Name
Abstract | ||
|---|---|---|
In multi-echelon inventory systems the performance of a given node is affected by events that occur at many other nodes and at many other time periods. For example, a supply disruption upstream will have an effect on downstream, customer-facing nodes several periods later as the disruption cascades through the system. There is very little research on stock-out prediction in single-echelon systems and (to the best of our knowledge) none on multi-echelon systems. However, in real the world, it is clear that there is significant interest in techniques for this sort of stock-out prediction. Therefore, our research aims to fill this gap by using DNN to predict stock-outs in multi-echelon supply chains. |
Year | Venue | Field |
|---|---|---|
2017 | arXiv: Learning | Mathematical optimization,Industrial engineering,sort,Supply chain,Stockout,Mathematics |
DocType | Volume | Citations |
Journal | abs/1709.06922 | 0 |
PageRank | References | Authors |
0.34 | 4 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Afshin Oroojlooyjadid | 1 | 0 | 0.68 |
| Lawrence V. Snyder | 2 | 448 | 31.03 |
| Martin Takác | 3 | 752 | 49.49 |