I work on these sort of industrial optimization problems, and you're absolutely right about the amount of money left on the table. I had one problem at a previo

I work on these sort of industrial optimization problems, and you're absolutely right about the amount of money left on the table. I had one problem at a previous megacorp that I worked on for about 8 months, and then passed off as a two part solution. the first part was a new optimization engine design, but it also required a second part, a new physical process for crossdocking. To this day, 10 years later, my former colleagues still report actual measured savings of $300M/year, which I still find to be mind-boggling. You're also right about the complications and the need to be adaptable to various situations that arise. Some of that just comes from people who are afraid of getting their hands dirty and really understanding the domain they're working on. Operations Researchers and Industrial Engineers are not much different from Computer Scientists: sometimes the desire for a beautiful and simple solution prevents you from building something useful. Sometimes in order to build something useful, your idealized fast-converging LP turns into a QP/MIP bastardized rat king that only converges on an optimal solution half the time, but still gets a better-than-before solution the rest of the time. And some of us really hate that it has to be that way. The rest of us laugh their way to the bank.