Category Archives: SaaS

In the Software World, It Is Never Build vs. Buy!

In a LinkedIn post, THE REVELATOR asks “Why is the build versus buy debate a moot exercise?”.

The answer to this question is super simple.

If you are NOT a software* company, it is NEVER build. NEVER, EVER. Especially since “Build” typically means outsourcing to a Big X who are typically specialist implementors, not builders, and will just have to outsource to a Dev Shop and add a high margin to manage that outsourced project for you IF they want to get it right. (Just Google “Accenture Hertz Lawsuit” to see what happens when they get it wrong, so the smart Big X really do add a layer between you and an outsource Dev Shop in South America, Eastern Europe, or India … and trust us when we say that the last option ain’t always great either!) In the end, the project will cost 5X to 10X, take significantly longer than you expect, and rarely deliver entirely what you want.

The debate today should be “assemble vs. buy”, because the most you should do is determine whether its best to go with one provider who provides some functionality across the board for a function, but maybe not as deep as you want in certain areas, or if you want to assemble a slew of best of breed modules that go deep everywhere you want deep. In the latter case, you are deciding whether you are going to select a slew of best of breed modules from a slew of vendors and oversee the integration yourself (one time cost plus incremental costs on the update of each component solution) or go with an “orchestration” solution (and its year over year SaaS fee) vs. just selecting one of the same old Big Suite providers that will handle everything (with a fee to match).

The only thing that remains correct about the “build” vs buy debate is that you need to maintain the “build” mentality, in that you may have to lego-block “build” from a collection of best-of-breed modular solutions. However, the “build” will never be a build from scratch, just a build from components, the same way we used to assemble our own desktop systems.

* and even if you are a software company, if the type of software needed is not the type of software you build, and there is a reasonable SaaS solution, you should go with that;

Myth-busting 2025 2015 Procurement Predictions and Trends! Part 11

Introduction

In our first instalment, we noted that the ambitious started pumping out 2025 prediction and trend articles in late November / early December, wanting to be ahead of the pack, even though there is rarely much value in these articles. First of all, and we say this with 25 years of experience in this space, the more they proclaim things will change … Secondly, the predictions all revolve around the same topics we’ve been talking about for almost two decades. In fact, if you dug up a Procurement predictions article for 2015, there’s a good chance 9 of the top 10 topic areas would be the same. (And see the links in our first article for two “future” series with about 3 dozen trends that are more or less as relevant now as they were then.)

In our last instalment, we continued our review of the 10 core predictions (and variants) that came out of our initial review of 71 “predictions” and “trends” across the first eight articles we found, in an effort to demonstrate that most of these aren’t ground-shattering, new, or, if they actually are, not going to happen because the more they proclaim things will change …

In this instalment, we’re again continuing to work our way up the list from the bottom to the top and ending with “AI”.

AI

“AI” is the only “prediction” or “trend” that would not have appeared ten years ago. (Ten years ago it would have been “analytics”, the favourite precursor technology.) There were 10 predictions across the eight articles, and this was the only category where they were not in synch (because the technology, as well as the usage thereof, is not only still evolving but not well understood). Given the vendor hyper-focus on AI (and especially Gen-AI) over the past few years, it is yet another “prediction” or “trend” that is not new, as we are still in the (over)hype(d) cycle, but one that should be adequately addressed as it’s where we have the biggest gap between expectation (pushed by the vendors and the analyst firms and the consultancies) and reality.

Before we go any further, here were the ten predictions from the articles:

  • Advancements in AI and Automation
  • AI: overhyped or underestimated?
  • AI and The Digital Transformation Revolution will Continue
  • Artificial Intelligence in Procurement
  • Automation and Artificial Intelligence
  • Digital Transformation, Automation, and AI
  • Focus on AI Talent in Procurement and Skill Upgrading
  • From AI adoption to AI adaption
  • Integration of AI and Advanced Analytics
  • We’ll Evolve from AI Adoption to True Integration

They range from continued adoption to adaption to analytics enhancement to seamless integration to true advancement in underlying technology, and with the exception of continued, mostly unbridled, and definitely unresearched, adoption, they are more-or-less all off the mark.

The analyst firms are still overhyping this technology to the max (despite continuing to publish studies that 85%+ of technology projects fail)). At least six (6) in seven (7) vendors are overhyping (Gen-)AI to the max, if not nine (9) in ten (10). The Big 3 (Google, Microsoft, and, of course, “Open”-AI) are promising miracles for all who adopt their technology. It’s being marketed as the ultimate panacea, the magic elixir of your dreams, and the silicon snake oil that actually works (among other things). And when you combine the facts that most people don’t have the mathematical and technological background to understand what a given “AI” technology is and, as Bertrand kindly pointed out, humans are biologically wired to be lazy, most are happy to close their eyes, cover their ears, sing “la la la la la la”, and buy in to the BS promises hook-line-and-sinker. So, whether the technology is right or not (and we’ll give you a hint, it usually isn’t), they’ll buy it. (And then blame the vendor when it fails to deliver, who will blame the consultant for improper implementation and training.)

So how accurate were these predictions? Did any hit the mark? Come back for Part 12!

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

This originally published on April (29) 2024.  It is being reposted because MDM is becoming more essential by the day, especially since AI doesn’t work without good, clean, data.

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

666+ S2P+ Solutions … But Key Problems Are Still Not Addressed!

You’ve seen the Mega Map and the 666 solution logos on it.

You’ve heard the doctor and THE REVELATOR say repeatedly that another massive purge is coming to our space over the next 18 to 24 months (which will be the greatest since 2009-2011 where hundreds of companies were acquired, merged, or went insolvent), and that it’s already starting (with a few notable insolvencies, at least as far as the doctor is concerned, already occurring).

You’ve heard us say that there isn’t room for this many companies because even if you account for market size and vertical, we still only need so many solutions of the following varieties:

  • Sourcing
  • Supplier Management
  • Spend Analysis
  • Contract Mangement
  • e-Procurement
  • Invoice to Pay (I2P) / Accounts Payable (AP)

And even when you consider the wide variety of needs across all possible size – vertical -region combination, two to three dozen solutions in any category is more than enough to handle all of the complexities when you take even the most varied companies into account, but we now have over a hundred options in some of these categories. Only the strong, sorry, the smart, will survive … and only if they have enough money to do so (and enough control to make smart decisions, i.e. if they are controlled by greedy investors who double and triple prices that force them out of their target market, they will be the next casualty).

But even with all these solutions, core needs are not met. The reason being: in today’s business environment that is seeing a return to protectionism, sanctions, and border closings; a continual rise in natural disasters (thanks to global warming that will once again reign unchecked under administrations coming into power in multiple “first world” countries); and a continual disruption in logistics (due to epidemics, pandemics, reduced capacity, Panamanian droughts, and Houthis in the Red Sea), solutions are needed that go beyond siloed Procurement.

Back in 2022, THE PROPHET first tried to get the message out there with his proclamation that alt-suites would rise. (They still haven’t, but we do need new types of cross-functional applications.) He also made five predictions. They varied in terms of usefulness and vision (in the doctor‘s view, two in particular are desperately needed, although one of these needs to be broader than defined; one is nothing more than just an enhanced dashboard across various S2P applications and needs to be rethought, and two aren’t quite right [but contain ideas that can be built on]).

But THE PROPHET was right in that we need to rethink Procurement Technology in some organizations, who needs to contribute to Procurement, and how Procurement Process fits into overall operational processes. The solutions that worked for the last 20 years aren’t always enough anymore, and it’s not just a question of “intake” (which is not new despite what the providers will have you believe, see our prior posts on the subject) or “orchestration” (which is just a fancy term for SaaS middleware).

Here are three solutions that are needed now more than ever:

1. Design for Supply (DFS)

THE PROPHET was right on the money here. Not only is 80% of the cost locked in during design, but so is 80% of the risk. You not only need cost control, but you need supply assurance. This means that R&D needs to work with Procurement during design to ensure the products can be sourced affordably at low risk, and that Procurement needs to work with Supply Chain / Logistics to make sure the products can be reliably sourced in a timely manner (and the organization won’t have to stock months and months of inventory). Product design and development organizations need integrated DFS solutions that span R&D, Procurement, and Supply Chain.

2. Supply Chain Sourcing (SCS)

In the world of Direct, when organizations need to source for BOMs (Bill of Materials), they need to do it Supply Chain Aware. Under pressure, Procurement will always search for the lowest cost — but what if that is from a supplier in an unstable region; that is not part of the current, optimized, supply network; that can’t offer timely and secure delivery? Sourcing needs to be supply chain aware. And Supply Chain needs to be aware of what Sourcing is looking at so they can do network planning if the current supply network is not sufficient.

In fact, it would be even better if the DFS and SCS solutions were hosted on the same underlying platform.

3. Risk 360

This was the second platform where THE PROPHET was almost right on the money as well with his Assess-to-Monitor alt-suite. Risk is everywhere, both inside and outside the organization, inside and outside your partners’ organizations, inside and outside your suppliers’ organization, and its everywhere your physical, financial, and digital supply chains touch. Supplier risk, supply chain risk, cybersecurity risk, personnel risk, etc. can’t all be separate solutions. They need to be one integrated platform that constantly monitors, assesses, and protects your organization.

There are, and will continue to be, a need for new solution types, in S2P+, but these would be a great start!

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.