Category Archives: Sourcing Innovation

The Risk Disconnect is Still Big But …

As pointed out in a post a year ago on how there are at least 12 risk disconnects … but one you should never overlook! we talked about how the disconnect between risk and cost is one of the most critical in our view because:


  1. not only can one identifiable supply chain disruption wipe out all of the savings of a single sourcing event, but also increase costs well beyond that point

  2. only an understanding of the true cost of risk will convince most stakeholders and executives to look beyond cost, reliability, marketing differentiation, or whatever else matters most to them — money talks and (imminent) (potential) loss is the one thing that gets noticed

But that’s pretty hard as most sourcing and procurement solutions not only have no concept of risk, but neither do most platforms. And many of those that do are pretty basic — you can import third party risk scores, define risks to track, and query them occasionally. And that’s about it — and that is clearly not enough given that an organization’s chance of experiencing a significant disruption is now about 90%.

But that might change soon. Not that long ago (in late 2017 to be precise), Spend Matters released the Solution Maps for Strategic Procurement Technologies (Sourcing, Contract Management, Analytics, and Supplier Management) were released — with the Sourcing, Analytics, and Supplier Management maps designed (in entirety) by the doctor and the Contract Management map co-designed by the doctor and the maverick.

Each of these maps had a few elements of risk, but not many. And they were application-based, not platform based. But with the newly revised Solution Maps coming out in June, Risk Management will now be a key component of the common sourcing – supplier management component of the strategic procurement technology maps that measures the assessment, mitigation planning, [risk] model definition, monitoring & risk identification, regulatory compliance monitoring, and supplier risk management capabilities of the platform. Going forward, both Spend Matters and Sourcing Innovation will be putting a greater focus on risk management capabilities to help your organization cope with the turbulent times ahead.

2020 is Less Than a Year Away. And we still haven’t crossed the supply chain plateau. Part II

In yesterday’s post, we referenced a post from six years ago where we commented on a piece by the Supply Chain Shaman who believed we had reached the supply chain plateau. And while we do not agree that the plateau has been reached, despite the extensive objective analysis of balance sheets, we certainly agreed that progress was, and still is, stalled.

We also referenced our post from a year ago today, where we asked will this be the year we traverse the supply chain plateau, that we believed the root of the issue was manpower capability. And we conjectured the root of the issue was a lack of education. But good information, good training, good consulting, good peer groups, and good courses — while still few and far between — have been available for years now but there has not been much improvement in the overall education level and manpower capability.

And while it’s true that most Supply Chain / Supply Management / Sourcing / Procurement / etc. managers don’t leave college or university with a solid supply chain background, as few institutions offer such programs, with the right foundational program in STEM (Science, Technology, Engineering, and Mathematics), the fundamentals of supply chain can be rather easily taught to intelligent and capable STEM grads.

So why aren’t they properly trained — especially when there are professionals out there more than capable of training them? And while supply is scarce, and they command top consulting dollar, when you think about the ROI a top performing team can deliver in just a few weeks (which can be in the millions), even a top dollar trainer can deliver the organization a ROI 10 to 50 times her price.

Well, because at the end of the day, management is not as well-intentioned as the Shaman or the doctor gave them credit for. Or, more accurately, their good intentions are more focussed on what’s good for them or their management peers today, not what’s best for the organization (and, at the end of the day, the shareholders) over the long-haul.

That’s why, year after year, when dollars get tight, the training budget is the first to get cut. Management believes that when times are tight, spending should be cut, and rushes to be the first to cut their budget to look good in the eyes of the CFO and CEO. Instead of investing today to take more off the bottom line tomorrow, they take the short-cut to look good today.

Instead of going over budget and buying a modern, 3rd generation, S2P platform, they cheap out and buy a first generation or low-cost, low-capability, second generation platform with limited capabilities that limits the eventual performance gains the system can provide to one that barely makes sense. A 3x ROI with an average 2% to 3% savings vs a 5X to 10X ROI with a 5% to 10% savings.

Instead of owning up to their own incompetence and own short-sightedness, they hire analysts and consultants to do market assessments and find ways to blame the market, the supply base, the systems, or even the staff instead of themselves.

In other words, we haven’t reached the plateau yet because less-than-well-intentioned management won’t do what is necessary to hire and elevate the organizational manpower to the skill levels necessary to scale the walls that surround the plateau and hide the even higher plateau blocked from view.

And while this is a dark and dreary view, what other reason could one give?

2020 is Less Than a Year Away. And we still haven’t crossed the supply chain plateau. Part I

Six years ago tomorrow we commented on a piece by the Supply Chain Shaman who believed we had reached the supply chain plateau. This was based not on a gut feeling, but on an objective analysis of balance sheets of process companies over the course of a decade. The result: the average process manufacturing company has reached a plateau in supply chain performance. As bluntly stated:

Growth has stalled. To compensate and stimulate revenue, the companies increased SG&A margin by 1%. However, the conditions were more complex; the average company, over the last ten years, experienced a decline of 1% in operating margin, and an increase in the days of inventory of 5%. While cycle times have improved, the majority of the progress has come from lengthening of days of payables and squeezing suppliers.

And while SI still believes, as it did last year, that we have not reached the plateau, SI believes that growth is still stalled. As the Shaman conjectured, complexity has increased, but many well-intentioned executives still lack the understanding of the supply chain’s potential or how to manage the supply chain as a system. So while select projects in the hand of gifted buyers, departments as a whole are not performing as well, and often being managed even worse.

The core problem has not changed — manpower capability has not kept up. While leading vendors are building assisted intelligence technologies (and a few are experimenting with augmented intelligence technologies on the way to delivering cognitive, almost AI, experiences), the average organization, if they are lucky, are running on first generation Sourcing and Procurement systems from the early 2000s. And if they aren’t, they are running on spreadsheets and thoroughly outdated ERPs (as noted by the Supply Chain Shaman in the aforementioned article).

A year ago tomorrow we conjectured, in our post where we asked will this be the year we traverse the supply chain plateau, we conjectured the manpower capability issue was a lack of education. While the average practitioner is not educated enough, it’s certainly not a lack of education opportunities, so we’re obviously still missing part of the puzzle.

So what are the missing pieces?

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?