SIM? Is It Old News or a Shiny New Pair of Shoes? Part I (Updated)

Supplier Information Management, also known as SIM (but which has almost nothing to do with your Subscriber Identity Module card in your cell phone, which is what you probably think of when you hear SIM), is not new. The early leader in this space, Aravo, which boasted the likes of GE and CISCO as clients, was formed in 2000 and followed not only by a slew of companies trying to be best of breed in SIM (including AECSoft, acquired by SciQuest which is now Jaggaer; Hiperos, now owed by Coupa; and Lavante; now owned by PRGX to name a few) but by a slew of suite vendors that began to implement enhanced SIM into their platforms (including Ariba, Iasta [now Determine], and Zycus).

And most of the basic features are now commodity. Try to find a vendor that sells SIM that doesn’t track all headquarter location, financial, core product, service, insurance, and third party risk information associated with a tier 1 supplier. Most of the good vendors also track third party credentials, compliance information against all relevant laws and directives, internal performance metrics and third party ratings, and even integration with third party supplier directories, databases, and or networks.

And the uses are well known.

  • Where are the bulk of my suppliers located?
  • What is the financial health (risk score) of my top 100 suppliers?
  • Are any of my products out of compliance with regulations in one or more countries?
  • Do all of my suppliers have their relevant insurance certificates up to date?
  • Who are my riskiest suppliers?
  • Have all of my suppliers verified their primary contacts in the last six months?

And the more mature companies, to try and maintain an edge, maintain their customer base, and expand into new companies and additional verticals have started to integrate additional, and related, functionality. Aravo evolved into a full Supplier Lifecycle Management solution that balanced compliance, performance, and risk management. Hiperos, before its acquisition by Opus Global and then Coupa, focussed on Third Party Management and on Compliance and Risk Management in particular. For example, their compliance management solutions included code of conduct, diversity management, insurance attestation, social accountability, and sustainability. Lavante focussed on on-boarding and integrating SIM with audit recovery services and advanced to the point where it was acquired by the leading audit recovery services provider, PRGX.

When all is said and done, SIM seems like a very mature space that is very old news. Typically when a technology gets to a point that all the suite vendors are just gobbling up what’s left, there’s nothing new. And betting on it definitely musters the image of an old gambler clutching dice in one hand and his last dollar in the other mumbling “baby needs a new pair of shoes“. But is it a bet you would lose?

AI: Applied Indirection in Contract (Lifecycle) Management

Continuing our expose of why you should think “Applied Indirection” and not “Any form of Intelligence” when you hear AI, because most solutions claiming to be AI are really just dumb systems with RPA (robotic process automation) and classic statistical models from the 90’s, we move onto Contract (Lifecycle) Management which, like analytics, is almost universally touted to have AI, even when there isn’t even a shred of anything that comes close.

This doesn’t meant that there aren’t vendors with true AI, especially when you classify it as Assisted Intelligence (and sometimes even Augmented Intelligence), in the space, just that, as the buzz-acronym reaches new heights, there will be many more vendors claiming AI than those that actually have AI and you will need to do your homework to find out which is which.

Example #1 of Applied Indirection in C(L)M: Auto-Renewal Detection & Management

Yes, evergreen contracts can be a big problem in Procurement when they have outsourced their usefulness, but detecting and managing these is not hard, and certainly doesn’t require any AI whatsoever. All you have to do is specify the contract as “evergreen” or “auto-renew” by checking a box and enter a notice-by date (to prevent an evergreen renewal” as well as the start date and end date and most contract management platforms can alert you in sufficient time to take action, escalate to your supervisor if you don’t, and kick-off a termination process at the push of a button.

For anything close to AI, you require a system that can detect when a contract is evergreen or auto-renewing when there isn’t a spelled out and easily identified auto-renewal clause that can be found with a simple reg-ex search. For example, when a crafty supplier buries an auto-renewal requirement in the liability section under the notices subsection titled “methods for delivering official notices” which starts off “Official notices shall be sent by X, Y, or Z, to A or B and only treated as an official notice upon proof of receipt. This includes a notice of non-renewal, as the contract will automatically renew 30 days prior to expiry otherwise.” Even a good lawyer might miss that in a fifty page contract when it’s snuck in on the third revision.

Example #2 of Applied Indirection in C(L)M: Off-Contract Purchasing

Maverick purchasing is a big problem. But it’s not one that you need AI to detect. If you encode all the products, services, and / or categories that should be bought on contract, it’s pretty easy to identify when a purchase for that product, service, or category is not bought from that supplier. And if the contract only applies to a region, it’s pretty easy to encode that too and it’s just a simple check.

And even if you have two or three suppliers in a multi-supplier contract for risk mitigation purposes, then it’s just a matter of making sure at least one of the supplier got the purchase, and if each supplier had a geographic area, that the right one for the area. Again, simple rule checks. No AI needed.

The key is to detect when something is off-contract when it is not specifically coded to a contract, either because it’s a new product, missing a category designation, required to hit a volume commitment, and so on. And while this can often be accomplished by identifying the closest product or service (using a document likeness statistic or weighted field match), sometimes advanced NLP may be employed for better results (and this would constitute weak AI).

Example #3 of Applied Indirection in C(L)M: Clause Suggestion

On the surface, this sounds pretty smart … point out clauses that should be in my contract to protect me. Under the hood, in most CLM systems that include authoring, it’s basically a set of templates that are used to specify what to look for in a contract type, with additions or subtractions for well defined industries that the provider serves. It’s basically a check list. And it’s about as dumb as it gets.

Can it be smarter? Of course, but the smarts are more around proper contract identification than clause selection. Because the clauses that should be included generally depend first and foremost on the type of contract, secondly on the product or service, and thirdly on the regulations that affect the products and services in the origin country, the destination countries, and any points in between. Then, identifying which regulations come into play and which types of clauses will be needed. This requires good NLP, probabilistic selection, and, preferably, adaptive learning that learns over time when Legal or Procurement chooses an alternate clause over a standard clause. A system should have assisted intelligence here to be useful, and augmented intelligence to be truly useful. But few do.

Note that SI is not saying that systems with the non-AI abilities discussed above are not valuable, as any system that automates tactical processes and minimizes non-strategic busy work is valuable. We are just saying you shouldn’t pay for what you’re not getting, or overpay for what you are. Buy what you need, and pay accordingly.

One Hundred and Twenty Five Years Ago Today …

The Americans got themselves an official, federal, US Holiday that goes by the name of Labor Day. While this may not mean much to SI’s readers across the pond (where it seems that they get a bank holiday every other week in the UK and over a month of vacation every year in much of the Western EU), this is pretty significant when you consider that paid holidays in the US are not required under any government regulations and
blue collar and service workers in the US average only 7 paid holidays (while federal employees get 10).

And while this might not sound bad, US law does not require employers to grant any vacation and about 25% of all employees in the US receive no paid vacation time (or even paid holidays). And even in companies where workers get vacation, vacation starts at one week for entry level / new blue collar / service employees and two weeks for white collar jobs (after a year of service). It usually takes five to ten years at the same job for an employee to accrue three weeks, and twenty years of service to get four weeks.

In short, even though Labor Day has been a federal holiday in the US for 125 years, the plight of labour in the United States still needs to be recognized!

AI: Applied Indirection in Sourcing

As we said yesterday, hopefully we have made it clear by now that most of the time you hear AI you should think “Applied Indirection” and not “Any form of Intelligence” because most solutions claiming to be AI are really just dumb systems with RPA (robotic process automation) and classic statistical models from the 90’s (which were available in SAS in the 90’s as well, you just didn’t have enough memory on your PC to run all the data you wanted to run).

But since we want to make it abundantly clear that most of the “AI”, even in our space, is not “AI” at all, we are going to continue to take the major areas of SPT (Strategic Procurement Technology) and highlight some areas where AI is commonly claimed, but rarely found, continuing with Sourcing.

This doesn’t meant that there aren’t vendors with true AI, especially when you classify it as Assisted Intelligence (and sometimes even Augmented Intelligence), in the space, just that, as the buzz-acronym reaches new heights, there will be many more vendors claiming AI than those that actually have AI and you will need to do your homework to find out which is which.

Example #1 of Applied Indirection in Sourcing: Automated Auctions

Some of the shiny new sourcing platforms are really slick and can run an automated auction and do everything from the time you do a product/service selection all the way to final award recommendation. Now, I’m sure you’re thinking such a platform must be at least on the order of augmented intelligence, approaching cognitive, to do all this, but the reality is that you can do all this with simple RPA and a rules-driven workflow. Supplier selection? Just select the past suppliers and pre-approved suppliers from the last sourcing event. RFIs? Use the template with the standard terms and conditions. Ceiling prices? Use current price or current market price if the price has been relatively flat for the past year or falling. Floor prices? Pull in the should-cost model, marked up by a fair margin, if it is available, or use the lowest of the lowest paid historical price and lowest market advertised minus the typical savings percentage in the category. Minimum Decrement? 1%, rounded to three significant digits. Pre-populated bids? Current prices or last bids or advertised market price. Duration? Standard auction duration for the category. And so on. It’s literally just a set of rules, with tolerances, and RPA.

There’s only AI if the platform can run a sophisticated market and category analysis on internal, external, and automatically identified market data, identify a prime set of products in a category for an automated auction, determine the appropriate contract length (and projected demand) to result, and basically take a bunch of the analysis off of your plate for non-strategic / low-value, regular, purchases.

Example #2 of Applied Indirection in Sourcing: RFX Auto-Fill

One of the most time-consuming parts of the Sourcing process is waiting for the suppliers to fill out the RFIs with not just the bids but all of the other information you require. However, there’s no reason that a lot of this information can’t come from their profile, their catalog, and previous RFI responses (where you asked the same questions).

It doesn’t take AI to encode meta-data mappings between profile fields and standard RFI fields, catalog fields and standard RFI fields, and re-use of the same question across RFIs (and previous responses) to pre-fill the majority of an RFI and simply require the supplier to confirm and enter new, additional, or changed data (and, of course, check the boxes that say they accept the T’s & C’s, verify the data, and confirm their bid).

Unless the platform can seek out additional data on the supplier’s web-site, third party directories, third party audit sites, and process semi-structured and un-structured data using natural language processing and identify and extract new data and new information relevant to the RFI (and the sourcing event), it likely doesn’t have anything close to AI (even in AI’s weakest form).

Example #3 of Applied Indirection in Sourcing: Outlier Identification

Here’s another capability that doesn’t require anything close to AI. Statistical algorithms to identify outliers have existed for decades and decades. Many even pre-date modern computers. Run a simple mean/modal comparison, do a simple clustering maybe even use a regression. Easy-Peasy. Even Kitty can do it!

The trick is to identify outliers that aren’t easily identifiable mathematical outliers. A bid in range that is not sustainable for a supplier because the bids were supposed to be landed cost bids (and included shipping as the buyer wasn’t taking possession until the goods hit the warehouse) and one supplier’s bids clearly didn’t, when you think about it, is the type of outlier we want the system to detect for us.

If four of the suppliers are near-shore, and the cost of shipping for them on a unit basis is typically 10% of unit cost, but a fifth supplier is offshore, and the cost of shipping for that supplier by unit is typically 30% of unit cost, even if the bid looks okay, it might not be. For example, if the bids for all the nearshore suppliers are between $100 and $120, their actual unit prices are about $90 to $110. If the off-shore supplier, with a shipping cost around $30, comes in at $100, a mathematical outlier algorithm won’t detect that it’s underlying unit bid is roughly $20 less than the lowest bid, and if the driving costs are expensive raw materials, and the should cost model for that supplier indicates a production cost of $80 (even though their production and overhead costs are significantly less), then a bid of $100 is unsustainable and an outlier (or will not be honoured as the supplier misunderstood and expected to bill shipping separately). If the platform truly has assisted intelligence, it should detect this, even when a human doesn’t. (While you always want the lowest cost, you want the lowest sustainable cost — it doesn’t do you any good if the supplier goes bankrupt halfway through the contract and you have to scramble to find another.)

Note that SI is not saying that systems with the non-AI abilities discussed above are not valuable, as any system that automates tactical processes and minimizes non-strategic busy work is valuable. We are just saying you shouldn’t pay for what you’re not getting, or overpay for what you are. Buy what you need, and pay accordingly.