System Engineering and Productivity

System Engineering and Productivity

Designing a Predictive Model for Product Acceptance Rate in Lean Manufacturing Using an Artificial Neural Network

Articles in Press

Document Type : Research Paper

Authors
Mehrnaz Bahramzad 1
Sadegh Abedi 2
Reza Ehtesham Rasi 3
1 Ph.D. Student, Department of Industrial Management, Qazvin Branch, Islamic Azad University, Qazvin, Iran
2 Corresponding author: Assistant Professor, Department of Industrial Management, Qazvin Branch, Islamic Azad University, Qazvin, Iran
3 Associate Professor, Department of Industrial Management, Qazvin Branch, Islamic Azad University, Qazvin, Iran
10.22034/sep.2026.2079391.1442
Abstract
The aim of the present study is to develop a data-driven and predictive model for forecasting the product acceptance rate in a lean manufacturing environment using an artificial neural network. In the first step, potential variables influencing product acceptance were identified through a systematic review of the literature and by eliciting the opinions of experts in lean manufacturing and quality management. The final selection of input variables was then carried out using the fuzzy Delphi method. Subsequently, 4,800 data records extracted from food industry production lines over a two-year period were used as the quantitative data for the study. The predictive model was designed and trained based on a feedforward artificial neural network, and its performance was compared with several benchmark regression models, including linear regression, decision tree, random forest, and support vector machine. The results showed that the artificial neural network model, with a coefficient of determination of 0.88, achieved higher predictive accuracy than the benchmark models and demonstrated a strong capability in modeling nonlinear relationships between quality- and process-related variables and the product acceptance rate. Furthermore, variable importance analysis revealed that raw material quality and the percentage of production waste played the most significant roles in predicting the product acceptance rate. The findings indicate that improving a single factor in isolation does not guarantee an increase in product acceptance, and that sustainable outcomes can only be achieved through the simultaneous management of multiple key variables.

Highlights

  • Identification of six key variables as model inputs for predicting product acceptance in lean manufacturing.
  • Use of an artificial neural network to analyze complex and nonlinear relationships.
  • Development of a data-driven model to support more accurate decision-making for quality improvement and waste reduction.

Keywords
Lean Manufacturing
Product Acceptance
Artificial Neural Network
Production Quality
Food industry;
Subjects

Copyright © Mehrnaz Bahramzad, Sadegh Abedi, Reza Ehtesham Rasi

 

License

This article is released under the Creative Commons Attribution (CC BY 4.0) license. Anyone is free to copy, share, translate, and adapt this article for any purpose, whether commercial or non-commercial, as long as proper citation is given to the authors and original publication.

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System Engineering and Productivity

Articles in Press, Accepted Manuscript
Available Online from 30 April 2026

  • Receive Date 29 November 2025
  • Revise Date 06 February 2026
  • Accept Date 28 March 2026
  • First Publish Date 30 April 2026
  • Publish Date 30 April 2026