Impact by design: translating Lead times in flux into an R handbook with code

TL;DR

This paper provides a practical R handbook translating the concepts of lead time divergence into code, enabling reproducible analysis and forecasting evaluation using synthetic data.

Contribution

It offers an implementation of lead time divergence analysis in R, including scripts, examples, and evaluation methods, making the methodology accessible and reproducible.

Findings

Normalized L1 distance measures lead time divergence

Bound links divergence to forecast error

Implementation is fully reproducible with synthetic data

Abstract

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.