Category Archives: Sourcing Innovation

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?

RFX Creation – Kicking You When You Are Down (Part III)

In our last two posts we’ve been arguing that the RFX process, at least traditionally, has been unnecessarily manually intensive and painful, almost taking the “strategic” out of “strategic sourcing” as so much manual time and effort is required to get it done that you can lose sight of the cost savings forest as you try to cut your way though the individual trees that continually block your way.

We indicated that much of the manual work that is typically required in RFI and RFP creation is relatively easily automated in an appropriate, modern, system — in addition to being much easier to accomplish in modern interfaces designed for efficiency and productivity — and that is why newcomers continue to rise, and profit, in an enterprise software space that should be mature and crowded enough to prevent this from happening.

We also indicated that a lot of time was required to vet potential suppliers for an RFP (even after an initial RFI round), that an organization might not be able to cull the list even if it wanted to, and that neither of these situations should be the case. Why?

First of all, it should be possible to not only auto-score the models against appropriate thresholds of suitability, defined by industry best practices and fine-tuned over time using machine learning techniques that learn the appropriate characteristics and scoring along multiple axes based upon suppliers you select and suppliers you don’t, but rank the suppliers in suitability based on the RFI alone.

Secondly, a modern platform should be able to absorb industry intelligence to predict quality, cost, and delivery and determine how likely a new supplier will fare against incumbents and market average. And then refine the rankings based on this data.

With this data, you could then predict if it’s (very) likely or (very) unlikely that a supplier would receive an award (now or in the future) and allow you to determine if you want to invite the supplier now or not.

How? RPA, ML, AR, and “AI” integration of these technologies.

How specifically? That’s a discussion for a later article, but hopefully, by now you get our point — most RFX technology is kicking you when you’re already down.

RFX Creation – Kicking You When You Are Down (Part II)

Yesterday we explained how, just from an RFI perspective, many S2P “e-Negotiation” or “e-Sourcing” platforms kicked you when you were down and reeling from an unnecessarily intensive, and painful, supplier discovery process — a process that should be mostly automated (as per our lead up articles). But, as we all know, the RFI is just the first stage of the process.

Once a supplier passes the RFI, you need to

  1. actually create the RFP
  2. determine if you are going to invite a supplier to the RFP (and monitor the process once you do)

Generally speaking, creating an RFP is no walk in the park either as the platform is even less likely to contain a relevant RFP template, especially if you are sourcing direct materials or custom manufactured products and need details on processes, raw materials, warranty, maintenance, and delivery methods as well as detailed cost breakdown models. If the RFI process was manual and painful, the RFP will be ten times as manual and painful.

You will have to:

  • identify the relevant bill of materials for each product (and possibly build them from scratch)
  • identify the non-cost information required at each level (raw material, source, quality specs, etc.)
  • identify the cost models required at each level (and possibly build them from scratch)
  • identify the roll-up models for costs and quality scores
  • identify the evaluation models that you will use
  • put all this together into a cohesive and comprehensive RFI

When all you should have to do is:

  • identify the products you are sourcing

Since a modern system, especially one built for easy direct material sourcing, should automatically, for each product:

  • pull in the relevant bill of materials
  • identify the relevant non-cost information based on the compliance requirements noted in the RFI and organizational policy
  • identify the relevant cost models based on the bill of materials (and preferred production processes)
  • build the roll-up models based on embedded intelligence in the platform and defined relationships between the different levels of the BoM
  • apply a standard evaluation model for the category to the RFI
  • … and integrate all of this into a comprehensive RFP for your manual review

Once you have this RFP, you need to determine if you still want to invite the supplier, especially if you have more potential suppliers than you really need.

And, right now, platforms don’t help you here at all.

You see, you only want to invite the supplier if there is a chance you will actually make an award to the supplier in this, or a future, event. If the quality is too low, the prices are too high, the necessary services do not exist, or the necessary culture is not present, the RFP process will be a waste of time on both sides.

Now, you might say that there’s no way to know this before going through the RFP, but is that really the case?


But we’ll take this up in the next part of this series.

RFX Creation – Kicking You When You Are Down (Part I)

If you are an outside observer of the S2P space, like an enterprise software analyst that covers the more traditional enterprise spaces like (ERP, AP, CRM, etc. ), you might wonder how in this day and age a startup that just offers e-RFX, e-Auction, and basic SIM — technology that has been around for 20 years — could not only survive, but in the case of some new entrants like ScoutRFP and Bonfire (which only have a fraction of the breadth and depth of the market leaders, see their analyst rankings in Spend Matters SolutionMaps) thrive!

Well, it all comes down to usability, efficiency, and effectiveness. Most of the first, and even second, generation platforms only focussed on the third measure of effectiveness, and only measured it from a financial ROI perspective on completed events (not on adoption, categories under management, suppliers under management, etc.). Efficiency only mattered from the viewpoint of the implementation or services team (and only to the degree necessary, if billable hours was a major revenue center, and the teams were keeping up, then efficiency was good enough). And usability, well, the software was digital and that was better than paper — so whatever the platform provided was deemed good enough.

But it wasn’t. And we don’t need to offer any proof. ScoutRFP and Bonfire wouldn’t exist if it was, and niche plays like EC Sourcing would not have not have quietly grown from niche players to full S2C offerings with a constantly expanding customer base that is as large as some of the more prominent S2P players (which, despite the abundance of marketing they throw in your face, only have a few hundred customers).

So why does RFX creation in most platforms kick you when you are already down (in the mud trying to scavenge for potential suppliers, as per our last two pieces on supplier discovery)?

First of all, when it comes to basic supplier qualification RFIs:

  • most platforms have limited templates when it comes to the data you need to collect for regulatory compliance
  • you have to manually identify which templates you will need to collect necessary organizational data, regulatory data, location and production data on the supplier
  • search is limited and determining which templates, generally incomplete, you can start from is difficult
  • new template construction (to build what is not present) or existing template modification is usually painful as it is not responsive drag and drop as it was developed using old-school frameworks on older versions of HTML and not kept up to date
  • you have to manually define gating and scoring scales on each template individually
  • there is limited workflow and you often don’t have the ability to define logical, conditional, workflows which will block a supplier as soon as a mandatory requirement is not met or include a template that is only required if a certain process or restricted material is used — which means you often have to go through multiple rounds (as you can’t ask a supplier to fill out anything not necessary or they won’t even answer the first email)
  • there is limited or no auto-scoring and many fields have to be scored manually

In comparison, a more modern platform will:

  • either provide templates, a repository, or integrations to partners that have the templates you need (or make it easy to auto-build them from document or spreadsheet imports)
  • will index core data requirements, compliance requirements, and industry requirements against products and services you source and automatically identify which data and templates will be required
  • automatically search your library to suggest starting template (sections)
  • help you build templates for newly identified requirements
  • allow you to build, modify, and conditionally link templates in a workflow using drag-and-drop and responsive design
  • automatically define critical gating questions based on organizational policy and mandatory compliance requirements and make it easy for you to define additional gating questions
  • allow for the definition of auto-scoring across all fields and RFI sections
  • auto-score each RFI response for you

Complex RFIs that used to literally take days (upon days) to build in the first (and second) generation platforms can now be built in a matter of hours. (We’ve heard multiple, verifiable, stories of some companies that used to spend two days building an RFX on Industry Leading Platform X switching to someone like EC Sourcing and building the same RFX in 15 minutes. That’s why one of the industry leaders released a brand new, slimmed down, redesigned platform targeted at the mid-market last year. You might want the power of tank, but if it takes way too long to get from 0 to 60, you’ll never use it when everyone else has fighter jets that get to the destination first.)

But if only this was the whole story!