Analysis and correction of supply chain design through machine learning
US10832196B2 · kind B2 · utility
1Cited by
5References
15Claims
0Family size
Assignee
Inventors
Key dates
| Filing date | Sep 28, 2018 |
| Grant date | Nov 10, 2020 |
| Priority date | — |
| Expiry date | Apr 16, 2039 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06N5/022
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
A dynamic supply chain planning system for analysis of historical lead time data that uses machine learning algorithms to forecast future lead times based on historical lead time data, and to divide historical lead time data into clusters based on seasonality and linearity. The machine learning results are further processed to adjust future planned lead times and to identify sources in the supply chain that contribute to large deviations between historical planned lead times and actual lead times.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.