Analysis and correction of supply chain design through machine learning
US11188856B2 · kind B2 · utility
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12Claims
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Key dates
| Filing date | Oct 1, 2020 |
| Grant date | Nov 30, 2021 |
| Priority date | — |
| Expiry date | Oct 1, 2040 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06N5/022
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
A method and system for a machine learning cluster analysis of historical lead time data, which is augmented by one or more features. The data can also be divided into groups, based on time-density of the data, with clustering performed on each group. Furthermore, clustering can also be projected onto two dimensions. In addition, the historical lead time data is separated into a plurality of tolerance zones based on tolerance criteria. The clusters are separated in accordance with a tolerance zone of each group; and further separated according to one or more lead time identifiers to provide one or more separated clusters.
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