Category Archives: Exact Purchasing

Can You Truly Have Structured Risk Conversations without Exact Purchasing?

We’ve been talking a lot about the Busch-Lamoureux Exact Purchasing Pocket-Cube model lately because we’re never going to solve the exponentially proliferating Procurement problems unless we fix the fundamentals. And when it comes to risk management, it’s pointless unless the risks being managed are the ones that really matter relative to their criticality which should be defined not by Risk Management, but by Procurement based on the importance of the categories they impact.

If you look at risk in isolation, you’re going to focus on:

Traditional Risks

  • limited commodities, especially foodstuffs, where bad yields or natural disasters wipe them out, or minerals that come from limited mines
  • transportation shortages, where routes are at capacity and any man-made or natural event that impacts the lane in any way causes a shortage
  • factory limitations, as it’s a custom product that can only be produced by a few existing factories without extensive customization

And you’re going to completely ignore:

  • restricted commodities, where a significant percentage of global production comes from a single region, or country (and when that gets cut off, a glut of supply suddenly becomes a dearth of supply)
  • global transportation chokepoints, and what happens when a lack of rainfall limits the amount of traffic that can pass through the Panama Canal, the Red Sea closes, the Strait of Hormuz is cut off, etc.
  • local transportation chokepoints, such as the ILA controlled east-coast ports in US or the ILWU controlled west-coast ports in the US, and a strike cuts off your routes and back-up routes
  • skilled worker limitations because it’s not just the factory, it’s the work force, and if most of the workforce is > 60 and the educational/mentorship programs that trained the next generation workers were shut down … that factory is gone in a few years

And what you address might not be that important.

If you’re a traditional mechanical manufacturer, you’re only dependent upon rare earths for magnets and lighting, as most rare earths are in electronics. If you’re monitoring anything beyond the rare earths used in the magnets and lights you need to make/source, you’re wasting your time.

If everything being sourced through a taxed transportation network could be sourced from somewhere else through a network with a lot of capacity, at only a slightly higher price point, then you don’t really care about that transportation network.

If you’re dependent on two factories, and you aren’t monitoring the turnover, the influx of new workers, and the output of future workers in the local economy, you will someday, without warning, find yourself needing to find a new factory with a new supplier that will need to customize their production lines, processes, and workforce to your needs … which they may not be able to accomplish in time to keep your supply chain flowing and main product line in stock — which could risk your entire business model.

Meanwhile, you don’t notice the risk above where

  • 60% of the rare earth you depend on for your magnets are coming from different suppliers in China, so when a pandemic strikes and China institutes a no tolerance policy against a virus that can’t be eliminated, your supply goes up in smoke (and you had no warning to secure as much supply as you could while you still could)
  • you weren’t watching for events that could close the Strait of Hormuz (thinking the Red Sea was the end of it) and aren’t watching the Strait of Malaca (which carries almost 25% of global trade … so if the pirates leave Africa …)
  • you will get shocked when the ILWU contract expires on July 1, 2028 and the US West Coast ports shut down as the pay increase that was negotiated in the last round is NOT keeping up with the inflation your current administration is creating;
  • and so on.

And if you attempt to solve your supply chain risk identification by acquiring a multi-tier supply chain visibility and monitoring solution, you’ll get sucked down every risk rabbit hole that is identified based upon every raw material used anywhere in your supply chain and detected impact event.

Unless you are properly categorizing your purchases using Busch-Lamoureux Exact Purchasing, identifying those categories both high-risk and high-impact, and identifying what risks would be devastating to you, you aren’t addressing the right risks and any attempt at a structured conversation will be a waste of time.

And only then will you be able to identify:

  • where the impacts will be felt,
  • which functions need to be involved,
  • who should own the risk,
  • why identified monitoring via subscription data feeds is needed,
  • when a risk-related event is significant and needs to be manually assessed/addressed,
  • what needs to be done if significance is determined, and
  • how response success will be determined.

And then you can use the tips offered up by Greg Schlegel in his We Discuss Risk Regularly post to have truly successful risk management conversations.

Four Broken Procurement Processes You Wouldn’t Have With Exact Purchasing

Gaurav Sharma recently penned a truly great post on LinkedIn on 5 dated procurement processes/setups you can get rid of without AI or any tool whatsoever. This is the type of post we need more of because AI isn’t always the answer (and in fact, it’s rarely the answer), and AI shouldn’t even be considered until the technological needs are identified (which, if the right process is followed, require AI a lot less than the hype machine would lead you to believe).

Not only should you not have the five processes he outlined if you are a best-practice Procurement department, but 4 of them wouldn’t exist in the first-place if you built your Procurement programs off of Busch-Lamoureux Exact Purchasing.

Let’s take them one-by-one.

Spend it or Lose it at the end of the year.

Proper Procurement, and the proper business operations it would dictate, would never have this because budgeting based on historical spend is bad budgeting, as is pricing based on historical price points. The era of global stability is over, natural disasters are on the rise, critical resources are becoming scarcer by the day, and logistics becoming uncertain and unpredictable due to the fallouts of sanctions, wars, disasters, strikes, and uprisings. As a result, what you will pay this year, and what you will have to charge to maintain a fair profit margin, will not necessarily have any correlation to what you paid last year, especially in categories where more than half of supply comes from a single country (such as rare Earths from China) or flows through a single chokepoint (like oil, fertilizer, etc. through the Strait of Hormuz).

Budgets need to be based on expected costs using the most up to date data at the time, and revisited every time a category comes up for (re)sourcing. And, most importantly, be based on forecasted demand, which needs to be up-to-date when budgets are created and updated regularly based upon category velocity and actual sales/utilization over a typical, statistically significant, time window (which will be different for every category). This is the key: budgets should be based on expected, and approved, demand and cost ranges — not fixed spend buckets.

And you need to make three critical changes to budget management to be successful.

  1. If the purchases are needed (i.e. buying less will shutdown a production line, result in a costly stockout, etc.), the expected spend can be exceeded (as long as all efforts are made to keep it as low as possible) and the organization will react by either increasing pricing or cutting elsewhere if they can’t.
  2. If the forecasted demand has been reached, it cannot be increased without approval (or approved forecast updates for input components), even if the expected spend hasn’t been reached.
  3. For discretionary categories, if the organization was able to delay demand (by finding a way to get one more year out of that cell phone or laptop, delaying hiring through better automation and occasional overtime, or simply pushing off MRO restock until the next major project started), expected baseline demand is NOT reduced for the next year. In fact, if a valid argument exists, unused demand may even be carried over. Organizations that can reduce or defer demand need to be rewarded. In the long term, you’ll save money if you encourage delay of spend until absolutely necessary.

And if you use Busch-Lamoureux Exact Purchasing, you’ll have an infrastructure where you are able to re-compute forecasts as needed, query current pricing as needed, monitor for events in high risk or highly volatile categories, get alerted when you may need to accelerate an event, and have an infrastructure to take the right action at the right time where you aren’t sourcing based on a fantasy budget but a real, up-to-date, demand with real, up-to-date, market pricing.

Approval Chains

If you’re using Busch-Lamoureux Exact Purchasing, you have regularly updated, agreed upon, forecasts and expected demand. You have pre-vetted suppliers. You have market pricing, contracts, purchase orders, and m-way match. Once the demand, suppliers, and contracts / bids have been accepted and approved, if everything matches, there is no need for a human in the loop. You configure the (A)RPA and let it issue the okay-to-pay to the payment system and let the payment happen. Unnecessary approvals add unnecessary time, create unnecessary work, and potentially cost you not only the opportunity for early payment discounts, but even fines if you don’t make the payment windows mandated by the UK, EU, and other countries for paying small suppliers.

PowerPoint Category Strategies

A PowerPoint dies as soon as it is presented. No one ever goes back to it. With Busch-Lamoureux Exact Purchasing, for any high complexity, high risk, or high impact category (which are 7 of the 8 categories), you setup the necessary price, risk, quality, delivery, etc. monitoring systems from day one. You have alerts whenever a significant event occurs that could significantly impact your pricing, quality, or supply. And, for any category that is high impact, you have mitigation or response strategies already defined in your procurement systems that you can action.

KPIs that incentivize activity

In Busch-Lamoureux Exact Purchasing, you define KPIs based upon success factors, NOT activity factors. You’re concerned with savings against market (i.e. cost avoidance), not historical budgets. If market prices went up 15%, you’re not saving over last year in any managed category. But if market prices went down 10%, you shouldn’t count any decrease in spend against last year’s spend of less than 10% as savings, because if you didn’t reduce spend by 10%, you’re doing a lousy job. It’s not resolved issues, it’s straight through processing. And so on. With Busch-Lamoureux Exact Purchasing, you don’t have worthless KPIs in the first place.

The only process/setup it doesn’t eliminate is tolerating underperformance. That’s entirely a people issue, and if you have people that tolerate underperformance, they need to go. No process can fix that. Only your willingness to take action can.

* as a certain Western society does everything it can to pretend climate change doesn’t exist while its greatest ally does everything it can to bomb us to the next great flood as it unleashes over 2 million metric tons of carbon dioxide (tCO₂e) a month with the bombs it uses in a single conflict — a measurable level of emission equal to 0.05% of total monthly (tCO₂e) global emissions

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.

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.