This commentary translates the central ideas in Lead times in flux into a practice ready handbook in R. The original article measures change in the full distribution of booking lead times with a normalized L1 distance and tracks that divergence across months relative to year over year and to a fixed 2018 reference. It also provides a bound that links divergence and remaining horizon to the relative error of pickup forecasts. We implement these ideas end to end in R, using a minimal data schema and providing runnable scripts, simulated examples, and a prespecified evaluation plan. All results use synthetic data so the exposition is fully reproducible without reference to proprietary sources.
@article{arxiv.2511.12763,
title = {Impact by design: translating Lead times in flux into an R handbook with code},
author = {Harrison Katz},
journal= {arXiv preprint arXiv:2511.12763},
year = {2025}
}