Category Archives: Technology

The Key to Procurement Software Selection Success: Affordable RFPs!

Modern supply chains are risky. Very risky. Nothing made this fragility more clear than COVID where the world essentially broke down due to an illogical over-reliance on outsourcing, especially to China. (There’s a reason that SI has been promoting near-sourcing, home-shoring, and home-sourcing for over sixteen years — because this breakdown was inevitable, the only unknown was whether or not it was to be geopolitical instability/war, a massive natural disaster, or a pandemic that would be the first card to topple in the house of cards.)

Despite the best laid plans, and all the precautions you can implement, something will inevitably go wrong. Very wrong. And the disturbance will cost you greatly. That’s you you buy supply chain insurance which, depending on exposure, limits of dependency, and regionalization, will cost you between 1% and 10% of the policy value (maximum claim amount). If we take 5% as an average (which is not unreasonable), that says for every 1,000,000 of at-risk inventory you need to insure (to prevent devastating loss), you are paying $50,000.

But do you know what’s just as risky as your supply chain? The investment in the technology that you use to power your supply chain. Therefore, you should do everything you can to ensure you get it right! The best way to do this is create a good, proper, RFP to help hone in on software vendors that have appropriate solutions that should be able to fill your need while ensuring that they have the minimum globalization, size, and services you will need to consider giving them an award.

But, as per previous articles, including our last article on why THERE ARE NO FREE RFPs!, you’re probably not capable of doing this on your own. This is because a proper RFP requires

  • understanding your current Procurement Maturity
    (and while you may understand what you’re doing, it’s doubtful you understand how you are faring against the market or best-in-class)
  • understanding your current processes (based on this) vs. your target processes (based on where you should get to within a reasonable time-frame, taking into account that The Hackett Group, based on their book of numbers, discovered that it was typically an eight-year journey to best in class for large global enterprises)
  • understanding how these translate into use cases that must be supported by technology
  • understanding what technological capabilities will be required to get you there and …
  • what additional capabilities would be beneficial to simplify your tasks, identify additional value, or help your team progress in Procurement maturity over time and …
  • understanding which types of solutions / modules on the market contain the bulk of those capabilities so you know which segment of vendors to send the RFP to
  • understanding if the backbone solutions in place are worth keeping or if they should be replaced instead of augmented (i.e. would the solution with the missing capabilities completely subsume these solutions [rending them unnecessary], like simple RFPs in a Sourcing Suite or catalogs in a Procurement suite, or would they still be needed, like an ERP backbone)
  • understanding the globalization needs not just of the company, but the (potential) suppliers
  • understanding the services that will be required for installation, migration, and integration
  • understanding any unique requirements of the organization that will need to be addressed by a vendor (to ensure they can meet them) before negotiations can begin

and if you don’t know

  • what the state of the market is, or what best in class is
  • how your processes should be transformed to advanced up the maturity curve
  • how to define the appropriate use cases
  • … and the key technology capabilities that will be required
  • … and which optional capabilities will be true value add
  • how to identify solution/module types based on these capabilities
  • which solutions you have that you should keep, and which you should replace
  • the full breadth of globalization needs across the extended enterprise
  • the full breadth of services that will be required
  • which of your organizational requirements are truly unique and need to be spelled out

then you CANNOT write a good RFP. So you really, really, should pay an expert, independent, advisor (or consultancy that does not have any preferred provider partnerships) to do the appropriate Procurement and platform maturity assessments and write the RFP that you need.

Especially since this can usually be done for less than 10%, if not 5%, of the 5-year cost of the investment. (Face it, you’re going to be locked into at least three years no matter what you buy, usually five years, and even if not, it’s going to be too costly to switch out even the worst solution in less than five years.) For example, as per previous Sourcing Innovation posts on how much should you pay for a starting platform, as a mid-market you would be looking at about 250K/year in license fees for a good suite across the board (120K for a starter, but that wouldn’t have all the modules or advanced capabilities where you need them), plus implementation, migration, and integration that will run you anywhere from 125K to 500K (or more) up front. Assume 250K, and this gives you a five year baseline cost of 1.5M. 10% of that is 150K, and you can definitely get the help you need for that — and it’s a SMALL price to pay to make sure you get the acquisition right of this make-or-break technology (that can deliver a 3X to 5X+ ROI done right, and cost you Millions done wrong). (And if you’re a larger enterprise, you’d be looking at 3M to 6M for a suite for 5 years, which gives you a budget that even the Big X would be interested in, but which they SHOULD NOT automatically be considered for as they are all preferred implementation partners for at least one of the major suites.  In other words, if you have shortlisted to their partners, definitely consider use them — they have deep insight.  If your shortlist is for a majority of vendors who are not their partners, or if you want them to consider non-partners equally, make sure they are willing, able, and available to make this commitment up front before giving them the work!  [Remember, when it comes to enterprise suites, some of them have minimum referral / implementation requirements for partners, who can get in trouble and lose the partnership if they don’t recommend that system as one of the options in certain situations!]  Also remember, even if you go elsewhere for RFP creation, depending on whom you select, you may want to move back to a Big X for implementation — keep in mind when you should use a Big X!)

So if you want true success, big savings (10% for the appropriate strategic sourcing/procurement technologies), and real ROI (3X to 5X or more), put those “FREE” RFPs in the trash where they belong and find the right expert to help you create the right Affordable RFP that will ensure the successful selection that your organization needs.

Have all the Big X fallen for Gen-AI? Or is this their new insidious plan to hook you for life?

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.)

Almost every single Big X and Mid-Sized Consulting firm  is putting “Gen-AI” adoption in their top 10 (or top 5) strategic imperatives for Procurement, and its future, and that it’s essential for analytics (gasp) and automation (WTF?!?).

It’s absolutely insane. First of all there are almost no valid uses for Gen-AI in business (unless, of course, your corporation is owned by Dr. Evil), and even less valid uses for Gen-AI in Procurement.

Secondly, the “Gen” in “Gen” AI stands for “Generative” which literally means MAKE STUFF UP. It DOES NOT analyze anything. Furthermore, automation is about predictability and consistency, Gen-AI gives you neither! How the heck could you automate anything. You CAN NOT! Automation requires a completely different AI technology built on classical (and predictable) machine learning (where you can accurately calculate confidences and break/stop when the confidence falls below a threshold).

Which begs the question, have their marketers fallen for the Gen-AI marketing bullcr@p hook, line, and sinker? Or is this their new insidious plan to get you on a never-ending work order? After all, when it inevitably fails a few days after implementation, they have their excuses ready to go (which are the same excuses being given by these companies spending tens of millions on marketing) which are the same excuses that have been given to us since Neural Nets were invented: “it just needs more content for training“, “it just needs better prompting“, “it just needs more integration with your internal data sources“, rinse, lather, and repeat … ad infinitum. And, every year it will get a few percentage points better, but if it gets only 2% better per year, and the best Gen-AI instance now is scoring (slightly) less than 34% on the SOTA scale, it will be (at least) 9 (NINE) years before you reach 40% accuracy. In comparison, if you had an intern who only performed a task acceptably 40% of the time, how long would he last? Maybe 3 weeks. But these Big X know that once you sink seven (7) figures on a license, implementation, integration, and custom training, you’re hooked and you will keep pumping in six to seven figures a year even though you should have dropped the smelly rotten Gen-AI hot potato the minute you saw the demo (and asked them for a more traditional enterprise application they can deliver with guaranteed value).

So, maybe they aren’t misled when it comes to Gen-AI. Maybe they are just shrewd financial managers because it’s their biggest opportunity to hook you for life since they convinced you that you should outsource for “labour arbitrage” and “currency exchange” (and not materials / products you can’t get / make at home) and other bullsh!t arguments that no society in the history of the world EVER outsourced for. (EVER!) Because if you install this bullcr@p and get to the point of “sunk cost”, you will continue to sink money into it. And they know it.   Or do they?

In our view, the sad reality is that while one or two financial managers may have gone deep enough down the Gen-AI rabbit hole to figure this out, most of them likely just don’t see the downside for them or their clients.  Given all the hype the creators of these Gen-AI* models are pushing, with prolific examples only of success cases and upside, with very little education on the realities (because few of us are highlighting all of the risks of Gen-AI and failures when misapplied), maybe all they are seeing are promises that are just too good to ignore.

So, please, ignore the Gen-AI until you’ve validated a use case and instead remember When You Should Use Big X. Every solution and services provider has strengths and weaknesses. Please use them for their strengths, be successful, and increase the project success rate. (Post-Edit: As of 2024, technology project failure is at an all-time high. We don’t want to see any more of it!)

*Remember that AI, and Gen-AI in particular, is a fallacy.

Don’t Zip Through the Zip-sponsored Spend Matters authored Intake and Procurement RFP! [2024] (Collected Links)

Don’t Zip Through the Zip-sponsored Spend Matters authored Intake and Procurement RFP!

Please note this is NOT coverage of Zip. See this post for Zip solution coverage!

BONUS

BONUS 2

The Gen AI Fallacy

For going on 7 (seven) decades, AI cult members have been telling us if they just had more computing power, they’d solve the problem of AI. For going on (seven) 7 decades, they haven’t.

They won’t as long as we don’t fundamentally understand intelligence, the brain, or what is needed to make a computer brain.

Computing will continue to get exponentially more powerful, but it’s not just a matter of more powerful computing. The first AI program had a single core to run on. Today’s AI program have 10,000 core super clusters. The first AI programmer had only his salary and elbow grease to code, and train the model. Today’s AI companies have hundreds of employees and Billions in funding and have spent 200M to train a single model … which told us we should all eat one rock per day upon release to the public. (Which shouldn’t be unexpected as the number of cores we have today powering a single model is still less than the number of neurons in a pond snail.)

Similarly, the “models” will get “better”, relatively speaking (just like deep neural nets got better over time), but if they are not 100% reliable, they can never be used in critical applications, especially when you can’t even reliably predict confidence. (Or, even worse, you can’t even have confidence the result won’t be 100% fabrication.)

When the focus was narrow machine learning/focussed applications and accepting the limitations we had, progress was slow, but it was there, was steady, and the capabilities, and solutions improved yearly.

Now the average “enterprise” solution is decreasing in quality and application, which is going to erase decades of building trust in the cloud and reliable AI.

And that’s the fallacy. Adding more cores and more data just accelerates the capacity for error, not improvement.

Even a smart Google Engineer said so. (Source)

Challenging the Data Foundation ROI Paradigm

Creactives SpA recently published a great article Challenging the ROI Paradigm: Is Calculating ROI on Data Foundation a Valid Measure, which was made even greater by the fact that they are technically a Data Foundation company!

In a nutshell, Creactives is claiming that trying to calculate direct ROI on investments in data quality itself as a standalone business case is absurd. And they are totally right. As they say, the ROI should be calculated based on the total investment in data foundation and the analytics it powers.

The explanation they give cuts straight to the point.

It is as if we demand an ROI from the construction of an industrial shed that ensures the protection of business production but is obviously not directly income-generating. ROI should be calculated based on the total investment, that is, the production machines and the shed.

In other words, there’s no ROI on Clean Data or on Analytics on their own.

And they are entirely correct — and this is true whether you are providing a data foundation for spend analysis, supplier discovery and management, or compliance. If you are not actually doing something with that data that benefits from better data and better foundations, then the ROI of the data foundation is ZERO.

Creactives is helping to bringing to light three fallacies that the doctor sees all the time in this space. (This is very brave of them considering that they are the first data foundation company to admit that their value is zero unless embedded in a process that will require other solutions.)

Fallacy #1. A data cleansing/enrichment solution on its own delivers ROI.

Fallacy #2. You need totally cleansed data before you can deploy a solution.

Fallacy #3. Conversely, you can get ROI from an analytics solution on whatever data you have.

And all of these are, as stated, false!

ROI is generated from analytics on cleansed and enriched data. And that holds true regardless of the type of analytics being performed (spend, process, compliance, risk, discovery, etc.).

And that’s okay, because is a situation where the ROI from both is often exponential, and considerably more than the sum of its parts. Especially since analytics on bad data sometimes delivers a negative return! What the analytics companies don’t tell you is that the quality of the result is fully dependent on the quality, and completeness, of the input. Garbage in, garbage out. (Unless, of course, you are using AI, in which case, especially if Gen-AI is any part of that equation, it’s garbage in, hazardous waste out.)

So compute the return on both. (And it’s easy to partition the ROI by investment. If the data foundation is 60% of the investment, it is responsible for 60% of the return, and the ROI is simply 0.6 Return/Investment.)

Then, find additional analytics-based applications that you can run on the clean data, increase the ROI exponentially (while decreasing the cost of the data foundation in the overall equation), and watch the value of the total solution package soar!