Transforming Supply Chain Forecasting Using Transformer Models and K-NN Analysis
DOI:
https://doi.org/10.59890/ijarss.v2i6.2026Keywords:
Big Data Analytics, Supply Chain Optimization, K-Nearest NeighborsAbstract
The study optimizes supply chain logistics in Asia using the K-Nearest Neighbors (K-NN) algorithm to enhance delivery efficiency and profitability. It suggests that future research should explore ensemble methods and deep learning models for better accuracy and robustness. Comparative analyses with traditional models provide valuable insights. Investigating the impact of real-time data analytics and IoT can improve visibility and control. Big data analytics for predictive models in risk management and resilience against disruptions like natural disasters and geopolitical instability is crucial. Exploring collaborative networks where stakeholders share data and resources can significantly advance logistics efficiency. These directions will help develop efficient, resilient, and sustainable supply chain systems, offering practical solutions for businesses in Asia's complex market.
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Copyright (c) 2024 Moch. Nauval Faris Muzaki, Muhamad Fatchan, Irfan Afriantoro
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