تحلیل فرآیند پیش‌بینی تقاضا از طریق زنجیره‌تامین و مقایسه وضعیت فعلی و مطلوب درصنعت نشر کتاب

نوع مقاله : مقاله پژوهشی

نویسندگان

1 گروه تحقیقاتی سامانه‌های هوشمند صنعتی مروارید، ایران.

2 مرکز تحقیقات، شهرداری شیرود، مازندران، ایران.

چکیده
هدف: امروزه مدیریت زنجیره‌تامین و پیش‌بینی دقیق تقاضا از عوامل کلیدی در بهبود بهره‌وری، کاهش هزینه‌های عملیاتی و افزایش انعطاف‌پذیری در صنایع مختلف محسوب می‌شوند. صنعت چاپ و نشر کتاب، به‌عنوان یکی از حوزه‌های تاثیرپذیر از نوسانات تقاضا، نیازمند اتخاذ استراتژی‌های کارآمد برای مدیریت زنجیره‌تامین و تخصیص بهینه منابع است. هدف این پژوهش بررسی تاثیر زنجیره‌تامین بر فرآیند پیش‌بینی تقاضا در این صنعت و تحلیل تفاوت‌های میان وضعیت موجود و شرایط مطلوب است.
روش‌شناسی پژوهش: در این مطالعه، از رویکرد کمی و تحلیل داده‌های واقعی فروش و تقاضای کتاب استفاده شده است. روش‌های مبتنی بر یادگیری ماشین، به‌ویژه شبکه‌های عصبی مصنوعی از نوع پیشرو با الگوریتم یادگیری پس‌انتشار خطا، برای مدل‌سازی و پیش‌بینی تقاضا به‌کار گرفته شده‌اند. عملکرد این مدل‌ها با روش‌های سنتی مانند مدل‌های سری زمانی مقایسه شده است تا میزان بهبود دقت پیش‌بینی ارزیابی شود.
یافتهها: نتایج تحقیق نشان می‌دهد که مدل‌های یادگیری ماشین، به‌ویژه شبکه عصبی پیشرو، دقت بالاتری در پیش‌بینی تقاضا نسبت به مدل‌های سنتی دارند. همچنین استفاده از این مدل‌ها موجب کاهش اثر شلاقی در زنجیره‌تامین و بهبود هماهنگی میان اعضای آن شده است.
اصالت/ارزش‌افزوده علمی: این پژوهش با ارایه یک مدل ترکیبی از مدیریت زنجیره‌تامین و پیش‌بینی تقاضا مبتنی بر شبکه‌های عصبی، رویکردی نوآورانه برای بهینه‌سازی تصمیم‌گیری در صنعت نشر ارایه می‌دهد. تلفیق روش‌های یادگیری ماشین با تحلیل زنجیره‌تامین می‌تواند مبنایی برای توسعه راهکارهای هوشمند در مدیریت موجودی و برنامه‌ریزی تولید در صنایع مشابه باشد.

کلیدواژه‌ها

موضوعات

عنوان مقاله English

Analysis of the demand forecasting process through the supply chain and comparison of the current and desired states in the book publishing

نویسندگان English

Fatemeh Zahra Montazeri 1
Zahra Joorbonyan 1
Habibeh Karimi 2
1 Morvarid Intelligent Industrial Systems Research Group, Iran.
2 Department of Research Center, Shiroud Municipality, Mazandaran, Iran.
چکیده English

Purpose: Today, supply chain management and accurate demand forecasting are considered key factors in improving productivity, reducing operational costs, and enhancing flexibility across various industries. The printing and publishing industry, as one of the sectors highly influenced by demand fluctuations, requires efficient strategies for supply chain management and optimal resource allocation. The objective of this study is to examine the impact of supply chain management on demand forecasting in this industry and to analyze the differences between the current state and the desired conditions.
Methodology: This study adopts a quantitative approach and utilizes real sales and book demand data for analysis. Machine learning techniques (particularly feedforward artificial neural networks with the backpropagation learning algorithm) are used to model and forecast demand. The performance of these models is compared with that of traditional approaches, such as time-series models, to assess improvements in forecasting accuracy.
Findings:  The results reveal that machine learning models, especially feedforward neural networks, achieve higher accuracy in demand forecasting compared to traditional methods. Moreover, the application of these models reduces the bullwhip effect in the supply chain and enhances coordination among its members.
Originality/Value: By presenting a hybrid model integrating supply chain management and demand forecasting based on neural networks, this research introduces an innovative approach to optimizing decision-making in the publishing industry. The integration of machine learning techniques with supply chain analysis can serve as a foundation for developing intelligent solutions in inventory management and production planning across similar industries.

کلیدواژه‌ها English

Demand forecasting
Supply chain
Machine learning
Artificial neural network
Bullwhip effect
Printing and publishing industry
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