Category Archives: Technology

Another Supply Chain Misconception That Should Be Cleared Up Now

Yesterday we discussed one supply chain misconception that should be cleared up now because, despite all of the misconceptions mentioned in an Inbound Logistics Article, it was not addressed. However, there is a second misconception that is almost as critical that was not addressed either, so today we will address it. And while, there are, dozens of common misconceptions (including the 22+ mentioned in the article), these are the two that are the most critical to understand, as they are two that pose the most risk in most of today’s Procurement organizations.

 

THE SECOND BIGGEST SUPPLY CHAIN MISCONCEPTION

Supply Chains Have Reached (A New) Normal

Supply chains have never been, and will never be, normal as they will always be in flux due to perturbations, delays, and disruptions that happen daily. You may not see all the trial and tribulations a third tier supplier goes through every day, but trust the doctor when he says they have just as much turmoil as you do. Nothing is predictable in supply chains. When you accept this misconception in conjunction with the first misconception, it’s easy to see how almost all of the others are also misconceptions (that highlight slices of the bigger misconceptions).

For example:

  • cost becomes much less important than supply assurance due to the unpredictable nature of supply chains
  • since it’s not a linear, closed, model, zero-sum doesn’t apply
  • we made up the stages of planning, buying, transportation, and warehousing silos to fit a theoretical definition of normal that doesn’t exist
  • there is at least a hand-off at every stage, so the process is not disconnected but linked, if not intertwined
  • etc. etc. etc.

When you accept the reality, Supply Chain Management, as well as Source-to-Pay, will become a lot easier to manage because you will realize that

  1. only human expertise can adapt to new situations and find real-world solutions to the new challenges that arise
  2. technology will allow you to automate the tactical / semi-normal operations and instead focus on the exceptions and challenges, making you more productive as you focus the majority of your effort on strategy and thinking vs (e-) paper pushing and thunking which is the only thing the machine is good at (and, based on current technological understanding, ever be good at — which is exactly why we can limit it to the thunking because it can do over 3 Billion calculations a second flawlessly [if we ditch the “AI”] while we struggle to do 3)

In other words, only intelligent, adaptable, humans can manage constantly changing supply chains. Good technology can alert them and give them the intelligence they need to make good decisions, but technology cannot make those decisions for them.

(And the doctor, who dreaded saying Bye, Bye to Monochrome UIs can’t wait for the day he can say bye, bye Gen-AI.)

One Supply Chain Misconception That Should Be Cleared Up Now

Not that long ago, Inbound Logistics ran a similarly titled article that quoted a large number of CXOs that made some really good observations on common misconceptions that included, and are not necessarily limited to (and you should check out the article in full as a number of the respondents made some very good points on the observations):

The misconceptions included statements that supply chains should:

  • reduce cost and/or track the most important metric of cost savings
  • accept negotiations as a zero-sum game
  • model supply chains as linear (progression from raw materials to finished goods)
  • … and made up of planning, buying, transportation, and warehousing silos
  • … and each step is independent of the one that proceeds and follows
  • accept they will continue to be male dominated
  • become more resilient by shifting production out of countries to friendly countries
  • expect major delays in transportation
  • … even though traditional networks are the best, even for last-mile delivery
  • accept truck driver shortage as a systemic issue
  • accept the blame when anything in them goes wrong
  • only involve supply chain experts
  • run on complex / resource intensive processes
  • … and only be optimal in big companies
  • … which can be optimized one aspect at a time
  • press pause on innovation or redesign or growth in a down market
  • be unique to a company and pose unique challenges only to that company
  • not be sustainable as that is still cost-prohibitive
  • see disruption as an aberration
  • return to (the new) normal
  • use technology to fix everything
  • digitalize as people will become less important with increasing automation and AI in the supply chain

And these are all very good points, as these are all common misconceptions that the doctor hears too much (and if you go through enough of the Sourcing Innovation archives, it should become clear as to why), but not the biggest, although the last one gets pretty close.

 

THE BIGGEST SUPPLY CHAIN MISCONCEPTION

We Can Use Technology to Do That!

the doctor DOES NOT care what “THAT” is, you cannot use technology to do “THAT” 100% of the time in a completely automated way. Never, ever, ever. This is regardless of what the technology is. No technology is perfect and every technology invented to date is governed by a set of parameters that define a state it can operate effectively in. When that state is invalidated, because one or more assumptions or requirements cannot be met, it fails. And a HUMAN has to take over.

Even though really advanced EDI/XML/e-Doc/PDF invoice processing can automate processing of the more-or-less 85% of invoices that come in complete and error free, and automate the completion and correction of the next 10% to 13%, the last 2% to 5% will have to be human corrected (and sometimes even human negotiated) with the supplier. And this is technology we’ve been working on for over three decades! So you can just imagine the typical automation rates you can expect from newer technology that hasn’t had as much development. Especially when you consider the next biggest misconception.

Valid Uses for Gen-AI!

the doctor has been told he’s too hard on Gen-AI. He doesn’t think he’s hard enough, but there are those who keep insisting that Gen-AI has some valid uses. And they’re right, it has some. Not the uses that you need it for, but actual uses nonetheless.

So today, in a rare moment of weakness, he’s going to acknowledge those uses. Soak it in. He may never do so again.

1. Ensure your insurance / bank only covers and lends to people you like.
One of the great things about Gen-AI is that almost all models are biased, and it’s really easy to train them to be as biased as you want. Only want your health insurance to accept only young people between 25 and 40 with no family history or indicators of any illness whatsoever? No problem. Don’t want your bank to approve a loan to anyone who isn’t an all American Christian white? No problem. Race-Biased Gen-AI to the rescue!

2. Have it make up a new story for your child who constantly wants new stories every night.
Train it on thousands of stories kid suitable and it will make up a new story every night (with a high probability of most those stories being safe and suitable — chances are only a few will scare them into therapy). Your kid will be happy (at least until they get scared into therapy) and your brain will get the rest it needs at night (so it can start worrying about how it’s going to pay for that therapy). Put those constant hallucinations to use. It’s your own personal Scheherazade, with just a little bit of Grimm and occasionally a bit of King (Stephen).

3. Incite the mob.
Need a mob behind you to get your cause front page on the headlines? Incite a mob to cover your theft attempt at a corporate headquarters above a luxury department store? Maybe even help you overthrow a capitol? No sweat! Program that Gen-AI to be as hateful and incitory as possible and have it pump out fake news propaganda 24/7 until you have the mob you need on your side and there you go!

4. Scam the Scammers. (Or at least keep them busy and out of your inbox.)
Most scammers will keep trying as long as someone is responding to them (and eating up their time). Guess what AI has a lot of — GPU time. Most models have 10,000 (or more) GPUs at their disposal. That’s a lot of scammers an AI can tie up for you. (Especially if they can’t differentiate easy pickings Grandpa Joe from a very agreeable but completely broke GrandpAI Joe.)

5. Take down a rival’s network.
Simply train in some sleeper behaviour for a few months into the future, and once the competition is done with their tests and trust it … poof … down goes their network.

And if you want to be truly evil, you can always use Gen-AI to

6. Ensure your terror campaign is as lethal as possible.
We’ve read the stories of how even recent tests of self-driving systems decided to ignore the shadows of what were actually people RIGHT in front of them and drive into those shadows at full speed. A few minor alterations and instead of avoiding people-like figures and shadows, it will be the murderous trolley that tries to kill as many as possible. And who says you have to limit it to trolleys? Use it to program bomb-bearing drones and it will seek out the densest crowd possible. And so on. And yes, we went to a very dark place, but just where do you think AI is taking us? There are currently NO bright outcomes. Ponder that before you go singing its praises.

Of course, if you just want to be a little chaotic around the house, and only take that first step down the dark path, just hook up it’s hallucinatory outputs to a random direction generator and use it to:

7. Power your Roomba.
Your pets will think it’s truly possessed!

So there you go — 7 valid uses of Gen-AI. You decide how many of them you want to use.

Enterprises have a Data Problem. And they will until they accept they need to do E-MDM, and it will cost them!

insideBIGDATA recently published an article on The Impact of Data Analytics Integration Mismatch on Business Technology Advancements which did a rather good job on highlighting all of the problems with bad integrations (which happen every day [and just result in you contributing to the half a TRILLION dollars that will be wasted on SaaS Spend this year and the one TRILLION that will be wasted on IT Services]), and an okay job of advising you how to prevent them. But the problem is much larger than the article lets on, and we need to discuss that.

But first, let’s summarize the major impacts outlined in the article (which you should click to and read before continuing on in this article):

  • Higher Operational Expenses
  • Poor Business Outcomes
  • Delayed Decision Making
  • Competitive Disadvantages
  • Missed Business Opportunities

And then add the following critical impacts (which is not a complete list by any stretch of the imagination) when your supplier, product, and supply chain data isn’t up to snuff:

  • Fines for failing to comply with filings and appropriate trade restrictions
  • Product seizures when products violate certain regulations (like ROHS, WEEE, etc.)
  • Lost Funds and Liabilities when incomplete/compromised data results in payments to the wrong/fraudulent entities
  • Massive disruption risks when you don’t get notifications of major supply chain incidents when the right locations and suppliers are not being monitored (multiple tiers down in your supply chain)
  • Massive lawsuits when data isn’t properly encrypted and secured and personal data gets compromised in a cyberattack

You need good data. You need secure data. You need actionable data. And you won’t have any of that without the right integration.

The article says to ensure good integration you should:

  • mitigate low-quality data before integration (since cleansing and enrichment might not even be possible)
  • adopt uniformity and standardized data formats and structures across systems
  • phase out outdated technology

which is all fine and dandy, but misses the core of the problem:

Data is bad (often very, very bad), because the organizations don’t have an enterprise data management strategy. That’s the first step. Furthermore this E-MDM strategy needs to define:

  1. the master schema with all of the core data objects (records) that need to be shared organizational wide
  2. the common data format (for ids, names, keys, etc.) (that every system will need to map to)
  3. the master data encoding standard

With a properly defined schema, there is less of a need to adopt uniformity across data formats and structures across the enterprise systems (which will not always be possible if an organization needs to maintain outdated technology either because a former manager entered into a 10 year agreement just to be rid of the problem or it would be too expensive to migrate to another system at the present time) or to phase out outdated technology (which, if it’s the ERP or AP, will likely not be possible) since the organization just needs to ensure that all data exchanges are in the common data format and use the master data encoding standard.

Moreover, once you have the E-MDM strategy, it’s easy to flush out the HR-MDM, Supplier/SupplyChain-MDM, and Finance-MDM strategies and get them right.

As THE PROPHET has said, data will be your best friend in procurement and supply chain in 2024 if you give it a chance.

Or, you can cover your eyes and ears and sing the same old tune that you’ve been singing since your organization acquired its first computer and built it’s first “database”:

Well …
I have a little data
I store it on my drive
And when it’s old and flawed
The data I’ll archive

Oh, data, data, data
I store it on my drive
And when it’s old and flawed
The data I’ll archive

It has nonstandard fields
The records short and lank
When I try to read it
The blocks all come back blank

I have a little data
I store it on my drive
And when it’s old and flawed
The data I’ll archive

My data is so ancient
Drive sectors start to rot
I try to read my data
The effort comes to naught

Oh, data, data, data
I store it on my drive
And when it’s old and flawed
The data I’ll archive

Let the Bloodbath Continue!

Note the Sourcing Innovation Editorial Disclaimers and note this is a very opinionated rant!  Your mileage will vary!  (And not about any firm in particular.)

In a recent LinkedIn post, THE PROPHET tells us there is a Consulting Bloodbath starting, especially in the Big 5 (and their strategy firms). All the doctor can say to this is Good Riddance! and It would be even better if they battled it out Gladiator style! (After all, it’s been 28 years since American Gladiators ended, time for a rebrand and a relaunch with a little bit of MXC, which ended 17 years ago.) But we’re getting ahead of ourselves here …

Basically, according to THE PROPHET, firms are worried about the economy and growth headwinds ahead (this is also why investors have yanked money from equities and lessor-rated debt in recent weeks), and this includes tech/dev teams within consulting firms. In some cases lucky consultants are put on the bench and told they have six or nine months to find their next gig, and in others (and maybe the doctor is reading a bit between the lines here) they received their pink slips faster than they could say please Jack Robinson.

The bit about tech/dev teams makes the doctor happy because,

  • these are not tech firms, and they are selling modern analytics/automation/AI solutions they often have no business selling (and no real capability to deliver at even an average level unless they recently acquired a firm that does — remember what they initially got big doing, that is what they do better than anyone else)
  • they are not structured for proper SaaS development and deployment and are NOT SaaS enterprises
  • most of the “talent” they are using are not “top” talent, and if if they are “top” of their class when they are hired, they still need mentorship and experience to become “top” talent, mentorship and experience they are NOT going to get a lot of at a Big X until they start climbing the ranks (as there are too many hires each year for one-on-one mentorships to be practical, it’s usually one mentor per team)
  • the Big X cost structures are too high for mass market penetration; only the F500 / G3000 can afford them, but they still shouldn’t be using them automatically because overpaying for anything that can be commoditized by a SaaS or servifes vendor doesn’t deliver the value they need in inflationary times where supply chains are breaking daily (and instead the Big X should be used for where they deliver the best value — see when should you use a Big X)

And before you chastise me from apparently taking pleasure in people getting fired, think it through! If you do you will realize

  • the true “top” talent is going to end up at appropriate SaaS/Tech companies (or SaaS+IP powered niche automated services consultancies where their true talent/drive really is) where they can get the mentorship they need to grow and reach their full potential (and possibly rejoin a Big X later, either by choice or through acquisition0 because
  • Big X being forced to pull out of (chasing) inappropriate custom SaaS/tech deals/engagements will open up the market back up for those companies that are well positioned, who can start growing and pick up this top talent, and, moreover, give Big X a chance to focus on where they offer the greatest value, can easily guarantee a return on a high dollar investment, satisfy the customer on the first project, and get repeat business for life (see when should you use Big X)
  • the “talent” that is not ready for the tech market will either go back to school or find their true calling (before going down a path where they will eventually get overwhelmed, be unhappy, or both; we can’t have the next generation burn-out in first world countries where a very significant portion of the aging population will not be of working age in the very near future)

Plus, shift happens! (How many of us have been restructured, rightsized, or outsized from a job by financiers and lawyers who think they can run a complex enterprise from a balance sheet or understand advanced technology and engineering when they can barely gas up the Jaguars and Mercedes they drive to work everyday?*) Furthermore, given that the average life expectancy at a job these days is 4 years, this talent might as well learn about, and get used to it, now when parts of the economy will be rebounding (and they have opportunity ahead of them), versus getting their @ss3s unceremoniously throw to the curb next time the market drops.

And if, for some reason, a Big X Consultancy (which did not start in tech but in accounting/tax, operations, strategy, etc.) is where they belong, then let them prove it in a battle royale! Forget about sitting on the bench waiting and hoping to get invited to a sales call where they can sell a project to work on, put them in the Arena! When a Fortune 500/Global 3000 needs a consultancy, force them to make their selection in the arena where the consultant leads will battle it out modern gladiator style! Not just a Dragon’s Den pitch, they have to battle it out to even get the opportunity to pitch — prove they’ll do whatever it takes to deliver value at the hourly rates their employer is charging!  (Yes, we’re kind of joking here, but if it is where they belong, they should have no problem proving their worth!)

Thoughts?

 

* If the apocalypse is nigh it is largely because some rich benefactors, not even involved in the day to day running of the company, and likely never involved with the company at all, looked at their spreadsheet models and forced the engineers who actually know how to build things out of the C-suite, allowed Gen-AI to tell them how to do technical jobs, and then elected populist pinheads as Prime Ministers and Presidents to tell them balance-sheet management is okay. And let’s not forget that, as per the OECD PISA data, statistically most of them shouldn’t even be able to do high school math competently!