Category Archives: Procurement Innovation

Next Generation Analytics NEEDS to Surface Root Cause Analysis …

… but relationship modelling alone is NOT going to get us there!

In another great article by Xavier Olivera of Hackett Spend Matters, he dives into the topic of how procurement analytics needs to work – from visibility to orientation because current procurement analytics offerings, while reasonably good and actionable at the process level compared to where they were a few years ago, are poor at helping users orient themselves when a specific goal or problem comes into focus.

He notes that when a procurement leader decides they want to improve X, the challenge is no longer visibility. It is knowing which analytics matter for that objective and which do not. But all the analytics platforms give them today is metrics, they don’t give them direction. Even if the user knows what metric to drill into first (because it is the highest, lowest, or outlier), all they can see is the data that contributed to that metric. For spend, the transactions. For a supplier rating, the Net Promoter Scores. For a process, the time in each step.

The users see the immediate “what”, but not the “why”. Why were the transactions high? Is this market price, has the quantity gone up, or is the supplier charging above the agreed upon rate. For a rating, is it because the performance wasn’t up to spec, the delivery is consistently late, or the service/interactions are very poor. For a process, which time was too long (compared to average), unless you can dig into another level (and even then, why it was too long).

According to Xavier, in situations like these, analytics has to work different. When a procurement leader wants to improve contract compliance, the starting point should not be a full review of all compliance metrics, benchmarks and dashboards. It should be a guided path that surfaces the specific reports, KPIs and comparisons most likely to explain the gap, given the organization’s operating context.

Which is a great start, but just surfacing those reports, KPIs, and comparisons that are statistically relevant or deviations from a norm doesn’t explain the gap, it just captures the gap. Not only is it the case that a KPI only becomes meaningful once it is examined in the right context, but it only becomes useful if there is enough data to allow the system to determine, with high statistical likelihood, the root cause and actions to take that could address the root cause (and not just the symptom these systems surface today).

Xavier than tells us that the ability to orient analytics effectively depends on the data’s structure, which is partially right, but doesn’t quite capture the entire requirement. He goes onto state that Procurement outcomes do not arise from isolated transactions … they emerge over time from relationships and analytics is most effective when the underlying data model can express these relationships explicitly. Which is closer. But the reality is that this still isn’t enough for proper root cause analysis.

It’s critical, because without relationships you can’t trace the end metric back to the source data, but just being able to identify the source data only tells you what is fundamentally wrong, not why, or what you need to do about it.

That’s where analytics needs to get to.

If your steel category transactions are high, you can trace back to the contracts and whether or not the rates are per contract, the shipping is per carrier quote, the tonnage as expected, and the breakdown across steel categories appropriate for your current product lines or construction products. If any rates or tonnage don’t add up, you know the issue is the invoices — but you don’t know why they are being paid. Were the new rates not properly encoded? Were the tolerances within acceptable limits and the automatic OK-to-Pay issued despite the mismatch? Are category managers blindly overriding the system because the supplier was threatening late shipments if payments didn’t appear on time?

In Xavier’s example, if contract compliance is low, why? Is it just a few suppliers, or even a single supplier, across a category. If just a few suppliers, are they unaware of the contract because of personnel changeover? Did a new industry regulation adversely affect them? Was it actually the fault of a carrier or sub-tier supplier they had no control over? This is what you need to determine to ensure that compliance actually improves and stays improved.

In other words, you need more than the data, you need models that capture what the data element used in a KPI is, who or what creates the data in the first place (and how they create that data), what the data range and typical mean/median/mode values are, what positively or negatively impacts the data, and what can be done if a shift is desired in the data.

Without this baked in intelligence into the model, even if the root data in the system can be uncovered, a user won’t understand what it means or where to start doing something about it. That’s where analytics needs to get to for analysts to be proactive instead of reactive.

And this is another area where the Busch-Lamoureux approach to Exact Purchasing will help. When you define your categories at a granular level appropriate to to the quadrant of the pocket cube they occupy, you not only know what influences their cost, but what also influences their supply, what defines their quality, and what role third parties (that you may have to monitor) play. You have the foundations for doing real proactive analysis and identifying not only what “good” is but what is most likely contributing to a “not good” metric or data point and what standard options exist to address, and try to improve, the data point (as you need to mitigate high risk and manage high complex categories at a detailed level).

In other words, the future is knowledge-based models that capture more than data points and calculations, but what the data points actually mean and what factors (represented by other data points) directly influence the data points you are analyzing.

The Best Article Xavier Olivera Has Ever Written!

In what “good” looks like today, and what it enables next, Xavier writes:


The next phase of P2P evolution will not be defined by who adds the most AI features fastest. It will be defined by who builds systems that make better decisions easier, safer and more repeatable, without losing the discipline that P2P was designed to enforce in the first place.

Truer words have never been spoken, especially in the Age of AI hype where the A.S.S.H.O.L.E. floods us with AI BS faster than we’ve ever been flooded with tech propaganda before!

Gen-AI LLMs (which are now powering the AGI craze, because if the first offering flops, just tweak and relaunch it with a few new buzzwords and claim it just needed more time, processing power, and tweaking) are not intelligent. They’re not even reliable. Hallucinations are a core function, Predictions are based on data available, even if it’s incomplete, incorrect, or indicative of actions known to be wrong for the situation in question that is typically an exception to the rule (or pattern). And many actions that can be taken automatically by these systems can’t be reversed (as there is not only no mechanism, but when they trigger an external event, the ability to reverse an incorrect action is completely out of your control).

Given this harsh reality, while they can monitor and make suggestions on how to govern, they can not govern and they do not count as governance. Governance is the only way to get to better, safer, and repeatable decisions. In reality, these Gen-AI /AGIs count as risk. Any error made with respect to a commitment (transaction, obligation, contract, large financial transfer) is an error that increases organizational jeopardy!

Governance is predictability, determinism, explainability, and traceability. This is not modern LLM-based Gen-AI / AGI system, but a traditional RPA or modern ARPA system (where all suggested rule and workflow changes and adaptations to prevent a future exception from occurring must be approved by a human) where all actions are governed by unbreakable rules, all exceptions are approved by a human, and all actions are completely traceable and 100% explainable — with no lies.

Remember that when you’re looking for your next Procurement solution, or you’ll end up with one that is worse, more dangerous, and less repeatable than the last generation solution you have now. For example, let’s say you implement an agent that monitors the inbound email channel for supplier communications regarding payment instructions and invoices. A communication comes in requesting a change of banking details for a supplier. The IPs and source domain look good so the change, and the change is to another bank local to the supplier (that they did business with in the past), so the update is sent to the AP system. The next day, an invoice comes in from the supplier for 10 times the number of units on the last PO. It’s from a supplier where shipment quantities never match the PO and where the buyer always approves the discrepancies, so the invoice is automatically paid. The next day another request comes in to change the bank account back to the original. It also passes the AI’s sniff test, so it happens. No one notices that a multi-million dollar payment was made to a fake supplier on a fake invoice, until the real invoice comes in a few days later, gets rejected because the PO has been matched, and the supplier flags an issue two weeks later when its AR team finally gets around to processing the exception, the AP team investigates, tells the supplier an invoice was paid, a back and forth occurs, and when the supplier finally gets the “proof”, informs the buyer that is NOT their bank account. By now, over three weeks and a day have passed, and the funds are unrecoverable as the thieves transferred the money out of the country and closed the fake account the day the fake invoice was paid. This is the “governance” you’ll get from an unintelligent agentic solution (masquerading as an AI employee) that does everything on probabilities.

When a Conflict Starts, It’s Already Too Late For Procurement To Pay Attention!

Supply Chains are not only hurting, they are breaking, and they have been since the US and Israel renewed the conflict with Iran and more-or-less brought the Strait of Hormuz to a close for pretty much every western country that is associated with the US.

A Strait that is critical not only for

  • global energy (as it normally sees 20% to 25% of global oil passing through it daily)

but also for

  • natural gas (up to 25%, at least it will further delay the AI Data Centers)
  • fertilizer (as it saw up to 50% of urea, ammonia, and sulphur supply passing through it daily, with the former a key fertilizer component)
  • methanol (but at least bootleggers will have to use real grain alcohol now) and petrochemicals
  • etc.

In other words, the Strait being close off is not just a logistics nightmare for the shipments you were expecting that needed to pass through the Strait on time, it’s a nightmare across your entire supply chain as all of your suppliers dependent on the oil, natural gas, chemicals, gasses, etc. that normally pass through the Strait daily are also suffering their own nightmares. Delays will compound through the chain for the lucky ones, and the rest will see shipments just stop.

And articles that tell you this is a leadership moment are missing the point.

Where it was critical, you should already have known your exposure, had monitoring in place, and been alerted the day the conflict started that an issue was coming your way.

Where supplier Force Majeure was unacceptable, you should already have had the flexibility in your contract to shift, pause, or end the contract immediately upon supplier failure.

Where supply was critical, you should have been geographically dual-or-tri sourcing with order escalation clauses built into the contracts so you can quickly secure supply when potential shortages are detected.

Where margins are tight or costs can vary widely based upon external events, your cost models should already be taking this into account, should be monitoring for market price changes, and should be updated upon such changes with immediate alerts if prices shift beyond typical market fluctuations.

And strategic and critical suppliers will already be treated as such. They will be given fair margins, access to buyer expertise that will help them with efficiency and negotiating their own raw material contracts, and placed in a financial position where they too can dual or tri-source and explore optionality in their own supply chains.

Because, as Paul Martyn commented on one of the many articles on why the conflict is apparently time to pay attention and step up (even though, as we stated in our opening, it’s already too late):

If you:

  • defer supplier investment –> you pay in disruption
  • squeeze supplier margin –> you pay in resilience loss
  • ignore (supply chain) optionality –> you pay in constrained decisions and lack of supply

The answer, of course, is to be paying attention to any high risk or high impact category from the day you identify it to the day you end the last product line that uses it. And to use the Busch-Lamoureux Exact Purchasing model to properly place your category, determine which cost factors and risks you need to track, how often, when alerts should be triggered, what mitigations can be taken up front, and what actions need to be taken when an issue likely to cause a disruption arises.

Analytics Must Drive Source-to-Pay, but not necessarily Gen-AI

Xavier recently penned another great piece on Analytics in P2P: From visibility to actionability where he highlighted the failures in analytics in traditional P2P:

  • static, backward looking, spend by category, invoice cycle time, approval rates, compliance rates
  • insights only after transactions are processed, payments are made, and cycles completed
  • late payments multiplying, exceptions accelerating, and supplier risk accumulating
  • lack of operational insight

According to Xavier, P2P can only be modernized if the embedded analytics shift from descriptive to diagnostic.

  • don’t report KPIs, explain the root causes (which approval paths contributed the most to approval time)
  • don’t report exception rates, identify suppliers that consistently cause them
  • don’t report spend anomalies, break it down and identify root causes

It’s a great start, but where it needs to get to is actionability. Xavier begins to address this point by stating the next step is “predictive awareness” where the system anticipates likely outcomes within active processes, such as predicting which invoices are likely to miss payment terms, which requisitions are likely to stall in approval or which suppliers are likely to generate disputes based on current patterns as that allows a Procurement professional to intervene before issues arise.

Finally, Xavier gets to the main point — the real inflection point comes when analytics begin to recommend actions and influence execution paths. Prescriptive analytics in P2P requires tight coupling between insight and control. If analytics identify a high-risk transaction, the system must be able to route it differently, apply additional validation or prompt a specific decision. If analytics detect a low-risk, repetitive transaction, the system must be able to reduce friction without manual intervention.

But it needs to go one step further. It must not only route differently, and apply more controls, but it must still do so automatically based on the diagnostic and predictive analytics. It can’t just apply a “one-size-fits-all” approach for automation and kick every exception out for human processing. You can’t always make the default path smarter because there should be different paths depending on the cost of the purchase, the risk associated with the purchase, the discrepancy between the invoice, goods receipt, PO, and/or contract terms and conditions. You need multiple streams that are auto-selected by predictive analytics that support the right actions given the assessment of the conditions.

The reality is this — except for truly exceptional situations, once you’ve made the decision on what to purchase, procurement should be 100% automated. It’s all e-document exchange, analysis, authorizations, and (payment) transactions. Unless something is really off, a buyer should never be involved once all the workflows, rules, and authorizations are setup.

But this automation should extend back into, and through, source-to-contract. Building on the Busch-Lamoureux Exact Purchasing pocket-cube framework, there are categories that are low risk, low value, and low complexity — you should NOT be buying these manually. “Agentic” automation should be taking care of these for you, considering that even a worst-case screw up will be of little impact. Then there are categories of moderate risk, value, and/or complexity which can be fully automated if all of the necessary data is available and there is a cost and supply history to build on, there are no special situations that need to be taken into account, and a worst-case analysis indicates that even a statistically unlikely “bad buy” will be of minimal impact. These should be 90%+ automated from the decision to buy to the recommended award, with extensive analytics and augmented intelligence for human review. And if the buyer likes the default recommendation, it should be just one click for the process to go from award to e-signed contract.

All of this requires very extensive descriptive, diagnostic, predictive, and actionable analytics and intelligence with extensive, adaptive, robotic process automation ([A]RPA) that can automate everything that should be. The reality is that while everything should be sourced (or exactly purchased), when you have all of the (market) intelligence, the standard processes, and the organizational goals encoded, then there’s no reason that the systems shouldn’t do the majority (or the entirety) of the work for you.

While buyers won’t be replaced by agentic systems (despite the over-hyped BS claims of AI Employees), they will be heavily augmented by them when most categories aren’t complex, risky, or strategic enough to require human review or intervention.

How Do We Fix The Procurement, Logistics and Supply Chain Disconnect? (Part 2)

Yesterday we gave you a history lesson on how we got here, from the time it worked back in 1885 to now where it’s completely broken. The reason? You can’t understand how to fix it if you don’t understand why it broke. (The Short Answer: McKinsey and peers who echoed their thought leadership to break down functions, outsource staff, outsource production, outsource logistics, and downsize until there’s almost nothing left in the organization.)

Procurement was never meant to be divorced from Supply Chain or Logistics. It was all supposed to be an integrated function. Buy. Ship. Manage. Repeat. But it’s not, and because no one will give up a fiefdom, and because almost no one has enough cross-disciplinary training to understand the intricacies of most functions, yet alone manage them, it won’t become part of Supply Chain again (or at least not for a long time — at least not before the fall of the modern house of Usher).

So fixing the disconnect is going to be hard. The departments will remain separate. The business language will remain distinct. The goals and KPIs will be local and different. Procurement is going to want lowest cost from a supplier it believes can supply the necessary volume over the lifetime of the (contract) award. Logistics is going to want suppliers that are on lanes served by existing carriers they have contracts with who have excess capacity and from whom they can get a good rate.

Supply Chain is going to want to utilize warehouses, ocean routes, FTZs, and other networks they have in place that they believe are low risk and high performance — they have to assure supply to manufacturing. Each goal would likely select a different supplier — there will be one supplier that is lowest price, one that is lowest shipping, and one that fits in best with the current network with a high supply assurance rate. Furthermore, if you “work” with them and then select the supplier they don’t want, they’ll think you weren’t actually “working” with them.

But, fortunately, you don’t need to work with them to work with them. You just need their priorities and their data.

You can make the best balanced decision for Procurement, Logistics, and Supply Chain if:

1. You understand the competing objectives AND their relative priority

You sit down with logistics and ask them what their most critical criteria are in supplier selection, ask them to rank them, and ask them to relatively weight them. Then you sit down with supply chain and do the same. Then you take your critical (generic) criteria, rank them, and relatively weight them. Then you create an integrated list based on the relative rankings across your three departments, weight based on C-Suite priorities, and get the final, general purpose, rankings blessed by the CFO and COO.

2. You integrate into all of the internal AND external data sources at your disposal

When you go through this exercise, you’ll realize that multiple factors affect landed cost, supplier risk, supply assurance (and supply chain risk), quality, and other key factors. You’ll realize that the only way to evaluate all of this is to pull in key sets of data from the logistics system, supply chain systems, and external data feeds (so that risk analyses are done on complete, up-to-the-minute, data) and integrate them into a decision matrix.

3. You make a decision that satisfies the objectives subject to the data available

And you do this using modern decision optimization that balances all of the objectives relative to the weightings, hard constraints, soft goals, and supplier offerings. As per our prior article, thanks to the introduction of multiple “buy local” acts (US CHIPS, EU IAA, Chinese local content) , The Time for Optimization is Finally Here as it’s the only way you will be able to satisfy all of these requirements with a single multi-vendor buy as you have to match
procurement decisions with supply chain factory requirements as you have to buy for different requirements and produce to those requirements and produce where its most economical to get those goods to the countries they were produced for.

In other words, you fix the disconnect with:

  1. shared priorities,
  2. integrated data,
  3. integrated decision optimization that takes procurement, production, and logistics requirements into account.

And if you don’t fix the disconnect, your organization’s entire existence may soon come into jeopardy.