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Research Paper | Information Technology | India | Volume 10 Issue 12, December 2021 | Rating: 5.2 / 10

Autonomous Systems for Algorithmic Network Consolidation in Supply Chain Operations

Bidyut Sarkar ^[5] | Rudrendu Kumar Paul ^[4]

Abstract: The rapid increase in Mergers and Acquisitions (M&A) in the e-commerce and Supply Chain sector prompts the need for effective strategies to consolidate networks, thus enhancing cost savings. This study introduces a data science-driven approach for algorithmic consolidation of networks following M&A. The process encompasses assessing the supply chain functions of the merging and acquired organizations, pinpointing clusters of locations within a specific radius, and determining the comprehensive cost savings from merging fulfillment hubs. The study suggests a well-defined procedure that sequentially details how to employ this strategy, yielding advantages such as augmented operational competence, minimized transport expenses, amplified bargaining power with carriers, and simplified inventory control. Nevertheless, certain obstacles and restrictions, such as data precision, reluctance to adapt, potential implications on customer service, and legal issues, need to be considered. The presented data science-driven approach for algorithmic network consolidation holds substantial potential for increasing savings following M&A and refining e-commerce and Supply Chain procedures, with prospective applications across various sectors.

Keywords: Autonomous Systems, Data Science, Supply Chain, Mergers and Acquisitions (M&A), Network Consolidation, Fulfillment Centers, Operational Efficiency, Algorithmic Approach, Supply Chain Optimization, Algorithm, Automation and Scaling

Edition: Volume 10 Issue 12, December 2021,

Pages: 1425 - 1428