A Model for Process Control in Agile Supply Chain Networks Using Artificial Neural Networks

Document Type : Original Article

Authors

Department of Industrial Management, Islamic Azad University, Tehran Central Branch, Tehran, Iran

Abstract

model to enhance agility in supply chain networks, focusing on optimizing decision-making
under demand fluctuations and cost constraints.
Methods: A quantitative descriptive design was employed, utilizing cluster sampling (n=384)
from a manufacturing company’s customer base. The ANN model integrated key variables
(e.g., raw material flow, production volume, storage capacity) and was tested via sensitivity
analysis to evaluate the impacts of production cost, service level, and factory capacity changes
on objective function values.
Results: The model significantly improved supply chain agility, enabling a dynamic response
to demand shifts. A 111% production cost variation altered costs by ±15.7%, while a 91%
capacity reduction rendered the model infeasible. A 411% demand surge disrupted service
levels (91%), highlighting capacity constraints. Flexibility indicators (e.g., production
adaptability) emerged as critical agility drivers.
Conclusion: The ANN-based model optimizes supply chain performance, particularly
in healthcare contexts where responsiveness and cost efficiency are paramount. Practical
recommendations include workforce skill development, just-in-time production systems, and
enhanced supplier IT integration.

Highlights

Alireza Aliasgharzadeh (Google Scholar)

Mahmood Mohammadi (Google Scholar)

Keywords

Main Subjects

References

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Health Management & Information Science
Volume 12, Issue 4
October 2025
Pages 267-273

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