  Time-series forecasts play a critical role in business planning. However,
forecasters typically optimize objectives that are agnostic to downstream
business goals and thus can produce forecasts misaligned with business
preferences. In this work, we demonstrate that optimization of conventional
forecasting metrics can often lead to sub-optimal downstream business
performance. Focusing on the inventory management setting, we derive an
efficient procedure for computing and optimizing proxies of common downstream
business metrics in an end-to-end differentiable manner. We explore a wide
range of plausible cost trade-off scenarios, and empirically demonstrate that
end-to-end optimization often outperforms optimization of standard
business-agnostic forecasting metrics (by up to 45.7% for a simple scaling
model, and up to 54.0% for an LSTM encoder-decoder model). Finally, we discuss
how our findings could benefit other business contexts.
