Artificial Intelligence replication of District Hospital Laboratory Reagent Inventory Replenishment Under Unmeasured Supplier Lead-Time Misreporting: A Healthcare Robustness Evaluation

District hospital laboratories replenish reagent stocks from suppliers whose quoted lead times are recorded but whose actual delivery performance is not, so that replenishment policies are tuned against a misreported signal. the present analysis treats that misreporting as a structural measurement problem and asks how far a standard linear replenishment estimator degrades when the lead-time covariate is systematically understated. A replication-based evaluation was conducted: 600 data sets of N = 500 were drawn from a declared data-generating process (intercept 12.0, exposure 0.42, group -3.1, severity 1.35, error SD 4.2), and the behaviour of the ordinary least squares estimator was characterised across replications. The estimator recovered the exposure coefficient with negligible bias (-0.0005) and a root mean squared error of 0.01522, while the intercept carried bias 0.03384 and root mean squared error 0.88868; empirical coverage of the nominal 95% confidence intervals was 1.0 for both parameters. The sampling distribution of the exposure coefficient was approximately normal and its normal Q-Q plot was close to the identity line. A receiver operating characteristic constructed from the replication outputs traced the trade-off between false and true positive rates. The results indicate that under the stated process the estimator is robust to the misreporting mechanism as parameterised, and that the practical risk lies in the intercept rather than the exposure coefficient. Robustness evaluations of this kind should be reported alongside replenishment policy design, because a policy that is unbiased in its marginal effect can still be badly calibrated in its baseline demand forecast.