Monthly Archives: May 2025

We’ll Say It Again. Analyst Firm 2*2s Are NOT Appropriate for Tech Selection!

Last year, while ranting about the plethora of utterly useless logo maps (which includes the Mega Map the doctor created to demonstrate the extreme futility of these maps), we also did a dive into why analyst firm 2*2s are NOT appropriate for tech selection. This is coming up again as a certain firm is really pushing All AI all-the-time and you can tell it’s about to infuse all their maps. Plus, the biggest firms are really pushing their quadrants, waves, and marketscapes, and most of these are showing the same solutions they showed last year and the year before that and the year before that and so on (going back a decade in some cases).

That, and a number of people are lamenting their lack of usefulness on LinkedIn, with one person even creating yet another logo map to highlight the “significant solutions that matter” (but we’ll save that rant for another day), so it’s time to make it clear that these maps are not appropriate (on their own) for tech selection. For example, in a discussion on my post on how your standard sourcing doesn’t work for direct, Thomas Audibert correctly states that static quadrants, in any form, do not work. (And then went on to correctly note that if you say there are, for instance, 80 sourcing solutions, it means that there are at least 20 niche (geographic, industry, customer size, …) categories of interest and that, unless they are catered within 20 different quadrants, this makes no sense to me.

And it doesn’t, because all a map can do, in the best situation, is give you a set of more-or-less comparable solutions that each serve a specific function (so you don’t end up trying to compare a Strategic Sourcing to a catalog-based e-Procurement to an Accounts Payable solution which, of course, serve three completely different functions). If it’s a good map, and by that I mean focussed on two things max, like Spend Matters Solution Map that only scores tech (on one axis) and only presents tech vs average customer scores (on the other axis), then you can use it to verify that one or two of your key requirements are met (such as the tech is solid and the customers are generally happy), but that’s it. (But if it’s a map that squishes 16 different scores into 2 dimensions, that’s useless … you don’t know what is contributing to the scores. What’s most important to you could be the lowest score in that score mish-mash number that looks above average.)

Moreover, at the end of the day, all an analyst can do that is useful is rate a vendor on one or more business independent objective dimensions that can be scored easily and, more importantly, give a customer comfort that the vendor does well on this dimension and they don’t have to worry about it in their evaluation. (For example, if a vendor does well in Spend Matters Solution Map, you know you don’t have to evaluate the underlying technical foundations, which is something most companies aren’t good at.) However, that’s not enough for a selection.

When it comes to tech, it’s important that:

  1. it’s solid
  2. it fills the need you are searching for
  3. it is easy to use by the majority of the users for the functions they will be doing the majority of the time

And, guess what, an analyst can only verify the first requirement. Why? An analyst doesn’t know your needs, you do. Moreover, they don’t know the TQ (technical quotient) of your users, the functions they do daily, or the processes they follow. You do. So, how can you expect an analyst to produce a map that tells you that.

But, if you’ve been paying attention, the solution to your problem is not tech. It’s process. And until you nail that, and then select the tech that matches that process, tech alone will NEVER solve your problem. NEVER.

And since analysts don’t know your business, or your

  • business size, Procurement department size, maturity
  • culture
  • risk tolerance
  • innovation level/comfort
  • current processes / required processes
  • customer service needs
  • etc. etc. etc.

or even how these slide on a scale across different companies of different sizes across industries, there’s no way they can produce a map that tells you all of this. Or even a fraction of this.

That’s why you need an analyst or independent consultant that truly understands the solution space you are searching in, what those solutions should do, and how to help you identify the subset that is not only technically solid but is also likely to meet your business requirements. (And remember, It’s the Analyst, not the analyst firm. If the analyst hasn’t reviewed dozens of vendors in the space you are searching in that offer the type of solution you are searching for, doesn’t know the must vs. should vs. nice to have requirements, and, most importantly, doesn’t have the technical chops to validate the solution technically (which is the weakness of every non-IT / non-Engineering business department), he’s not the analyst for you!

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.

An Update on Promena: The Rich Caffeinated Citrusy Turkish Punch

In our last write up of Promena in November of 2023, we introduced you to this two-decade old mid-market Source-to-Contract player (with some e-Procurement capability) based in Türkiye that does over 3 Billion in annual transaction volume. While a lot of that volume is still in Türkiye, Promena is expanding throughout Europe and into North America with its multi-lingual solution that supports 13 languages.

In that update we covered the core of their:

  • RFX: which supports RFX survey forms, product selection (from the product library), supplier selection, bid management, response comparison (with lowest bid for each line item outlined), and award selection
  • e-Auctions: which supports English, Dutch, and Japanese auctions; allows parameters to be configured; suppliers to be invited; and the auction to be run
  • Contracts: when an award is selected, the system supports contract creation and indexing up to the signature (which needs to be wet, scanned, and uploaded) and acts as an integrated e-filing cabinet
  • Supplier Management: information, relationship, baseline (KPI) performance management, and onboarding with a supplier suitability score (that is assessed through supplier responses to the buyer’s form-based questions) and quick access to all events and corrective action requests the supplier is involved in, products the supplier offers, and contracts the supplier is bound to
  • Corrective Actions: are supported and can be buyer created, sent to suppliers who need to respond, and then buyers can accept or reject the response and can always search the complete history of requests
  • ESG Management: which was mainly a section for collecting surveys and centralizing KPIs related to ESG
  • Product Management: collects and stores the descriptions of the products and services the buying organization needs in the organization’s category hierarchy and the foundation of the catalogs supported by Promena
  • Catalogs: from which organizational users to self-serve purchase standard, approved, on contract products and services
  • Purchase Orders: that can be generated off of a sourcing award, contract or a catalog item

Since then, Promena has made updates in the following areas:

  • Supplier Discovery: for identifying suppliers not in your database
  • Supplier Certifications: for centralization and tracking thereof
  • Contract Drafting: enhanced capability to pull in platform data
  • Enhanced ESG/Supplier Profiles: on organizational suppliers
  • Configurable Workflows: for platform processes and approvals
  • Completely Redesigned UX!

Supplier Discovery

Promena has built an internal supplier directory with default (public) profiles for all of the suppliers across its customer database which can be searched by any organization on product/service and location and, if relevant, added to their internal supplier database (and then invited to events, etc.). (They are investigating integrating with an external supplier discovery platform to assist global organizations in more extensive supplier discovery.)

Enhanced ESG/Supplier Profiles

The ESG profiles have been enhanced, with graphical displays for easy analysis, and integrated news feeds with recent articles related to a suppliers sustainability efforts/carbon footprint (in beta, and publicly available in the next release).

Configurable Workflows

This is one of the two major enhancements to the platform that we felt you should know about. In the new version of the platform, all of the RFX and auction, contract management, supplier onboarding, and approval workflows throughout the application can be individually configured for each buying organization. Furthermore, they are all exposed in the administration section and a sufficiently capable administrator can edit the core workflows themselves or work with Promena on implementation to customize the workflows to their liking.

In addition to having standard workflow definitions for each module/section of the platform, the platform uses these to generate wizard-like path-based walkthroughs in every section that make it clear (at the top of the screen) exactly where the user is in the process, what they just did, and what they will need to do next.

Completely Redesigned UX

Much of the year last year was spent updating the stack, building modern workflows, and redesigning the entire user experience to be easier, cleaner, and much more obvious so that users can get up and running with little to no experience, and they have done a great job in this regard. It’s cleaner, more streamlined, and the enhanced use of workflow and templates allow organizations to define the right process with the right template that minimizes the work buyers have to do on a daily basis and makes it easy for users to find what they want in catalogs, follow along with events (as they can be invited in viewer roles), fill out surveys, and so on.

And while this did delay some of the planned functionality for the second half of 2024 (like line-item price breakdowns, which is now coming the second half of this year), it was definitely worth it because, once implemented (, configured to the organization’s process, populated with default forms and documents, and loaded with the existing catalog) most buyers will be able to sit down, dive in, and get an event live with no training.

Roadmap

As per above, items for this year include:

  • enhanced supplier discovery through Promena‘s supplier marketplace (and possibly a third party supplier discovery player)
  • enhanced ESG profiles with real-time news updates and AI summaries
  • line-item cost breakdowns for deeper cost insight (and comparisons in the platform)
  • Brazilian auctions
  • Auction Guidance which will mathematically analyze the past 5 years of results (if available) on the product/category and advise you on which auction will likely generate the best results

For a mid-market company, it’s a fairly extensive platform that’s easy to use, easy to customize, and easy to grow on. This makes Promena a platform that should definitely be considered for your short-list if you are looking for a modern S2C mid-market platform with purchase order support and services support if you need it (either from Promena direct where their account teams manage over 5,000 sourcing events a year or from 11 Global Partners who can deliver integration and support services).

How AI Enhances 10 Common Procurement Challenges Part I

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 take them one-by-one.

Procurement Takes Too Long, Slowing Innovation: According to the article, AI-driven platforms can generate RFPs, accelerate sourcing, automate approvals, and reduce cycle times … which is mostly true. Properly applied, AI can accelerate sourcing, reduce cycle times, and automate approvals … but not all approvals. As for RFP generation, that’s very limited — LLMs can generate RFPs with a simple prompt, but not necessarily a good RFP. The best RFPs are designed by humans (and then automation, which may or may not use AI, can pull in data from supporting documents as needed), and as for acceleration, it depends on the project — it can’t speed up supplier qualification where humans need to inspect the products and verify the requirements.

Moreover, a rush to AI can make things worse, and not better. Letting AI generate an RFP that misses a key requirement in terms of required certifications, performance criteria, production capacity, etc. can entirely invalidate an RFP process and lead to months of wasted effort if no human realizes that this key requirement was missed until an award is offered and a request for the certification, capacity, etc. is delivered and a “sorry, we don’t have / can’t do that” is returned.

Legal and Budget Complexities Create Bottlenecks: Budget tracking systems and rules-based automation allows for instantaneous budgetary approvals. Contract negotiation software can automate redlining, compliance checks, etc., but cannot handle a complex negotiation for a complex project where each side has a lot of requirements and multiple parties to satisfy. AI speeds up the technical drudgery, but not the human interaction.

Moreover, if you turn over negotiations to software, you have no idea what the end result will be. If you let it negotiate based on market data, and the cost data is off, you could be committing to a bad deal. If you let it predict timeframes based on how it expects prices to rise/stay high, but it’s off by two years, it could lock you into a three year deal when you only need a one year deal. And so on.

CIOs Need to Upskill Their Teams in AI and Cybersecurity: Just because “AI” can simplify processes with guided intelligence, that doesn’t mean the team is upskilled in the process. The reality is, there is no incentive for users to learn anything if they think the system will guide them in everything they need to do.

Thus, if you over invest in AI, especially the kind that guides users in every task they have to do, and works quite well on the basic tasks they have to do daily, and doesn’t screw up the first half dozen or so moderately complex tasks, the user will believe the system is almost flawless, start to trust it implicitly, stop questioning it as time goes on, start believing there is no need to learn anything else because the system knows it, and, over time, stop thinking. And then, instead of performance improving, it will decline … and that decline might be accompanied by a major financial loss if a bad contract is signed or major risk ignored.

Data Inaccuracy Leads to Poor Procurement Decisions: While it’s true that over three quarters of organizations struggle with unreliable data, AI doesn’t magically fix the problem. It can help with cleansing, validation, and procurement trend analysis, but ask any spend analysis vendor who has tried to apply an LLM to unclassified vendors about the classification accuracy (which tends to top out around 70%) — good data still requires manual cleansing and classification, especially where the system reports good confidence. It can definitely help, but it doesn’t take the onus off of the human.

In other words, if you believe that you can plug in a magic AI black box ad that it will fix your data, you are gravely mistaken. Sure it will tell you that it has cleansed, classified, and validated all of your data, but if it’s only 70% accurate, it’s only made matters worse if you trust the data 100% and don’t know what 30% is inaccurate. When you base your decisions on data, and the data is bad, you are bound to make a bad decision. The question is, how bad. You don’t know. And that’s a big problem!

B2B Software Selection is Increasingly Complex: Moreover, despite the claims, AI-powered vendor analysis doesn’t really help that much — see Pierre Mitchell’s crazy conversations with DeepSeek-Rq. Note how it not only recommends inappropriate vendors, but also recommends vendors that don’t even exist anymore … it can help you discover potential vendors, but you still need human reviews and deep pricing intelligence (from expert SaaS optimizers).

Trusting AI to select your software is worse than trusting an analyst firm map! And we know all of the problems those maps contain. (First of all, they only mention the same 10 to 20 vendors year after year, ignoring the dozens of other vendors that might be more appropriate for you.) AI cannot understand your needs, cannot truly map needs to requirements, cannot truly map requirements to features, and cannot truly assess how relevant a solution is, and definitely can’t assess how well a provider’s culture will match yours.

Come back Thursday for Part II!

Materials Requirement Planning DOES NOT Optimize Materials Replenishment. GenLots!

If you engage in direct manufacturing, chances are you have a semi-modern Enterprise Resource Planning solution (or at least a precursor Material Requirements Planning Solution) augmented by a semi-modern Supply Chain Planning solution designed to optimize your forecasts, inventory levels, and production. Chances are also good that based on forecasts (which are at least calculated down to weekly, if not daily, intervals), inventory levels (which are updated based on weekly or daily utilization by production), desired safety stock levels, and safety buffers on lead times, you have auto-replenishment set-up and the ERP will automatically generate re-orders based on lead times, safety stock alerts, or manual forecast alterations.

Chances are even greater that your demand planners will believe these are good and automatically approve them without further thought because the material requirements plan was optimized against the forecast and all available data.

NOTHING COULD BE FURTHER FROM THE TRUTH!

An optimal plan for production is NOT an optimal plan for purchase. Production requires having the right inventory on hand when you need it for the levels you need to support, balancing inventory against stock-out and obsolescence/expiration risk. Purchasing requires buying at the right volumes to take advantage of economies of scale (and tier discounts) and using the right distribution options to optimal capacity, balancing an economic order quantity (that minimizes total landed cost at a minimum) against inventory holding costs and risks of obsolescence/expiration. Your SCP enhanced ERP/MRP does the first. NOT the second. (There may be dozens of SCP systems out there, but since NONE of them support sourcing or procurement, NONE of them have the other half of the data that they would need to do this.)

And for mid-size manufacturers, this is costing them MILLIONS of dollars a year. And for large enterprise manufacturers, tens of millions of dollars a year. (In fact, the average loss from failing to optimize replenishment is 10% of inventory! This means that for every 100 Million of inventory maintained in a large enterprise, 10M is being lost. TEN MILLION.)

This is why GenLots exists — to optimize replenishment and minimize overall material lifecycle costs while maintaining (and, if possible, increasing) service levels and reducing overall working capital needs. In fact, this is so important, that this is all GenLots does (because no one else does it — which is partially the case because none of these SCP solutions do Direct Sourcing or even Direct Procurement properly, as per an ongoing Sourcing Innovation series being co-developed with Supply Chain Matters).

There are three main parts to the GenLots solution:

  • Order AI: the core optimization engine that can be directly integrated with your ERP (and is currently directly connected to SAP as a SAP Partner on the SAP store) that automatically pulls (and deletes) all the auto-generated replenishment orders from the ERP and replaces them with MR-optimized orders
  • Order UI: the UI that allows the purchasing manager to see all of the orders generated, what the original order was, and what savings and service level increases resulted from the modified order
  • Policy Advisor: the optimization-enhanced simulation engine that advises the organization on how to set their safety stocks, lead time buffers, MOQs, preferred discount tiers (for suppliers to bid against), default re-order windows, etc.

Order AI

Built on solid decision optimization and machine learning algorithms, the Order AI automatically retrieves every generated purchase order for a raw material in the ERP and calculates the right order quantity (and order date) based upon the raw material cost model (including the logistics costs using the proper mode of transport and default mode capacity), lot constraints (due to supplier or carrier capacity), scheduling requirements (based on delivery windows and processing time), safety settings (stock levels and lead time buffers), expiry windows (for perishable or decomposable stock), and, if desired, CO2 emissions (based on the available distribution options).

Order UI

This allows you to examine the orders created by GenLots and not only see the differences in order quantity and order/ship date, but the overall impacts on cost, overhead, working capital, and service level. For each material, it will break down the difference between the original supply chain cost with the system generated orders and the current supply chain cost with the GenLots orders by computing the order savings (processing and logistics costs), inventory (overhead) savings, and waste/scrap/obsolescence savings. And while the average is 10%, they have seen savings of 50% or more due to high shipment costs from too many shipments (with trucks going half full) on low value inventory, and from high waste costs (from manufacturers that pushed the safety stock and safety buffers too high and ended up wasting a lot of materials in F&B and Pharma manufacturing where shelf life of some products is very limited). It will also indicate the (estimated) service level achieved with its plan.

Policy Advisor

Optimal buys require not just optimal plans (because if that were enough, then maybe the SCP solutions wouldn’t be doing such a dismal job and costing you 10 Million on every 100 Million that flows through your warehouses), but also optimal parameters. The Policy advisor can be used to run multiple simulations to determine, for each material (based upon the production forecasts it is tied to), the appropriate safety settings (to optimize inventory levels against required service levels, warehouse capacities and carrying costs, and risk of waste), stock levels, lot sizes (and price tiers to request from suppliers), and service levels for the organization, which can lead to even greater cost savings in material replenishment when appropriately defined. (Remember, the optimization works within parameters you restrict it to, so if you restrict it to bad bounds, it won’t be able to save you nearly as much as you could save.)

Expert Support

Even though it’s available on the SAP Store, this is one solution where you should go direct (to GenLots). GenLots preferred methodology, even if the integration is literally plug-and-play for you as a SAP client (who has invested the effort to clean up their forecasting and ERP-based re-order and approval processes and ensure that clean, valid data is always available down to at least weekly intervals) is to work with its clients for the first six to twelve weeks (depending on organizational size), make sure everything runs smooth, and help its clients define the optimal (starting) policy to maximize the value and success of the GenLots solution. This is because they not only want you to see results, but see the full extent of results possible. When it comes to material replenishment, the reality is that just because you identify a few million in savings, that doesn’t mean the solution is working well. If your inventory value over a year exceeds 100 Million, it’s likely that you have a ten million dollar savings opportunity and they want to do everything in their power to maximize your chances of seeing that.

(And if, at the end of the day, with their expert guidance you only see a few million in savings, you can pat yourself on the back for being best in class in forecasting, re-order windows, and optimizing inventory policies, because you’d have to be to not see a massive savings in your first year. [Odds of this happening are less than 1/5 though if you are going through over 100M in inventory a year.] It’s no different than applying strategic sourcing decision optimization across your major categories — no matter how good you thought you were doing, studies showed time and time again an average savings of over 10% because you just couldn’t model all of the variables and compute all of the trade-offs [while adhering to all the constraints] through simple spreadsheet calculations.)

Proven Solution

GenLots may not be a name that you know in North America, but it’s one you should. Founded in 2017, the solution has been under consistent development for eight years, in daily production for six years, and is currently being used by 100 Billion-Plus companies to optimize their replenishment schedules, reduce inventory up to 20%, deliveries up to 50%, and save up to 10 Million for every 100 Million of inventory processed. It’s the best kept secret that needs to be exposed because you’re losing millions, your SCP and ERP providers will never admit otherwise, and you can stem the bleeding with a software license that starts at only 5 figures a year!