Category Archives: SaaS

There is No Super-Selection Map for Source-to-Pay

In a post on comparing the Hansen Fit Score to other analyst ranking maps and methodologies, THE REVELATOR asks “which would you choose, and why”, to which the doctor responds that THE REVELATOR has to be a lot more specific, because, depending on your context, there could be three choices

1) The Hackett Group Inc. KPIs for zeroing in on what type of technology you should choose for the biggest boost to your business as there’s no arguing with their book of numbers. But this doesn’t give you a shortlist.

2) Spend Matters, A Hackett Group Company Solution Map for deep tech assessments, allowing you to qualify tech for consideration before doing a deep dive assessment on business needs (and we all know that most people can’t do this effectively). Once you know what module, or modules, you need, SolutionMap will give you a qualified list of the best, rated, vendors with those modules.

3) Jon W. Hansen fit score for sieving a shortlist of relevant vendors who make the tech cut into the 3 most likely to be the best organizational fit to invite to the RFP where they can prove their worth AND interest in actually making your organization successful

However, the optimal route, if you have the time and money, is 1, 2, 3 … (and let’s face it, since this could save you millions, you likely do). Why? When you use

1) you focus in on the specific problem set/module (set) to attack first for the biggest impact

2) you filter down to those providers who have the tech to do it

3) you filter down to those that would be right for your business on the other dimensions 1 and 2 does not address.

However, none of these approaches can

0) perform a gap analysis, determine what problems you need to solve, and help you center your analysis on the right metrics or numbers or

5) take the short-list you are left with after using Spend Matters Tech Match (built on Spend Matters Solution Match) or the Hansen fit score and construct a proper RFX to help you determine which vendor will provide you with more than a license but work with you to implement, and execute, a proper solution.

And that’s why there’s no super selection map for source-to-pay!

(And please remember, never use a big analyst firm quadrant map because vendors have lured big analyst firms astray.)

Data is Too Darn Expensive Today … But It Won’t Be For Long

THE PROPHET, who has recently discovered ranting is his new favourite thing to do (on LinkedIn), recently complained that Procurement, Commodity, and Supplier Data is Too Darn Expensive.

And while he’s right in that data is often too expensive for what it is, it’s not going to stay that way. Next generation providers are going to commoditize quality data and lower anonymized community data subscriptions to win (and keep) clients, because they know that there’s no value in advanced technology alone (and especially in analytics, optimization, and AI wihtout quality data to feed it) but there are three key points he missed in his rant where he complained about data prices and advocated the use of LLMs and Gen-AI as a substitute (which they are not, and considering how much they hallucinate, we wouldn’t even trust them to be directionally accurate — just feed the historical data you can get your hands on into Excel and do some basic trend plotting if directionality is enough).

1) As Lisa Reisman noted in the comments, sometimes you need highly granular accurate data by geography, volume, and production methodology. When pennies make a difference, because you are buying tens or hundreds of millions worth of the material for a global operation, it matters.

2) Most firms are still ignoring their own data, which, when run through something like Covalyze (which THE PROPHET should love as it was founded and designed by economists), gives very accurate target cost models on any category the firm has enough historical data on, allowing them to pinpoint where they need more data and why for cost breakdowns (and should cost models to refine the target cost models), and which suppliers they actually need those expensive profiles on. Then they can go to pay by the sip providers like Veridion for basic supplier data or other emerging commodity and supplier data portals.

3) The amount of data most firms need is much less than they think. In the tail, most of the spend is not significant enough for any market data to provide insight on a significant savings potential beyond what you will get from analyzing your own historical data and market quotes. When pennies won’t make a difference, you don’t do detailed cost breakdowns by raw material. When the product is a commodity that can be supplied by multiple suppliers at similar price points and equal quality levels, you don’t do deep risk profiles because you can just go to the next supplier in the queue if the first one fails you. And so on. You only do detailed analysis where there is statistical likelihood of a real opportunity or a real risk. Otherwise it’s a waste of time, money, and resources as no organization today even comes close to fully analyzing the significant categories and risks they have in any given year. Thinking you will do more is delusional and not worth it if you don’t have the basics covered.

By the time firms actually need more data, you can bet a next generation of data providers will have it readily available and cheap by today’s standards.

Despite Attempts to Simplify It, There Are MANY Categories of ProcureTech Solutions

When selecting a ProcureTech Solution, you have all the following buckets:

Function X Classic Type X SaaS Category X Integration
Sourcing
SXM
CLM
Analytics
e-Procurement Best-of-Breed Standalone App
(full function)		 Suite EcoSystem
Invoice-to-Pay Mini-Suite Lightweight App
(task specific)		 I2O Ecosystem(s)
ESG/Sustainability Suite Bolt-On
(extends a module)		 Open API
GRC
Category/Cost Intel
Niche (Legal, Marketing,
Hospitality, SaaS/Tech, etc.)
I2O

And if you do the multiplication, that’s 297 combinations … and that’s just the tip of the iceberg when there are 10 core areas of SXM, multiple niche areas being addressed (some classic solutions were just for print/telco), multiple buckets of risk management solution, generic and scope-3 specific sustainability solutions, different approaches to intake-to-orchestrate, and that’s just addressing the functional areas of Source-to-Pay+.

Then you have the situation where some vendors only offer a single best of breed (BoB) module, others offer a mini-suite, and others still offer a mega-suite with all of the core modules and often a half dozen more on top of that.

While most are SaaS apps these days, they vary from heavy standalone apps that implement full functions to lightweight apps designed for specific tasks (that are usually missing from larger standalone apps that purport to completely cover a function but don’t) to bolt-ons that offer advanced functionality, but require a core module to work on top of.

One also has to consider how you integrate them into a comprehensive workflow that supports Source-to-Pay+. Sometime modules integrate into one-or-more suite ecosystems out of the box (like the SAP Store or The Coupa Store), other times they just come with a (semi) open API, and now some, not built for integration, are integrating into one or more of the new orchestration ecosystems.

And while functionality should come first, you have to consider all of these other factors as well because if you select a suite for a module, you’re probably locking yourself into the other modules you need as those the suite offers due to cost and integration cost considerations, if you select light-weight or bolt-on apps, then you better have something to integrate them into, and you better be sure the ecosystem has all of the modules you will need to implement over the next five years or so before locking yourself into an ecosystem.

So even though THE REVELATOR believes that everything is going to be a bolt-on or an app and that’s all your going to have to worry about, unfortunately the ProcureTech world is NOT going to make it that simple. Overlooking traditional category and integration can completely destroy the value you require if you can’t easily integrate with complementary modules/apps (and especially if you are in a [primarily] direct industry and need to integrate with supply chain applications for the data you need to make good supply chain aware decisions).

However, it will be interesting to see the primary solution category, breadth, and integration of ProcureTech Solutions (by, and independent of, function) in the future.

With Suites, What you are Sold Vs. What You Get Vs. What You Need are Three VERY Different Things!

A while back, Dan Gianfreda published a piece on LinkedIn on how what you need is not what you are sold when you buy a a shiny, “all-in-one” procurement platform that is 10X bigger than what you will actually use (on a multi-year contract with a massive implementation that takes months longer than promised and ensures you don’t have the majority of the functionality you need until the contract is almost up), and he was right. But it missed the full picture. The reality is that not only are you sold 10 times more than you will use, but what you will use doesn’t cover what you need, and with a poor selection, might only be one 10th of what you actually need!

In other words, you need to see the full picture:

As outlined in the response post, just because a suite has a module, there’s no guarantee that module is anywhere close to what the organization really needs, especially when the capabilities can vary greatly (and the definitions even more so). Sourcing can be a simple RFX or a multi-staged integrated RFX/Auction platform with embedded strategic sourcing decision optimization. We still see canned reporting modules sold as “modern spend analysis” when they are anything but. And most AI claims are pure BS (or an indication that you should probably run for the hills if that’s the only selling point).

Even if the suite theoretically has the core/must have functionality the organization needs, that’s only meaningful if that functionality is implemented in a way that supports the organizational processes and policies. If approval chains are required, tamper-proof audit logs need to be in place, validated process steps are needed for public sector compliance, and so on — and the suite has none of those, it don’t matter how user friendly, integrated, or “powerful” it is because the organization will NOT be able to use it.

Moreover, the core functionality differs by organizational type and since most platforms only do one of indirect, direct, services, capex projects, or tailspend well, selecting the wrong suite will render it totally useless for the majority of sourcing/procurement projects, which will add insult to injury of the huge cash outlay you agreed to (for an ROI that will never, ever, materialize).

Moreover, as previously indicated, you can NEVER assume that all (or sometimes, even any) of the solution providers will:

  • ask the right questions to understand the challenges
  • do the right due diligence to ensure their solution will solve those challenges
  • be honest about their capabilities (or, outside of the dev team, even understand those capabilities)

because, chances are, as I have indicated many times, everyone in the ecosystem exists to make money off of YOU, but not necessarily to help you. (Especially when too many vendors took too much money and are now under extreme pressures to fulfill ridiculous growth requirements in just a few years or risk massive layoffs, being folded into a bigger player, or getting dropped from the portfolio entirely before going bankrupt.) There’s no time to do it right, just to sell, sell, sell. (Which is why we keep advocating employing an independent consultant to help you with selection, project planning, and project assurance — since their remuneration depends on helping you, not someone else.)

So remember this before you start looking at big suites as there is a good chance you’ll likely be paying 10 times what you should be (based on what you are using) while still only getting 25% of what you actually need in the best case. (And there’s nothing wrong with building your own Best-of-Breed ecosystem, even if you need to add an orchestration player to that mix, if that is what maximizes the return on every dollar spent.)

How AI Enhances 10 Common Procurement Challenges Part II

A recent CIO article drew my ire because it claimed that AI Overcomes 10 Common Procurement Challenges as it oversimplified the problems and overstated the benefits of AI. Let’s finish them one-by-one.

Legacy Systems Complicate the Adoption of New Technology: The article claims AI streamlines integration by assessing system compatibility, automating migration, and reducing downtime. While two out of three ain’t bad, it ain’t good when the critical requirement of assessing system compatibility cannot be met by AI — since simple text matching isn’t helpful if the interface of a legacy system isn’t specified in a standard format (as otherwise it’s essentially field-name matching, which is no different than human guesswork). The reality is that humans still have to define/verify the mappings before the AI can take over.

Letting AI do the mappings is fraught with errors. And its even worse when you let it automatically connect systems, pull and push data, replicate incorrectly mapped and bad data across systems, and “fix” data that was actually correct on system integration because the “bad” data in one system is used to overwrite the good data in another system just because it appeared to be more recent. Because it’s automated, AI can propagate and exacerbate errors at an unprecedented rate and in a matter of seconds make a mess that can take months to repair.

Managing Supplier Risks is a Growing Concern: AI can continuously monitor supplier performance, predict risks, and ensure compliance. This is one situation where they were almost perfectly correct, but, when they say vendor evaluation can be time intensive and imply that AI can speed it up, they overlook the fact that evaluations still have to be done by humans and tech can’t speed that up.

Moreover, if you think you can augment your data with third party data to speed up the evaluations, you’re just fooling yourself. You just make bad decisions faster.

Manual Procurement Process Drain Resources: AI can definitely automate repetitive tasks, reduce human error, increase efficiency, and free your team to focus on strategic initiatives, but only for tasks that are well defined, typically free from exception, and capable of being processed by standard rules. However, this can’t be done until the repetitive tasks are identified, processing rules defined, standard exceptions identified, and additional rules defined. Only then can the AI automate enough to be useful.

Moreover, using a next-gen LLM with chain-of-compute to try to break the requirements of a task down into subtasks, execute those subtasks automatically, and automate a process without any human intervention is just as likely to go wrong as it is to go right.

Demand Forecasting is Often Inaccurate : AI can improve demand forecasting, but only if you have the right data — it’s not a magic box, just a black box that you need to understand.

It’s not just demand trend based on utilization / point of sale data, its also market conditions which can sharply change a demand curve overnight … traditional curve fitting / machine learning that most “AI” is based on cannot detect a change in market conditions or a political situation that can cause a rapid change in demand.

Procurement Remains Transactional Rather Than Strategic: AI DOES NOT transform procurement into a strategic function that optimizes spend, improves supplier collaboration, and aligns purchasing decisions with your business! Only people-powered Human Intelligence (HI!) can do that. Remember — transforming Procurement requires defining a strategy, defining appropriate processes, identifying the right people to transform it, and then, and only then, identifying the right technologies.

Assuming that you can slap in AI and transform a tactical function into a strategic one is worse than a pipe dream, it’s a recipe for disaster. Running fast and hard doesn’t get you any closer to the finish line if it’s not in the right direction. For more details, see the dozens of posts about AI in the archives.

Again, we’re not saying that AI is bad. Technology is neither good nor bad. But, like any technology, it has to be ready for prime time, correctly identified, correctly implemented, and correctly used — and that requires a lot of Human Intelligence (HI!) and planning, and the right processes put in place. Shoving it in and expecting a miracle is dangerous. And this is yet another article that implies you can just shove it in and get results. And you can’t. Especially if it’s the wrong technology, which can enhance your problem instead of shrinking it. That’s the problem. This article, like many others, doesn’t tell you about the dangers and downfalls and what you have to do to avoid them.