Is There a Future for Stochastic Modeling in Business and Industry in the Era of Machine Learning and Artificial Intelligence?
ABSTRACT The paper arises from the experience of Applied Stochastic Models in Business and Industry which has seen, over the years, more and more contributions related to Machine Learning rather than to what was intended as a stochastic model. The very notion of a stochastic model (e.g., a Gaussian process or a Dynamic Linear Model) can be subject to change: What is a Deep Neural Network if not a stochastic model? The paper presents the views, supported by examples, of distinguished researchers in the field of business and industrial statistics. They are discussing not only whether there is a future for traditional stochastic models in the era of Machine Learning and Artificial Intelligence, but also how these fields can interact and gain new life for their development.
Authors
- Ansgar Steland (ORCID: https://orcid.org/0000-0001-9395-7458)
- Marco S. Reis (ORCID: https://orcid.org/0000-0002-4997-8865)
- Piercesare Secchi (ORCID: https://orcid.org/0000-0003-4048-9552)
- Emanuela Raffinetti (ORCID: https://orcid.org/0000-0002-0386-7975)
- Philip L. H. Yu (ORCID: https://orcid.org/0000-0002-9449-0420)
- Gilbert Saporta (ORCID: https://orcid.org/0000-0002-3406-5887)
- N. I. Fisher (ORCID: https://orcid.org/0000-0003-0209-7080)
- Rituparna Sen (ORCID: https://orcid.org/0000-0002-2112-7532)
- Marcos Escobar‐Anel (ORCID: https://orcid.org/0000-0001-9691-4322)
- Jean‐Michel Poggi (ORCID: https://orcid.org/0000-0002-8222-1653)
- Ron S. Kenett (ORCID: https://orcid.org/0000-0003-2315-0477)
- Fabrizio Ruggeri (ORCID: https://orcid.org/0000-0002-7655-6254)
- Wai Keung Li
- Paolo Giudici (ORCID: https://orcid.org/0000-0002-4198-0127)
- Dennis K. J. Lin (ORCID: https://orcid.org/0000-0003-2552-7709)
- Roger W. Hoerl (ORCID: https://orcid.org/0000-0002-0305-8676)
- David Banks (ORCID: https://orcid.org/0000-0002-2376-0710)
- Zhanpan Zhang (ORCID: https://orcid.org/0000-0002-3913-9654)
- William S. Cleveland (ORCID: https://orcid.org/0000-0002-9688-6728)
Institutions
- Conservatoire National des Arts et Métiers (FR)
- Western University (CA)
- The University of Sydney (AU)
- Duke University (US)
- Union College (US)
- Purdue University West Lafayette (US)
- University of Pavia (IT)
- Laboratoire de Mathématiques d'Orsay (FR)
- Samuel Neaman Institute for National Policy Research (IL)
- Education University of Hong Kong (HK)
- GE Global Research (United States) (US)
- Istituto di Matematica Applicata e Tecnologie Informatiche (IT)
- GE Vernova (United States)
- Indian Statistical Institute (IN)
- University of Coimbra (PT)
- RWTH Aachen University (DE)
- Politecnico di Milano (IT)
Publication Details
- Journal
- Applied Stochastic Models in Business and Industry
- Published
- 2025-03-01
- DOI
- https://doi.org/10.1002/asmb.70004
- Citations
- 6
- Primary Topic
- Fault Detection and Control Systems
- Type
- article
- Field-Weighted Citation Impact
- 7.29
Funders
- European Commission
- Fundação para a Ciência e a Tecnologia