How to use no-code artificial intelligence to predict and minimize the inventory distortions for resilient supply chains
Sunil Kumar Jauhar, Shashank Mayurkumar Jani, Sachin S. Kamble, Saurabh Pratap, Amine Belhadi and Shivam Gupta
| Year of publication: |
2024
|
|---|---|
| Authors: | Jauhar, Sunil Kumar ; Jani, Shashank Mayurkumar ; Kamble, Sachin S. ; Pratap, Saurabh ; Belhadi, Amine ; Gupta, Shivam |
| Published in: |
International journal of production research. - London [u.a.] : Taylor & Francis, ISSN 1366-588X, ZDB-ID 1485085-0. - Vol. 62.2024, 15, p. 5510-5534
|
| Subject: | Inventory distortion | No-Code Artificial Intelligence (NCAI) | out-of-stocks | overstocks | supply chain resilience | Lieferkette | Supply chain | Künstliche Intelligenz | Artificial intelligence | Lagermanagement | Warehouse management | Risikomanagement | Risk management | Lagerhaltungsmodell | Inventory model |
| Type of publication: | Article |
|---|---|
| Type of publication (narrower categories): | Aufsatz in Zeitschrift ; Article in journal |
| Language: | English |
| Other identifiers: | 10.1080/00207543.2023.2166139 [DOI] |
| Source: | ECONIS - Online Catalogue of the ZBW |
Persistent link: https://www.econbiz.de/10015053702