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

ArXiv ID: 2511.12763 “View on arXiv”

Authors: Harrison Katz

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.

Keywords: Booking lead times, Normalized L1 distance, Pickup forecast error, Normalized Distribution Distance, R implementation, Hospitality / Revenue Management

Complexity vs Empirical Score

• Math Complexity: 5.0/10
• Empirical Rigor: 7.0/10
• Quadrant: Holy Grail
• Why: The paper introduces an advanced mathematical metric (normalized L1 divergence) and derives a theoretical bound on forecast error, but it is heavily implemented in R with runnable code, synthetic data generation, and clear evaluation protocols.

  flowchart TD
    A["Research Goal: Replicate Lead Time<br>Distribution Analysis in R"] --> B["Methodology: Normalized L1 Distance<br>vs. Reference Periods"]
    B --> C["Data: Synthetic Booking Data<br>Minimal Schema for Reproducibility"]
    C --> D["Computation: Calculate Divergence<br>Track Monthly Flux"]
    D --> E["Outcome: R Handbook & Scripts<br>Bound on Forecast Error"]