Monthly Archives: September 2024

So You Admit You Might Be a Dead-Company Walking. How Do You Avoid the Graveyard? Part 3

In short, as per Part 1, you

  1. keep admitting to every mistake you are making and do something about it, then
  2. continue by looking for cost-effective opportunities for improvement and pursue them and finally
  3. never, ever, ever forget the timeless basics.

Today, we’ll continue by describing what you do when you identify, and admit to, one of the next two mistakes (mistakes 3 & 4) we chronicled in our two part introduction to our “dead company walking” (Part 1 and Part 2) series (where we helped your potential customers identify problems that signify you are a SaaS supplier they should be walking away from). (You can find Part 2 here.)

3) Shiny New Tech is More Important than Tried and True Methodology

Tried and true ALWAYS trumps shiny new tech in enterprise software. Especially when the total cost of ownership after factoring in license fees, maintenance, hardware & software updates, services, data feeds, integration fees, etc. usually push a solution into the realm of seven figures (and eight figures for large enterprises). Companies want a return, and shiny doesn’t generate a return. So focus on algorithms, processes, and approaches that are guaranteed to yield solutions. Do this by:

i) As THE REVELATOR would say, take an agent-based approach and focus in on what the solution should do

It’s supposed to be people-process-technology (or, in the doctor‘s words, talent-transformation-technology since you should first look for opportunities to improve your processes before investing in technology), not technology-forced workflow-prisoner! When you focus on the tech first and forget the process it is supposed to support and the people who have to use it, you’re never, ever, ever going to get it right. And believing that an over-hyped unproven technology will eventually show emergent behaviour (that it has zero chance of doing because of the underlying technology) and “evolve” to solve the problem is just stupid.

Real tech encodes real solutions identified by real human intelligence that have been implemented, repeatedly verified, tested in real world operating conditions, and understood to the point that the success can be repeated predictably. Sometimes it’s traditional AI, sometimes it’s RPA, sometimes it’s workflow, and sometimes it’s a simple calculation. It all depends on the problem and the people-process combo that can be applied to solve that problem. Tech is only transformative when it supports the business. You don’t lead with tech, you follow.

ii) Then identify the best tech options for each step

Once you have fully documented the problem(s) the people need to solve, the processes they can support, and the solutions that will work acceptably (maybe not perfectly, but it’s rare that a solution is perfect — plus, as we’ve repeatedly indicated, most enterprise users would cry tears of joys if they found something that just worked), identify all the potential tech options at your disposal, from open source to out-of-the-box from third parties to custom development.

Then, for each option, evaluate:

  • it’s cost
  • the relative return (for the customer and how that will translate into sales)

And identify the options with the best balance. You’ll be surprised at how often it’s not the new shiny. The best hotness is the old busted hotness.

iii) Select tried-and-true when all things are equal (and save)

Finally, when all things are equal, go for the most stable, tried-and-true, methodology — don’t do experimental new “AI” development if a sold optimization or analytics-based solution already exists.

4) Over Reliance on Third Party Tech is a Sustainable Business, Especially if its Gen-AI!

Reliance on third party tech, especially that which has not been proven to be on stable ground in the market, is not a good business plan. What do you do if the provider goes bankrupt, or, realizes they are on the path to bankruptcy and turns the tech off? If you have nothing else to pivot to, you go bankrupt too. Not a good scenario!

Even if you are building on tried-and-true stable tech (with a large install base), unless your business plan is to be acquired by the tech you’re building on and you actually have a chance of making that happen (you came from the provider, have good connections, etc.), your options are very, very limited if you don’t succeed.

You need to focus on a solution to a real problem that companies will pay to address first. And while it can be based 100% on third party tech to start, that shouldn’t be the game plan. The plan should be to acquire it, build something better in house, or at least find three or four options that will serve the same function until you can acquire or build appropriate tech. (The exception being a product maintained for you by a third party that is based on open source that will always be there for you to take ownership of and then assign to a new team.)

Once you have a solution, and know what tech you need, do the cost benefit analysis of:

  • licensing someone else’s tech
  • acquiring the third party tech
  • building on, and contributing to, open source
  • building your own

for each aspect of the solution, as well as the points at which one option becomes better.

In addition, even when you select a solution, always keep your eyes out for an alternative that has been demonstrated to be more reliable, efficient, or cost effective. When a problem has been newly identified, some companies will just throw anything at it to see what appears to work, without doing a proper analysis and designing a proper solution from the ground up. Eventually, some smart minds powered by Human Intelligence (HI!) will come up with a better solution. Once that becomes economical, you’ll want to switch to that if you don’t have an equivalent solution in house, while keeping back-up options open.

And definitely don’t (over) rely on third party Gen-AI — even the biggest companies can only afford to bleed for so long. There’s nothing Gen-AI can do that traditional tech can’t. It’s best uses are as an interface layer for (very) low TQ (Technology Quotient) folk who are being forced to use tech but just don’t get it (as a more natural language chatbot interface) or as a massive document store search and summarization solution (when traditional semantic / neural networks would just be overloaded). That’s it. And in all of these cases, it needs an underlying application that actually does the work and manages the data.

Stay tuned for Part 4!

the doctor explains the Procurement Alphabet

I would say that you approach them with caution
You should not let them overwhelm you with cheer
Pays to know what you’ll find
To understand their kind
’round here

Drawing their lines,
they look so radical
Tracking their curves
I get so lacrimal
Something deep down reveals they’re tragical

I think I’ve had enough …

It’s a strange alphabet
That’s what I know
But it’s a strange alphabet
We’ve got to follow …

Seventeen years ago I explained the whole numbers. Steve Martin explained the alphabet (but not the Procurement alphabet), and who better to explain the whole numbers than a trained mathematician.

But lately I’m seeing a lot of glossaries and “complete sourcing/procurement guides” that are anything but … so I thought I’d fill in a few basics for you …

A is for Analyst, who pretends to understand
B is for Buyer, with cash in hand
C is for Contractor, lost in Legal land
D is for DEI, now on the witness stand

E is for Equity, where can it be found
F is for Finance, who pay by the pound
G is for Goods, which make the supply chain go round
H is for Hedge, on currencies sound

I is for Insight, desperately needed
J is for Jazz, the hope has been seeded
K is for Kanban, its use exceeded
L is for Legal, its advice unheeded

M is for MRP, technology ancient
N is for Negotiate, done in plainchant

O is for Onboard, suppliers aplenty
P is for Purchase, multiples of twenty
Q is for Quality, often absent-ee
R is for RFX, created by cognoscenti

S is for Supply, critical to success
T is for Tariff, always assessed
U is for UNSPSC, classification coalesced
V is for Vendor, marketing obsessed

W is for Warranty, never enough
X is for Xennial, weary and gruff
Y is for Yardmaster, full of chuff
Z is for Zealotry, Procurement tough

… just to make it clear that the depth and breadth of the space is well beyond what a short glossary or guide can ever hope to address. The Procurement alphabet is not a character alphabet, or even a phonetic or syllabic writing system, it’s a logographic one. One that takes years, if not decades to fully master with all of its global dialects.

But that doesn’t mean you shouldn’t try.

Advanced Sourcing Yesterday — No Gen-AI Needed!

Back in late 2018 and early 2019, before the GENizah Artificial Idiocy craze began, the doctor did a sequence of AI Series (totalling 22 articles) on Spend Matters on AI in X Today, Tomorrow, and The Day After Tomorrow for Procurement, Sourcing, Sourcing Optimization, Supplier Discovery, and Supplier Management. All of which was implemented, about to be implemented, capable of being implemented, and most definitely not doable with, Gen-AI.

To make it abundantly clear that you don’t need Gen-AI for any advanced enterprise back-office (fin)tech application, and that, in fact, you should never even consider it for advanced tech in these categories (because it cannot reason, cannot guarantee consistency, and confidence on the quality of its outputs can’t even be measured), we’re going to talk about all the advanced features enabled by Assisted and Augmented Intelligence (as we don’t really have true appercipient [cognitive] intelligence or autonomous intelligence, and we’d need at least autonomous intelligence to really call a system artificially intelligent — the doctor described the levels in a 2020 Spend Matters article on how Artificial intelligence levels show AI is not created equal. Do you know what the vendor is selling?) that have been available for years (if you looked for, and found, the right best-of-breed systems [many of which are the hidden gems in the Mega Map]). And we’re going to continue with Sourcing. (Find our series on Advanced Procurement — No Gen-AI Needed! Yesterday, Today, and Tomorrow at the following links.)

Unlike prior series, we’re going to mention some of the traditional, sound, ML/AI technologies that are, or can, be used to implement the advanced capabilities that are currently found, or will soon be found, in Source-to-Pay technologies that are truly AI-enhanced. (Which, FYI, might not match one-to-one with what the doctor chronicled five years ago because, like time, tech marches on.)

Today we start with AI-Enhanced Sourcing that was available yesterday (and, in fact, for at least the past 5 years if you go back and read the doctor‘s original series, which will provide a lot more detail on each capability we’re discussing. (This article sort of corresponds with AI in Sourcing Today that was published in January, 2019 on Spend Matters.)

YESTERDAY

Workflow / Project Automation

Once a sourcing project is defined, which typically consists of identifying the required products and demand, the critical requirements of the supplier pool, the RFI, the RFP/Q, the evaluation criteria and weightings, the award rules, and the initial award offers, the entire project is easily automated using rules-based automation. Best-of-breed platforms will integrate fuzzy matching to identify additional suppliers who provide similar SKUs, RFI/P/Q templates which will automatically be pulled in and modified based upon the particular items in the category and organizational risk/compliance rules using semantic characteristic matching (traditional NLP will be fine), and built in “cherry-pick” algorithms that will compute standard award scenarios (lowest price, max 3 suppliers, geo-split, etc.) and create a default recommendation — which only requires math and traditional analytics.

Auto-Fill

For the better part of the past decade, the best platform auto-fills not just successive rounds, but auto-fills / pre-populates all of the supplier, item, and RFI data based on available information in all integrated systems — be it from past events, the supplier master, the forecasting platform, or market(place) data (for products).

This just requires rules-based automation and workflow with reg-ex pattern matching, and simple trend analysis and market data matching for price / demand population. Easy peasy on the tech ladder.

Outlier Identification

As we wrote years ago, it only takes one bad data element to make a good sourcing process go bad. Just one. One bid too low that takes a buyer down the wrong path. One risk rating too high that steers a buyer away from what would be their best supplier. One demand error that steers the best supplier away. But all of these “outliers” can be easily detected with traditional mathematical clustering algorithms used as the back-bone of machine learning — k-means, nearest neighbour, etc. — and identifying any values too far off the norm and then alerting the buyer to (have the supplier) correct them.

Rule-Based Auto-Award Identification

For simple scenarios where it’s always lowest cost, simple mathematical calculations can identify the supplier-item awards, and these can be limited to a max # of suppliers as then it’s just computing some combinations. No “AI” required.

SUMMARY

Now, we realize this was very brief, but again, that’s because this is not new tech, that was available long before Gen-AI, which should be native in the majority (if not the entirety) to any true best-of-breed Sourcing platform, that is easy to understand — and that was described in detail in the doctor‘s 2019 article for those who wish to dive deeper. The whole point was to explain how traditional ML methods enable all of this, with ease, it just takes human intelligence (HI!) to define and code it.

How Do You Say Bye-Bye to DEI Without Customers and Suppliers Going Bye-Bye

DEI is going a lot of blowback. Much of it deservedly so since

  • many initiatives are led by people, who’ve never read a dictionary, that confused “opportunity” with “outcome” (and they’re not the same thing at all),
  • many initiatives are led by people who are misusing DEI to discriminate against unrecognized groups (specifically, religious minorities, white candidates, etc.), and
  • it was so bad in some jurisdictions that it is triggering legal responses (not just board and investor responses).

But ripping it out without a plan or even a thought about the blowback is not a good idea.

First of all,

  • a properly defined initiative is NOT illegal, or even immoral,
  • not all are being used to illegally discriminate against religious minorities or non-minorities, and
  • education can help ensure that a well-defined program is tweaked to be perfectly in alignment with federal and state laws with respect to equal opportunity.

Secondly, just because you’re doing it wrong, doesn’t mean everyone is. As pointed out in this recent opinion article on Supply Chain Dive on how Harley-Davidson’s DEI rollback is a procurement mistake, Harley Davidson’s removal of their support for supplier diversity could be seen as going too far.

And it could be. Ripping out or killing a program that doesn’t work, and then publicly stating that you’re instead going to focus on complying with all state and federal equal opportunity legislation, especially if that’s what customers want is definitely a good thing. (If you’re not convinced, read Jason Busch’s article on why Harley Davidson Dumping Supplier Diversity is more-or-less a good thing.

But you want supplier diversity to the extent there are diverse suppliers that can support your business. It may be your right to buy from who you want, when you want, where you want, and how you want, but if it upsets your supplier base, that’s a problem. Especially if your best suppliers walk away, or, even worse, walk away and sue you. Just like you need happy customers, you need happy suppliers. Plus, a good policy encourages diversity, it doesn’t mandate it when one supplier is inferior to another.

Moreover, even if the DEI program is not working, killing it too fast can also result in customer blowback who might think that you are not about equal opportunity and diversity. It’s a tricky situation, and any action needs to be well thought out, including any potential blowback and how you respond to it in a matter that dispels it before it snowballs.