Category Archives: Sourcing Innovation

AI Doesn’t Drive Savings, Innovation, or Performance. Sourcing Excellence Does.

And Sourcing Excellence requires (Strategic Sourcing) Decision Optimization.

As the Sourcing Optimization Grand Master Paul Martyn has clearly stated in his post on how Procurement is at an Inflection Point:

  • AI won’t fix Procurement.
  • Dashboards won’t fix Procurement.
  • Better Data won’t even fix Procurement.

ONLY structured, modelled decision making that gets executed in the practice of true Sourcing Excellence will.

And that structured decision making will be based on true multi-objective sourcing optimization that takes costs, risks, and goals into account to help you, the intelligent human, make the right decision that a dumb machine will never see.

And if you want to find out how that’s done, reach out to the Sourcing Optimization Grand Master himself who has saved Billions in his career WITHOUT increasing risk, liability, or complexity and find out how your organization could be the next to save millions (upon millions) while making less risky and more valuable decisions.

Sourcing Excellence Is Predictability in Tough Times

Sourcing Mediocrity, or worse, Bad Buying, leads to chaos.

Your costs are up.

Your delivery predictability is gone.

Your energy supply is intermittent and brown outs are becoming normal while those costs go up too.

Your taps are running dry.

Your workforce benefit costs are going up as healthcare costs skyrocket.

Your AI costs are going up as compute and consulting skyrockets and more consultant time is needed to deal with the results of bad, bad, hallucinations, that have gone beyond wrong orders, 3-way mismatches, and fraudulent payments to bad customer advice and legal claims that have put you in legal jeopardy.

This isn’t inflation. This is bad buying.

With good buying and sourcing excellence:

Your costs are stable — because you didn’t select risky suppliers, squeeze their margins to dangerously low levels, or make ridiculous asks that only add cost and not value.

Your deliveries are predictable as you’ve selected carriers that can support multiple routes and have re-routing plans in place if a route gets shut down due to a port strike, border closing, or “Geopolitical conflict” (i.e. war).

Your energy supply is regular as you were sure to build where the grid could support your energy needs, select providers (where you had a choice) that could guarantee the supply, and installed backup generators for key functions (and batteries for minimal lights and on-site computing requirements).

Your water pressure is through the roof as you ensured there was adequate supply and put contracts in place to guarantee it.

You manage your benefit negotiations carefully, put long term contracts in place, and work with the provider to prevent fraud (which makes you a customer of choice).

You don’t buy Gen-AI just because every brain-fried consultant and their favourite cognitively atrophied analyst is telling you to. You buy classic AI that works hallucination and error free at a fraction of the compute and cost.

In other words, you apply sourcing excellence end-to-end.

And you make good use of (strategic sourcing) decision optimization.

And you realize savings twice the savings of your peers.

But don’t take my word for it. Take the word of Paul Martyn, one of the original Sourcing Optimization Grand Masters who has sourced over 20 Billion dollars, and seen consistent results doing so over the past two decades.

And saved oodles of cash. To find out how much, check out this post on how you’re seeing your sourcing decisions repriced from bad buying. Then do the math on how much you could be saving (and, of course, reach out to Paul if you’d like someone to help you put a plan in place to save that money).

P.S. If you haven’t figured it out yet, if you were using Busch-Lamoureux Exact Purchasing you’d not only know that you should already be using optimization, but where, why, and would have already reached out to Paul to help you define the program.

Sourcing Excellence IS Optimization!

Sourcing Excellence requires optimization. Not AI. Optimization. We have finally reached a point where nothing else will get you there.

And Sourcing Excellence requires Paul Martyn. You need someone who has built and led programs, evaluated and employed multiple tools, and has the decades of experience to bring the insights you need instantly to the table. With many of the sourcing optimization greats (who founded CombineNet, VerticalNet Tigris, Trade Extensions, etc.) retired or moved on, the number of people left who have over two decades of practical experience are countable on your fingers (just like the number of analysts who have been consistently covering this space for two decades). Paul Martyn is one of the few, true, optimization masters left. So if you want to save your supply chain, reach out to Paul.

If you want to understand why, as well as why sourcing excellence truly requires optimization (as it’s time has finally come), since I know you won’t listen to me, read Paul’s ongoing Sourcing Excellence series, which just saw Part 11 published.

  1. Part 1: (Optimization is Thinking)
  2. Part 2: (Optimization Frames Reality)
  3. Part 3: (Optimization is More than a Capability)
  4. Part 4: (Optimization Changed the Game)
  5. Part 5: (Optimization Must Always Be On)
  6. Part 6: (AI is NOT Yet Fly in Procurement)
  7. Part 7: (Innovation is Just an Input)
  8. Part 8: (Orchestration is the Key)
  9. Part 9: (Value is a Game)
  10. Part 10: (Constraints Dictate)
  11. Part 11: (Constraints Vary)

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.