| Title |
Predicting Green Supply Chain Impact With SNN-Stacking Model in Digital Transformation Context |
| ID_Doc |
62290 |
| Authors |
Li, T; Donta, PK |
| Title |
Predicting Green Supply Chain Impact With SNN-Stacking Model in Digital Transformation Context |
| Year |
2023 |
| Published |
Journal Of Organizational And End User Computing, 35, 1 |
| DOI |
10.4018/JOEUC.334109 |
| Abstract |
Green supply chain management is crucial for sustainable enterprises. Achieving it hinges on creating a greener supply chain through AI-driven data analysis. This enables precise market alignment, optimized management, and sustainable development. This study explores the link between digital transformation and green supply chain management. It leverages AI, specifically the XGBoost algorithm, to gauge sample contributions to market demand. It extracts multi-dimensional features in green supply chain management using NSCNN and CSCNN, combining them with the Stacking ensemble learning algorithm to form a new predictive model. This model, SNN-Stacking ensemble learning, outperforms traditional models, aiding resource planning, enhancing supply chain transparency, and promoting sustainable development by reducing environmental risks and resource waste. This research underscores the potential of digital technology in green supply chain management. |
| Author Keywords |
Demand forecast; Digital transformation; Green supply chain management; SNN-Stacking ensemble learning; XGBoost algorithm |
| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI) |
| EID |
WOS:001173163000012 |
| WoS Category |
Computer Science, Information Systems; Information Science & Library Science; Management |
| Research Area |
Computer Science; Information Science & Library Science; Business & Economics |
| PDF |
https://www.igi-global.com/ViewTitle.aspx?TitleId=334109&isxn=9781668478912
|