Author Archives: thedoctor

Don’t Throw Away That Old Spend Cube, Spendata Will Recover It For You!

And if you act fast, to prove they can do it, they’ll recover it for free. All you have to do is provide them 12 months of data from your old cube. More on this at the end of the post, but first …

As per our article yesterday, many organizations, often through no fault of their own, end up with a spend cube (filled with their IP) that they spent a lot of money to acquire, but which they can’t maintain — either because it was built by experts using a third party system, built by experts who did manual re-mappings with no explanations (or repeatable rules), built by a vendor that used AI “pattern matching”, or built by a vendor that ceased supporting the cube (and simply provided it to the company without any of the rules that were used to accomplish the categorization).

Such a cube is unusable, and unless maintainable rules can be recovered, it’s money down the drain. But, as per yesterday’s post, it doesn’t have to be.

  1. It’s possible to build the vast majority of spend cubes on the largest data sets in a matter of days using the classic secret sauce described in our last post.
  2. All mappings leave evidence, and that evidence can be used to reconstruct a new and maintainable rules set.

Spendata has figured out that it’s possible to reverse engineer old spend cubes by deriving new rules by inference, based on the existing mappings. This is possible because the majority of such (lost) cubes are indirect spending cubes (where most organizations find the most bang for their buck). These can often be mapped to 95% or better accuracy using just Vendor and General Ledger code, with outliers mapped (if necessary) by Item Description.

And it doesn’t matter how your original cube was mapped — keyword matching algorithms, the deep neural net de jour, or by Elves from Rivendell — because supplier, GL-code, and supplier and GL-code patterns can be deduced from the original mappings, and then poked at with intelligent (AI) algorithms to find and address the exceptions.

In fact, Spendata is so confident of its reverse-engineering that — for at least the first 10 volunteers who contact them (at the number here) — they’ll take your old spend cube and use Spendata (at no charge) to reverse-engineer its rules, returning a cube to you so you can see the results (as well as the reverse-engineering algorithms that were applied) and the sequenced plain-English rules that can be used (and modified) to maintain it going forward.

Note that there’s a big advantage to rules-based mapping that is not found in black-box AI solutions — you can easily see any new items at refresh time that are unmapped, and define rules to handle them. This has two advantages.

  1. You can see if you are spending where you are supposed to be spending against your contracts and policies.
  2. You can see how fast new suppliers, products, and human errors are entering your system. [And you can speak with the offending personnel in the latter case to prevent these errors in the future.]

And mapping this new data is not a significant effort. If you think about it, how many new suppliers with meaningful spending does your company add in one month? Is it five? Ten? Twenty? It’s not many, and you should know who they are. The same goes for products. Chances are you’ll be able to keep up with the necessary rule additions and changes in an hour a month. That’s not much effort for having a spend cube you can fully understand and manage and that helps you identify what’s new or changed month over month.

If you’re interested in doing this, the doctor is interested in the results, so let SI know what happens and we’ll publish a follow-up article.

And if you take Spendata up on the offer:

  1. take a view of the old cube with 13 consecutive months of data
  2. give Spendata the first 12 consecutive months, and get the new cube back
  3. then add the 13th month of data to the new cube to see what the reverse-engineered rules miss.

You will likely find that the new rules catch almost all of the month 13 spending, showing that the maintenance effort is minimal, and that you can update the spend cube yourself without dependence on a third party.

Is That Old Spend Cube Money Down the Drain?

How many times has this happened? You hire some experts to help with a sourcing effort, they produce a one-off spend analysis, you run some initiatives and realize some savings, and … a year later, you’ve got an obsolete spend cube with IP you’ve paid a lot of money for, but can neither use nor extend, because either the experts didn’t share the process they used to create the cube or, even worse, they used “AI” with “intelligent transaction pattern matching” and there simply aren’t any rules to share.

Or, as often happens (due to the competitive landscape), maybe your original vendor has lost interest in spend analysis, or has left the business, or was acquired and sidelined — and your spend analysis system is either end-of-life, largely unsupported, or obsolete. What then?

Well, you have two options:

  1. Write it off, throw it away, and start all over again
  2. Recover the cube

And yes, you read that right, recover the cube!

You’re probably saying, how can that be done, especially if the original cube was mapped with AI or one-time overlay rules that were created by an expert and lost in the sands of time?

With intelligence, observation, and an application of proper, inverse, AI that sifts through the evidence left behind and generates real rules to start you off — rules that can then be extended in a system that supports layering in a logical fashion to not only allow for a re-creation of the original cube, but an improvement that fixes original errors and takes into account changes in the business since the cube was created.

And yes, this is possible, because mappings leave evidence, the same way a suspect at a scene leaves evidence, and that evidence can be unearthed by applying the digital equivalent of classic archaeological techniques that have been used for over a century to interpret the past. (the doctor has given presentations on this and if you are intrigued, contact him)

And it’s even easier in the case of spend analysis when you remember that you can completely map even a Fortune 100’s spend by hand in less than a week to high accuracy by using the classic secret sauce of:

  1. map the GL codes
  2. map the suppliers
  3. map the suppliers and GL codes
  4. map the exceptions
  5. map the (significant) exceptions to the exceptions

… and then run the rules in the same order.

This works because the vast majority of spend cubes are on indirect spend, and indirect spend cubes can almost always be mapped effectively this way. Even if there is no specific GL code in the data set, there should be similar patterns around the key fields that determine GL code (product description, SKU, etc.) And what doesn’t match defines the exceptions.

In other words, it’s theoretically possible to do a reverse engineering when you understand the foundations of most spend cubes and learn how to interpret the mapping evidence left behind.

But, is anyone doing this?

Be Wary of FREE Supplier Discovery

As per our recent pieces on how supplier discovery shouldn’t be a kick in the pants, at least today, it shouldn’t be free either — because a good supplier discovery solution costs a lot of money to maintain.

A number of vendors are now offering, or considering an offering of, free supplier discovery bundled with their Sourcing or Procurement Solution because, just like it shouldn’t cost suppliers to do business on a network, it shouldn’t cost you anything to do searches (when search engines are free), in their view.

And while it sounds great in theory, at least today, it’s not practical in practice. Computing power, storage, internet access, and electricity costs money … as does a lot off the software used to enable this FREE supplier discovery (as there is no free software, someone still has to compile it, integrate it, maintain it, etc. And this resource time is costly as well). Google only enables free search because it makes money on ads and services that it sells, which subsidizes the internet search.

This means that the only way a provider could really offer free discovery is if it was subsidizing that search with other software offerings (which means you’re still paying for it as it could charge less for those offerings if it was not subsidizing supplier discovery). And if it this is its main offering, you need to ask how it’s making money as it costs a lot of money to maintain a good supplier discovery solution, and if the provider tells you it is cheap (and some providers are making this argument), then the solution is not good.

I’ve heard some providers argue that since there is so much supplier information out there freely available on public directory sites (paid directories that are open, supplier associations, government registries, investment sites, etc.) that it would be cheap to scrape and combine all off this information if you have a good AI engine and all you really need is just a lot of storage and fast internet access, which can be relatively low cost. And while this sounds good in theory, it’s not good in practice.

First of all, the majority of all supplier listings are micro-businesses, and most of these aren’t big enough to serve a corporation in any capacity. Many have never done any substantial business and there’s not enough information to assess risk or capability. Many listings are outdated and incorrect and many more are for out of business suppliers. Many listings don’t have enough information to determine products or services to any level of accuracy. In other words, the majority of free information is bit-garbage.

In order to have a good supplier directory, you have to have information that has been manually validated to a reasonable extent. Which means that either the vendor needs to spend a lot of expensive manpower validating or start with third party databases that have been manually validated, which cost money to access. Either way, good information costs money, which means that a supplier discovery vendor can’t create or maintain anything good for free.

Which also means that if the information is good, it’s likely also limited to a directory supplier discovery vendor has built up over time from its customer base, which will only be good for you if there are like organizations doing business in like geographies already in that customer base.

So, just like there’s no such thing as a free lunch, there’s no such thing as a good, free supplier discovery service. At least not today or tomorrow.

One Hundred and Forty Nine Years Ago Today …

An American Legend was born when Jesse James commits his first *confirmed* bank robbery.

What does this have to do with Procurement? Besides the fact that, when you think about it, many suppliers will rob you blind on a daily basis if you are unprepared during the negotiation, during the invoice review, or during the warranty process.

Well, if you think about it, sometimes if you want to get famous, you have to take big risks.

But, more importantly, if you take risks, you can get famous … but in the case of Procurement, you don’t have to rob a bank to make money. You just have to get smart about how you buy. There are savings to be had in every category, and all you have to do is find them to bring millions to the bottom line.  And take the risk of doing something new.

And all you need to do to figure out how is to read the archives, strategy, process, and the tools you need to make it all happen.

Are You Sick of the “Digital Transformation”?

the doctor is certainly sick of the terminology. Not a day goes by that some backwoods yahoo doesn’t think this makes the perfect headline, twenty years after we were introduced to specialized Procurement tools, almost thirty years after the introduction of the ERP, and more than forty years since specialized MRP systems were introduced to the market. The “digital transformation” is now new and hasn’t been since the internet evolved to the world wide web and every software company started transitioning to the cloud (which, by the way, is just someone else’s computer!).

the doctor is also sick of all the article stating that the digital transformation will not displace (real) Procurement professionals because that’s obvious. Besides the fact that we are nowhere close to real AI systems, most of Procurement today is not number crunching. It’s fire-fights. Stakeholder-pleasing. Countering disruption blights. Supplier appeasing. It’s a lot of relationship management, which is something a piece of software just can’t do. (There are a few good SRM platforms that enable SRM, but they do not accomplish SRM — that is accomplished by the expert relationship managers that astutely use the system.)

the doctor is also sick of the futurists who are stuck in the past and still predicting a great digital renaissance to come. Our collective IQ has dropped since the renaissance started; Twitter is making us dumber than goldfish (and you wonder why the doctor despises Twitter); the more we trust the machine, the more blind we become to the risks involved; it’s creating an unparalleled digital divide worse than anything William Gibson and his Neuromancer mind can come up with; and Ready, Player One might be the best possible future if we continue down the current road (assuming a certain dictator-want-to-be doesn’t start World War III first).

For better or for worse (and its for worse if we don’t stabilize our power grids and shield the hard drives that contain all of the data that drives our economy, as a natural EMP could wipe out economies in a second), we’re going to keep moving down the digital highway at ever increasing speeds, which means pending something drastic, the next twenty years are going to the be the same as the last twenty and all this hullaballoo about digital transformation, at this point, is just unnecessary noise.