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