Abstract
Unpredictable fluctuations in customer demand at physical facilities can have substantial operational impacts. Current demand forecasting approaches tend to be simplistic and reactive as they rely primarily on internal historical data, which provides inadequate accuracy to support future resource allocation. This disclosure describes techniques to provide accurate medium-term demand forecasts for physical operational facilities by synthesizing a diverse set of relevant real-world factors obtained from a fusion of numerous heterogeneous data sources related to human practices and needs. The factors can be continually aggregated and input to a suitably trained machine learning model trained to generate specific actionable output designed to facilitate operational planning and resource allocation. The output can be presented in multiple modalities as appropriate to fit the resource allocation workflow of the various types of end users, such as administrators, managers, etc. By leveraging unique and extensive data factors beyond internal historical data for a single facility, the techniques described in this disclosure can enable demand forecasting that has improved accuracy compared to existing approaches.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
Parakh, Kapil and Klein, Daniel, "Forecasting Demand at Physical Facilities Using Diverse Heterogeneous Real-World Data", Technical Disclosure Commons, (August 14, 2026)
https://www.tdcommons.org/dpubs_series/11377