Authors: Md Abrar Jahin, Md Sakib Hossain Shovon, Jungpil Shin, Istiyaque Ahmed Ridoy, M. F. Mridha

Published on: July 24, 2023

Impact Score: 8.22

Arxiv code: Arxiv:2307.12971

Summary

• What is new: A novel framework for supply chain forecasting incorporating Big Data Analytics, focused on comprehensive data analysis, and optimization of forecasting effects on various supply chain aspects.
• Why this is important: The need for improved supply chain forecasting strategies that adequately leverage Big Data Analytics to enhance overall supply chain management, transparency, and efficiency.
• What the research proposes: Proposing a systematic framework that outlines the process from problem identification, through data collection and analysis, to optimization of forecasting effects on inventory, workforce, and the supply chain.
• Results: Outlined a standard process for supply chain forecasting, demonstrated the potential improvements in operations management, transparency, and efficiency, and suggested directions for future research.

Technical Details

Technological frameworks used: Proposed novel framework incorporating Big Data Analytics for Supply Chain Management

Models used: Machine-learning models for forecasting

Data used: Supply Chain data including inventory, workforce, and performance metrics

Potential Impact

Supply chain management firms, logistic companies, retailers, and manufacturing businesses could benefit or need to adapt to these insights.

Want to implement this idea in a business?

We have generated a startup concept here: OptiChain Analytics.