Category Archives: Spend Analysis

When Selecting Your Prescriptive, and Future Permissive, Analytics System …

Please remember what Aaron Levenstein, Business Professor at Baruch College, said about statistics:

Statistics are like bikinis. What they reveal is suggestive, but what they conceal is vital.

Why? Because a large number of predictive / forecasting / trending algorithms are statistics-based. While good statistics, with good sufficiently-sizeable data sets, can reach a very high, calculable, probability of accuracy a statistically high percentage of the time, if a result is only 95% likely 95% of the time, then the right answer is only obtained 95% of the time (or 19 / twenty times), and the answer is only “right” to within 95%. This means that one time out of twenty, the answer is completely wrong, and may not even be within 1%. It’s not the case that one time out of twenty the prediction is off more than 5%, it’s the case that the prediction is completely wrong.

And if these algorithms are being used to automatically conduct sourcing events and make large scale purchases on behalf of the organization, do you really want something going wrong one in twenty times, especially if an error that one time could end up costing the organization more than it saved the other nineteen times because it was primarily sourcing categories that were increasing with inflation or decreasing according to standard burn rates as demand dropped on outdated product offerings, but one such category was misidentified. If instead of identifying the category as about to be in high-demand, and about to sky-rocket in cost due to the reliance on scarce rare earth metals (that are about to get scarcer as the result of a mine closure), it identified it as low-demand, cost-continually-dropping, over the next year and chose a monthly-spot-buy auction, then costs could increase 10% month over month and a 12M category could, over the cost of a year, could actually cost 21.4M (1M + 1.1M + 1.21M …), almost double! If the savings on the other 19, similarly valued, categories was only 3%, the 5.7M the permissive analytics system saved would be dwarfed by the 9.4M loss! Dwarfed!

That’s why it’s very important to select a system that not only keeps a record of every recommendation and action, but a record of its reasoning that can be reviewed, evaluated, and overruled by a wise and experienced Sourcing professional. And, hopefully, capable of allowing the wise and experienced Sourcing professional to indicate why it was overruled and expand the knowledge model so that one in twenty eventually becomes one in fifty on the road to one in one hundred so that, over time, more and more non-critical buying and automation tasks can be put on the system, leaving the buyer to focus on high-value categories, which will always require true brain power, and not whatever vendors try to pass off as non-existent “artificial intelligence” (as there is no such thing, just very advanced machine-learning based automated reasoning).

Are We About to Enter the Age of Permissive Analytics?

Right now most of the leading analytics vendors are rolling out or considering the roll out of prescriptive analytics, which goes one step beyond predictive analytics and assigns meaning to those analytics in the form of actionable insights the organization could take in order to take advantage of the likely situation suggested by the predictive analytics.

But this won’t be the end. Once a few vendors have decent predictive analytics solutions, one vendor is going to try and get an edge and start rolling out the next generation analytics, and, in particular, permissive analytics. What are permissive analytics, you ask? Before we define them, let’s take a step back.

In the beginning, there were descriptive analytics. Solutions analyzed your spend and / or metrics and gave you clear insight into your performance.

Then there are predictive analytics. Solutions analyzed your spend and / or metrics and used time-period, statistical, or other algorithms to predict likely future spend and / or metrics based on current and historical spend / metrics and present the likely outcomes to you in order to help you make better decisions.

Predictive analytics was great as long as you knew how to interpret the data, what the available actions were, and which actions were most likely to achieve the best business outcomes given the likely future trend on the spend and / or metrics. But if you didn’t know how to interpret the data, what your options were, or how to choose the best one that was most in line with the business objectives.

The answer was, of course, prescriptive analytics, which combined the predictive analytics with expert knowledge that not only prescribed a course of action but indicated why the course of action was prescribed. For example, if the system detected rising demand within the organization and predicted rising cost due to increasing market demand, the recommendation would be to negotiate for, and lock-in supply as soon as possible using either an (optimization-backed) RFX, auction, or negotiation with incumbents, depending upon which option was best suited to the current situation.

But what if the system detected that organizational demand was falling, but market demand was falling faster, there would be a surplus of supply, and the best course of action was an immediate auction with pre-approved suppliers (which were more than sufficient to create competition and satisfy demand)? And what if the auction could be automatically configured, suppliers automatically invited, ceilings automatically set, and the auction automatically launched? What if nothing needed to be done except approve, sit back, watch, and auto-award to the lowest bidder? Why would the buyer need to do anything at all? Why shouldn’t the system just go?

If the system was set up with rules that defined behaviours that the buyer allowed the system to take automatically, then the system could auto-source on behalf of the buyer and the buying organization. The permissive analytics would not only allow the system to automate non strategic sourcing and procurement activities, but do so using leading prescriptive analytics combined with rules defined by the buying organization and the buyer. And if prescriptive analytics included a machine learning engine at the core, the system could learn buyer preferences for automated vs. manual vs. semi-automated and even suggest permissive rules (that could, for example, allow the category to be resourced annually as long as the right conditions held).

In other words, the next generation of analytics vendors are going to add machine learning, flexible and dynamic rule definition, and automation to their prescriptive analytics and the integrated sourcing platforms and take automated buying and supply chain management to the next level.

But will it be the right level? Hard to say. The odds are they’ll make significantly fewer bad choices than the average sourcing professional (as the odds will increase to 98% over time), but, unlike experienced and wise sourcing professionals, won’t detect when an event happens in left-field that totally changes the dynamics and makes a former best-practice sourcing strategy mute. They’ll detect and navigate individual black swan attacks but will have no hope of detecting a coordinated black swan volley. However, if the organization also employs risk management solutions with real time event monitoring and alerts, ties the risk management system to the automation, and forces user review of higher spend / higher risk categories put through automation, it might just work.

Time will tell.

UNSuitable Procurement Spend Classification!

Brian Seipel of Source One Management Services recently shared his Pros and Cons of using UNSPSC for spend classification, indicating that the best taxonomy for you, including UNSPSC, was determined by your primary goal.

According to Brian, if your goal was to hit the ground running fast and base analysis on a tried-and-true standard, then UNSPSC was a great start because, as a standard, it is:

  • pre-developed and ready-to-use,
  • capable of expressing a good degree of granularity, and
  • widely available from vendors and a significant number of data enrichment options exist.

And this sounds great, but, any services vendor with a spend analysis offering (Insight Sourcing Group – SpendHQ, Spendency, Sievo, etc.)

  • has one more standard taxonomies designed for Procurement that it has been using for years and years (that has been refined across dozens, if not hundreds, of clients) and that it regularly achieves great results with
  • and these taxonomies are highly granular, usually to at least four levels of detail, and sometimes more and
  • can be enriched from dozens of sources using pre-defined mappings that the expert spend services group has ready-to-go

And when you look at it this way, there are really no benefits. (Well, there is one benefit to UNSPSC, and that is easy H(T)S code mapping, but that’s a Finance/AP benefit, not a Procurement one!)

However, the benefits of a custom Procurement taxonomy:

  • alignment to organizational Procurement/Sourcing needs
  • flexibility and capability to be re-organized on the fly
  • ability to support different levels of granularity in different categories (so that drill down is only available where it makes sense)

can not be found in UNSPSC. It’s one rigid unaligned structure. It can’t be remapped and re-organized as needed to support changing spend responsibility (such as department-specific IT services being taken out of IT spending and mapped to the appropriate departments). And the granularity cannot be altered. Allowing spend to be analyzed in some cases down to nonsensical levels.

So while it may be standard and universally supported (and even useful from a Finance/AP point of view), it really is an UNSuitable Procurement Spend Classification. So, when it comes time to do spend analysis, do NOT use it. (Select a system that supports multi-classification and finance can have their UNSPSC pound-cake and you can have your feathery souffle.) Are we clear?

(And yes, if asked, even consultants who do not like UNSPSC will say it’s a reasonable option because they are told to never directly contradict a client who signs the cheque, and if the CFO who signed the PO wants it, for whatever half-baked reason, guess what is all of a sudden a viable option … )

AnyData: Another Analytics Arriviste from Across the Atlantic

Maybe some good is coming of all the gross incompetence in public sector spending, unreasonably long payment terms, and multi-nationalization of contemporary British companies … the last few years have seen more Analytics companies start in the UK than in the rest of the English speaking world. Anydata, founded in May, 2013, is one in the long list of UK-based spend analysis providers that have been receiving coverage here on SI and over on SM over the past year or so.

It’s one of the more unique offerings as, in some ways, it has more in common with Agiloft, a BPM (Business Process Management) vendor which recently forayed into Contract Management, building its first application in a matter of days using its visual development environment.

Like Agiloft, and unlike many other vendors in analytics, Anydata started out by building a visual development framework upon which it built its spend analysis offering. This gives it a number of advantages which include, but are not limited to, rapid configuration, rapid report and dashboard construction, rapid visualizations (that is on par or faster than Tableau, QlikView, PowerPivot, Birst, and other platforms they are typically compared against), and rapid development of workflows to support additional data collection.

The analysis platform is centered around powerful dashboard-driven analytics that can be customized by client from dozens of dashboard templates that include historic, strategic, geographic, vendor, company, office, cost-center, and chart-of-account overviews as well as savings opportunities, invoice opportunities, and category opportunities.

The categorization is quite powerful, and currently second only to Sievo in functionality currently on the market. Sievo’s unique multi-pivot drill-down approach allows users to classify data in chunks in any way they want to define those chunks in any order in a very collaborative fashion, which is currently unique on the market today. And while the AnyData approach is not as collaborative, it is as powerful as you can define chunks not on pivots and values, but on queries which can then be replayed, in the order of your choosing, as data is reloaded. So instead of having to define a three level breakdown to select a specific group of transactions for a category, it’s a simple query — which allows for much faster categorization if you are a power user good at creating SQL queries. Much faster.

And, rather uniquely, it has a very powerful data intelligence feature that allows an analyst to query and inspect the data and meta-data on a recently imported data source for the purposes of validating the accuracy and completeness — an activity that should go well beyond just validating the basic check-sums (against the annual financial reports). With AnyData’s platform, you can quickly identify sums, trends, and outliers for any time period of interest, use sliders to zone-in and zone-out on potentially anomalous data, use filters to restrict to dimensions (and even facts) of interest, and understand the characterization of the data you are importing. Not only does this help immensely in cleansing, but helps you pinpoint errors that standard techniques miss in cleansing and classification.

It has additional strengths, and, of course, weaknesses compared to other tools on the market — which can be explored in depth in the Spend Matters Pro series co-authored by the doctor and the prophet [membership required] (Part I) — but this should give you a good introduction to, and flavour for, what Anydata is.

PRGX: Optics on Optix

In our last post, we noted that, as written by the doctor and the prophet over on Spend Matters Pro (membership required) in the PRGX Intro, PRGX is one of a select number of dominant services provider in the niche market for recovery audit services — a market that unlike other procurement services faces tremendous price pressure for its core recovery, statement and related auditing and profit recovery services but also a vendor that has started to remake itself quietly from within.

As a result, as indicated in our last post, PRGX has built the most complete, and in many ways the most advanced, analytics and recovery solution for the retail sector and, in doing so, has built one of the most complete and advanced analytics and recovery solutions for just about any sector that buys and relies on goods. It does this via two platforms, Optix, which has deep Payment, Spend, and Product analytics, and Lavante, which has deep SIM and automated recovery prevention analytics. (We expect they will eventually be merged, but, for now, they are separate.)

As we have covered Lavante multiple times in the past, we’re going to focus on introducing the features of Optix.

Spend Optix

Spend Optix is designed to help an organization get deep insight into their category spend like a typical spend analysis platform for Sourcing and Procurement. Reporting revolves around categories and suppliers. It is also the only PRGX product that today has a built-in report builder, which can build spend reports across pre-defined dimensions and fields. The product is designed to help you understand spend category performance, spend under contract, invoice-vs-supplier insights, item price variance, and commodity cost indices.

This product can also be configured to track all contracts, all meta data of interest, and relate the contracts to the relevant categories and products. This allows a user to drill into a contract from a category, a category from a contract, and create accurate “address spend” reports, as will be described below. The ease of use is not at the level of Lavante SIM, but we expect that will change over time.

Payment Optix

Payment Optix is designed to help an organization get deep insight into their payments and related metrics and, in particular, DPO (days payable outstanding), PO (purchase order) vs. Non-PO spend, deep AP analytics, and risk insights.

The home screen, as with the other OPTIX products, is a dashboard with key metrics and graphs, such as invoices processed by month, DPO, Benford’s law (by invoice amount or value), and related metrics that an organization wants to see on a daily basis. The platform is drill-down report oriented, and the reports are segmented into Invoice Processing, DPO, and Risk Management.

Product Optix

Product Optix is designed to help an organization get deep insight into their product pool, including net margin, equivalent products, and best supplier funding opportunities. Reporting revolves around categories, suppliers, and deals.

The best part is the product detail report which brings up not only detailed product information, but the most complete product margin breakdown report you ever did see. With their extremely strong background in retail, PRGX understands true lifecycle margin calculations as good as anyone and it shines through in their report.

This is just a brief overview of what PRGX can do. For a much deeper dive, see the Pro series (Part I, Part II, and Part III) by the doctor and the prophet over on Spend Matters Pro (membership required) that also dives into strengths and weaknesses and a very detailed SWOT analysis to help you understand where they fit.