Category Archives: Technology

You Don’t Need Gen-AI to Revolutionize Procurement and Supply Chain Management — Classic Analytics, Optimization, and Machine Learning that You Have Been Ignoring for Two Decades Will Do Just Fine!

Open Gen-AI technology may be about as reliable as a career politician managing your Nigerian bank account, but somehow it’s won the PR war (since there is longer any requirement to speak the truth or state actual facts in sales and marketing in most “first” world countries [where they believe Alternative Math is a real thing … and that’s why they can’t balance their budgets, FYI]) as every Big X is pushing Open Gen-AI as the greatest revolution in technology since the abacus. the doctor shouldn’t be surprised, given that most of the turkeys on their rafters can’t even do basic math* (but yet profess to deeply understand this technology) and thus believe the hype (and downplay the serious risks, which we summarized in this article, where we didn’t even mention the quality of the results when you unexpectedly get a result that doesn’t exhibit any of the six major issues).

The Power of Real Spend Analysis

If you have a real Spend Analysis tool, like Spendata (The Spend Analysis Power Tool), simple data exploration will find you a 10% or more savings opportunity in just a few days (well, maybe a few weeks, but that’s still just a matter of days). It’s one of only two technologies that has been demonstrated, when properly deployed and used, to identify returns of 10% or more, year after year after year, since the mid 2000s (when the technology wasn’t nearly as good as it is today), and it can be used by any Procurement or Finance Analyst that has a basic understanding of their data.

When you have a tool that will let you analyze data around any dimension of interest — supplier, category, product — restrict it to any subset of interest — timeframe, geographic location, off-contract spend — and roll-up, compare against, and drill down by variance — the opportunities you will find will be considerable. Even in the best sourced top spend categories, you’ll usually find 2% to 3%, in the mid-spend likely 5% or more, in the tail, likely 15% or more … and that’s before you identify unexpected opportunities by division (who aren’t adhering to the new contracts), geography (where a new local supplier can slash transportation costs), product line (where subtle shifts in pricing — and yes, real spend analysis can also handle sales and pricing data — lead to unexpected sales increases and greater savings when you bump your orders to the next discount level), and even in warranty costs (when you identify that a certain supplier location is continually delivering low quality goods compared to its peers).

And that’s just the Procurement spend … it can also handle the supply chain spend, logistics spend, warranty spend, utility and HR spend — and while you can’t control the HR spend, you can get a handle on your average cost by position by location and possibly restructure your hubs during expansion time to where resources are lower cost! Savings, savings, savings … you’ll find them ’round the clock … savings, savings, savings … analytics rocks!

The Power of Strategic Sourcing Decision Optimization

Decision optimization has been around in the Procurement space for almost 25 years, but it still has less than 10% penetration! This is utterly abysmal. It’s not only the only other technology that has been generating returns of 10% or more, in good times and bad, for any leading organization that consistently uses it, but the only technology that the doctor has seen that has consistently generated 20% to 30% savings opportunities on large multi-national complex categories that just can’t be solved with RFQ and a spreadsheet, no matter how hard you try. (But if you want to pay them, a Big X will still claim they can with the old college try if you pay their top analyst’s salary for a few months … and at 5K a day, there goes three times any savings they identify.)

Examples where the doctor has repeatedly seen stellar results include:

  • national service provider contract optimization across national, regional, and local providers where rates, expected utilization, and all-in costs for remote resources are considered; With just an RFX solution, the usual solution is to go to all the relevant Big X Bodyshops and get their rate cards by role by location by base rate (with expenses picked up by the org) and all-in rate; calc. the expected local overhead rate by location; then, for each Big X – role – location, determine if the Big X all-in rate or the Big X base rate plus their overhead is cheaper and select that as the final bid for analysis; then mark the lowest bid for each role-location and determine the three top providers; then distribute the award between the three “top” providers in the lowest cost fashion; and, in big companies using a lot of contract labour, leave millions on the table because 1) sometimes the cheapest 3 will actually be the providers with the middle of the road bids across the board and 2) for some areas/roles, regional, and definitely local, providers will often be cheaper — but since the complexity is beyond manageable, this isn’t done, even though the doctor has seen multiple real-world events generate 30% to 40% savings since optimization can handle hundreds of suppliers and tens of thousands of bids and find the perfect mix (even while limiting the number of global providers and the number of providers who can service a location)
  • global mailer / catalog production —
    paper won’t go away, and when you have to balance inks, papers, printing, distribution, and mailing — it’s not always local or one country in a region that minimizes costs, it’s a very complex sourcing AND logistics distribution that optimizes costs … and the real-world model gets dizzying fast unless you use optimization, which will find 10% or more savings beyond your current best efforts
  • build-to-order assembly — don’t just leave that to the contract manufacturer, when you can simultaneously analyze the entire BoM and supply chain, which can easily dwarf the above two models if you have 50 or more items, as savings will just appear when you do so

… but yet, because it’s “math”, it doesn’t get used, even though you don’t have to do the math — the platform does!

Curve Fitting Trend Analysis

Dozens (and dozens) of “AI” models have been developed over the past few years to provide you with “predictive” forecasts, insights, and analytics, but guess what? Not a SINGLE model has outdone classical curve-fitting trend analysis — and NOT a single model ever will. (This is because all these fancy-smancy black box solutions do is attempt to identify the record/transaction “fingerprint” that contains the most relevant data and then attempt to identify the “curve” or “line” to fit it too all at once, which means the upper bound is a classical model that uses the right data and fits to the right curve from the beginning, without wasting an entire plant’s worth of energy powering entire data centers as the algorithm repeatedly guesses random fingerprints and models until one seems to work well.)

And the reality is that these standard techniques (which have been refined since the 60s and 70s), which now run blindingly fast on large data sets thanks to today’s computing, can achieve 95% to 98% accuracy in some domains, with no misfires. A 95% accurate forecast on inventory, sales, etc. is pretty damn good and minimizes the buffer stock, and lead time, you need. Detailed, fine tuned, correlation analysis can accurately predict the impact of sales and industry events. And so on.

Going one step further, there exists a host of clustering techniques that can identify emergent trends in outlier behaviour as well as pockets of customers or demand. And so on. But chances are you aren’t using any of these techniques.

So given that most of you haven’t adopted any of this technology that has proven to be reliable, effective, and extremely valuable, why on earth would you want to adopt an unproven technology that hallucinates daily, might tell of your sensitive employees with hate speech, and even leak your data? It makes ZERO sense!

While we admit that someday semi-private LLMs will be an appropriate solution for certain areas of your business where large amount of textual analysis is required on a regular basis, even these are still iffy today and can’t always be trusted. And the doctor doesn’t care how slick that chatbot is because if you have to spend days learning how to expertly craft a prompt just to get a single result, you might as well just learn to code and use a classic open source Neural Net library — you’ll get better, more reliable, results faster.

Keep an eye on the tech if you like, but nothing stops you from using the tech that works. Let your peers be the test pilots. You really don’t want to be in the cockpit when it crashes.

* And if you don’t understand why a deep understand of university level mathematics, preferably at the graduate level, is important, then you shouldn’t be touching the turkey who touches the Gen-AI solution with a 10-foot pole!

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 …

The Best Way Procurement Chiefs Can Create a Solid Foundation to Capitalize on AI

As per our recent post on how I want to be Gen AI Free, the best way to capitalize on Gen-AI is to avoid it entirety. That being said, the last thing you should avoid is the acquisition of modern technology, including traditional ML-AI that has been tried and tested and proven to work extremely well in the right situation.

That being said, if you ignore the reference to Gen-AI, a recent article on Acceleration Economy on 5 Ways Procurement Chiefs Can Create a Solid Foundation had some good tips on how to go about adopting ML-AI with success.

The five foundations were quite appropriate.

1. Organize

A plan for

  1. exactly where the solution will be deployed,
  2. what use cases it will be deployed for,
  3. how valid use cases will be identified, and
  4. how the solution is expected to perform on them.

There’s no solution, even AI, that can do everything. Even limited to a domain, no AI will work for all situations that may arise. As a result, you need a methodology to identify the valid use cases and the invalid use cases and ensure that only the valid uses cases are processed. You also need to ensure you know the expected ranges of the answers that will be provided. Then you need to implement checks to ensure that no only are only valid situations processed but that only output in an expected range is accepted in any automated process, and if anything is outside the expected norms anywhere, a human with appropriate education and training is brought into the loop.

2. Create a Policy

No technology should be deployed in critical situations without a policy dictating valid, and invalid, use. Moreover, any technology definitely shouldn’t be used by people who aren’t trained in both the job they need to do and proper use of the tool. Even though most AI is not as dangerous as Gen-AI, any AI, if improperly used, can be dangerous. It’s critical to remember that computers cannot think, and only thunk on the data they are given (performing millions of calculations in the time it takes an average person to perform two). As such, the quality of output is limited both to the quality of data input and the knowledge built into the model used. Neither will be complete or perfect, and there will always be external factors not considered, which, even if normally not relevant, could be relevant — and only an educated and experienced human will know that. (Moreover, that human needs to be involved in the policy creation to ensure the technology is only used where, when, and how appropriate.)

3. Understand Your Platform(s) of Choice

Just like there are a plethora of Gen-AI applications, a lot of different vendors offer AI applications, and even if most are similar, not all are created equal. It’s important to understand the similarities and differences between them and select the one that is right for your business. (Consider the algorithms and models used, the extent of human validated training available, typical accuracy / results, and the vendor’s experience in your use case in particular when evaluating an AI solution.)

4. Practice

Introducing new tools requires process changes. Before introducing the tool, make sure you can execute the associated process changes, first by executing training exercises on the different types of output you might get and then, possibly by way of a third party who uses a tool on your behalf, using real inputs and associated outputs. While the AI may automate more of the process, it’s even more critical that you respond appropriately to parts of the process that cannot be automated or where the application throws an exception because the situation is not appropriate to either the use of AI or the use of the AI output. (And if you don’t get any exceptions, question the AI … it’s not likely not working right! And if you get too many exceptions, it’s not the right AI for you.)

5. ALWAYS Ask Yourself: “Does that Make Sense?”

Just like Gen-AI hallucinates, traditional AI, even tried-and-true AI that is highly predictable, will sometimes give wrong results. This will usually happen if bad data slips in, if the use case is on the boundary of expected use cases, or the external situation has changed considerably since the last time the use case arose. Thus, it’s always important to ask yourself if the output makes sense. For tried-and-true AI where the confidence is high, it will make sense the vast majority of the time, but there will still be the occasional exception. Human confirmation is, thus, always required!

With proper use, AI, unlike Gen-AI (which fails regularly and sometimes hallucinates so convincingly that even an expert has a hard time identifying false results), will give great results the majority of the time — so you should seek it out and implement it. Just also implement checks and balances to catch those rare situations it doesn’t and put a human in the loop when that happens. Because traditional use-cases are more constrained, and predictable, it’s a lot easier to identify and implement these checks and balances. So do it … and see great success!

The Public Sector is Giving Procurement Integrity A Bad Name … Can the Private Sector Fix It?

A recent article over on Global Government Forum on Procurement Integrity: A Big Problem That’s Worse Than Most Organizations Think, pointed out that errors, fraud and abuse in procurement cost governments and organizations millions of dollars every year, and even though recent headlines in the US (TriMark, Booz Allen Hamilton), UK (NHS, Royal Mail), and Canada (ArriveCan) are starting to shine the light on the extent of (public sector) procurement fraud, the problem is still bigger than you think. Much bigger.

Current estimates are that organizations, across the public and private sectors, lose 5% per year due to procurement errors, abuse, and fraud. Given that Global GDP is about 85 Trillion dollars, at 5%, that’s 4 TRILLION dollars estimated to be lost annually to errors, abuse, and fraud. And that’s probably a low-ball estimate due to the fact that we just calculated that Over One TRILLION dollars will be wasted on IT software and services due, primarily, to lack of knowledge and/or outright stupidity (and not malicious intent, but if it’s easy for consultancies and third parties to considerably over bill for legitimate goods and services that you need, imagine how much they are fleecing you for goods and services that you don’t need and may not even receive).

It’s highly likely that the true cost of errors, abuse, and fraud (internal, collusion, and external) is closer to 10% of total GDP, or close to EIGHT TRILLION. That’s at least twice the GDP of every country on the planet except China and the United States. That’s a BIG PROBLEM, which is definitely not being helped by the 100M to Multi Billion Procurement Frauds being reported almost monthly across major western economies — and multi-million dollar fines don’t repair the damage. (They don’t even come close.)

This is damage which Procurement needs to repair — because Procurement is the only department that has any hope of putting proper procedures, processes, and platforms in place to minimize the errors; training the organizational employees on proper procedures and monitoring the implementations to prevent abuse; and putting in place proper detection systems to detect, and prevent, potential fraud and quickly identify and track it when it happens.

Unless all the bucks go through, and stop at, a modern Procurement department run by a CPO who puts in place proper people, processes, and platforms, loss is going to continue to run rampant. Which means that while the public sector is failing us daily, the Private sector has to step up and restore the integrity of Procurement. It can start by utilizing some of the the techniques in the linked article, and continue by continually learning and implementing the best technology and processes it finds to not only uncover significant savings in inflationary times, but return integrity and trust into big business, and give governments who have lost their way a model to follow.

And for more details on Bad Buying to avoid, and how to achieve Procurement with Purpose, the doctor suggests you start by following the great public procurement defender, Peter Smith.

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!