Category Archives: Spend Analysis

The Lack of Adoption of Analytics is NOT Complicated!

According to THE PROPHET, the reason that we’ve never seen a breakout $100M+ pure-play (spend) analytics vendor is it’s complicated. (Source: LinkedIn)

But the reality is that it’s really not.

First of all, approximately one third of all multi-nationals are headquartered in the US. In other words, one third of global enterprise is based out of the US, where the strategic decisions are made. Let’s say that again, one third!

Secondly, and this is the real explanation, in our age of participation trophies and only focusing on the positive (when there really isn’t any), no one is willing to state the truth, and that is most of the employees responsible for strategic [spend] analysis are just too math stupid.

Analytics, at its core, requires good mathematics skills and, with traditional analytics applications, good computer skills.

However, the US, where many multi-nationals are based, consistently ranks in the lower part of the OECD international rankings and is currently 34th in the PISA [out of 79 scored countries] (with an average numeracy score of 249, below the TOTAL OECD average of 263, with over 1/3 of its adult population at level 1! This means they can’t even do basic arithmetic and problem solving [or calculate a tip FFS, but that does explain why they believed their administration when they lied and said other countries pay the tariffs] — and that’s the average business employee in the US, since anyone with a level 2 on the OECD can likely fake it in a STEM career in the US.

As for THE PROPHET‘s reasons as to why Spend Analysis has consistently underperformed the hype:

  • While 3/4 of solutions have always been reporting in drag, I’ve been highlighting at least a dozen Best of Breed solutions consistently for the past decade. They have existed for the past 20 years, you just had to look (and understand what to look for. But this site did a great job of helping you with that!)
  • Yes, scale came at the cost of dumbing down the UX (for the US market in particular)!
  • Unfortunately there is no faster way to die as a Spend Analysis vendor then to get scooped up by a (mega) suite or a Big X Comsultancy.
  • Actually, the analytics and optimization is not powerful or complex enough in most solutions. Again, the problem is that the vendor didn’t add incremental levels of simplification (i.e. dumbing down) so each user could take advantage of it at their mathematical (in)competency level.

But the real reason, as hinted above, is that employees resisted these advanced spend analytics solutions because they knew they didn’t have the mathematical skills to use them. (Which the US Education System should be blamed for [and why it should be fixed, not dismantled], not the employees, unless those employees went to University and chose not to take math courses to try and make up for the failings of the public education system they were subjected to.)

As for THE PROPHET‘s signals that the times they are a changin’:

  1. Good + Cheap = Dangerous
    Faster? Check! Cheaper? Check! Smarter? Well … Ask Woody!
  2. Analytics is Merging with Execution
    This is key for adoption of analytics — do it when you need it and apply the findings right away.
  3. Intake, Orchestration and Agentic Tech
    I guess I have to say it again!
    𝐒𝐩𝐞𝐧𝐝 𝐎𝐫𝐜𝐡𝐞𝐬𝐭𝐫𝐚𝐭𝐢𝐨𝐧 𝐢𝐬 𝐂𝐥𝐮𝐞𝐥𝐞𝐬𝐬 𝐟𝐨𝐫 𝐭𝐡𝐞 𝐏𝐨𝐩𝐮𝐥𝐚𝐫 𝐊𝐢𝐝𝐬!
    When what we really need is a Revenge of the Nerds! (If the USA even has any left!)

However, the real reason that we may finally be entering a new era in analytics is the following:

4. Most companies are trying to stave off bankruptcies as a result of US trade, market, etc. decisions that have already bankrupted many SMEs and they now realize that analytics is a key part of that solution. You can’t optimize spend you don’t understand, or understand the impact of a sudden 145% increase in tariffs if you don’t understand how much you are sourcing from the country in question.

The Green Cabbage Grows Another Leaf

When we first invited you to Take a Leaf from the Green Cabbage eighteen months ago, we covered one of the most extensive services-backed indirect-focussed spend analysis players in the market (serving clients like Delta, Home Depot, Dell, Adobe, etc.) with deep support for:

  • SaaS Subscriptions: going well beyond many of the dime-a-dozen SaaS cost analyzers (and there are quite a number of those now, see our coverage of the Sacred Cows), Green Cabbage can unpack the purposely confusing consumption models the big players throw at you (to try and get you to spend more than you need to), do SKU level price comparisons, provide you deep insight into negotiation opportunities, and, through their MITs, provide guidance into how to achieve actual savings
  • Contingent Workforce (CW): detailed insights into over 70,000 position-level-market (geography) combinations for deep negotiation insights across 120 countries
  • Clinicals: deep knowledge and insights into clinical SKUs and sourcing
  • Invari: their Invoice management platform, which allows invoice and payment data to automatically be extracted into the appropriate spend cubes while also providing core I2P capability (and eliminating the need for YAP — yet another platform)
  • MITs (Market Intelligence Theses) across SaaS, CW, and Clinicals: which could be lightweight, comprehensive, or competitive; guaranteed to be completed within 3 days, and usually completed in 1 to 2 days for lightweight and comprehensive
  • Contract Library: Green Cabbage starts by loading your contracts, not your spend data, extracting the key terms and pricing, and then loads your spend data, tying as much as they can to your contracts (for immediate insights into any pricing violations); this is important because this is the foundation for the deep insights they can provide via Elegion, which we mentioned, but didn’t get into as it was in earlier stages at the time

Now, since it’s only been six months, you’re probably wondering how much new stuff could there be that would entice Sourcing Innovation to pen an update after such a short time. Quite a bit actually. There are five improved and new offerings in particular that need to be addressed:

Elegion (formerly GC Legal)

Elegion, their in-depth contract clause repository, contains hundreds of business, commercial, and legal terms; conditions; and standard contract clauses with an explanation of what each term is along with best-in-class definitions of each clause.
The platform makes it a point to call out the highly-relevant “mousetraps” that suppliers will use to (often unfairly) protect themselves through inclusion, exclusion, or modified language. The best-in-class definitions are drafted by licensed attorneys with expertise in the relevant subject matter and areas specifically supported by Green Cabbage (IT, Marketing, and Contingent Workforce). Moreover, the attorneys who drafted these clauses have collectively negotiated thousands of deals from both sides of the table in these areas.

In addition, directly through the platform, via secure end-to-end encryption, users can use their credits* to asynchronously request input on specific clauses in the agreements presented to them during their negotiation with a response guaranteed within 48 hours. Moreover, they can also request synchronous 30 min or 60 min 1-on-1 consultations with an on-staff Green Cabbage Attorney who is an expert in the contract they are currently negotiating. (Green Cabbage‘s top attorneys used to work for the top tech giants and contingent workforce providers.) This service, of course, uses up credits much faster than one-time asynchronous message requests, but can be invaluable when negotiating a multi-million dollar contract. However, the best part of the offering is the deep insight into terms, conditions, clauses, and best language/practice that can allow a buyer to address most of their legal questions self-serve with confidence!

* each subscription comes with a certain number of credits, and clients can always buy more

Better Support for Corporate Hierarchies

As we indicated in our first article, Green Cabbage supports a number of big Private Equity firms, including Private Equity Firms that manage a number of other Private Equity Firms (where each has specialized funds). As such, they have built an infinitely extensible corporate hierarchy with appropriate view and access permissions, that allow an individual, with the right permissions in any department or company, to see all of the spend in all of the departments and companies under their purview down the chain to identify opportunities for contract (re)negotiations through the utilization of a common platform provider, CWM provider, etc. (You may not believe it, but even the mega-corps will respond to a renegotiate now or we stop using you across 10 of our companies … no need to wait for the renewal.) This helps them answer questions like “how much do I spend across my portfolio with supplier S or on category X”, which is very powerful information to have for leverage and can often support contract negotiation at a group or higher level.

Receptio Integration (GPA)

If you’re a large mid-market or Global 3000, you use a lot of tech. These will range from small task/function/department specific small SaaS apps that go on the P-Card to multi-million contracts with the likes of Microsoft, Oracle, or Google. And while there is savings available in virtually every contract in every price range, for a large mid-market or global multi-national, it’s not worth chasing 5K on a 50K contract when there is likely 200K to be saved on a 1M contract. In this situation, you’ll just blindly accept the renewal for the small SaaS app if the price increase is 10% or less, and spend your hours negotiating an extra 10% from the behemoth because that’s big bucks. But what about the mid-range? The 50K to 250K contracts where there’s likely 5K to 50K of savings? That’s nothing to scoff at, but the cost of the effort involved, especially if you need to involve Legal, sometimes negates the value you realize.

So what’s the answer? Pre-negotiated Terms and Conditions from specialized GPOs that specialize in the best rates, on average, from these providers by bringing these, usually smaller, providers more business volume than they’d get on their own. That’s Receptio — an integration platform that connects you to Green Cabbage‘s Group Purchasing Arm partners that can get you better deals from many SaaS providers and Contingent Workforce providers than you can get on your own when your needs fall into the mid-range of spending.

Supplier Newstand

What’s the one thing missing from most supplier management and contract management platforms? Supplier Insights that are meaningful at contract negotiation and renewal time. Why is this typically missing? Because it requires scouring the internet to find relevant news that relates to the supplier, pulling in the links and creating summaries (hallucination free), and then tagging the articles to relevant subject matter (ownership, management, product, etc.) to allow a buyer to determine not only the org type and management but whether that’s likely to change (e.g. the organization just brought in an expert consultant on going public, announced a new CEO, etc.).

Green Cabbage recently released the first version (that currently scours over 750 news channels and vetted news sources) of this that allows you to see and search all of the recent news associated with a supplier — including, but not limited to: acquisition/merger activity, pricing changes, layoffs, executive movement, etc. — and is currently working on the next release that will associate these articles with pre-defined tags to provide quick insights.

Marketing Spend Intelligence

This is the newest offering of the Green Cabbage platform — deep, specialized insight into marketing spend. IT Spend may be the biggest at 5 Trillion Spend, but Marketing is no slouch either at an extrapolated estimated value approaching 2 Trillion when you correlate various sources and metrics, with Advertising spending alone topping 1 Trillion in 2024 (with the E&M industry being a 2.8 Trillion industry in the US alone). And, like SaaS, there is a huge amount of overspend in this category as well, especially in the non-creative spend in production and distribution when material and standardized service costs are not analyzed. Moreover, very few spend analysis, procurement software, or consultancies can provide these deep insights and guidance — and fewer still with guidance for marketing professionals with little-to-no Procurement experience. This alone almost warrants an update as market intelligence alone unveils huge opportunities, but, as you just noticed, this is just one of many improvements.

In other words, Green Cabbage has been advancing their offering at a rapid rate, as they also expand globally, with offices in the United Kingdom and India to complement their US office, and expansion into AustralAsia imminent.

2025 Is Just Another Year … But Is It All Doom and Gloom? Part 2 (Real Tech!)

As per our first instalment, it all depends on your point of view and whether you are willing to look beyond the hype, buckle down, and get the real job done.

For instance, just the following five technologies will eliminate 95% or more of your tactical sourcing, procurement, and supplier monitoring work — and all you have to do is find them, properly implement them, and use them. Let’s talk about them briefly.

Real DIY Analytics

The ability to analyze the data you want, when you want, how you want, enriched and augmented using the auxiliary data you want … and not in predefined dash-boards or hidden “AI Agents” which may, or may not, do the analysis you want (and need) … cannot be underestimated! Real value comes from ad-hoc analysis and investigating hunches, abnormalities, and trend changes when you discover them; not days, weeks, or months later when the “cube” has been refreshed, and it might be too late to correct a problem or capture an opportunity!

Remember, this is not 2005, this is 2025, and there are at least half a dozen great DIY (spend) analysis solutions that will do most of what you want, for a price tag that is a fraction of what you might expect, and if you are okay with full DIY, some of these start at a price you can put on a P-Card. For example, Spendata Classic (which can handle data sets up to 5 Million Rows) can be obtained for $699 a year, and Enterprise, which can handle data sets up to 15 Million records, which comes with unlimited use for 5 users (and view licenses for more), and some consulting and setup, starts at an amount that will surprise you. (You can still put it on a P-Card if you pay monthly.) And there is literally nothing you can’t do in it if you’re willing to apply a little elbow grease. It truly is The Power Tool for the Power Analyst.

(Strategic Sourcing Decision / Supply Chain Network) Optimization

Yes, it’s math. But you know what? Math works! And when you use deterministic math, it’s 100% accurate, every time! And it’s one of only two technologies in S2P+ that was been proven (by multiple analyst firms) to repeatedly identify 10%+ savings year-over-year (but since this was pre-COVID and pre- the 47th, we need to amend this finding to adjust for inflation and tariffs). And as an FYI, the other technology was NOT AI. (It was proper DIY spend analysis. Only Human Intelligence can intuit where to look for previously unidentified opportunities, the best AI can do is just follow a script and run standard analysis. Furthermore, the thing about spend analysis is that an analysis that identifies an opportunity only helps you ONCE — once you capture the opportunity, the analysis is useless. You need to do a new, and different, one.)

Rule Based Automation

When you think about most tasks across Source to Pay, most of them are just execution of simple, easily defined processes — most of which don’t require much (if any) intelligence and, thus, don’t need AI (and shouldn’t use an unpredictable AI agent when you can encode a process that gets it right, guaranteed, every single time. (Plus, the way you want to source, buy, pay, track, manage, etc. is probably a little bit different than your peers, and who knows how the AI Agent would do it for you. You certainly don’t!)

With rule based automation, you can easily execute an entire sourcing event in the background all the way to award if you like. It can run auctions, it can run multi-round RFPs with detailed feedback (it’s all calculations, response comparisons, and decisions on what data you want to share and how blinded you want it), it can run analyses and optimizations, it can calculate recommended award decisions subject to the constraints and goals that matter to you, present that to you for acceptance, or, if it’s a simple winner take all or top 2 situation, create the award automatically, send it out, get supplier acceptance, assemble the contract, and send it for e-Signature. You don’t need Agentric/Gen-AI, just tech we’ve had for over a decade!

Machine Learning

Now, when it comes to Enterprise Master Data Management and Administration (E-MDMA) and Invoice Processing, it can be quite a lot of work to keep up with the mapping, cleansing, and enrichment rules, and you don’t want to have to manually define all the new rules every time a new data element appears or a new invoice format arrives, especially if the system can auto-detect/”guess” 90% of the time through rule re-use and generalization. With machine learning, the system can keep track of your corrections, mathematically extract models, and adjust it’s rules to handle the new mapping again automatically as well as improve its suggestion logic when it doesn’t know what to do — increasing the chance that you just have to “accept” a new rule vs. defining it from scratch. (Unlike Gen-AI which just tries to find similar patterns somewhere to present you with something that may or may not have any correlation to your business and even reality!) And we’ve had great non-(pure-)Neural Network machine learning that works great with enough data for decades! Predictive analytics was making huge progress late last decade before this Gen-AI BS took over and could have helped Procurement departments automate 90%+ of what they wanted to automate with just a bit more development and effort by the leading vendors — it just required a bit more time, money, and focus. (Gen-AI has set us back a decade!)

Analytics Backed Augmented Intelligence

We don’t need machines to make decisions for us (especially when they can’t think, or even reason), we need machines to do calculations for us that help us make the right decision quickly and effectively. We need the machine to automatically identify and retrieve all of the relevant data, do all the relevant situational and market analysis, do all the predictive trend analysis, identify all of the typical responses with respect to the situation, predict the likely success of each, and present us with a set of ordered recommendations, complete with the calculations and supporting analysis, so we can pick one or realize that the machine didn’t/couldn’t know about a recent event or a human factor and that none of the responses are right (and that only we could craft one, with full information on the situation). The machine may not think, but the thunking it can do far exceeds our computational ability (billions of computations a second, all flawless), and that’s EXACTLY what we should be using the machine for.

If we give up on this Artificial Intelligence BS (even if the current models are right, machines need to be 100 Million times more powerful for it to even “mimic” human intelligence. That’s not happening any time soon) and instead just give all the machines all the (boring) grunt work, leaving us free to do what they can’t (strategy and relationships). If we do so, we can be at least 10 times as productive as we are now and deliver on the promises Gen-AI / Agentric AI / AGI never will, and do so at a small fraction of the cost. And oh, we have that tech today … we just need to deploy and integrate it properly!

And this is just the beginning of what you can do when you look beyond the hype and use your Human Intelligence [HI!] to cut through all the BS.

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!

This originally posted on March 22 (2024).  It is being reposted because we need solutions, Gartner (who co-created the hype cycle) published a study which found that Gen-AI/technology implementations fail  85% of time, and its because we have abandoned the foundations — which work wonders in the hands of properly applied Human Intelligence (HI!).  Gen-AI, like all technologies, has its place, and it’s not wherever the Vendor of the Week pushes it, but where it belongs.  Please remember that.

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, Mid-Sized Consultancy, and the majority of software vendors are 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, an expert consultant will still claim they can with the old college try if you pay their top analyst’s salary for a few months … and at, say, 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 and Mid-Sized 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 / Mid-Size- 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!

Ghosts in Data Can Indicate Fraud but …

… so can the telltale signs, and if you don’t even know how to spot those signs, why even look for the ghosts (which are very hard to find).

A recent article over on Dev Discourse on Spotting the Ghosts Using Big Data to Detect Fraud in Government Purchases described the results of a study by the University of Craiova, Romania, Institute of Financial Studies, Bucharest, and three other universities that examined how big data and online systems can help make public procurement more transparent and fair.

They analyzed the data from Romania’s public procurement system in 2023, where the government made 2.25 million purchases that totalled about 3.22 Billion Euros. In this study, the researchers were particularly interested in “exclusive” relationships, where a vendor only works with one public entity. They found that over 14% of all public purchases fell into this category, which is concerning as these exclusive deals can indicate problems like favouritism or fraud because they don’t follow the usual rules of fair competition.

This is just one standard way to identify potential fraud. Other ways, as noted by the article, are to

  • look for unusual transaction values,
  • look at the geographical distribution of unusual transactions or sole-source relationships (and for clusters in particular) as many happen in specific regions (suggesting that certain areas have higher risks of fraud)
  • look at deals that were completed too quickly (such as those completed within minutes of posting) and that were awarded considerably after hours or on weekends,

If you’re not even doing these basics to identify potential fraud, then you’re not ready to look for ghosts in the data.

And when you are doing this, and you’re struggling to weed out the likely fraud in sole-source, unusual transaction value, and transactions completed at weird times, the next step is to do a basic analysis on the supplier. As the report indicates, the correlation between award level and supplier financial performance should be correlated, not inverted. If a supplier with poor financial performance keeps getting sole-source awards, that’s a BIG RED FLAG.

Then, run the standard contract-purchase_oder-invoice matching to make sure the amounts line up. And if you do all of the above, you’ll find more fraud, non-policy compliance, and overpayments than you ever thought possible. No ghosts needed. (But if you ever get to the point that all of the above comes up blank, reach out and the doctor will tell you how to find ghosts in the data as well as ghosts in the machine.)