 3 will actually be the providers with the middle of the road bids across the board and 2) for some areas/roles, regional, and definitely local, providers will often be cheaper — but since the complexity is beyond manageable, this isn’t done, even though the doctor has seen multiple real-world events generate 30% to 40% savings since optimization can handle hundreds of suppliers and tens of thousands of bids and find the perfect mix (even while limiting the number of global providers and the number of providers who can service a location)
global mailer / catalog production —
paper won’t go away, and when you have to balance inks, papers, printing, distribution, and mailing — it’s not always local or one country in a region that minimizes costs, it’s a very complex sourcing AND logistics distribution that optimizes costs … and the real-world model gets dizzying fast unless you use optimization, which will find 10% or more savings beyond your current best efforts
build-to-order assembly — don’t just leave that to the contract manufacturer, when you can simultaneously analyze the entire BoM and supply chain, which can easily dwarf the above two models if you have 50 or more items, as savings will just appear when you do so

… but yet, because it’s “math”, it doesn’t get used, even though you don’t have to do the math — the platform does!

Curve Fitting Trend Analysis

Dozens (and dozens) of “AI” models have been developed over the past few years to provide you with “predictive” forecasts, insights, and analytics, but guess what? Not a SINGLE model has outdone classical curve-fitting trend analysis — and NOT a single model ever will. (This is because all these fancy-smancy black box solutions do is attempt to identify the record/transaction “fingerprint” that contains the most relevant data and then attempt to identify the “curve” or “line” to fit it too all at once, which means the upper bound is a classical model that uses the right data and fits to the right curve from the beginning, without wasting an entire plant’s worth of energy powering entire data centers as the algorithm repeatedly guesses random fingerprints and models until one seems to work well.)

And the reality is that these standard techniques (which have been refined since the 60s and 70s), which now run blindingly fast on large data sets thanks to today’s computing, can achieve 95% to 98% accuracy in some domains, with no misfires. A 95% accurate forecast on inventory, sales, etc. is pretty damn good and minimizes the buffer stock, and lead time, you need. Detailed, fine tuned, correlation analysis can accurately predict the impact of sales and industry events. And so on.

Going one step further, there exists a host of clustering techniques that can identify emergent trends in outlier behaviour as well as pockets of customers or demand. And so on. But chances are you aren’t using any of these techniques.

So given that most of you haven’t adopted any of this technology that has proven to be reliable, effective, and extremely valuable, why on earth would you want to adopt an unproven technology that hallucinates daily, might tell of your sensitive employees with hate speech, and even leak your data? It makes ZERO sense!

While we admit that someday semi-private LLMs will be an appropriate solution for certain areas of your business where large amount of textual analysis is required on a regular basis, even these are still iffy today and can’t always be trusted. And the doctor doesn’t care how slick that chatbot is because if you have to spend days learning how to expertly craft a prompt just to get a single result, you might as well just learn to code and use a classic open source Neural Net library — you’ll get better, more reliable, results faster.

Keep an eye on the tech if you like, but nothing stops you from using the tech that works. Let your peers be the test pilots. You really don’t want to be in the cockpit when it crashes.

^* And if you don’t understand why a deep understand of university level mathematics, preferably at the graduate level, is important, then you shouldn’t be touching the turkey who touches the Gen-AI solution with a 10-foot pole!

Why are Big X training so many “consultants” on AI?

Especially Gen-AI? For the longest time, the doctor couldn’t understand why so many Big X consultancies were training so many “consultants” on AI, especially Gen-AI. Most of their junior “consultants” can’t even use advanced functionality in today’s analytics applications (as you need advanced degrees in mathematics, computer science, data science, and/or Operations Research to do so) or deliver significant value on traditional analytics and advisory projects relative to the price they charge (unless they are being led by a more senior person with the analytics knowledge and real-world experience). (Read our previous articles and comments on where this talent ends up [which is typically not a Big X] and where these Big X firms offer unparalleled value [and where you should be using Big X].)

But it was recently all made clear to me. These consultants, who struggle with basic projects (as reflected in the high tech failure rates they are regularly a part of as the typical first choice for a third party implementation team when the vendor does not provide them adequate training and support on the platform they are implementing), are barely up to doing the work (as they are usually straight out of school with no real world experience or deep knowledge of anything not taught in a textbook MBA program), and definitely not up to doing strategic engagements out of the gate!

However, with companies wanting to rapidly digitize across the board (which they need to, but, not all digitization requirements should have equal priority), they need strategic advice and direction, and these firms just don’t have enough senior consultants to handle all the engagements and, most importantly, do the work required to put those book-sized briefs and presentations together.

But the one thing Gen-AI can do is take in millions of pages of strategic plans and presentations, take in instructions of what is desired, then generate pages of text from bits and pieces of these historical plans and presentations for each instruction, amalgamate them all together, and produce a detailed report and presentation that they can present to the client. And do this in a few hours under the guidance of a junior analyst with a (Gen-) AI playbook! Then all the senior person has to do is a quick tweak and review!

We’re not joking! The crazy thing is, with so much free material on the internet, with a little bit of elbow grease, and some very creative prompt engineering, you can do this yourself. And someone on LinkedIn already showed you how — giving you this information for FREE in this LinkedIn article. (And should that article disappear, here’s a link to the author’s article on his site.)

So now you know. It’s not about getting you better results (which may or may not happen, every project is different), it’s to give them the ability to take on more projects that they wouldn’t otherwise have the manpower to do.

And if you really want good results, note that you can always hire a real strategic senior consultant from a specialist niche consultancy who often won’t be on multiple projects at the same time, and who can give the insights you need without wasting trees printing out book sized presentations for you. After al, relative to the value the right consultant will bring, Consultants are Cheap and, in our space, the key to Affordable RFPs!

It’s Not AI (First,Led,Powered,etc.) or Autonomous. It is Solution with Augmented Intelligence!

By now you know our stance on Gen-AI (and how it should be relegated to the rubbish heap from which it came) because it’s not about “AI”, it’s about outcome. And outcome requires a real, predictable, usable solution that helps Human Intelligence (HI!) make the right decision. Such a solution is one that uses tried and true algorithms that support tried and true processes that provide a human with the insight needed to make the right decision at the time, every time a decision needs to be made.

This requires a solution that walks the human user through the process, step by step, and presents them with the information required to make a decision as to whether to progress to another step, what the next step is, and any conditions that need to be put on that next step. This requires a solution that automatically runs all of the typically relevant analysis, on all of the available data, and presents the insight, along with any typical decisions (as [a] default recommendation[s]) made on any similar situations that can be found in the organizational history.

Automation should only occur in situations the organization has defined as acceptable according to well defined, human reviewed, and verified rules. Not default vendor rules or unverified probabilities or unverified random computations from a random algorithm. A good solution is one that walks a user through the process, often allowing each step to be completed with a single choice or click. It’s not one that makes the choice for the user, which may or may not be the right one, but one that helps the user makes the right choice. It might seem like a subtle difference, but it is a very important one.

Even though an AI-powered autonomous solution might seem to make the right decision over 90% (or 95%) of the time, it doesn’t mean it actually is. If it looks right, it might be a good decision, but it doesn’t mean it’s a good decision for the organization at the time, or the best decision that can be made. Only human review, at the time, can make that decision. A good solution runs all the analysis it can, summarizes the results, and lets a human verify the data for any recommendation made by the system.

To better understand the the subtlety, consider a situation where the organization lets the system automatically re-auction all regularly purchased products and commodities for manufacturing or MRO where the price is typically constant over time using a lowest bidder takes all e-Auction that results in the auto-generation and auto e-Signature of a one year contract. Now, most of the time this is probably going to work okay, but imagine you let it run on full auto-pilot and in the e-Auction queue is your regular RAM contract that expired three days after a major RAM plant factory fire (that happens about once every decade if you trace back through the last forty years), and prices have just skyrocketed about 50%. Prices which would drop back down as soon as the plant comes back online in three months. Locking in a full year contract would result in excessive cost overruns on the items for almost nine months longer than necessary, instead of just three months or so. A human would know to buy the bare minimum on the spot market at overly inflated rates and wait until the market stabilized before running an e-Auction to lock in the next contract. But a system told to just re-auction and re-order at every contract expiration would do this that. It wouldn’t know that the current market rates are just temporary, why, and how to change course. This is just one example where over-automation and AI will lead to failure without Human Intervention.

A good system presents the user with the products/commodities that are typically automatically auctioned, the history of costs, the current market costs, the recommendation for auto-sourcing and term, the expected results, and whether the recommendation is for the auction to auto-award and contract or, when the auction is complete, pause and include a human in the loop to make a final decision. A well designed system minimizes the work and input required by a human, eliminating all the tactical data analysis and e-paperwork, making it easy to make the right strategic decision without a lot of effort. Technology isn’t about trying to replace human intelligence (which it can’t), but about eliminating unnecessary drudgery or computation (“thunking”) that humans are not good at (or don’t have the time for), so that humans can focus on strategic decisions and value add.

That’s why the right answer is always a solution with augmented intelligence. Not autonomous AI solutions.