Navigating Uncertainty: Human vs. Algorithmic Forecasting Approaches in Times of Crisis
Type
working paper
Date Issued
2024-07-08
Author(s)
Abstract
Forecasting plays a pivotal role in effective operational management, providing critical insights for decision-makers. This paper endeavors to discern the comparative performance of human and algorithmic forecasting, especially within crises, to test the resilience and adaptability of these methodologies. Drawing on data from a Swiss automotive distributor, the research distinguishes between four crisis types, where we focus on external sudden and external smoldering crises. Employing a mixed-method research design, we find that independent of the specific crisis situation, overall, algorithmic forecasts outperform human forecasts. However, for both human and algorithmic forecasts, variations in forecasting accuracy are observed, with smoldering and sudden crises exhibiting less accurate forecasts than non-crisis situations. This study contributes valuable insights into the effectiveness of different forecasting methods in diverse crises, enhancing decision-making knowledge and resilience in times of uncertainty. Furthermore, we hope that by showing the superiority of algorithmic forecasts and delineating their applicability, we can relieve decision-makers of the inherent distrust in algorithms.
Language
English (United States)
Keywords
Forecasting
Crisis
Machine Learning
Data Analytics
Management Accounting