Monthly Archives: December 2018

One Hundred Years Ago Today …

The UK began its effort to leave the dark ages with the first general election where women were permitted to vote and the first woman was elected to the Commons.

If only it would finish its exit of the dark ages in Procurement, which, sadly, in many organizations is still controlled by white males in their fifties.

While the doctor does not want to be stereotypical, he does want to be realistic — Procurement is simply better when there are multiple perspectives (and skills) at the table. And without the second gender, you’re clearly leaving half the perspective and skill off of the table (and that is simple, irrefutable, math).

M&A Mania – Will it Ever End?

As per our posts on Sourcing Innovation earlier this year, the M&A Mania has been in full swing for the past couple of years, and as per the acquisition news that came out Monday, it seems the mania hasn’t abated. But will it abate in 2019?

We hope so.

Sometimes M&A makes sense, but sometimes it’s too much too fast. The theory behind M&A is that it’s easier for the customer to have all the related solutions under one vendor’s roof than three, four or six when they need to build an end-to-end S2P support solution than to have to deal with six vendors when they have integration issues, support issues, or system errors.

It’s a great theory, but it doesn’t work any better in practice if all a vendor is doing is buying up smaller vendors to sell them under one roof. If all of the development teams are separate, all of the product management teams are separate, and all of the support teams are separate, you’re still trying to sync with six different groups in order to resolve integration issues, support issues, or system errors. What difference is it if they are under one roof, three roofs, or six? From your perspective, none at all!

The reality is that it doesn’t help you as a Procurement Practitioner at all if the solutions aren’t integrated, and we don’t just mean data-based end-point integration — where it’s easy to push data out of one tool and pull it into the next. It has to be a deeper integration that integrates process and workflow. And that type of integration doesn’t happen fast. It takes many months in the best of cases, and many years in the worst.

So when a vendor goes on a buying spree, without forethought as to how it’s going to integrate all those solutions into a cohesive platform in a reasonable amount of time, it’s just bringing the integration and support nightmare for its clients under one roof, and not adding any value.

The best M&A is when a company buys a company with a great complementary solution and then steps back, takes the time to get the teams fully integrated and the solution integrated at least at the process level with its solution (not necessarily deep workflow configuration but more than just end-point data integration), and only then thinks about the next acquisition.

Right now the big players have made so many acquisitions that the doctor thinks they are all at full capacity to manage integrations, and in a couple of cases, maybe beyond. So he certainly hopes that the M&A Mania winds down, at least until there is settling across the space.

Plus, any company that acquires too many solutions too rapidly puts itself at risk of acquisition by someone bigger still. Just look at what happened to CA Technologies — the Acquirer became the acquired … by a hardware company! The last thing we want is a big S2P play to be acquired by a big hardware or generic platform vendor that doesn’t understand the space.

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.