Category Archives: Decision Optimization

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)

The optimization era is finally beginning!

In a recent article, Koray Köse states that the EU just killed global supply chain optimization.

When, actually, they just ushered in the real optimization era.

If you are a true multi-national, as Koray has said, you have to pick 2 options out of the 3 options available since you can not simultaneously satisfy US CHIPS Act, EU IAA origin/low-carbon requirements, and Chinese local content rules. So you have to decide which 2 options are the most valuable to you (based on costs and revenue opportunity in the market). That’s an expected profit optimization based on predicted sale prices and the localized supply chain optimizations for cost computation.

So you have to run 3 different sets of scenarios against different assumptions and Pareto efficiencies — and as humans we just can’t do that, and today’s AI can’t do that either (despite the over-hyped claim to the contrary). You need optimization to pick/justify your options, and then ongoing optimization to keep costs, and revenue, in line with prediction as global events force you to reroute regionalized and localized supply chains, substitute materials due to shortages, etc.

What was killed was the simple concept of global optimization that was relatively easy to do without optimization (and what passed as optimization for the past 25 years). Up until now, the reality was that, if you had even a few constraints, and the ability to do simple math, you could quickly eliminate the most expensive suppliers and the suppliers that couldn’t meet your constraints, and then, using your constraints, cherry pick the lowest or second-lowest cost supplier/distributor, and come up with a solution that was within 1% to 2% of theoretical optimal, but that was actually more optimal in practice as it was more stable and easier to maintain.

Optimization is only needed when you need to make choices that can’t be made without considering multiple sub-cases, regionalizations, and localizations — and this is exactly what this messed up world has given us!

It becomes even more important if you are a true multi-national with business in, and government commitments in, the US, EU, and China. You have to adhere to all of the rules globally, but you can’t with any one product formulation, so you have to create at least 2 different products where you figure out what 2 of the three combinations are easiest AND cheapest to make, where to make them, and how to supply them to the countries you serve (with there will be one country each mix cannot be imported into). This requires a host of scenarios to be run before a selection to be made, and a host of models to be continually run during production and distribution to ensure everything aligns with changing market conditions.

So while the classic optimization vendors who can’t do anything more than minimally constrained global optimization are now dead, it’s finally opened up the era of real optimization. The question is, what vendors are going to step up to fill the void?

Optimization CAN NOT Be Automated!

Not long ago, THE PROPHET said that the future of optimization is self-adjusting autonomous systems that just “do it”.

And while future systems should:

  • automatically aggregate, verify, and enrich data from multiple sources
  • adapt constraint and model recommendations based on organizational and market trends
  • continuously monitor environments and suggest the next events based upon the opportunity
  • suggest categorization and framework refinements that would allow for more successful events
  • consider volatility and risk in its models and recommendations

These models should not:

  • autonomously seek out and integrate data without human validation
  • autonomously change constraints and models
  • automatically run events for categories still under contract
    (on the probabilistic expectation the savings will exceed the penalty)
  • change your categorization and framework without approval
  • replace deterministic models with probabilistic ones with unknown weightings on volatility and risk

and these models should definitely not run fully autonomously in the background and make commitments without human approval and intervention.

Going back to basics, which THE PROPHET says he knows well, there’s a very simple reason you need a human in the loop for sourcing, and the simple way to explain it is this. To a machine, a 3.5″ lid is a 3.5″ lid, especially when it’s not!

Apply this next generation fully autonomous optimization platform concept to a global fast food chain, and the first thing it’s going to identify is that the human is following a “hidden constraint” by always buying matching cup and lid sizes from the same vendor, and doing away with this arbitrary constraint will save a global operation millions a year.

The new junior buyer, upon seeing this, will jump and down and tell the platform to “Lock the order and output the savings report so I can demonstrate this new AI optimization tool saved millions”.

But that “hidden constraint” is a real constraint because 3.5″ is not 3.5″ across manufacturers who are still running on decades old production technology as the process to create the cups and lids for those fountain drinks hasn’t changed since we were kids, there were no standards then, and the measurements were always off a bit.

If you’ve ever wondered why sometimes the lid just stopped fitting when the “serve yourself” trend started, this is why — someone broke the unwritten rule — and the chain tried to pretend the problem didn’t exist.

Why did they try to pretend that the problem didn’t exist? That’s because the “fix” is to order the matching inventory from the same supplier, sit on double inventory, and send costs through the roof.

In other words, this twenty five year old hidden constraint that the doctor personally saw sourcing optimization consultants overlook (when they were told by the client that you couldn’t use manufacturer’s X lids with manufacturer’s Y cups and that constraint should, obviously, be part of the model) is still a valid constraint today. And other examples abound across categories. The specs seem the same on the spec sheet, but only the engineers and buyers know when they are not and apply “unnecessary” or “hidden” constraints to account for these situations.

Moreover, going back to the suggestions of THE PROPHET:

  • machines don’t know truth from lies, so if someone publishes false data, they will use that false data in enrichment, and there goes your model!
  • as we just demonstrated, sometimes AI will remove necessary constraints or not detect “hidden” constraints that need to be included
  • you don’t break a contract on a hunch — you break it when it’s not working out; if you find a better product or lower cost, you start switching over as soon as you can or by diverting as much as you can from an un-contracted/contractually satisfied supplier to that new supplier
  • you don’t completely change categorization and upend the financial reporting and other dependent processes because it suits the optimization module
  • you use the probabilistic assessments, you don’t replace your deterministic model, where you can compute optimality and confidence, with them

When it comes to optimization, you want Augmented Intelligence and a system that, with input and verification at the right points, does all of the tactical drudgery and thunking that the machines are great at (and we are not). You don’t want it autonomously making strategic decisions it doesn’t understand.