مهندسی سیستم و بهره‌وری

مهندسی سیستم و بهره‌وری

طراحی مدل پیش‌بینی نرخ پذیرش محصول در تولید ناب با استفاده از شبکه عصبی مصنوعی

مقالات آماده انتشار

نوع مقاله : پژوهشی

نویسندگان
مهرناز بهرام‌زاد 1
صادق عابدی 2
رضا احتشام راثی 3
1 دانشجوی دکتری، گروه مدیریت صنعتی، دانشکده مدیریت، واحد قزوین، دانشگاه آزاد اسلامی، قزوین، ایران
2 نویسنده مسئول: استادیار، گروه مدیریت صنعتی، دانشکده مدیریت، واحد قزوین، دانشگاه آزاد اسلامی، قزوین، ایران
3 دانشیار، گروه مدیریت صنعتی، دانشکده مدیریت، واحد قزوین، دانشگاه آزاد اسلامی، قزوین، ایران
10.22034/sep.2026.2079391.1442
چکیده
هدف پژوهش حاضر طراحی یک مدل داده‌محور و پیش‌بینانه برای پیش‌بینی نرخ پذیرش محصول در محیط تولید ناب با استفاده از شبکه عصبی مصنوعی است. در گام نخست، متغیرهای بالقوه مؤثر بر پذیرش محصول از طریق مرور نظام‌مند ادبیات و اخذ دیدگاه خبرگان حوزه تولید ناب و مدیریت کیفیت شناسایی شد. سپس، انتخاب نهایی متغیرهای ورودی با بهره‌گیری از روش دلفی فازی انجام گرفت. در ادامه، ۴۸۰۰ داده استخراج‌شده از خطوط تولید صنایع غذایی طی یک بازه زمانی دوساله به‌عنوان داده‌های کمی پژوهش مورد استفاده قرار گرفت. مدل پیش‌بینی بر پایه شبکه عصبی مصنوعی پیش‌خور طراحی و آموزش داده شد و عملکرد آن با چند مدل رگرسیونی مرجع شامل رگرسیون خطی، درخت تصمیم، جنگل تصادفی و ماشین بردار پشتیبان مقایسه گردید. نتایج نشان داد که مدل شبکه عصبی مصنوعی با ضریب تعیین ۰٫۸۸، عملکرد دقیق‌تری نسبت به مدل‌های مرجع دارد و توانایی بالایی در مدل‌سازی روابط غیرخطی میان متغیرهای کیفی و فرایندی و نرخ پذیرش محصول ارائه می‌دهد. همچنین تحلیل اهمیت متغیرها نشان داد کیفیت مواد اولیه و درصد ضایعات تولید بیشترین نقش را در پیش‌بینی نرخ پذیرش محصول ایفا می‌کنند. یافته‌ها بیانگر آن است که بهبود تک‌بعدی یک عامل به‌تنهایی تضمین‌کننده افزایش پذیرش محصول نیست و مدیریت هم‌زمان چند متغیر کلیدی برای دستیابی به نتایج پایدار ضروری است.

تازه های تحقیق

  • شناسایی شش متغیر کلیدی به‌عنوان ورودی مدل برای پیش‌بینی نرخ پذیرش محصول در تولید ناب
  • استفاده از شبکه عصبی برای تحلیل روابط پیچیده و غیرخطی
  • ارائه یک مدل داده‌محور جهت تصمیمات دقیق‌تری برای بهبود کیفیت و کاهش ضایعات

کلیدواژه‌ها
تولید ناب
پذیرش محصول
شبکه عصبی مصنوعی
کیفیت تولید
صنعت مواد غذایی
موضوعات

عنوان مقاله English

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

نویسندگان English

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
چکیده English

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.

کلیدواژه‌ها English

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

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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مهندسی سیستم و بهره‌وری

مقالات آماده انتشار، پذیرفته شده
انتشار آنلاین از 10 اردیبهشت 1405

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