Are improvements in forecasting less impactful as long as the optimization process is well-structured and focuses on improving decision-making based on those forecasts?

Also, why does Lokad use stochastic gradient descent instead of tools like Seeker from InsideOpt, which leverages metaheuristic algorithms, robust optimization, and customized methods? Is it due to interpretability or another reason?