Systems and methods for probabilistic parts forecasting based on machine utilization patterns
US12039554B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Mar 31, 2021 |
| Grant date | Jul 16, 2024 |
| Priority date | — |
| Expiry date | Jun 18, 2042 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06Q30/0205
- WIPO fieldIT methods for management
- WIPO sectorElectrical engineering
Abstract
A method for forecasting part sales, including collecting sales data for a part over a series of sales time periods and collecting activity data for a plurality of activity types over a series of activity time periods for a plurality of machines including the part. A mean activity time can be calculated for each activity type for each time period in the series of activity time periods based on the collected activity data. An activity probability density function of the mean activity times for each activity type is created and a machine learning model is trained using an expectation of activity derived from the probability density functions for each activity type and the collected sales data. Machine activity data for a set of machines can be fed into the trained model to derive a part sales probability density function for the set of machines.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.