Tobit Exponential Smoothing, towards an enhanced demand planning in the presence of censored data

TL;DR

This paper introduces Tobit ETS, an extension of exponential smoothing models designed to effectively handle censored data, such as stockouts, thereby improving demand forecasting accuracy in supply chain management.

Contribution

It proposes Tobit ETS, a novel method that extends traditional ETS models to efficiently estimate censored data, reducing forecast bias in practical applications.

Findings

Tobit ETS significantly reduces forecast bias.

The method performs well on real airline and supply chain data.

Simulation results confirm improved accuracy over existing models.

Abstract

ExponenTial Smoothing (ETS) is a widely adopted forecasting technique in both research and practical applications. One critical development in ETS was the establishment of a robust statistical foundation based on state space models with a single source of error. However, an important challenge in ETS that remains unsolved is censored data estimation. This issue is critical in supply chain management, in particular, when companies have to deal with stockouts. This work solves that problem by proposing the Tobit ETS, which extends the use of ETS models to handle censored data efficiently. This advancement builds upon the linear models taxonomy and extends it to encompass censored data scenarios. The results show that the Tobit ETS reduces considerably the forecast bias. Real and simulation data are used from the airline and supply chain industries to corroborate the findings.