Category Archives: Supplier Information Management

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.

oboloo: Bringing e-Sourcing to the SME masses

e-Sourcing is a critical part of proper strategic procurement, but one that not a lot of SMEs and lower-end mid-markets have access to due to the cost of most strategic sourcing suites designed for the upper mid-market and enterprise that are beyond their budget, typically leaving them with only ultra-basic RFX solutions which are not enough.

In contrast, oboloo offers a Source-to-Contract platform with basic supplier management, contract document management, and savings management capability which can be obtained for $1,000 / user / year, allowing a SME Procurement department of 5 users to obtain decent sourcing software for 5K a year and put it on a P-card.

e-Sourcing

Their new, V2, e-Sourcing module is the core of the recently upgraded platform and allows an organization to build and issue RFIs, RFPs, and RFQs custom tailored to their needs for every event.

The entry dashboard to the Sourcing module allows a user to search the sourcing event database by opening and / or closing date, location, department, category, sub-category, event type, and event creator. From this dashboard, the user can access an existing event they have access to or create a new event.

If they choose to create a new event, they start with the sourcing wizard that allows them to configure the RFX event as a collection of (pre-defined) (standard) sections for internal use, standard information gathering (supplier questionnaires), event specific supplier sections, and a pricing section. (There’s only one standard product/item pricing template at the moment, but they are looking at including more for services [based on rate hours] and/or [manufacturing] cost breakdowns in future releases. If the user desired a more detailed price breakdown, they can attach an Excel spreadsheet.)

The platform walks the buyer through the process of

  • defining the activity that captures all the sourcing meta-data
  • selecting the sections for internal use
  • selecting the sections for supplier response
  • selecting the standard questionnaires (sustainability, security, etc.)
  • defining the pricing request
  • attaching any supporting documents
  • defining the scoring criteria
  • inviting the suppliers

Internal sections might consist of information on evaluation criteria and current pricing and cost structures.

Supplier sections consist of relevant criteria on required confidentiality, contact information, implementation plans, and future roadmap. Once a section is selected, it can be edited as needed.

Questionnaires are for the gathering of standard security and privacy information, sustainability information, service and support information, quality assurance, and other standard information required of any supplier for the product, category, or doing business requirements.

The pricing section is where the products are defined, by name, code, unit of measure, and quantity. The buyer can add as many products as she wants.

Once the products are defined, the buyer moves on to the scoring section where she defines the weighted scoring across each section included in the RFX.

Finally, the user selects the suppliers she wants to send the RFX to as well as the contacts at each supplier who will receive the RFX. If she chooses, she can switch to a supplier view before issuing the RFX. When she’s done, she presses send, and the RFP is complete.

The whole process can be completed in 10 minutes if the products are defined in the system and the buyer is okay with standard templates.

With regards to the construction process, the platform comes with a suite of standard sections and questionnaires that the buying organization can start with, and then the buying organization administrator can alter these as desired upon implementation.

Once the RFX is complete, and issued, the buyer can easily access the current status at any time. They can see which suppliers have responded, what they have responded to, and where the RFX is in the process. Once the RFX is closed, the buyer can start scoring and once scoring is complete, make an award.

Scoring is done on a section by section basis, with the information for each supplier displayed in consecutive rows for each supplier. The platform supports multiple scorers, and the weighted average will be used across scorers if multiple scorers are defined. Once scoring is done, the buyer sees the average score by section by supplier as well as the average score by supplier and can then mark a supplier for the award.

Contract Management

oboloo defines their contract management as a customized document management system, and that’s essentially what it is. It’s simply a repository for tracking organizational contracts, indexing them with metadata, defining relevant dates and alerts, and providing some basic reporting. But for most small organizations, that’s all they really need. They don’t use complicated contracts, they don’t want a separate document management system they won’t use, and they certainly don’t need the ability to define extensive clause libraries with multiple versions of each clause.

With respect to reporting, the system tracks expiring contracts by month, and can break them down by department, location, category, manager, type, etc. The user can also search across all of these criteria to quickly identify contracts of interest. It also tracks the number of documents (not) approved, the number of documents that have expired, and the number of contract records associated with suppliers that have been marked approved. And, of course, it can be setup with automated alerts/notifications to let the buyer know when contracts are coming up for expiry, when they are expired, etc. (And, of course these alerts/notifications exist throughout supplier management, RFX, and savings tracking when tasks are due.)

Each contract is a record consisting of key metadata classifiers, owners, financials, termination information, associated information, savings tracking, and a change log.

Supplier Management

The platform is defined as a basic supplier information and performance management platform that can maintain records for all suppliers used, or invited, by the buying organization and these can be searched by key identifiers that include industry, sub-industry, supplier type, preferred status, location, active (status), and contract as well as supplier name.

Supplier records are rather basic and consist of basic identifying information, owners, contacts, contracts, and scorecards. Performance management is scorecard centric in the application, and scorecards are also used to manage risk and track sustainability in the platform, as the buying organization can start with oboloo templates and set up their own to track the information they are interested in from a supplier performance management perspective.

Like contract management it is also fairly basic, but that’s what most SMEs and small midsized organizations need. Most of them don’t need extensive records on suppliers they are mainly buying indirect and MRO products from, and performance management is just a matter of ensuring quality, timely delivery, sustainability, low risk, and adherence to contract(s). This makes it easy for the buying organization to define and manage their suppliers.

Savings Tracking

Savings tracking is a simple module where, on a product, or contract, basis, a buyer can setup a savings tracking project on a fixed or variable time period for a set number of milestone dates. The buyer defines the product(s), current baseline spend (adjusted for the quantity, the projections, and then, at every milestone, defines the actual spend and the platform automatically computes the savings (or the lack thereof) and, once the last milestone is entered, computes the savings for the project.

As with the contract module, the system can then create meta-reports across all savings projects by month, year, and all time, as well as the remaining projected savings (and percentage). This can be broken down by category, sub-category, location, department, saving project type, and the responsible buyer.

Configuration

The platform supports roles-based security, comes with four pre-defined roles, and more can be configured to define created, read, edit, approval, archive, and similar rights across each module. Each user has a role as well as a unique profile.

It also supports the ability to define the industry and sub-industry hierarchy, the category and product hierarchy, corporate locations, departments, and each element type used by the system as identifying metadata (such as sourcing, contract, supplier, saving, document, etc. type). In addition, all of this information can be uploaded from excel (csv). The backend is built on APIs and the next version will have well-defined open APIs for data import as well as third party software integrations by 2025 Q1. Finally, users can select their currency (and define their preferred exchange rate) as well as their language, with approximately 15 languages currently supported.

Finally, the user can see their current licence and the corporate administrator can see all the currently active user licenses on the platform.

At the end of the day, oboloo contains the basic functionality you found in best-in-class first generation sourcing platforms almost two decades ago, with a few key differentiators. It’s a fully modern cloud-native SaaS stack, which can be fully self-implemented and self-configured, with a streamlined UX for SMEs, fully customizable template sections to allow for supplier records, contracts, RFXs, and savings projects to be created in minutes (vs hours or days). Most importantly, all of this comes at a cost that is a fraction of what these early SaaS platforms used to cost (and of what most [mini-]suites targeted at large mid-market and above, with a lot more bells and whistles than SMEs need, cost today), allowing a small sourcing team to get started for under 10K instead of having to spend 100K or more.

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.

Advanced Supplier Management YESTERDAY — 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 application, 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 (as we don’t really have true appercipient [cognitive] intelligence or autonomous intelligence, and we’d need at least autonomous intelligence to really call a system artificially intelligent — the doctor described the levels in a 2020 Spend Matters article on how Artificial intelligence levels show AI is not created equal. Do you know what the vendor is selling?) that have been available for years (if you looked for, and found, the right best-of-breed systems [many of which are the hidden gems in the Mega Map]). And we’re going to continue with Supplier Management. (Find our series on Advanced Procurement — No Gen-AI Needed! Yesterday, Today, and Tomorrow; our series on Advanced Sourcing — No Gen-AI Needed! Yesterday, Today, and Tomorrow; and our series on Advanced Supplier Discovery — No Gen-AI Needed! Yesterday, Today, and Tomorrow through the embedded links.)

Unlike prior series, we’re going to mention some of the traditional, 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, might not match one-to-one with what the doctor chronicled five years ago because, like time, tech marches on.)

Today we move on to AI-Enhanced Supplier Management that was available yesterday (and, in fact, for at least the past 5 years if you go back and read the doctor’s original series, which will provide a lot more detail on each capability we’re discussing). (This article sort of corresponds with AI in Supplier Management Today Part I and Part II that were published in April, 2019.)

YESTERDAY

Auto-Fill Onboarding

While early 1st and 2st generation supplier management platforms required a supplier to create a full profile from scratch and enter all of their information, third generation platforms, which define expected formats for each field and have contextual awareness, can pull in the data from third party profiles, market databases, supplier forms, and even csv or xml exports of a supplier’s profile from another site.

Using classical semantic parsing, pattern matching, flexible reg-ex rules based data format validations, and any available meta data, even yesterday’s platforms could auto-fill the majority of a supplier profile form if the data was available in textual format for parsing.

Basic Community Intelligence

As per our coverage of supplier discovery, the reality is that this “AI” like functionality doesn’t require any “AI” at all. Community Intelligence just requires the amalgamation of data across customers, which is easy to do with multi-tenant SaaS as long as the customer agrees to sharing their reviews and insights (which could be part of the contract), and the supplier is made aware (which is part of the waiver to participate in customer events) of what is being shared.

It’s just math for averages, time series for trend series on those averages over time (of quality ratings, performance ratings, OTD ratings, etc.), and consolidation of tagged reviews. The only AI that would be needed is semantic processing if the platform provided a sentiment analysis across the community.

Real Time Performance Monitoring

As written five years ago, the last thing you want is to find out without warning that your primary supplier for a critical component in your new engine, control system, or IoT platform is bankrupt and no more shipments are coming; that a recent shipment has a 10% defect rate that is 10 times the acceptable, contracted, level; or that the custom factory redesign you just contracted for is going to take an extra six months when it should be 80% done.

Also, as written five years ago, none of this needs to be the case. There’s no reason a good platform could not alert you to leading indicators correlated with bankruptcy. Or a pattern of (slightly) late deliveries that is getting worse over time. That defect rates, even if within tolerance levels, have been increasing rapidly in recent shipments. Or that the last three key project milestones haven’t been met and the project is tracking to at least three months late.

With regards to early detection of bankruptcy, pull in financial risk scores monthly from your financial risk provider, look for downward trends (simple math), and monitor for alerts. Use the community intelligence identified above to identify late deliveries. Alternatively, if that’s not available, and it’s a big supplier with multiple customers in your country, monitor the public port data for its shipments … if they used to be every two months, but are now every three or four months, with an average volume per shipment that’s going down, that’s an indicator of trouble. With regards to your needs, track all of the rejected shipments at the warehouse, the returns, and keep a running tab on defect rate over time, again looking for trends in the wrong direction in terms of defects per shipment or returns per month.

There is so much you can do with just math. So do it!

Automated Issue Identification

As per our article five years ago, if the supplier management platform is integrated with organizational Sourcing, Procurement, and/or ERP systems, then the platform can automatically import objective supplier metric data as well as subjective supplier performance data from individuals across the organization that interact with the supplier.

Building on real time performance monitoring, the platform can monitor a whole host of metrics, trend them over time, identify drops that can signify issues, and alert the buyer if a dangerous drop is detected. Again, it’s just math.

Automated Risk Identification

The automated issue identification capabilities of a properly implemented and integrated supplier management platform are great, but as we have hinted above, the best platforms can also detect potential risks using leading indicators spit out by cross-organization metrics, trends, reports, and sentiment.

Remember, in addition to metric data, it can also take advantage of the community intelligence to identify early risk indicators. It can track the overall trend of promotion (against pre-existing tags) of a supplier for specific capabilities and the overall tone and sentiment of comments, and then compare that to the overall trend of anonymized price and performance data, and so on to detect when the performance or rating of a supplier is improving or declining, and, possibly, even how fast a rating might be declining which could indicate not just potential problems but risk.

Now integrate this to third party intelligence platforms with financial, CSR, operational, etc. risk and you start getting 360-degree risk profiles — and super early warning indicators since you never know where they are going to come from (the risk assessors, the community intelligence, or your own metrics). It’s all metrics, trends, and thresholds. Math. Good ol’ math.

Automated Resource Assignment

The best platforms support corrective action management, new product development, and supplier development initiatives. Each of these typically require project plans that require resources to support them, Always human resources and sometimes even physical organizational assets or IP assets (including software licenses).

If the platform is connected into a project management platform which has all of the information on organizational resources, and the organization’s asset management software, since the platform will know what skills are needed for the project, as well as what assets the supplier needs, it’s just a matter of best-match mapping. A great supplier management platform could do that through simple match computations and allocation tracking. When there are conflicts, it’s just a simple optimization problem for the best match.

SUMMARY

Now, we realize this was very brief, but again, that’s because this is not new tech, that was available long before Gen-AI, which should be native in the majority (if not the entirety) to any true best-of-breed Supplier Management platform, that is easy to understand — and that was described in detail in the doctor’s 2019 articles for those who wish to dive deeper. The whole point was to explain how traditional ML methods enable all of this, with ease, it just takes human intelligence (HI!) to define and code it.