Optimizing Operations: a Convergence of Business Analytics, Machine Learning, and Blockchain for Employee Performance and Supply Chain Integrity

EasyChair Preprint 12186

Abstract

This research explores the integration of business analytics, machine learning, and blockchain technologies to optimize operations, focusing on enhancing employee performance and ensuring supply chain integrity. Employing a comprehensive methodology, the study demonstrates promising results in streamlining processes, enhancing transparency, and improving overall efficiency. Despite encountering challenges in implementation, effective treatments are proposed to overcome these obstacles. The conclusion highlights the transformative potential of this convergence in reshaping contemporary business operations.

Keyphrases: Blockchain, Business Analytics, Employee, Optimization, Performance, Supply Chain Integrity, machine learning

@booklet{EasyChair:12186,
  author    = {William Jack and Roki Eolis},
  title     = {Optimizing Operations: a Convergence of Business Analytics, Machine Learning, and Blockchain for Employee Performance and Supply Chain Integrity},
  howpublished = {EasyChair Preprint 12186},
  year      = {EasyChair, 2024}}