Great talk - some notes/rough translation. The questions in the Q&A are a little difficult to hear :)
"The more ill-structured the domain, the poorer the guidance for knowledge application that ‘top-down’ structures will generally provide. That is, the way abstract concepts (theories, general principles, etc.) should be used to facilitate understanding and to dictate action in naturally occurring cases becomes increasingly indeterminate in ill-structured domains."
Great talk - the in-memory approach makes more sense when you have a lot of global dependencies. I would imagine you get some thrashing behavior in cases where you spill the "wrong" thing.
Seems to me that supply chain can very easily become the enabler or barrier to competing with time. He mentions an interesting example on optimizing for full truck loads and the effects on the business as a whole.
Not sure I agree with everything here - however the bit on the different supply chain flows and their priorities is helpful (Efficient, Agile, Responsive, Seasonal, Low Volume). Helps to answer those questions whose answer is typically "it depends".
Begs the question of segmentation since you are measuring performance by product/market/....
I like the analogy of “increased attack surface”, particularly as it increases your chances of being infected by a vague, but attractive, idea-virus-meme. Reminds me of Robert Greene in 48 Laws of Power on charlatanism - “…on the one hand the promise of something great and transformative, and on the other a total vagueness. This combination will stimulate all kinds of hazy dreams in your listeners who will make their own connections and see what they want to see”
In my experience it is quite common for the stated goal of an organization to be “improve XYZ business metrics with ABC type system” but the ulterior motive to be “make a defensible and risk-free decision to look like we’re progressing”
Is there a solution to this problem for someone inside an organization evaluating vendors? How about as a vendor?
Github here: https://github.com/Blue-Yonder-OSS/cyclic-boosting
When you say glossary - you mean between people to understand the meaning of terms? Or in the sense of lookup table for values in data?
The section on "Conquering Complexity" immediately reminds me of [Out Of The Tar Pit](https://curtclifton.net/papers/MoseleyMarks06a.pdf)
Agreed, the current paradigm with token limitations restricts the use cases on raw data, i.e. giving the LLM your entire hypercube to look for things.
However if instead you were pointing it at the documentation for the 2-3 tools you're using plus excel and asking it to tweak XYZ functionality.... then the fuzziness/randomness is confined to the configuration/setup layer which then drives a consistent and performant tool to generate results.
...Train teams to model variability and build a planning master data layer to understand layers of variability. (A planning master data layer measures and tracks shifts in lead times, conversion rates, and quality.....
This was the most insightful bit... many companies see the value in doing this, however modelling "new" problems in a rigidly implemented system doesn't often lend itself to experimentation.
Are there any good examples of processes/software that "self adjusts" planning/modelling parameters? Seems like something that could easily lead to crazy and infeasible results if left alone....
A helpful allegory for today's software flexibility vs. ease-of-use tradeoff and how LLMs may lead to more extensible and malleable software for the end user.
To be quite honest it's the first time I've seen something that helps inform how we may use LLMs as a supply chain community in the context of spreadsheets and rigid tools.
"...LLM developers could go beyond that and update the application. When we give feedback about adding a new feature, our request wouldn’t get lost in an infinite queue. They would respond immediately, and we’d have some back and forth to get the feature implemented."