Category Archives: Supplier Information Management

Features ARE NOT Applications; But Applications Require Features!

THE PROPHET recently asked What Procurement Tech Product Categories Were Really Just Features All Along? Which is a great question, except he cheated.

He cheated with the first 5!

  • Supplier performance management
  • Supplier quality management
  • Supplier information management / supplier master data management
  • Supplier diversity
  • Supplier risk management (not supply chain risk!)

We’ve known for years it should be one Supplier 360 solution! (Even though no one offers that when you consider all of the elements that should be there. Heck, none of them even offer the 10 basic CORNED QUIP requirements … in fact, good luck finding a solution that offers 5 of those requirements among the 100+ supplier management solutions).

He you cheated again with the next 3!

  • Should cost / cost modeling (for procurement, not design engineers)
  • RFX and reverse auctions (when not bundled with broader capabilities or services)
  • Sourcing optimization

We’ve also known for yours it should be cost-model and optimization backed sourcing (auction, RFX, hybrid, single source negotiation, etc.) … otherwise, it’s an incomplete solution. But only a fraction of the 80+ sourcing platforms offer true optimization (less than 10) and fewer still do extensive cost modelling. (Note that we are focussed on modelling, not cost estimation — that requires data, and that can, and probably should, be a third party data feed.)

And he was wrong on the last front.

Real Spend Analytics should be standalone. Wrapping restricts it! The modules you use should provide all the specific views you need, but the reason that spend analysis quickly becomes shelfware in most organizations today is the same reason it became shelfware 20 years ago … once you exhaust the limits of the interface its wrapped in, it becomes useless. Go back to the series Eric and I wrote 18 years ago (which you can since Sourcing Innovation didn’t delete everything more than a decade old when it had to change servers in 2024, unlike Spend Matters when it did its site upgrade in 2023).

But Very, Very right in that features are not applications!

And very, very right in that too many start-ups are launching today as features (which will only survive if acquired and rolled up into existing applications and platforms), and not solutions. While apps dominate the consumer world, in business there is not always an app for that, and, frankly, there shouldn’t be. This focus on point-based apps is ridiculous. It’s not features, it’s functions. It’s not apps, it’s platforms. It’s not orchestration (and definitely not spend orchestration), it’s ecosystems!

Recent stats, such as those published by Spendesk put the average number of apps a business uses at 371, with an average of 253 for SMBs and 473 for enterprise firms. WHAT. THE. F6CK? This is insane. How many departments does an average organization have? Less than 10. How many key functional areas? Less than 12. Often less than 10! How many core tasks in each function? Usually less than 6. That says, in the worst case, an enterprise might have 72 distinct critical tasks which might need their own application (but probably not). This says that SMBs have at least 3 times the app they should have, mid-size organizations at least 5 times, and enterprises at least 7 times. That is insane! No wonder there are so many carbon copy SaaS optimizers (as we covered in our piece on sacred cows), because if you have that many SaaS apps, you have features, not applications. And you need to replace sets of these with functional applications that solve your core problems.

(And if you want to know how to prevent app sprawl, before buying yet-another-app, ask yourself “is this supporting a function that should be done on its own, or just a task that should be part of an existing function” … if the latter, it’s a feature, not an application, and if the application it should be part of does not have an upgrade/module that supports the task, then you have the wrong application and it’s time to replace it, not pointlessly extend the ecosystem!)

You’re Not Doing Supplier Performance Management (SPM) Right Unless it Improves You!

This post was inspired by a LinkedIn post from Celia, founder of Vendor Score IT, who says that if your suppliers aren’t evolving, they’re holding you back.

Celia is perfectly correct in that you will be held back by suppliers who refuse to progress, but another key point that really needs to be addressed that all of the supplier performance management advocates miss is the following:

If you’re not evolving, you’re holding your suppliers back.

When you need to step up performance, you can’t put all of the blame or all of the responsibility on the supplier. You have to take some too. First of all, you selected the supplier. Secondly, you didn’t monitor the supplier closely to ensure that the supplier performed up to your level of expectation. Thirdly, you know what the customer wants, as well as the performance you expect, better than your supplier. Fourthly, you should be leading the innovation charge, as the one responsible for value-add for the end-customer.

Furthermore, when it comes to Supplier Performance Management (SPM), while it’s super easy to just drop the under-performing suppliers and replace them with better performing ones … simply adopting better suppliers doesn’t make you any better as an organization. In fact, not only will you have a new set of suppliers in the lower median who then become under-performing, but overall performance scores will go down because you are not enabling them to perform better.

You don’t want to ditch poor metrics because they are holding you back, vendor reviews to ensure they are striving to get better, or take a growth mindset to make them perform better.

You want to ditch poor metrics because they are forcing suppliers to perform sub-optimally to score well in your system, you want to do “vendor reviews” to open dialogues about how you can help them improve (because, with a three year commitment, they’ll buy a new machine, upgrade their warehouse and use new pallets to reduce breakage, improve quality control processes, etc.), and use your growth to fuel theirs as well.

How do you identify the bad metrics? How do you identify where you are holding them back (vs. them holding you back)? How do ensure that you get the growth you need? By taking the mindset that it’s at least as much your fault as there’s (and probably more), by going in with a joint improvement mindset, by listening to them (and, if necessary, reading between the lines to see how their focus on certain metrics, such as OTD or year-over-year production cost decrease [when energy costs are going up and it’s forcing them to sacrifice quality], is actually degrading their performance), and asking them to contribute to improvement plans. (i.e. You make it clear that you are going to work with them on joint improvement, not just dictate plans to them or expect them to do all the work. And that they should take advantage of that because, otherwise, you’ll find another supplier who will work with you if they won’t. A good supplier will jump at this opportunity.)

By improving your organizational performance, you will encourage your suppliers to improve their performance as well!

Myth-busting 2025 2015 Procurement Predictions and Trends! Part 4

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 continuing with “supplier management”.

Supplier Management

There were 6 predictions across the eight articles which basically revolved around “collaboration” with some focus on “development”. This is yet another topic that is overhyped and needs to be addressed, but, as with our last two articles, we will start by listing all of the individual predictions:

  • Agile Supplier Management
  • Collaborative Platforms
  • Enhanced Supplier Collaboration
  • Enhanced Supplier Collaboration
  • Supplier Collaboration and Strategic Partnerships
  • Supplier Development and Growth

Here’s the thing. For anything not a commodity, an organization’s success ultimately depends on supplier performance. While supplier performance will be good from the start for some suppliers, it won’t be so good for others. In these cases, it won’t always improve just be rejecting shipments. Sometimes it will require collaboration, which means that collaboration has always been, and will always be, important. So it’s nothing new. The only difference is that, as disruptions become more common, products require more differentiation and rapid advancement, and supply chains need to rapidly shift as raw material sources and distribution routes become unavailable, we are in a situation where collaboration is becoming increasingly more critical.

As a result, collaboration will increase in some supply chains as it is needed, but you won’t see a sudden shift en masse for Procurement to all of a sudden become more collaborative with its suppliers unless it needs to. While there is always a lot of talk about how collaborative an organization is, especially at RFP time, the reality is, as we all know, once the contract is inked, unless the supplier is considered very strategic, the chance of actual collaboration is very low.

The best one can hope for is that the organization selects supplier management software that enables better communication and collaboration than is usually supported by such software, which will mean that, over time, collaboration may increase before a disaster scenario that requires it to do so.

The only prediction that may become true in a small number of Procurement organizations that install more modern, collaborative, agile platforms is they become more agile in supplier management, begin collaboration when potential issues are detected, see how easy it is, and actually start supplier development before major problems arise.

What Should Happen? (But Won’t!)

Organizations should acquire supplier performance management and development systems that allow them to track supplier performance, identify blips and downward trends, and immediately identity, and implement, appropriate supplier development programs … in a collaborative fashion with the suppliers. This will identify which suppliers need more collaboration, when, and help you get to the why. That’s it. It’s not giving collaboration lip service, looking for “agile” systems, creating new “partnerships”, etc. It’s just identifying which suppliers need collaboration, when, why, how, and getting it done … with straight-forward supplier performance management and development systems.

Three down, seven to go.

Advanced Supplier Management TOMORROW — No Gen-AI Needed!

Back in late 2018 and early 2019, before the GENizah Artificial Idiocy craze began, the doctor did a sequence of AI Series (totalling 22 articles) on Spend Matters on AI in X Today, Tomorrow, and The Day After Tomorrow for Procurement, Sourcing, Sourcing Optimization, Supplier Discovery, and Supplier Management. All of which was implemented, about to be implemented, capable of being implemented, and most definitely not doable with, Gen-AI.

To make it abundantly clear that you don’t need Gen-AI for any advanced back-office (fin)tech, and that, in fact, you should never even consider it for advanced tech in these categories (because it cannot reason, cannot guarantee consistency, and confidence on the quality of its outputs can’t even measured), we’re going to talk about all the advanced features enabled by Assisted and Augmented Intelligence that are (or soon will be) in development (now) and you will see in leading best of breed platforms over the next few years.

Unlike prior series, we’re identifying the sound, ML/AI technologies that are, or can, be used to implement the advanced capabilities that are currently emerging, and will soon be found, in Source to Pay technologies that are truly AI-enhanced. (Which, FYI, may not match one-to-one with what the doctor chronicled five years ago because, like time, tech marches on.)

Today we continue with AI-Enhanced Supplier Management that is in development “today” (and expected to be in development by now when the first series was penned five years ago) and will soon be a staple in best of breed platforms. (This article sort of corresponds with AI in Supplier Management The Day After Tomorrow that was published in May, 2019 on Spend Matters.)

TOMORROW

Supplier Future State Predictions

Supplier management platforms of today can integrate market intelligence with community intelligence, internal data, and external data sources and give you a great insight into a supplier’s current state from a holistic perspective.

Along each dimension, future states can be predicted based on trends. But single trends don’t tell the whole story. Now that we have decades of data on a huge number of companies available on the internet across financial, sustainability, workforce, production, and other dimensions which can be analyzed overtime and cross-correlated, we can do more, and know more.

Based on this correlated data, machine learning can be used to build functions by industry and company size that can predict future state with high confidence based upon the presence of a sufficient number of sufficiently accurate data points for a company in question. Now that these platforms can monitor enough internal, community, and market data and pull in a plethora of data feeds, they can accurately compute metrics with high confidence along a host of dimension, and this in turn allows them to compute the metrics that are needed to predict future state if the vendor’s platform has enough historical data on enough companies to define trends and define predictor functions using machine learning.

Not only can you enter a relationship based on a current risk profile, but on a likely future risk profile based on what the company could look like at the end of the desired contract term. If you want a five year relationship, maybe taking advantage of that great deal due to a temporary blip in supplier or market performance may not be a good idea if suppliers historically in this situation typically went into a downward spiral after accepting a big contract they ultimately weren’t prepared to deliver on.

Category Based Supplier Rebalancing

We could actually do this today, as a few vendors are now offering this capability, but it’s not yet part of supplier management platforms and the newly emergent offerings are often limited to a few categories today. But tomorrow’s platforms will continually analyze your categories holistically (along the most relevant dimensions, which could include cost, supply assurance, environmental friendliness, etc.) to determine if the supply mix you are currently using is the best one, let you know if there could be a better one, and suggest changes to orders (as long as it doesn’t jeopardize contracts where that jeopardy could come with a financial or legal penalty).

It’s just a matter of re-running an optimization model on, say, a monthly basis with updated data on price, supply assurance, and environmental friendliness (using the appropriate data for each, such as market quotes, current supplier risk, carbon per unit, etc), and comparing the optimal result to the current allocation plan. If it’s within tolerance, stay on track; if it’s slightly out of tolerance, notify a human to conduct and review a thorough analysis to see if something might need to change; if it’s way off of tolerance, recommend a change with the data that supports the change.

Supply Base Rebalancing

Once you have a platform that is continually reanalyzing categories and supplier-based assignment, you can start looking across the supply base and identify suppliers which are hardly used (and an overall drain on your company when you consider the costs of maintaining a relationship and even maintaining the supplier profile) and supplier that are potentially overused (and pose a risk to your business simply based on the level of supply [as even the biggest company can stumble, fall, and crash to the ground on a single unexpected event, such as the unexpected installation of a spreadsheet driven Master of Business Annihilation as CEO who has no clue what the business does or how to run it effectively and, thus, causes a major stumble, as summarized in Jason Premo’s article).

And, more importantly, identify new suppliers who have been performing great with slowly increasing product / service loads and should be awarded more of the business over older suppliers that are becoming less innovative and more risky to the operation at large. Now, this will just be from a supply perspective, and not a supply chain perspective (as these programs focus on suppliers and not logistics or warehousing or overall global supply issues), but this will be very valuable information for Sourcing and New Product Development who want to always find the best suppliers for a new product or service requirement.

Real-Time Order Rebalancing

Since tomorrow’s platforms will be able to recommend category rebalancing across suppliers, they will also be able to quickly recommend real-time order rebalancing strategies if a primary supplier is predicted to be late in a delivery (or a human indicates an ETA for a shipment has been delayed by 60 days). This is because they will be integrated with current contracts, e-procurement systems, and have a bevy of data on projected availability and real historical performance. Thus, it will be relatively simple to recommend the best alternatives by simply re-running the machine learning and optimization models with the problematic supplier taken out of the picture.

Carbon-Based Rebalancing

Similarly, with the rise of carbon-calculators and third-party public sources on average carbon production per plant, and even unit of a product, it will be relatively easy for these supplier management platforms to build up carbon profiles per supplier, the amount of that carbon the company is responsible for, how those profiles compare to other profiles, and what the primary reasons for the differentiation are.

The company can then focus on suppliers using, or moving to, more environmentally friendly production methods, optimize logistics networks, and proactive rebalancing of awards among supplier plants to make sure the plants producing a product are the ones closest to where the product will be shipped and consumed. It’s simply a carbon focussed model vs. a price focussed one.

SUMMARY

Now, we realize some of these descriptions are dense, but that’s because our primary goal is to demonstrate that one can use the more advanced ML technologies that already exist, harmonized with market and corporate data, to create even smarter Supplier Management applications than most people (and last generation suites) realize, without any need (or use) for Gen-AI. More importantly, the organization will be able to rely on these applications to reduce time, tactical data processing, spend, and risk while increasing overall organizational and supplier performance 100% of the time, as the platform will never take an action or make a recommendation that doesn’t conform to the parameters and restrictions placed upon it. It just requires smart vendors who hire very smart people who use their human intelligence (HI!) to full potential to create brilliant Supplier Management applications that buyers can rely on with confidence no matter what category or organization size, always knowing that the application will know when a human has to be involved, and why!

Advanced Supplier Management TODAY — No Gen-AI Needed!

Back in late 2018 and early 2019, before the GENizah Artificial Idiocy craze began, the doctor did a sequence of AI Series (totalling 22 articles) on Spend Matters on AI in X Today, Tomorrow, and The Day After Tomorrow for Procurement, Sourcing, Sourcing Optimization, Supplier Discovery, and Supplier Management. All of which was implemented, about to be implemented, capable of being implemented, and most definitely not doable with, Gen-AI.

To make it abundantly clear that you don’t need Gen-AI for any advanced enterprise back-office (fin)tech, and that, in fact, you should never even consider it for advanced tech in these categories (because it cannot reason, cannot guarantee consistency, and confidence on the quality of its outputs can’t even be measured), we’re going to talk about all the advanced features enabled by Assisted and Augmented Intelligence that were (about to be) in development five years ago and are now (or should be) available in leading best of-breed systems. And we’re continuing with Supplier Management.

Unlike prior series, we’re identifying the sound, ML/AI technologies that are, or can, be used to implement the advanced capabilities that are currently found, or will soon be found, in Source to Pay technologies that are truly AI-enhanced. (Which, FYI, may not match one-to-one with what the doctor chronicled five years ago because, like time, tech marches on.)

Today we continue with AI-Enhanced Supplier Management that was in development “yesterday” when we wrote our first series five years ago but is now available in mature best of breed platforms for your Procurement success. (This article sort of corresponds with AI in Supplier Management Tomorrow Part I and Part II that were published in May, 2019 on Spend Matters.)

TODAY

Auto Profile Updates with Smart Information Selection

In our last article, we noted that in first, and many second, generation Supplier Management solutions, a supplier was always forced to create a profile by scratch, filling out a bevy of pre-defined form fields — even if they had all of that data in a well formed (metadata rich) xml or csv file. That’s why yesterday’s Supplier Management solutions contained functionality to auto-complete profiles wherever this data was easily available in standard formats.

But the biggest problem remained — supplier profile maintenance. A supplier profile is only accurate the second a supplier hits confirm/complete. Then, their main contact changed. They changed their mailing address. They moved HQ. They offered a new product. They dropped an old one. And so on. And, of course, they never maintained their profile, and you never verified it until you went to call, mail, or order and that person wasn’t there, the mail got returned, or the order was rejected (because the supplier no longer made the product). Then, you went to the website, found the new main line, called, navigated to the right person, got the right info, and maybe remembered to update the system.

So, as errors were discovered, some critical ones would be corrected, but most would remain unchanged or unnoticed and over the years errors — including information on critical insurance, regulatory approvals, and other key business requirements that put the organization at high risk if not verified — continued to pile up. After a few years, the record becomes more wrong than right. Not good.

So today’s solutions make use of the fact that information typically gets updated somewhere, even if not in the application. They monitor the supplier’s website for changes in contact information, invoices for address and product information, state and country registries for business information, and so on and when changes are detected, automatically update the supplier profile if the changes can be independently verified (through a third party authority, to prevent hacks or fraud from changing the system) or present the new data for approval to the relationship manager. All this takes is simple website and data source monitoring, scraping, reg-ex based pattern matching, and automated workflows. For complex information, a bit of semantic processing. Nothing beyond classical, proven, tried-and-true AI is needed.

Market Based Supplier Intelligence

Today’s supplier management platforms can integrate with multiple marketplaces, communities, partners, GPOs, and specialized compliance, sustainability, and risk data platforms, use rule-based transformations to harmonize all the data, and use built-in algorithms to extract intelligence at a market level.

Your company data gives you one view into a supplier; your vendor-based community, which is usually limited to similar companies in your industry that the vendor was able to sell, gives you another view; but the market gives you yet another view yet. Mathematically, one data point doesn’t tell you anything. If only nine other customers use the vendor and share their data through community intelligence, that gives you 10 data points, which gives you some data on the supplier’s performance and their performance for you relative to others, but 10 data points is not statistically significant. But if 30, 50, 100 data points can be collected from the market, that gives you deep insight with deep statistical significance.

On top of the data, and a few powerful cores (few, not a few thousand), all these platforms need is basic statistical calculations, trend analysis, classical machine learning, semantic processing, and sentiment analysis … all of which have been market ready for over a decade.

Real Time Relationship Monitoring

Relationships are more than just performing to a contract. They are about building a working arrangement that is beneficial to both parties. One where both are willing to admit problems, collaboratively explore potential solutions, and work together to achieve them. One where, when there are no problems, both are willing to find ways to improve.

As a result, relationship monitoring is more than just supplier performance monitoring. Especially since the relationship can be bad even when the performance is (still) (surprisingly) good, and the relationship can be (reported as) good when the performance is bad.

However, if you turn that semantic and sentiment analysis that was typically done on market data and public comments on internal communications, you can start to build up a picture of the overall viewpoint and sentiment on the relationship from both sides, what successes or issues are contributing to that, and if the situation is improving or deteriorating over time (by trending the number of spikes in communication with sentiment that is overly positive or negative). It’s not foolproof, as both sides could adopt strict, formal, communication no matter what, but since people are human, they tend to get hotheaded and lose tempers (and let the words fly) if they are really upset or jubilant when they are really happy (and let the praise fly), and while minor changes in relationship sentiment might not be caught (within tolerance), major changes will. Moreover, you’re not going to get rigid, controlled, strict, formal communication until threats of a lawsuit fly, but then it’s too late!

Automated Resolution Plan Creation, Monitoring, and Adjustment

Not only can supplier management platforms automatically detect issues (by rapid increases or decreases in trends or metrics), they can also correlate them to included resolution plan templates, automatically instantiate them and customize them to the issue in question, walk the supplier relationship manager through the resolution process, monitor progress, and automatically adjust the plan, and timeline, as needed as new information, good or bad, comes in.

Each default template can be correlated to a particular metric, trend, or sentiment driven situation, so selecting it is just a lookup. Instantiation is just filling in the blank with the appropriate category, product, service, and metric information, through reg-ex matching and search and replace. Robotic Process Automation (RPA) walks both sides through the process. Monitoring alerts either side when something is updated or not completed on time through more RPA. And adjustments can be made to trend lines based on average timelines on similar projects and current trends at each milestone.

Automated Risk Mitigation Strategy Identification

It’s one thing to detect risk, which is pretty easy along many dimensions when you have a lot of data at your disposal, and relatively straightforward to predict the likelihood of some risk events, but it’s a lot harder to determine which mitigation strategies should be employed when it looks like a risk is going to materialize.

But that doesn’t mean it can’t be done, or isn’t doable by the best of platforms. Just like a platform can come equipped with issue resolution plan templates, it can also come equip with standard risk mitigation strategies, which are essentially action plans to be automatically customized with the specific category, product/service, logistics, and supply line details. This is just pattern matching and semantic contextual awareness.

When all of this is combined with (near) real time monitoring across data sources, that are continually looking for relevant news sources, changes in metrics / prices / trends, etc, it’s like magic (although it isn’t). The platform detects risks, finds the most appropriate mitigations, and present it to the relationship manager. An all it uses is math, traditional machine learning, and traditional semantic/sentiment analysis. And, of course, a lot of up-front human intelligence (HI!) in the creation of this solution.

Automatic Real-Time Resource Re-Alignment

Corrective action plans and risk mitigation plans have something very important in common — people. People who create them, approve them, execute them, and monitor them. This requires resources to be constantly assigned, monitored, replaced as soon as they are unavailable or needed on more pressing assignments, and reassigned as the issue is resolved or the mitigation complete.

And while it will often be difficult for a project manager, or even a resource manager, to determine when to remove an organization’s best problem solver from a critical corrective action project to address a less critical risk mitigation project, or vice versa, even when the manager can’t think of someone else who could address the less critical risk mitigation project effectively, even when there is another moderately experienced problem solver that could step into the critical project, the software will be able to compute when that should happen if the organization defines the rules as to when that will happen based on hard metrics.

For example, if you define assignments to correlate resources to the projects with the highest cost (should the issue persist or the risk mitigate), and you define the cost of an issue based on its expected impact if unsolved, and the cost of a risk as its expected impact if unaddressed (using a fixed cost or a formula if those 10,000 processors don’t arrive and you have 10,000 vehicles you can’t complete), and you associate a seniority with each resource, it’s simply rank ordered matching.

If there aren’t enough resources for all problems, you can apply simple optimization to maximize the impact of your most senior resources. And, again, there is no Gen-AI needed!

SUMMARY

Now, we realize some of these descriptions, like yesterday’s, are also quite brief, but again, that’s because this is not entirely new tech, as the beginnings have been around for years, have been in development for a few years and discussed as “the future of” Procurement tech before Gen-AI hit the scene, and all of these capabilities are pretty straight-forward to understand. Moreover, if you want to dive deeper, the baseline requirements for most of these capabilities were described in depth in the doctor’s May 2019 articles on Spend Matters. The primary purpose of this article, as with the last, is to explain how more sophisticated versions of traditional ML methodologies could be implemented in unison with human intelligence (HI!) to create smarter Supplier Management applications that buyers can rely on with confidence.