Category Archives: Decision Optimization

Operations Research Lessons are Timeless … Especially in the Age of AI

Twenty years ago we published a piece focussed on Mark Daskin’s Everyday Lessons from Operations Research that he delivered at INFORMS 2006. They are just as relevant today, if not more so, in the age of AI (Hype).

  1. Service and Performance Gets Worse as Utilization and Variability Increase
    This is true in so many ways. Dumb users asking stupid questions with poorly formed prompts that don’t match the training prompts flooding the internet with poor and hallucinatory answers that are fed into the next AI model in the collective race to the bottom. Response times decreasing as the models need to get bigger to process ever more mediocre data and, hopefully, create better responses (even though that won’t happen). Costs going through the roof as a result of the constant need for more computing power.
  2. … But Variability is necessary.
    Because every request is fundamentally different, even if phrased the same as the lack of standardization, and training on that standardization, ensures every user has a different intent with their request.
  3. Expect the unexpected in today’s world.
    Most responses will be okay, some will be bad, a good number will be completely hallucinatory, and every now and again the hallucination will provide a revolution insight — which will go unnoticed because most users now lack the cognitive power, and possibly the intelligence, to even detect a hallucination in the first place due to the mass cognitive atrophy Gen-AI is, provably, inducing.
  4. If it is too good to be true, it probably is not.
    But yet we continue to believe the lies, damn lies, and hallucinatory hype spewed by the purveyors and pushers of Gen-AI and the garbage it spits out.
  5. Life is full of errors.
    And Gen-AI multiplies those errors tenfold to ten-thousandfold!
  6. A good decision can result in a bad outcome.
    Even when the AI gets it right on the data, the data is always backward looking, not forward looking, and the right decision based on a past trend can still be wrong if a key variable or assumption just changed and the data does not yet reflect that. So even when the AI actually gets it right, it can still be wrong!
  7. If you are not using all you have, don’t pay for additional quantity.
    If you only have a small amount of data to train the model on, don’t pay for the largest model available — not only will it not help your case, it will hurt as there won’t be enough data to “train” the model even to reasonable levels of accuracy (relative to what the model is theoretically capable of). Always go with the smallest model that will theoretically meet your needs — and, moreover, if you have multiple distinct needs, multiple distinct models will perform much better than trying to create one big mega model.
  8. You can never do better by adding a constraint.
    Never, ever, ever. Whatever you are trying to minimize will increase. Constraints restrict options and that will always result in increased cost, increased risk, etc.
  9. Keep it Simple
    The best AI, especially in the age of Gen-AI, is no AI. More specifically, don’t use AI where AI is not needed. Deterministic automation, when properly applied, has worked well for decades and now, in an age where we can encode adaptive workflows, suggest exception resolutions, have the rules automatically update when a resolution is accepted or created, and improve over time.
  10. Think about problem formulation.
    The formulation dictates the potential solutions … and the better you define a problem, and a stepwise algorithm to resolve that problem, the less need you will actually have for (Gen-) AI.
  11. Look for compromise solutions.
    If a problem ends up being defined particularly constrained or complicated when multiple parties get involved, work together to simplify the constraints or complexity where it truly isn’t needed. There’s a big difference between preference and requirement, and understanding that allows for the creation of good comprise models and solutions.
  12. Data is not information.
    It never has been, and never will be. Moreover, data pulled out of an LLM might not even be accurate, so the chance of it producing usable information is even less. Always remember that.

The Great Thing About Optimization: You Can Cost Constraints and Solve for Them!

The Sourcing Optimization Grand Master Paul Martyn, as part of his ongoing Sourcing Excellence, published two great posts on how The Most Important Constraint Is Often Left Unspoken and The Real Problem Is Usually Context.

In Part 16, Paul notes that:

Sometimes the most important part of the decision never gets discussed directly.
Nobody says: “We don’t trust ourselves to manage the transition.”
Nobody says: “If this fails, I own the fallout.”
Nobody says: “We’d rather pay more than explain a disruption.”

But they poison the model by insisting on:

Extra incumbent weighting.
Requirements that narrow the field.
Timelines that make supplier change almost impossible.
Risk language that quietly points one direction.

Which ensures that the “optimal” solution is one that keeps the incumbent, even though it’s far from the optimal solution.

Then, in Part 17, Paul notes that context is the harder problem. That while the sourcing team can model price, lead times, freight, capacity, payment terms, supplier performance, etc., the data doesn’t capture:

  • a plant manager [who] still remembers a shutdown tied to a supplier change
  • marketing does not want packaging changes during a seasonal launch
  • finance is suddenly focused on working capital
  • operations is protecting uptime
  • an incumbent relationship [that] carries more internal support than anyone says out loud

And this is what leads to the insistence of constraints and inflated switching costs in the model that mathematically ensures the “optimal” solution is the one where nothing changes, even though the CFO can clearly see that costs are 5% above market averages with no real explanation as to why the organization has to pay that much to minimize risk, ensure supply, yada, yada, yada.

But here’s the thing, if the real issues are surfaced BEFORE the event begins (and the model built):

  • Even though the plant manager is scared of a shutdown, the probability can be computed and the cost can be quantified — as the cost per hour/day/week is known
  • The cost of extending the current contract just through the launch can be quantified and the cost of keeping the supplier for the whole contract term vs. just the launch and then switching to a new supplier can be computed
  • Up front vs. over-time costs can be modelled and the cost of solutions that require more investment up-front vs. later can be compared and contrasted
  • The on-time expectancy per supplier and carrier can be modelled and accounted for
  • The true cost of the incumbent preference can be modelled

The great thing about strategic sourcing decision optimization with what-if scenario support is that all costs and constraints, if properly expressed, can be modelled and the true cost of a preferred, vs. market, scenario computed. If the cost difference is inconsequential, then you go with what the people want. If it’s significant, then some real discussions need to happen and some real decisions need to be made.

Sourcing and Procurement Are NOT The Same

And they are definitely NOT interchangeable, as per a recent article by Paul Martyn (the Sourcing Optimization Grand Master) on LinkedIn.

As per his article,

  • sourcing is strategic
  • procurement is transactional

And this is why they are not only not the same, as per Paul’s article, but not interchangeable.

In the age of AI (Hype), this is distinction becomes doubly important!

As technology advances rapidly, humans become less and less important in Procurement as rapid advances in automation allow more and more of the tactical process to be completely automated (as ARPA allows exceptions to be learned and future manual intervention requirements to be eliminated) but more and more important in sourcing as Gen-AI repeatedly proves just how Astonishingly Inept modern Artificial Idiocy is.

Many will argue that sourcing is tactical because modern software can assemble RFXs from existing specs, automatically select suppliers from your SXM and/or ERP, automatically distribute them, automatically validate the returned RFXs, eliminate vendors who don’t meet absolute requirements, analyze the responses against market data for validity, build and execute multi-objective models, and recommend and award. And while that certainly sounds like sourcing, it’s not. It is sourcing execution. The tactical part that has to be done to support the strategic, but NOT the strategic.

The strategic is creating the specs, identifying the real organizational requirements, determining the requirements for supplier inclusion, validating the suppliers, determining the proper (multi-round) event type, validating the generated RFXs, analyzing the responses for hidden risks and traps and idiosyncrasies, defining the right trade-off models, selecting and modifying the right award scenario, overseeing the negotiation, etc. Every part of the process that requires an actual decision with Human Intelligence.

This is because, as Paul points out, a dumb machine doesn’t understand:

  • lowest cost vs resilience
  • incumbent vs challenger
  • standardization vs innovation
  • savings vs service
  • global leverage vs local agility

Or any other trade-off that can’t be completely quantified and captured in fail-safe rules.

Systems can, and should, support all tactical bit-pushing — especially since we were promised they would do so over 40 years ago when the big push was made for every person and business to adopt them — but, like IBM said in 1979, a computer should never (EVER) make a decision. And that most definitely includes Sourcing decisions!

AI CANNOT TELL YOU WHAT TO DO!

And I’m so glad I’m not the only one saying it!

The (Strategic Sourcing Decision Optimization [SSDO]) Grand Master himself Paul Martyn recently wrote a great post on LinkedIn that made this exceptionally clear and how the real problem is knowing what to do.

Paul starts off with three critical statements:

  1. AI can tell you what’s happening
  2. AI can’t tell you what to do
  3. In sourcing (procurement) the hard part isn’t visibility, it’s choice.

More specifically, it’s making a decision when every decision has tradeoffs, constraints, and (sometimes dire) consequences.

Unless you have an operating model to make those decisions, powered by technology that can actually help you adhere to the constraints, make the tradeoffs, and understand the consequences, the best case with AI is you get overwhelmed with the complexity of what’s happening.

So if you want to be buried in data and complexity and pretend you know what you are doing, there are dozens of BS AI players ready to help you.

But if you want the ability to make good decision, understand tradeoffs, restrict your inquiries to scenarios that adhere to constraints, and model the potential consequences when things go wrong, you need decision optimization with multi-objective capability. That’s Coupa (Trade Extensions). Or Jaggaer (Bravo Solution). Or Keelvar (just Keelvar). Not some BS AI startup offering nothing more than a clod or chat, j’ai pété LLM wrapper.

And if you want to know how to build the right operating model backed up by the right multi-objective optimization model(s) (and save millions while reducing risk and increasing quality), you contact Paul Martyn. He’s saved Billions. (Whereas in 94% of companies, AI has effectively saved 0.)

Now for those who don’t know, not only am I one of the last original (independent) analysts standing in our space (20 years doing SI next month), but I am likely the last original strategic sourcing decision optimization model builder left standing too. (Mindflow [acquired by Emptoris], 2000. First multi-line item model. Before CombineNet [acquired by SciQuest, renamed Jaggaer]. Before Emptoris [acquired by IBM and sunset]. Before all of them. Twelve years before Keelvar. First model to do more: Trade Extensions, acquired by Coupa.)

So unless Thomas Sandholm or Arne Andersson want to come out of retirement and recommend someone better — it’s Paul Martyn. No one still active in our space goes as far back or has worked with as many platforms as he has. (And I helped a PM/Consultant who worked at 2 different optimization providers get hired at 3 others over the past 20 years, and even that doesn’t match Paul’s resume!)

Why Do You Need Frameworks and Systems for Sourcing Excellence?

I’m going to call out a comment made by the Sourcing (Optimization) Grand Master himself, Paul Martyn, to a recent post I made echoing Matthew Buckingham‘s statement that today’s procurement leaders aren’t enough for tomorrow:

“If tomorrow’s environment demands creativity and crusading, how do you see procurement leaders operationalizing that without it devolving into noise?

More specifically:

What does “creative” procurement look like in a measurable, repeatable way

And how does a “crusader” avoid becoming a blocker instead of an enabler when pushing the C-suite to abandon comfortable models?

Feels like the gap isn’t mindset, it is translation into decision systems that actually move outcomes.”

You need

3) outcomes

2) created by decision systems

1) that support your sourcing and procurement needs

This requires

1) Sourcing Excellence in execution

2) BIC multi-objective optimization and analytics that fit into

3) a proper category framework for your organization, like the Busch-Lamoureux Exact Purchasing Framework, that helps you understand the systems and processes you need to employ in each category

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