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.