Off the top of my head, I'd consider saying something like:
1) demand forecasting (of crude & distillates by geography)
2) optimization of replenishment (balancing inventory costs and shipping costs)
3) shipment duration estimation (for managing/scheduling network flows)
There's also a lot production applications of this to chemical engineering processes -- managing the refinery to maximize yield of valuable products at minimum energy cost and maximum asset utilization.
This is as old as the hills IMO. I think it's called Operations Research. And the Process industry is quite mature.1) demand forecasting (of crude & distillates by geography)
2) optimization of replenishment (balancing inventory costs and shipping costs)
3) shipment duration estimation (for managing/scheduling network flows)
There's also a lot production applications of this to chemical engineering processes -- managing the refinery to maximize yield of valuable products at minimum energy cost and maximum asset utilization.
We both do lots of commercial project, for many many years already.ended up not collecting the prize because of the endless extra work they wanted: code, paper, posters, etc etc
That's probably because you saw it as a piece of coding and not as a software project with several stakeholders and viewpoints.
Best to pin down the requirements _beforehand_ and in a contract.
It would have been more painful if it had been a real-life, fixed-price software project.
Items #1 and #3 are not OR. Instead, they provide more accurate inputs to OR optimization.This is as old as the hills IMO. I think it's called Operations Research. And the Process industry is quite mature.1) demand forecasting (of crude & distillates by geography)
2) optimization of replenishment (balancing inventory costs and shipping costs)
3) shipment duration estimation (for managing/scheduling network flows)
There's also a lot production applications of this to chemical engineering processes -- managing the refinery to maximize yield of valuable products at minimum energy cost and maximum asset utilization.
So, what part of the Venn diagram does ML bring to the table? There is nothing really new here?
Exactly, that's the core of where ML is successful in replacing classical models. You need data + 'elegant' simplistic models and you're good to go.Items #1 and #3 are not OR. Instead, they provide more accurate inputs to OR optimization.This is as old as the hills IMO. I think it's called Operations Research. And the Process industry is quite mature.1) demand forecasting (of crude & distillates by geography)
2) optimization of replenishment (balancing inventory costs and shipping costs)
3) shipment duration estimation (for managing/scheduling network flows)
There's also a lot production applications of this to chemical engineering processes -- managing the refinery to maximize yield of valuable products at minimum energy cost and maximum asset utilization.
So, what part of the Venn diagram does ML bring to the table? There is nothing really new here?
In theory, item #2 can be solved with OR. But that approach requires setting up the problem correctly and providing accurate numerical inputs. In practice, the OR solution involves a bunch of nuisance parameters and the OR equations may leave out crucial structure that makes the solution suboptimal or infeasible in real business situations. Sometimes the best model of the system is the system itself as revealed by data from the system. Moreover, ML does not require arbitrary and false mathematical assumptions about linearity, continuity, the existence of analytic solutions, IID, etc. that make the OR solution tractable but imperfect.
As for process control, it's mature but also subject to unmodeled variations -- discrepancies between how the process is supposed to perform versus how it actually performs. In the past, companies used human experts that tried to learn the quirks of each system and adjust the control logic for better performance. But ML systems can do a much better job of learning from data than people.