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.