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Aliasgharzadeh, Alireza, Mohammadi, Mahmood. (1404). A Model for Process Control in Agile Supply Chain Networks Using Artificial Neural Networks. سامانه مدیریت نشریات علمی, (), -. doi: 10.30476/jhmi.2025.108149.1306
Alireza Aliasgharzadeh; Mahmood Mohammadi. "A Model for Process Control in Agile Supply Chain Networks Using Artificial Neural Networks". سامانه مدیریت نشریات علمی, , , 1404, -. doi: 10.30476/jhmi.2025.108149.1306
Aliasgharzadeh, Alireza, Mohammadi, Mahmood. (1404). 'A Model for Process Control in Agile Supply Chain Networks Using Artificial Neural Networks', سامانه مدیریت نشریات علمی, (), pp. -. doi: 10.30476/jhmi.2025.108149.1306
Aliasgharzadeh, Alireza, Mohammadi, Mahmood. A Model for Process Control in Agile Supply Chain Networks Using Artificial Neural Networks. سامانه مدیریت نشریات علمی, 1404; (): -. doi: 10.30476/jhmi.2025.108149.1306

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

Health Management & Information Science
مقالات آماده انتشار، پذیرفته شده، انتشار آنلاین از تاریخ 07 مهر 1404
نوع مقاله: Original Article
شناسه دیجیتال (DOI): 10.30476/jhmi.2025.108149.1306
نویسندگان
Alireza Aliasgharzadeh* ؛ Mahmood Mohammadi
Department of Industrial Management, Islamic Azad University, Tehran Central Branch, Tehran, Iran
چکیده
Abstract
Objective: This study proposes an Artificial Neural Network (1)-based process control 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.
کلیدواژه‌ها
Process Control؛ Agile Supply Chain؛ Artificial Neural Network
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