ML-Driven Predictive Analytics to Anticipate Market Trends in Sustainable and Ethical Sourcing
Rohit Agarwal, Vivekanand Aelgani, Anita Sofia Liz.D. R, Karibandi Venkata Sai Lakshmi Harika, Haider Mohmmed Alabdeli, G. R. Vijayshankar · 2024
This study aims at identifying effectiveness of prognostication models and the use of the ML algorithms to the projection of the trends for sustainable and ethical sourcing within the context of the given industry. The metrics compare the performance of the developed model to that of the traditional model: With an accuracy of 90%, the neural network model demonstrates an enhanced capacity in performing more accurate forecasts. Quantitative analysis from 2022-2024 proves annual gradual increase in environmentally friendly, fairly traded, and organically sourced products consumers’ preferences, an emerging conscience among consumers. Also, the compliance audits of suppliers reveal a very high rate of suppliers’ compliance score with key suppliers getting 92 for GreenSolve ethical sources, 88 for EthicalSource and 95 for EnviroTech out of a scores of 100, thus highlighting their compliance to ethical loyalty. These findings point to the applicability of adopting datamining strategies to enhance sustainable and ethical sourcing systems that are helpful for coping with emergent market trends and needs and promoting ethical conduct along the global supply chain.