Vol. 4 No. 2 (2024): July−December 2024
Articles

CloudScale: A Lightweight AI Framework for Predictive Supply Chain Risk Management in Small and Medium Manufacturing Enterprises

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  • Sida Zhang,
  • Chenyao Zhu,
  • Jing Xin
Sida Zhang
Computer Science & Machine Learning, Seattle, WA, USA
Chenyao Zhu
Industrial Engineering & Operations Research, UC Berkeley, CA, USA
Jing Xin
Business Analytics, UW Madison, WI, USA

Published 2024-11-11

Keywords

  • Supply Chain Risk Management,
  • Artificial Intelligence,
  • Manufacturing SMEs,
  • Cloud Computing

How to Cite

Zhang, S., Zhu, C., & Xin, J. (2024). CloudScale: A Lightweight AI Framework for Predictive Supply Chain Risk Management in Small and Medium Manufacturing Enterprises. Spectrum of Research, 4(2). Retrieved from https://spectrumofresearch.com/index.php/sr/article/view/26

Copyright (c) 2024 Spectrum of Research

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Abstract

This paper introduces CloudScale, a lightweight AI framework for predictive supply chain risk management designed specifically for Small and Medium Manufacturing Enterprises (SMEs). While large corporations have invested heavily in advanced supply chain technologies, SMEs face significant barriers including limited financial resources, technical expertise, and computational infrastructure. CloudScale addresses these challenges through a multi-layered architecture comprising optimized serverless cloud components and resource-efficient prediction algorithms. The framework incorporates privacy-preserving federated learning approaches to enable collaborative risk assessment while maintaining data sovereignty. Implementation across three diverse manufacturing SMEs demonstrated prediction accuracy exceeding 90% with computational requirements within SME infrastructure constraints. Case study results showed average inventory reductions of 13.6%, prevention of critical stockout events with estimated savings of $139,000 per SME annually, and average disruption detection lead times of 12.1 days. Performance benchmarking revealed that CloudScale delivers comparable accuracy to enterprise solutions at 22% of the computational cost and 18% of the total cost of ownership. The evaluation methodology validated the framework's ability to maintain critical functionality even under resource-constrained conditions, with graceful performance degradation to 25% of recommended resources. CloudScale represents a significant advancement in democratizing sophisticated supply chain risk management technologies for the 580,000 manufacturing SMEs in the United States.

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