Category Archives: Technology

Always Remember That While the Second Mouse Gets the Cheese …

… the third mouse gets nothing — unless you count the opportunity to bury the first mouse in a shallow grave before he dies of starvation something.

As Pete Loughlin reminds us in his recent post, “when conventional wisdom goes wrong”, most enterprises make one of two mistakes when selecting enterprise technology. Either they go with the same-old, same-old incumbent technology (that never solved their problem in the first place), or they go with a bleeding edge start-up (because their eagerness to please can be exploited). For the vast majority of companies, neither solves the problem.

While many startups will have something new and innovative, most don’t have the breadth or depth required to support a large organization — even if that is their ultimate target market. Start-ups are good for (smaller) mid-size organizations that need a point solution, or large organizations that just need one thing done better — they are not good as platforms. Similarly, if your ERP has failed you for 10 years, starting yet another customization project with a big 8 consultancy that has yet to deliver what they promised on any project isn’t a good idea either.

The best solution for many organizations is typically a new vendor with a newer, or more appropriate, solution, but not one that is so new that it has not yet been around long enough to be adequately stress tested and proven to have the breadth and depth required for the organizational size and need. A (smaller) mid-size organization should already be using it successfully and it should be clear that the solution has enough scale-power.

It’s often a hard call, but that’s why impartial expert technology consultants — who do not (re)sell solutions* — should be engaged. In particular, they should be engaged to help an organization identify the processes it needs to support, the key functionality that a platform needs to offer (but not features, as sometimes multiple feature sets can solve the same problem), the scale the platform will need to support, the data that will be required, the integrations to other enterprise systems or external data sources to fetch this data, and the breadth of deployment that will be required to support the processes. Then, they should help the organization construct a proper RFP (that describes the current process, the problems, the desired process, and ultimate goals) and identify potential vendors to send the RFP too, as well as demo requirements, scoring and weighting systems, and best practices in vendor selection. But they should not sell, or have any interest in, any of the solutions — they are guides through the dangerous enterprise jungle, not treasure map peddlers.

And, most importantly, as Pete points out, if the expert is engaged, she should be listened to. Otherwise, the organization is not only wasting money on the executive’s gut-feel technology platform, but also on the advice that was going to be ignored anyway. Always remember, there’s no gold in them thar hills, but there is in the advice of a wise sage.

just a reminder that the doctor does not have any interest in, or receive any compensation for, any technology that he may or may not recommend, unlike many analyst firms that charge per lead and per sale

Eighty Three Years Ago Today …

Edwin Armstrong, an American engineer, presented his paper A Method of Reducing Disturbances in Radio Signaling by a System of Frequency Modulation to the New York section of the Institute of Radio Engineers (which merged with the American Institute of Electrical Engineers in 1963 to form the Institute of Electrical and Electronic Engineers), which described his 1933 invention of radio broadcasting using frequency modulation, now known as FM broadcasting, and LOLCats everywhere rejoiced!


I Love My FM Radio!

Just What Does Modern Sourcing Need?

Every year vendors, analysts, and even bloggers come up with their view of what next generation sourcing is, and what is going to get us there. This year, there is a big push towards AI (Artificial Intelligence) and not just predictive, but prescriptive analytics. Apparently, the Sourcing (and Procurement) of the future will be managed by computers, and not by experts. This is not only unnecessary, but a bit scary.

Why is it scary? Because computers run on algorithms and algorithms are not intelligence. Even though it’s theoretically possible for a sufficiently powerful computing platform to pass the Turing test*, all it does is point out the insufficiency of the Turing test for assessing the intelligence of a computer program. While computers can process significantly more data than we can, and modern predictive (trend) models, given enough data, can be more accurate than our intuition, they cannot detect when they are likely to fail or there is information or factors that need to be considered not baked into the model.

For example, if the prescriptive analytics relies on predictive analytics that relies on price trend modelling that simply takes into account price history, currency fluctuations, demand for related products or commodities, and demand for commodities that are usually used to hedge against price fluctuations in the product or commodity category, it will not detect when a natural disaster will result in a supply chain disruption that will result in less product or commodity availability in two months, which, of course, will have a drastic impact on price. As a result, the recommendation to spot-buy while the price is dropping is the wrong one, because as soon as supply drops, prices will skyrocket and it will be too late to lock in the current price.

But this isn’t the worst that can happen. If the AI that monitors multiple pricing trends, expiring contracts, and supplier performance not only ignores this blip but, instead, not only directs a resourcing for an expiring contract on an unrelated,but highly strategic, category, but encourages the inclusion of a supplier (that normally does not supply that category) that is currently in financial distress, the organization could end up blindly awarding a critical category (that is currently being served by a stable, reliable, reasonably low cost supplier) to a different supplier that is about to go bankrupt and then, seemingly without warning, stock-out on a critical product for months.

As you can probably guess, the doctor still believes that Sourcing and Procurement do not need AI and prescriptive analytics. What they really need are powerful and modifiable rules-based workflows, exception monitoring, suspicious transaction identification, and event monitoring.

The true power of a platform is to automate the tactical and streamline the strategic. Every minute of a professional’s time should be spent on strategic activities or issue resolution, not electronic paper pushing. Document matching, data collection and verification, contract monitoring, automated trend computation, etc. are all tasks that should be done automatically, but no actions with any strategic impact should be taken without intelligent human intervention.

A good rules-based workflow can allow tactical tasks, such as invoice matching and marketing monitoring, to be automated according to accepted rules and ensures that professionals only need to be involved when the parameters exceed the specified norms. Exception monitoring can insure that when something is out of expected norms, or exceeds ranges, or happens too often, it is immediately brought to the attention of the right individual. A suspicious transaction monitoring system, even if statistically based, minimizes the chances of duplicate payments, fraud, audit trail tampering, and so on. And event monitoring, even though it will produce a number of false positives, will enable a human to identify events that might impact the projected supply and cost trends for commodities and products purchased by the organization, and mark those for manual review and, possibly, re-sourcing if need be.

Modern sourcing does need better technology, but it doesn’t need artificial intelligence. It needs platforms that can help the sourcing professional focus appropriately, not guide the professional down a programmed path that will only give Sourcing and Procurement a false sense of security. The solution can track best practices for different situations, but the human still needs to determine if the system’s assessment is proper. Sourcing needs a system that empowers it with the intelligence it needs to make the right decisions, not a system that makes decisions and acts on those decisions (with automated contracts, orders, etc.) without human review and approval.

* A computer that is capable of sampling all conversations archived and currently taking place in real time, finding the one that best matches the conversation you are having, and providing that answer will provide a conversation indistinguishable from that provided from a real human, but it’s not intelligent. It merely proves the infinite monkey theorem.

Sixty Years Ago Today

Sixty years ago today, Fortran, possibly the first modern computer language, is shared with the coding community for the first time. Originally developed by IBM, Fortran is a general-purpose, imperative programming language that was designed for numeric computation and scientific computing that dominated science and engineering program for decades.

Updated significantly in FORTRAN II (procedural programming), FORTRAN III (inline assembly), FORTRAN IV (logical data types and statements), FORTRAN 66 (ANSI standard), FORTRAN 77 (structured programming and character-based data), Fortran 90 (array and modular programming), Fortran 95 (high performance), Fortran 2003 (object oriented programming), and Fortran 2008 (concurrent programming), this ancient language is still in use today. In fact, due to the continued widespread use in the scientific community, the next version of Fortran (currently dubbed Fortran 2015) is intended to be completed mid-2018.

What do you think, LOLCat?

Really? Why?

Spend360 – Applying Deep Machine Learning to Spend Analysis

Regular readers will know that, generally speaking, the doctor has not been impressed with the auto-classification and mapping offerings by any spend analysis vendor he’s ever blogged about as all have failed pitifully on tail spend, performed poor on any supplier or category the provider hasn’t processed extensively, and worked poor in new geographies and even poorer in foreign languages.

However, this year, he’s been impressed by two vendors with auto-classification. TAMR, which are trying to tame the data deluge, and now Spend360. While a new name on this side of the pond, it is not a new name across the pond, having opened its doors for business in 2011, after two plus years of intense development. Plus, it is gaining reputation pretty quickly since it’s foray to this side of the pond a couple of years ago and now has over 100 North American clients, which brings its total client base to over 400 global customers, which is impressive for any company in this space. (Even more impressive is the fact that, to date, it has processed over 1 Trillion of spend.)

While it’s still not perfect, and still can’t outmatch the best human expert with a multi-level priority mapping engine, it is decades ahead of its competition and has the ability to learn and evolve and, over time, approach 98%+ mapping accuracy, leaving little that has to be mapped, or corrected, by a human user (which is quite valuable when the user is not an expert in spend analysis but still wants to reap the benefits).

Not only can its deep machine learning identify tail spend suppliers, company specific categories, and even individual items coded in obscure ways, but it can learn over time and adapt to different data models, especially since it can use evolving knowledge bases. Whereas the majority of first generation classifiers used naive statistical classification that could not learn and had to map to a fixed (UNSPSC) model, Spend360’s uses deep machine learning (based on LSTM and encoder/decoder technology) that maps to custom data models using extensible knowledge bases (which can be created and maintained by the organization) that can encode organization and industry specific knowledge (and negate the need for custom mappings or override rules).

The fact that the knowledge base can be extended anytime a mis-classification occurs negates the need for manual mappings or override rules common in so many first generation spend analysis systems is a very powerful concept. It means that every erroneous mapping need only happen once and will never need to be manually corrected again. Plus, the fact that the data model can be extended as analytic needs evolve means that the platform can continue to deliver value year over year over year, unlike most first generation platforms that only delivered top N reports and failed to deliver value after the first twelve to eighteen months.

But this isn’t all Spend360 has to offer. In addition to a powerful classification ability, which can be trained to actually work, it also has a very powerful front end that allows the user to drill through the cube using custom filters in real time, compared to first generation systems that had fixed OLAP with limited filter capability. Reports can be cross-linked and all linked reports auto-update as one is drilled into. And data can be uploaded and incorporated into the cube in real-time if additional data is required.

And, to top it off, based on the 1 Trillion in spend they have classified over the years, Spend360 also has deep spend benchmarks across all of the major verticals and categories, which is often mapped down to UNSPSC level 4. This allows an organization to quickly understand how its spend on a category compares to the average in its vertical. Simply augmenting this data with pricing trend data can give an organization quick insight into where some significant cost normalization opportunities may lie.

In short, Spend360 is a provider the doctor expects you to be seeing a lot more of in the years to come, and recommends that you check out the upcoming deep dive, co-written with the prophet, over on Spend Matters Pro [membership required] if you are able. This is one best-of-breed provider you want to know.