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

Battle Count: 51

This paper is focused on hospitality booking lead time monitoring and revenue management operations. While the distributional divergence metrics and risk bounds have conceptual parallels to financial risk management, the domain (hotel/short-term rental bookings), data structures, and decision templates are specific to hospitality operations. There is no direct application to trading strategies, portfolio optimization, or financial market forecasting. The L1/total variation distance concept is general but the implementation and context are entirely non-financial.

The R package uses base R and common tidyverse packages with no external service dependencies. The minimal data schema requires one row per booking with standard fields. Core computations (histograms, L1 divergence, STL decomposition, bootstrap) are straightforward. The pickup bound formula is simple arithmetic. Decision templates are rule-based with explicit thresholds. The main complexity lies in proper handling of censoring, support selection, and bootstrap resampling, but these are well-documented in the package.

All results use synthetic data generated with fixed seeds. The R package (leadtimefluxR) is open-source on GitHub. Scripts regenerate all figures and tables. No proprietary data required. Executive summary and versioned code allow third-party reproduction. The paper explicitly states 'All results use synthetic data so the exposition is fully reproducible without reference to proprietary sources.'