Category Archives: rants

There are NO Perils of Big Data in Procurement!

First of all, no organization has enough data, and those that come close don’t have big data.

Secondly, the more data you have, the better.

Third, if you think you have too much data, you’re not getting it!

So where’s this rant coming from? The rant-inducing headline du jour. The CIO Review recently published an article on The Perils of Big Data in Procurement which is complete non-sense, as there are no perils to having more data (because there’s never enough), unless it’s bad data (but the assumption in the article was that all the data was correct), just perils in terms of how that data is presented and accessed.

The perils in terms of how that data is presented and accessed can be significant, but that’s not due to having big data, that is due to poor system design — and that’s a different issue!

According to the article, buyers and procurement managers … have available a huge and unprecedented amount of data … [and] start to measure everything in order to manage it and that with this approach, several data lakes are created, feeding various dashboards, scorecards, reports, and metrics as procurement professionals try to understand spend analysis, price trends, market fluctuations, volume, cost savings, negotiation performance, and other essential factors. And this is true.

It goes on to say it is very easy for a person to be lost in the sea of numbers and details and miss the big picture entirely because you don’t know what is the crucial data that would give you critical insights. And if that wasn’t enough, it goes on to say it is the same as someone that enters the hospital with a broken leg but has everything else checked. WTF?

This is so dumb it makes you angry!

  1. If a person gets lost in the sea of details and numbers it’s because they don’t know what they should be looking for and how they should be looking for it, not because there’s too much data.
  2. If they don’t know what is crucial, it’s because they don’t know enough about the project they are doing to identify what’s critical and what’s not.
  3. What health practitioner is going to be so stupid as to not see a broken leg on a triage? Come on now! And what Procurement practitioner would check all but one dashboard randomly and then not check the last remaining dashboard? (And that’s what the article is implying with its ridiculous statement.)

In other words, the headline, and claim, is bullcr@p. Don’t blame a mountain of data for a lack of capability in your people, poor vendor technology choices (that bury you in useless dashboards), and your unwillingness to train your talent in modern technology and best practices so they can do their job properly.

And while the author is completely right in that you need to

  • understand what matters
  • start with a top-down view
  • have people who are good at interpreting the data

It still misses the point in that you need to, for any application you buy and any project you wish to undertake

  • define what’s relevant up front
  • find a solution that is configured/configurable to show that up front
  • make sure the data is easy to interpret, is accompanied with written guidance, and that your talent is trained on how to properly interpret the data and
  • if the goal is opportunity finding, the solution needs to identify and present the top opportunities across all of the analysis done, with deep supporting dashboards buried under the high level summary dashboard

More data is always better, especially if you want to use machine learning. In other words, it’s not the data, it’s the application, or the people, so don’t blame the data for your organization’s shortcomings.

Procurement Automation: Good. Automated Procurement: Bad.

We shouldn’t have to say this. It should be very clear by now. But given that a number of vendors are using the terminology interchangeably, possibly to convince you they have the right solution, maybe it’s not clear. But it needs to be. Because procurement automation is NOT the same as automated procurement and while procurement automation, properly done, is the best investment an average over-burdened and under-resourced Procurement department can make, on the flip side, AI-driven automated procurement is the absolute worst. To put things in perspective, downgrading Excel to Lotus 1-2-3 would be a better move. But let’s back up, and start with some definitions.

Procurement Automation is the process of automating certain procurement tasks that can be best accomplished by machines and procurement automation technology is the technology that automates the tasks that can be best done by machines. In simpler terms, it automates the “thunking” by doing all of the tactical, almost mindless, work that is a waste of a senior Procurement professional’s time.

The Source-to-Pay cycle is full of tasks that are best done by machines when appropriate rules and boundaries are defined. For each major area, we’ll outline some of these tasks as an example.

Intake/Orchestration

Procurement Automation will analyze the request, identify similar requests made in the past, identify the actions used to resolve those requests, identify the suppliers considered and selected, the products and services used, and other information. It will present that information to the buyer, including the suggested actions, and allow the buyer to one-click initiate any of the suggested actions, which might include a sourcing event, contract renegotiation, catalog purchase, etc.

Sourcing

Procurement Automation will, when a user kicks off a sourcing event for one or more products, automatically bring up the suggested suppliers, automatically suggest the appropriate questionaries and forms, automatically suggest the appropriate Ts and Cs to insist on up front, automatically send the RFP to suppliers, automatically analyze the responses to make sure they are complete, in the correct format, and in an expected range; automatically compare the responses to find deviations from the norm; automatically highly the lowest and highest costs, CO2 factors, etc. and present all that information to the buyer.

Supplier Management

Procurement Automation will, when a supplier is selected, automatically handle the onboarding; monitor the data for changes; monitor the performance metrics; monitor the OTD; monitor third party financial and risk metrics; and alert the buyer to any issues and performance changes that are detrimental or may indicate forthcoming problems.

Contract Management

Procurement Automation will, when an award is selected, push the award into the Contract Management system, automatically generate the draft contract, send it to the supplier, highlight any redlines the supplier makes when it comes back and automatically inform the supplier if any non-negotiable terms and conditions (including those they agreed to when they responded to the RFP), and automate the generation of the response email when the buyer does their redlines.

e-Procurement

For catalog buys, it will automatically generate the POs, route them for necessary approvals, distribute them to the suppliers when approved, automatically match the ASNs when they come back, alert the buyers if ASNs are not received in a timely basis, and match the invoices when they come in.

Invoice-to-Pay

When the invoice comes in, it’s automatically matched to the purchase order, it’s checked for price accuracy, identified as partial or full, verified to be non-duplicate, and if any checks fail, it’s bounced back to the supplier with a description of the issues and a request for correction and resubmission. If the resubmission deals with the problems, it’s queued waiting for goods receipt/confirmation if not present, or matched if present. If the match is made, then it’s automatically sent down the approval chain, and if it’s not made within a certain time period, an alert is raised.

In all cases, it’s automating the tactical tasks that don’t require any decision making and only involving the human when necessary.

In contrast, Automated Procurement is the process by where entire procurement processes are handed over to the machine to fulfill instead of the human. In other words, when an intake request comes in and the buyer marks it for sourcing, an Automated Procurement solution will handle the entire event up to and including the award and auto-generate and distribute the Purchase Order(s). The buyer is completely bypassed and the right inventory showing up at the right time at the right price is left entirely up to the machine. Sounds good in theory. Looks good in practice when it actually works, which it will some of the time. But grinds the company to a halt when it fails.

A machine that pursues lowest cost will select an unproven non-incumbent supplier for a critical part when the suppler, who has not supplied that particular part to the company before, outbids the incumbent. It will not detect that the bid was made in an desperate attempt to help the financially struggling supplier stay in business, that the bid is not sustainable, and that the supplier is not capable of producing the part at the indicated level of quality. Then, when the first shipment is mostly defective, and the promised rush replacement order never arrives because the supplier goes out of business, the production line for the 75K luxury car folds all for lack of a single control chip. (A similar situation has occurred in the past. Recently, chip shortages stopped Cherokee production in 2021, and that wasn’t the first occurrence. Or even the second, or third.)

Machines are not intelligent. Not even close. And expecting them to make a good decision every time with no logic whatsoever (as modern Artificial Idiocy algorithms just stack probabilistic equations on top of probabilistic equations almost ad infinitum) is lunacy. So while you should invest in the best Procurement Automation tech you can get your hands on, you should steer clear of any and all Automated Procurement Solutions those fancy new startups try to sell you. While those solutions may work 90% of the time, that last 10% of the time, they won’t work that great. And, in particular, that last 1% of the time they will fail so miserable that the disruptions and losses that result will more than cancel out any and all savings and efficiencies you might get from the 90% of the time the tech worked in the beginning.

The 1-Step Guide to Responsible AI in Procurement

Forbes recently published an article on Responsible AI Procurement: A Practical Guide For Selecting Trustworthy AI Vendors. It wasn’t bad, but it missed the point.

Today, there’s only one way to responsibly address AI in Procurement.

JUST SAY NO!

1) We don’t really understand proper AI Governance (especially when most vendors are using third parties which are illegally scarping content, not checking for bias, and tweaking models on the fly without consideration for the new problems the on-the-fly tweaks will cause).

Plus, it’s not just ethical codes of conduct, it’s agreeing on what the ethics are, and, most importantly, making sure the models are transparent and unbiased — but we don’t know how to do that today, especially since all these models are huge black box models.

2) You can demand all the evidence you want from the vendor as backup for the vendor claims, but if you can’t verify it, how can you trust it?

3) These models require huge datasets to train. Even if you know the data set used and the processing method used, how can you be sure every element was properly vetted? Just like one bad apple can spoil the bunch, just one bad element in a clustering or optimization model can spoil the entire model. Just one!  It only takes a small amount of bad data to spoil a model, regardless of the model used.

4) These models can fail, and sometimes fail spectacularly. If you don’t understand the model, you don’t understand where it can fail, and thus what to look for. Also, many minor incidents (which can foretell future catastrophic failures) will go unnoticed if a human isn’t checking everything.

5) These models are not secure … the AI can leak any training data at any time without warning. Your vendor can have every security certification under the sun, and all will be for naught if they use LLMs.

So, JUST SAY NO!

Yes, McKinsey This Is Generative AI’s break out year, BUT:

We should NOT be celebrating the fact that it broke out of the prison it should be contained in only to:

So, even if your Global Survey confirms the explosive growth of AI, you should not be celebrating Generative AI’s breakout year and hold off celebrating until someone manages to put this destructive brain-dead genie we’ve unleashed back into the bottle it was released from!

DO NOT CONFUSE THE ILLUSION OF UNDERSTANDING WITH ACTUAL UNDERSTANDING!

Because if you do, you will believe AI is Actually Intelligent when, in fact, as we have pointed out again and again and again, it is Artificial Idiocy, and the best modern technology only uses AI for thunking, not thinking, as thinking needs to remain the domain of us humans (before X robs us of our ability to use actual words).

Not only is there no AI, but when you type a command, there isn’t even any understanding by the algorithm of what you are asking for when you type a query into an AI tool. NONE. It’s all based on a statistical algorithm that uses pre-computed similarity probabilities to infer what you are asking. That’s not understanding. Not even close.

The Guardian recently published a long read article on Weizenbaum’s nightmares: how the inventor of the first chatbot turned against AI that anyone who is even mildly contemplating an AI tool needs to read. Slowly and carefully. Three times.

Weizenbaum, who was a mathematician, computer scientist, and a student of psychoanalysis, was one of the founders of modern artificial intelligence who not only invented the first chatbot (Eliza), but also built early (mainframe) computers (back when they used vacuum tubes and took up entire rooms) for the University he was studying at, General Electric, and the Navy. In the 1960s, he was part of Project MAC at MIT, a Pentagon program for “machine aided cognition” that perfected time-sharing, created in-system messaging (like instant messaging or early email), and created new tools for word processing.

He was also one of the first to think about the implications of Artificial Intelligence years, if not decades, before anyone else and one of the founders of computer ethics. He was a genius, and when he said that Artificial Intelligence is an “index of the insanity of our world“, he was totally right — and he was right five decades before AI became the buzz-acronym-du-jour. Few people effectively saw that far ahead in technology, so maybe we should sit back and listen. Carefully.

So please take the time to read Weizenbaum’s nightmares: how the inventor of the first chatbot turned against AI and realize that AI is not the answer. Deterministic algorithms developed by smart people that have studied the problem, tested their assumptions, and been consistently proven reliable are the answer. They may be based on machine learning, but machine learning that is expertly selected, tuned, and monitored by validation code that detects when the algorithm is not performing to expectation and interjects a human into the process. Not a multi-layered pseudo-random statistical algorithm that randomly predicts the next seven days worth of orders, starting on Monday, are 210, 198, 307, 250, 185, 250, and 3095 and thinks everything is A-OK even though the store is closed on Sunday.