Print Demand Forecasting with Machine Learning at HP Inc.
Summary: An iterative, human-in-the-loop framework for SKU-level print demand forecasting, combining machine learning with expert feedback to improve forecast accuracy and decision alignment in dynamic supply environments.
Authors
- M. Harshvardhan (ORCID: https://orcid.org/0000-0001-8086-544X)
- Chuanren Liu (ORCID: https://orcid.org/0000-0001-9030-8495)
- Adam Ghozeil
- Pedro Neto (ORCID: https://orcid.org/0000-0003-2177-5078)
- Jenq–Neng Hwang (ORCID: https://orcid.org/0000-0002-8877-2421)
- Frédéric Marie
- Cara Curtland
- Chuck VanDam
Institutions
- Hewlett-Packard (France) (FR)
- Hewlett Packard Enterprise (United States) (US)
- University of Tennessee at Knoxville (US)
Publication Details
- Journal
- INFORMS Journal on Applied Analytics
- Published
- 2025-05-29
- DOI
- https://doi.org/10.1287/inte.2024.0126
- Primary Topic
- Forecasting Techniques and Applications
- Type
- article
- Field-Weighted Citation Impact
- 0.00