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Prediction-led prescription: optimal Decision-Making in times of Turbulence and business performance improvement

Schaefers, Andreas; Bougioukos, Vasileios; Karamatzanis, Georgios; Nikolopoulos, Kostas

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Authors

Andreas Schaefers

Georgios Karamatzanis



Abstract

Can you have prescription without prediction? Most scholars and practitioners would argue that a good forecast drives an optimal decision, thus promoting the concept of prediction-led prescription. In times of turbulence, Special events like promotions and supply chain disruptions are impacting businesses severely. Nevertheless, limited research has been carried out to date to accurately forecast the impact of, and consequentially prescribe in the presence of special events. Nowadays Artificial Intelligence (AI) predictive analytics methods and heuristics imitate and even improve human intelligence, progressively leading towards innovative cognitive analytics solutions. This research aims to contribute to applying advancements in AI-based predictive analytics to improve business performance. We provide empirical evidence that these AI solutions outperform the popular (especially among practitioners) linear regression models. We corroborate the stream of literature arguing that AI predictive analytics could − via a natural path-dependent process − enhance prescriptive analytics solutions, and thus improve business performance.

Citation

Schaefers, A., Bougioukos, V., Karamatzanis, G., & Nikolopoulos, K. (2024). Prediction-led prescription: optimal Decision-Making in times of Turbulence and business performance improvement. Journal of Business Research, 182, Article 114805. https://doi.org/10.1016/j.jbusres.2024.114805

Journal Article Type Article
Acceptance Date Jun 25, 2024
Online Publication Date Jul 3, 2024
Publication Date 2024-09
Deposit Date Jun 14, 2024
Publicly Available Date Jul 12, 2024
Journal Journal of Business Research
Print ISSN 0148-2963
Publisher Elsevier
Peer Reviewed Peer Reviewed
Volume 182
Article Number 114805
DOI https://doi.org/10.1016/j.jbusres.2024.114805
Public URL https://durham-repository.worktribe.com/output/2483650

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