Category Archives: Sourcing Innovation

Spendata: The Power Tool for the Power Spend Analyst — Now Usable By Apprentices as Well!

We haven’t covered Spendata much on Sourcing Innovation (SI), as it was only founded in 2015 and the doctor did a deep dive review on Spend Matters in 2018 when it launched (Part I and Part II, ContentHub subscription required), as well as a brief update here on SI where we said Don’t Throw Away that Old Spend Cube, Spendata Will Recover It For You!. the doctor did pen a 2020 follow up on Spend Matters on how Spendata was Rewriting Spend Analysis from the Ground Up, and that was the last major coverage. And even though the media has been a bit quiet, Spendata has been diligently working as hard on platform improvement over the last four years as they were the first four years and just released Version 2.2 (with a few new enhancements in the queue that they will roll out later this year). (Unlike some players which like to tack on a whole new version number after each minor update, or mini-module inclusion, Spendata only does a major version update when they do considerable revamping and expansion, recognizing that the reality is that most vendors only rewrite their solution from the ground up to be better, faster, and more powerful once a decade, and every other release is just an iteration, and incremental improvement of, the last one.)

So what’s new in Spendata V 2.2? A fair amount, but before we get to that, let’s quickly catch you up (and refer you to the linked articles above for a deep dive).

Spendata was built upon a post-modern view of spend analysis where a practitioner should be able to take immediate action on any data she can get her hands on whenever she can get her hands on it and derive whatever insights she can get for process (or spend) improvement. You never have perfect data, and waiting until Duey, Clutterbuck, and Howell1 get all your records in order to even run your first report when you have a dozen different systems to integrate data from, multiple data formats to map, millions of records to classify, cleanse and enrich, and third party data feeds to integrate will take many months, if not a year, and during that year where you quest for the mythical perfect cube you will continue to lose 5% due to process waste, abuse, and fraud, and 3% to 15% (or more) across spend categories where you don’t have good management but could stem the flow simply by identifying them and putting in place a few simple rules or processes. And you can identify some of these opportunities simply by analyzing one system, one category, and one set of suppliers. And then moving on to the next one. And, in the process, Spendata automatically creates and maintains the underlying schema as you slowly build up the dimensions, the mapping, cleansing, and categorization rules, and the basic reports and metrics you need to monitor spend and processes. And maybe you can only do 60% to 80% piecemeal, but during that “piecemeal year”, you can identify over half of your process and cost savings opportunities and start saving now, versus waiting a year to even start the effort. When it comes to spend (related) data analysis, no adage is more true than “don’t put off until tomorrow what you can do today” with Spendata, because, and especially when you start, you don’t need complete or perfect data … you’d be amazed how much insight you can get with 90% in a system or category, and then if the data is inconclusive, keeping drilling and mapping until you get into the 95% to 98% accuracy range.

Spendata was also designed from the ground up to run locally and entirely in the browser, because no one wants to wait for an overburdened server across a slow internet connection, and do so in real time … and by that we mean do real analysis in real time. Spendata can process millions of records a minute in the browser, which allows for real time data loads, cube definitions, category re-mappings, dynamically derived dimensions, roll-ups, and drill downs in real-time on any well-defined data set of interest. (Since most analysis should be department level, category level, regional, etc., and over a relevant time span, that should not include every transaction for the last 10 years because beyond a few years, it’s only the quarter over quarter or year over year totals that become relevant, most relevant data sets for meaningful analysis even for large companies are under a few million transactions.) The goal was to overcome the limitations of the first two generations of spend analysis solutions where the user was limited to drilling around in, and deriving summaries of, fixed (R)OLAP cubes and instead allow a user to define the segmentations they wanted, the way they wanted, on existing or newly loaded (or enriched federated data) in real time. Analysis is NOT a fixed report, it is the ability to look at data in various ways until you uncover an inefficiency or an opportunity. (Nor is it simply throwing a suite of AI tools against a data set — these tools can discover patterns and outliers, but still require a human to judge whether a process improvement can be made or a better contract secured.)

Spendata was built as a third generation spend analysis solution where

  • data can be loaded and processed at any point of the analysis
  • the schema is developed and modified on the fly
  • derived dimensions can be created instantly based on any combination of raw and previously defined derived dimensions
  • additional datasets from internal or external sources can be loaded as their own cubes, which can then be federated and (jointly) drilled for additional insight
  • new dimensions can be built and mapped across these federations that allow for meaningful linkages (such as commodities to cost drivers, savings results to contracts and purchasing projects, opportunities by size, complexity, or ABS analysis, etc.)
  • all existing objects — dimensions, dashboards, views (think dynamic reports that update with the data), and even workspaces can be cloned for easy experimentation
  • filters, which can define views, are their own objects, can be managed as their own objects, and can be, through Spendata‘s novel filter coin implementation, dragged between objects (and even used for easy multi-dimensional mapping)
  • all derivations are defined by rules and formula, and are automatically rederived when any of the underlying data changes
  • cubes can be defined as instances of other cubes, and automatically update when the source cube updates
  • infinite scrolling crosstabs with easy Excel workbook generation on any view and data subset for those who insist on looking at the data old school (as well as “walk downs” from a high-level “view” to a low-level drill-down that demonstrates precisely how an insight was found
  • functional widgets which are not just static or semi-dynamic reporting views, but programmable containers that can dynamically inject data into pre-defined analysis and dimension derivations that a user can use to generate what-if scenarios and custom views with a few quick clicks of the mouse
  • offline spend analysis is also available, in the browser (cached) or on Electron.js (where the later is preferred for Enterprise data analysis clients)

Furthermore, with reference to all of the above, analyst changes to the workspace, including new datasets, new dashboards and views, new dimensions, and so on are preserved across refresh, which is Spendata’s “inheritance” capability that allows individual analysts to create their own analyses and have them automatically updated with new data, without losing their work …

… and this was all in the initial release. (Which, FYI, no other vendor has yet caught up to. NONE of them have full inheritance or Spendata‘s security model. And this was the foundation for all of the advanced features Spendata has been building since its release six years ago.)

After that, as per our updates in 2018 and 2020, Spendata extended their platform with:

  • Unparalleled Security — as the Spendata server is designed to download ONLY the application to the browser, or Spendata‘s demo cubes and knowledge bases, it has no access to your enterprise data;
  • Cube subclassing & auto-rationalization — power users can securely setup derived cubes and sub-cubes off of the organizational master data cubes for the different types of organizational analysis that are required, and each of these sub-cubes can make changes to the default schema/taxonomy, mappings, and (derived) dimensions, and all auto-update when the master cube, or any parent cube in the hierarchy, is updated
  • AI-Based Mapping Rule Identification from Cube Reverse Engineering — Spendata can analyze your current cube (or even a report of vendor by commodity from your old consultant) and derive the rules that were used for mapping, which you can accept, edit, or reject — we all know black box mapping doesn’t work (no matter how much retraining you do, as every “fix” all of a sudden causes an older transaction to be misclassified); but generating the right rules that can be human understood and human maintained guarantees 100% correct classification 100% of the time
  • API access to all functions, including creating and building workspaces, adding datasets, building dimensions, filtering, and data export. All Spendata functions are scriptable and automatable (as opposed to BI tools with limited or nonexistent API support for key functions around building, distributing, and maintaining cubes).

However, as we noted in our introduction, even though this put Spendata leagues beyond the competition (as we still haven’t seen another solution with this level of security; cube subclassing with full inheritance; dynamic workspace, cube, and view creation; etc.), they didn’t stop there. In the rest of this article, we’ll discuss what’s new from the viewpoint of Spendata Competitors:

Spendata Competitors: 7 Things I Hate About You

Cue the Miley Cyrus, because if competitors weren’t scared of Spendata before, if they understand ANY of this, they’ll be scared now (as Spendata is a literal wrecking ball in analytic power). Spendata is now incredibly close to negating entire product lines of not just its competitors, but some of the biggest software enterprises on the planet, and 3.0 may trigger a seismic shift on how people define entire classes of applications. But that’s a post for a later day (but should cue you up for the post that will follow this on on just precisely what Spendata 2.2 really is and can do for you). For now, we’re just going to discuss seven (7) of the most significant enhancements since our last coverage of Spendata.

Dynamic Mapping

Filters can now be used for mapping — and as these filters update, the mapping updates dynamically. Real-time reclassify on the fly in a derived cube using any filter coin, including one dragged out of a drill down in a view. Analysis is now a truly continuous process as you never have to go back and change a rule, reload data, and rebuild a cube to make a correction or see what happens under a reclassification.

View-Based Measures

Integrate any rolled up result back into the base cube on the base transactions as a derived dimension. While this could be done using scripts in earlier versions, it required sophisticated coding skills. Now, it’s almost as easy as a drag-and-drop of a filter coin.

Hierarchical Dashboard Menus

Not only can you organize your dashboards in menus and submenus and sub-sub menus as needed, but you can easily bookmark drill downs and add them under a hierarchical menu — makes it super easy to create point-based walkthroughs that tell a story — and then output them all into a workbook using Spendata‘s capability to output any view, dashboard, or entire workspace as desired.

Search via Excel

While Spendata eliminates the need for Excel for Data Analysis, the reality is that is where most organizational data is (unfortunately) stored, how most data is submitted by vendors to Procurement, and where most Procurement Professionals are the most comfortable. Thus, in the latest version of Spendata, you can drag and drop groups of cells from Excel into Spendata and if you drag and drop them into the search field, it auto-creates a RegEx “OR” that maintains the inputs exactly and finds all matches in the cube you are searching against.

Perfect Star Schema Output

Even though Spendata can do everything any BI tool on the market can do, the reality is that many executives are used to their pretty PowerBI graphs and charts and want to see their (mostly static) reports in PowerBI. So, in order to appease the consultancies that had to support these executives that are (at least) a generation behind on analytics, they encoded the ability to output an entire workspace to a perfect star schema (where all keys are unique and numeric) that is so good that many users see a PowerBI speed up by a factor of almost 10. (As any analyst forced to use PowerBI will tell you, when you give PowerBI any data that is NOT in a perfect star schema, it may not even be able to load the data, and that it’s ability to work with non-numeric keys at a speed faster than you remember on an 8088 is nonexistent.)

Power Tags

You might be thinking “tags, so what“. And if you are equating tags with a hashtag or a dynamically defined user attribute, then we understand. However, Spendata has completely redefined what a tag is and what you can do with it. The best way to understand it is a Microsoft Excel Cell on Steroids. It can be a label. It can be a replica of a value in any view (that dynamically updates if the field in the view updates). It can be a button that links to another dashboard (or a bookmark to any drill-down filtered view in that dashboard). Or all of this. Or, in the next Spendata release, a value that forms the foundation for new derivations and measures in the workspace just like you can reference a random cell in an Excel function. In fact, using tags, you can already build very sophisticated what-if analysis on-the-fly that many providers have to custom build in their core solutions (and take weeks, if not months, to do so) using the seventh new capability of Spendata, and usually do it in hours (at most).

Embedded Applications

In the latest version of Spendata, you can embed custom applications into your workspace. These applications can contain custom scripts, functions, views, dashboards, and even entire datasets that can be used to instantly augment the workspace with new analytic capability, and if the appropriate core columns exist, even automatically federate data across the application datasets and the native workspace.

Need a custom set of preconfigured views and segments for that ABC Analysis? No sweat, just import the ABC Analysis application. Need to do a price variance analysis across products and geographies, along with category summaries? No problem. Just import the Price Variance and Category Analysis application. Need to identify opportunities for renegotiation post M&A, cost reduction through supply base consolidation, and new potential tail spend suppliers? No problem, just import the M&A Analysis app into the workspace for the company under consideration and let it do a company A vs B comparison by supplier, category, and product; generate the views where consolidation would more than double supplier spend, save more than 100K on switching a product from a current supplier to a lower cost supplier; and opportunities for bringing on new tail spend suppliers based upon potential cost reductions. All with one click. Not sure just what the applications can do? Start with the demo workspaces and apps, define your needs, and if the apps don’t exist in the Spendata library, a partner can quickly configure a custom app for you.

And this is just the beginning of what you can do with Spendata. Because Spedata is NOT a Spend Analysis tool. That’s just something it happens to do better than any other analysis tool on the market (in the hands of an analyst willing to truly understand what it does and how to use it — although with apps, drag-and-drop, and easy formula definition through wizardly pop-ups, it’s really not hard to learn how to do more with Spendata than any other analysis tool).

But more on this in our next article. For The Times They Are a-Changin’.

1 Duey, Clutterbuck, and Howell keeps Dewey, Cheatem, and Howe on retainer … it’s the only way they can make sure you pay the inflated invoices if you ever wake up and realize how much you’ve been fleeced for …

Strategic Sourcing & Procurement for Technology Cost Optimization

Given that we recently published a piece noting that Roughly Half a Trillion Dollars Will Be Wasted on SaaS Spend This Year and up to One Trillion Dollars on IT Services, it’s obvious that one has to be very careful with technology acquisition as it is very easy to overspend on the license and the implementation for something that doesn’t even solve your problem.

As a result, you need to be very strategic about it. While you certainly can’t put the majority of your technology acquisitions (which can be 6, 7, and even 8 figures) up for auction (as products are never truly apples to apples to apples), you definitely have to be strategic about it. As a result, you should be doing multi-round RFPs and then awarding to the vendor who brings you the best overall value for the term you want to commit to, once all things are considered.

But these have to be well thought out … you need to make sure that you are only inviting providers that are likely to meet 100% of your must haves, 80% of your should haves, and 60% of your nice to haves (and, moreover, that you have really separated out absolute vs highly desired vs wanted but not needed because the more you insist on, especially when it’s not necessary, the shallower the vendor pool, and the more you are going to end up paying*).

To do this, as the article notes, you have to know what processes you need to support, what improvements you are expecting, what measurements you need the platform to take, and what business objectives it needs to support. Then you need to align your go-to-market sourcing/procurement strategy with those objectives and make sure the RFP covers all the core requirements (without asking 100 unnecessary questions about features you’ll never actually use in practice).

You also need to know what quantifiable benefits the platform should deliver, both in terms in tactical work(force) reduction (as the tech you acquire should be good at thunking), and the value that will be obtained from the strategic enablement (in terms of analysis, intelligence gathering, guided events, etc.) the platform should deliver. If it is a P2P platform, how much invoice processing is it going to automate, and, based on that, how much is it going to reduce your average invoice processing cost? If it’s a sourcing platform, how much more spend will you be able to source (without increasing person-power) and what is a reasonable savings percentage to expect on that? Understand the value before you go to market.

Then you need to understand how much support and help you need from the vendor. If you just want a platform that does a function, then you just need to know the vendor can support the platform in supporting that function. But if you need help in process transformation or optimization, customized development or third party tool integration for advanced/custom processes, etc. you need a vendor that cannot only provide services, but also be a strategic provider for you as well.

And so on. For more insights, we suggest you check out a recent article by Alix Partners on Strategic Sourcing and Procurement for Technology Cost Optimisation. It has a lot of great advice for those starting their strategic procurement technology journey.

*Just remember, if you’re a mid-market, and you’re flexible (i.e. define what a module needs to accomplish for you vs. a highly specific process) you can get your absolute functionality and most of your desired functionality for 120K in annual SaaS license fees, excluding data feeds and services. If you’re not flexible, or not really strict in really separating out absolute vs strongly desired vs nice-to-have, you can easily be paying four times that.

Also remember, if you’re enterprise, your absolutes and strongly desired are much more extensive, typically require a lot more advanced tech (like optimization, predictive analytics, ML/AI, etc.), and licenses fees alone will cost you in the 500K to 1M range annually at a minimum, not counting the 100K to 1M you will need to spend on the implementation, data cleansing and enrichment, integration, training, and real-time data feed access, so it is absolutely vital you get it right!

Sourcing Success in these Turbulent Times Require Long Term Planning and Cost Concessions

In a McKinsey article a few months back on How medium-size enterprises can better manage sources, McKinsey said that small and medium-size enterprises often struggle to find Procurement cost savings. Yet there are ways to do it while still pursing growth and providing a superior customer experience. The article, which concluded with an action plan for procurement cost savings, recommended:

  • establishing CoE teams
  • improving forecasting
  • expanding (the) use of digital procurement tools
  • gaining greater market intelligence
  • establishing a culture of — and process for — continuous cost improvement
  • incorporating supplier-driven product improvements

which, of course, are all great suggestions, and mostly address four of the five reasons that McKinsey give that prevent companies from reining in spending, which included

  • a lack of spending transparency (which would have to be corrected to improve forecasting)
  • talent gaps (which can be minimized with the right tools, market intelligence, and CoE teams)
  • underused digital tools and automation (which is directly addressed by using more of them)
  • exclusion of procurement and supply chain in business decision (which would hopefully be a byproduct of a corporate culture for continuous cost improvement that only happens when procurement and supply chain is not involved higher up)

but the fifth is largely unaddressed — the myopic focus on the short term which McKinsey claims could be addressed by putting more effort into planning and forecasting. But that doesn’t solve the problem.

Better forecasting will allow for longer contracts to be signed for higher volumes, which can lead to long term strategic supplier relationships, and better planning can allow this to happen, but this does not completely address the need for long term planning.

Supply Chains today are not the supply chains of the last ten to twenty years.

  • rare earths are even rarer
  • many critical raw materials are in increasingly limited or short supply
  • transportation can be unpredictable in availability and cost; even though most of the world declared COVID over in mid-2022, China still had mandatory lockdowns, ocean carriers scrapped many of their ships for insurance (and in some cases, post-panamax ships that had never made a single voyage), airlines furloughed too many pilots who found other jobs or just flat out retired, and the long-haul trucking in North America (the UK, and many first-world countries) has been on a steady decline for over a deacde
  • ESG/GHG/Carbon Requirements are escalating around the globe and you need to be in compliance (both in terms of reporting 1/2/3 and ensuring you don’t exceed any caps)
  • human/labour rights are escalating and you have to be able to trace compliance down to the source in some jurisdictions; you need suppliers who insist on the same visibility that you do
  • diversity is important not just to meet arbitrary requirements for government programs or arbitrary internal goals, but to ensure you have the right insight and expertise to solve all types of problems that might arise

And you can’t effectively address any of these problems unless you think long term AND accept that some of the solutions will cost more up front.

  • In mid November, the trading price for Neodymium (a rare-earth that is critical for the creation of strong permanent magnets, which makes it possible to miniaturize many electronic devices, including the [smart]phone you might be reading this on) was over $87,000 USD/mt. In comparison, hot roll steel was around $850 USD/mt. In other words, Neodymium was 100 times more expensive than steel. And while you can still buy steel for about the same price you could 10 years ago (it was around $900 USD/mt), Neodynmium is almost $20,000 more (as it was around $69,000 USD/mt in November 2013). It’s not the only rare earth to increase about 26% in 10 years, with further increases on the horizon. You need to have a strategy to minimize your need (which could include product redesigns that use more sustainable alternatives or recycling strategies that use recovered materials from older phone models). And when it comes to recycled materials, due to a historical lack of recycling efforts, or research into technologies to make recycling efficient and cost effective, recycled materials are almost always more expensive at first. Always. But as adoption increases, plants, technologies, and processes get more efficient, and the cost goes down (while, at the same time, raw material prices for materials in limited supply continue to go up). In other words, if you want to mitigate the ever-increasing costs for rare earths and other materials that are in limited supply, you have to incorporate the use of recycled materials, and maybe even invest in your own plants (and recycle your own phones you buy back because it’s cheaper just to buy them back and extract the rare earths yourself than buy the recycled rare earths from someone else).
  • Global trade is costly and unpredictable. Supply assurance is finally dictating near-sourcing and home-sourcing (which SI has been advocating for almost fifteen years, as inevitable disaster was the logical conclusion of outsourcing everything to China as eventually a pandemic, global spat, natural disaster, or other event would send shockwaves through the world when it severely disrupted the trade routes [because even though the chances of a pandemic, natural disaster on the scale of Krakatoa or the Valdivia earthquake, or another catastrophic event is minimal in any given year, over the course of a century, it becomes very likely]), and that is going to require re-investing in those Mexican factories (that worked just fine, by the way) you shut down twenty years ago, training appropriately skilled workers in low cost North American (or Eastern Europe) locales, and paying a bit more per unit (and even transportation until the carriers rebuild those routes). But in the long term, as global transportation costs continue to rise, and the local-ish resources get much more efficient (using the best technology we have to offer), your costs, and transportation risks, will go down while your competitor costs continue to go up.
  • if you don’t insist, and ensure, up front that your suppliers can report the data you need, how will you get it; chances are those suppliers need help and modern systems, which temporarily increase their operational costs as they install, integrate, and learn the systems; not more than a few cents here and there per unit, but a noticeable blip on the overall costs none-the-less
  • if you want suppliers that monitor their supply chain and insist on no slave/forced/child labour, appropriately treated and well paid labour, and, better yet, a community focus throughout the supply chain (so that the humans who mine the materials, harvest the food stuffs, weave the silk, or otherwise do the foundational work have a reasonable quality of life, health, and safety), you’re going to have to put the effort in to find them and the extra money to support them in their humanitarian efforts; since most of these workers in remote low-cost locales are paid pennies on your dollar, it’s another blip on the total cost to ensure they are paid every penny they deserve, but it’s still a blip; but you can’t afford not to do it if your jurisdiction has laws making you responsible for slave labour that later gets discovered in your supply chain
  • and while diversity shouldn’t cost more, since it’s the same number of employees, the reality is that the supply base embracing it could be a minority, and if these minority suppliers suddenly become in demand, market dynamics may kick in and they may charge a premium that your competitor will pay; but, as new challenges continue to arise, you will need the diversity to solve them; so, another blip in the cost you need to absorb

In other words, you need the long term focus to guarantee success, and you need to understand that, up front, it may cost a bit more. However, done right, your costs will decrease over time while your competitors’ costs skyrocket. So if you truly want success, in any high dollar, strategic, or emerging category, plan for the long term. And you will truly succeed.

For Successful Sourcing Sometimes You Have to Listen to a Lawyer

the doctor is always on the look-out for good articles on (strategic) sourcing and its importance to overall procurement performance in today’s stressful supply environment. Especially if the article isn’t yet another seven steps to sourcing success article. This article by Chris Eastham of fieldfisher was really close, as it provided nine tips to improve sourcing efficiency after telling you that

  • Sourcing is a Live Wire
  • Geo-Political Drivers cause Chaos
  • Technological Drivers cause Complexity

and these collectively compound your struggles, which are exacerbated enough due to organizations taking approaches that don’t serve their overall strategic objectives such as:

  • an insufficient focus on business drivers
  • only seeking piecemeal advice from Legal
  • a lack of project management (that results in key activities happening out of order)
  • a lack of early stakeholder engagement
  • an unsuitable project team (that doesn’t have the right subject matter experts)

And the author is dead on here. These are common mistakes. The author, as noted in our introduction, also had nine tips that included the following six:

  • early engagement of all stakeholders
  • realistic (not overly ambitious unrealistic) plans
  • core team with (enough) subject matter experts from day one
  • the desired results and a business case up front
  • do multi-round tenders that include detailed RFIs
  • define rules of engagement for stakeholders and vendors

But it was the following three that need to be highlighted because the doctor doesn’t see this advice enough in seven steps to successful sourcing articles.

  1. document all key requirements for the project in advance
  2. document all key product and service requirements, including any necessary terms and conditions that must be agreed to for the vendor to engage with the business
  3. get the right Legal team on board before the event is launched

Why are these three tips so tantamount to triumphant sourcing events?

  1. multiple reasons to document all key requirements for the project
    • if you don’t document all key requirements, you can’t be sure you know them
    • you can’t get the project right if you’re missing key requirements
    • you need to make sure you don’t invite any vendors that can’t even meet the key requirements
  2. multiple reasons again to document all key product and service requirements
    • if you don’t document all key requirements, will you buy the right product or service for you and, more importantly
    • you can’t share all the key data with vendors, which is important to ensuring they quote on the right products or service and
    • they can’t self select out if they can not meet any of your core business requirements, which means you could waste time analyzing their bids, optimizing the perfect award, only to start over when they figure out they’re not right for you (or, even worse, when they deliver the first order and nothing works)
  3. multiple reasons to get the right Legal team (which might just be one in-house counsel, or might be three hot-shots from an external firm for a complicated, 100M purchase that can make or break the company)
    • they know the key requirements that need to be addressed and in the contract to minimize internal risks
    • they know the risks of the dealing with companies in other geographies and what you have to consider for the suppliers you are planning to invite to the event
    • they know what should be in the contract, and communicated as such up-front, what should be in an appendix, and what is not crucial
    • they know the data you have to collect to meet the different regulatory (reporting) requirements that your organization has to adhere to, and which requirements you have to communicate with suppliers to make sure they can, and agree to, give you the right data

In other words, Legal, especially for any buy that is financially significant, strategically significant, or risky, is key to get involved in the right aspects up-front. (And note that if you build a team with the right stakeholders, and document the right requirements, Legal will be able to work quickly and efficiently, and even more so if you are willing to use standard templates and insist that vendors agree to your standard contracts and standard terms.)

Procurement Automation: Good. Automated Procurement: Bad.

We shouldn’t have to say this. It should be very clear by now. But given that a number of vendors are using the terminology interchangeably, possibly to convince you they have the right solution, maybe it’s not clear. But it needs to be. Because procurement automation is NOT the same as automated procurement and while procurement automation, properly done, is the best investment an average over-burdened and under-resourced Procurement department can make, on the flip side, AI-driven automated procurement is the absolute worst. To put things in perspective, downgrading Excel to Lotus 1-2-3 would be a better move. But let’s back up, and start with some definitions.

Procurement Automation is the process of automating certain procurement tasks that can be best accomplished by machines and procurement automation technology is the technology that automates the tasks that can be best done by machines. In simpler terms, it automates the “thunking” by doing all of the tactical, almost mindless, work that is a waste of a senior Procurement professional’s time.

The Source-to-Pay cycle is full of tasks that are best done by machines when appropriate rules and boundaries are defined. For each major area, we’ll outline some of these tasks as an example.

Intake/Orchestration

Procurement Automation will analyze the request, identify similar requests made in the past, identify the actions used to resolve those requests, identify the suppliers considered and selected, the products and services used, and other information. It will present that information to the buyer, including the suggested actions, and allow the buyer to one-click initiate any of the suggested actions, which might include a sourcing event, contract renegotiation, catalog purchase, etc.

Sourcing

Procurement Automation will, when a user kicks off a sourcing event for one or more products, automatically bring up the suggested suppliers, automatically suggest the appropriate questionaries and forms, automatically suggest the appropriate Ts and Cs to insist on up front, automatically send the RFP to suppliers, automatically analyze the responses to make sure they are complete, in the correct format, and in an expected range; automatically compare the responses to find deviations from the norm; automatically highly the lowest and highest costs, CO2 factors, etc. and present all that information to the buyer.

Supplier Management

Procurement Automation will, when a supplier is selected, automatically handle the onboarding; monitor the data for changes; monitor the performance metrics; monitor the OTD; monitor third party financial and risk metrics; and alert the buyer to any issues and performance changes that are detrimental or may indicate forthcoming problems.

Contract Management

Procurement Automation will, when an award is selected, push the award into the Contract Management system, automatically generate the draft contract, send it to the supplier, highlight any redlines the supplier makes when it comes back and automatically inform the supplier if any non-negotiable terms and conditions (including those they agreed to when they responded to the RFP), and automate the generation of the response email when the buyer does their redlines.

e-Procurement

For catalog buys, it will automatically generate the POs, route them for necessary approvals, distribute them to the suppliers when approved, automatically match the ASNs when they come back, alert the buyers if ASNs are not received in a timely basis, and match the invoices when they come in.

Invoice-to-Pay

When the invoice comes in, it’s automatically matched to the purchase order, it’s checked for price accuracy, identified as partial or full, verified to be non-duplicate, and if any checks fail, it’s bounced back to the supplier with a description of the issues and a request for correction and resubmission. If the resubmission deals with the problems, it’s queued waiting for goods receipt/confirmation if not present, or matched if present. If the match is made, then it’s automatically sent down the approval chain, and if it’s not made within a certain time period, an alert is raised.

In all cases, it’s automating the tactical tasks that don’t require any decision making and only involving the human when necessary.

In contrast, Automated Procurement is the process by where entire procurement processes are handed over to the machine to fulfill instead of the human. In other words, when an intake request comes in and the buyer marks it for sourcing, an Automated Procurement solution will handle the entire event up to and including the award and auto-generate and distribute the Purchase Order(s). The buyer is completely bypassed and the right inventory showing up at the right time at the right price is left entirely up to the machine. Sounds good in theory. Looks good in practice when it actually works, which it will some of the time. But grinds the company to a halt when it fails.

A machine that pursues lowest cost will select an unproven non-incumbent supplier for a critical part when the suppler, who has not supplied that particular part to the company before, outbids the incumbent. It will not detect that the bid was made in an desperate attempt to help the financially struggling supplier stay in business, that the bid is not sustainable, and that the supplier is not capable of producing the part at the indicated level of quality. Then, when the first shipment is mostly defective, and the promised rush replacement order never arrives because the supplier goes out of business, the production line for the 75K luxury car folds all for lack of a single control chip. (A similar situation has occurred in the past. Recently, chip shortages stopped Cherokee production in 2021, and that wasn’t the first occurrence. Or even the second, or third.)

Machines are not intelligent. Not even close. And expecting them to make a good decision every time with no logic whatsoever (as modern Artificial Idiocy algorithms just stack probabilistic equations on top of probabilistic equations almost ad infinitum) is lunacy. So while you should invest in the best Procurement Automation tech you can get your hands on, you should steer clear of any and all Automated Procurement Solutions those fancy new startups try to sell you. While those solutions may work 90% of the time, that last 10% of the time, they won’t work that great. And, in particular, that last 1% of the time they will fail so miserable that the disruptions and losses that result will more than cancel out any and all savings and efficiencies you might get from the 90% of the time the tech worked in the beginning.