ارائه مدل هوشمند تعیین قیمت فولاد با رویکرد ‌‌‌‌‌ترکیبی نظریه بازی‌ها و الگوریتم‌های ‌‌‌یادگیری ماشین

نوع مقاله : مقاله علمی پژوهشی

نویسندگان

1 دانشجوی دکتری، گروه مدیریت صنعتی، دانشکده مدیریت، واحد علوم و تحقیقات، دانشگاه آزاد اسلامی، تهران، ایران

2 دانشیار، گروه مدیریت، دانشکده مدیریت، واحد تهران مرکزی، دانشگاه آزاد اسلامی، تهران، ایران

3 استادیار، گروه مدیریت صنعتی، دانشکده مدیریت، واحد تهران جنوب، دانشگاه آزاد اسلامی، تهران، ایران

4 استادیار، گروه مدیریت صنعتی، دانشکده مدیریت، واحد تهران مرکزی، دانشگاه آزاد اسلامی، تهران، ایران

چکیده

هدف: مدیریت زنجیره تأمین نوعی مدیریت سازمانی مدرن است که جریان اطلاعات، جریان سرمایه و مشارکت‌های تجاری را در زنجیره تأمین سازمان‌دهی و برنامه‌ریزی می‌کند و به اطلاعات کامل تجاری و بازار نیاز دارد (کویین و همکاران، 2012)؛ با این حال، هزینه به‌دست‌آوردن شرکت‌های زنجیره تأمین و اطلاعات محصول با روش‌های سنتی، بسیار سنگین است. فناوری اطلاعات نیرویی را برای شرکت‌ها فراهم می‌کند تا مدیریت زنجیره تأمین را پیاده‌سازی کنند و زنجیره تأمین را به‌راحتی به اشتراک بگذارند. همه شرکت‌ها در زنجیره تأمین، می‌توانند از طریق مدیریت اطلاعات ارزش ایجاد کنند (حسین نیاشاواکی و همکاران، 2023). استفاده از رویکردهای هوشمند برای پیش‌بینی قیمت و میزان تقاضا، عملکرد تحویل تأمین‌کننده، دقت پیش‌بینی تقاضا، افزایش دقت برنامه‌ریزی کارخانه و پیش‌بینی تقاضا برای محصولات جدید را بهبود می‌دهد و باعث می‌شود که ریسک تأمین‌کننده، هزینه حمل‌ونقل، هزینه‌های موجودی و عملیات و زمان پاسخ‌گویی کاهش یابد (تیرکلایی و همکاران، 2021). در مدیریت زنجیره تأمین، پیش‌بینی دقیق تقاضا که از قیمت تبعیت می‌کند، موضوعی حیاتی است که می‌تواند هزینه موجودی را کاهش دهد و سطح خدمات مطلوب را به‌دست آورد (زوقاق و همکاران، 2020). رویکردهای هوشمند قیمت‌گذاری در زنجیره تأمین، به شرکت‌های زنجیره تأمین کمک می‌کند تا با توجه به دانش به‌دست‌آمده، کیفیت نحوه ارائه محصول خود را در مدیریت زنجیره تأمین تطبیق دهند (کوتسیوپولوس و همکاران، 2021). در صنعت فولاد و مدیریت زنجیره تأمین، شناسایی و مدل‏سازی نوسان‌های بازار فولاد بسیار مهم است. با توجه به زنجیره عمودی در این صنعت و تعامل مابین بازیکنان این صنعت، از نظریه ‌بازی‌ برای مدل‏سازی قیمت بهینه بهره برده شده است. از طرفی با توجه به اینکه برای رسیدن به تعادل، به تعامل بازیکنان و تکرار بازی نیاز است، از مدل‌های شبکه عصبی برای تکرار بازی استفاده شده است. در ادامه با توجه به شرایط خاص کشور در خصوص تحریم‌های شدید در صنعت فلزات، متغیر تحریم به‌عنوان عامل تعدیل در مدل‏سازی قیمت این صنعت در نظر گرفته شده است.
روش: پژوهش حاضر از نظر هدف کاربردی است. بازه زمانی پژوهش برای پیش‌بینی قیمت فولاد و محاسبه شاخص تحریم، داده‌های فصلی سال‌های 2011 تا 2020 بوده است. نرم‌افزار استفاده‌شده در این پژوهش، نرم‌افزار متلب است.
یافته‌ها: برای پیش‌بینی قیمت فولاد، از سه شبکه عصبی بیزین، بردارهای پشتیبان و پاد انتشار گراسبرگ بهره گرفته شد. نتایج بیانگر این واقعیت است که مدل پاد انتشار گراسبرگ، در پیش‌بینی قیمت فولاد دقت بیشتری دارد. در ادامه، قیمت پیش‌بینی‌شده وارد فرایند نظریه بازی‌ها شد و نقطه تعادل نش مدل تعیین شد. با توجه به شرایط خاص کشور، متغیر تحریم در مدل نظریه بازی‌ها وارد شد. نتایج نشان داد که حضور تحریم‌ها در مدل، باعث افزایش قیمت‌ها و کاهش تولید در صنعت فولاد شده است. با توجه به اینکه در پژوهش حاضر، تغییرات قیمت ناشی از تغییرات عرضه و تقاضا، در حضور تحریم‌ها بررسی شد، به‌علت کاهش عرضه و افزایش سطح تحریم، سطح قیمت‌ها با رشد فزاینده‌ای نسبت به تغییرات عرضه مواجه شد؛ در نتیجه می‌توان گفت که فولاد یک نهاده کم‌کشش است. این امر موجب می‌شود که هر گونه اخلال در زنجیره تأمین فولاد، افزایش شدید قیمت این کالا و تلاطم در بازار آن را در پی داشته باشد. در نتیجه، این امر حساسیت مدیریت زنجیره تأمین در محصول فولاد را دوچندان می‌کند. بر این اساس، لازم است که از دیدگاهی سیستمی ‌و پویا در سیاست‌های تنظیم بازار، سیاست‌های تأمین مواد اولیه و حمل‌ونقل، انبارداری و... بهره‌گیری شود. باید توجه شود که استفاده از رویکردهای هوشمند و یادگیری ماشینی، در راستای هماهنگ‌سازی این امور نقش بسزایی را ایفا می‌کند.
نتیجه‌گیری: با توجه به اینکه در پژوهش حاضر از رویکرد استکلبرگ استفاده شده است، نتایج به‌ترتیب ورود بازیکنان به بازی، بر تعادل نش حساس است. تدوین قوانین و مقررات نظارت ورود به بازار در این صنعت باید بررسی شود؛ زیرا صنعت فولاد جزء صنایعی است که هزینه‌های ورود و خروج سنگینی دارد. با توجه به نتایج پژوهش، بایستی نظارت بر ورود و خروج بازیکنان در این صنعت، در کانون توجه سیاست‌گذاران و مدیران این صنعت قرار گیرد و تلاش شود که قواعد بازی و استانداردهایی برای فعالان این بازار تدوین شود.

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

Presenting Smart Steel Pricing Model: An Integration of Game Theory and Machine Learning Algorithms

نویسندگان [English]

  • Mina Kazemian 1
  • Mohammad Ali Afsharkazemi 2
  • Kiamars Fathi Hafashjani 3
  • Mohammadreza Motadel 4
1 Ph.D. Candidate, Department of Industrial Management, Faculty of Management, Science and Research Branch, Islamic Azad University, Tehran, Iran.
2 , Associate Prof., Department of Management, Faculty of Management, Central Tehran Branch, Islamic Azad University, Tehran, Iran.
3 Assistant Prof., Department of Industrial Management, Faculty of Management, South Tehran Branch, Islamic Azad University, Tehran, Iran.
4 Assistant Prof., Department of Industrial Management, Faculty of Management, Central Tehran Branch, Islamic Azad University, Tehran, Iran.
چکیده [English]

Objective
Supply chain management is a modern organizational management mode that organizes and plans information, capital flow, and business partnerships in the supply chain and requires complete business and market information (Quinn et al., 2012). However, the cost of acquiring supply chain companies and product information by traditional methods is very high. Information technology provides the power for companies to implement supply chain management and share the supply chain easily, and all companies in the supply chain can create value through information management (Shawaki et al., 2023). The utilization of intelligent approaches to predict prices and demand quantities enhances supplier delivery performance. It also refines demand forecasting accuracy, improves factory planning precision, forecasts demand for new products, and minimizes supplier risks, transportation costs, inventory, operational expenses, and time (Tirklai et al., 2021). In supply chain management, accurate forecasting of demand reflects the price. It is a critical issue that can reduce inventory costs and achieve the desired service level (Zouqaq et al., 2020). Intelligent supply chain pricing approaches can help supply chain companies to adapt the quality of their product offerings in supply chain management according to the knowledge gained (Kotsiopoulos et al., 2021). Identifying and modeling steel market fluctuations is very important in the steel industry and supply chain management. Considering the vertical chain in this industry and the interaction between the players of this industry, game theory has been used to model the optimal price. Neural network models were employed to replicate the game, as interaction and repeated gameplay are required for achieving balance among players. Taking into account Iran's unique circumstances, notably its confrontations with substantial sanctions in the metal industry, the sanctions variable was integrated as an adjusting factor in the pricing model for this sector.
 
Methods
This is a practical study. The research time frame for predicting steel prices and calculating the sanctions index spans from 2011 to 2020, with quarterly data. The MATLAB software was used.
 
Results
Three Bayesian neural networks, support vectors, and Grassberg's anti-diffusion were used to predict the price of steel. The results showed that the Grossberg anti-diffusion model is more accurate in predicting steel prices. Next, the predicted price entered the game theory process and the Nash equilibrium point of the model was determined. According to the country's specific conditions, the sanctions variable was introduced in the game theory model. The results showed that the inclusion of sanctions in the model led to price increases and production reductions within the steel industry. The present study delved into price fluctuations resulting from shifts in supply and demand, particularly in the context of sanctions. The findings reveal that a reduction in supply coupled with escalated sanctions led to substantial price hikes, surpassing the impact of supply changes. Consequently, steel exhibits a heightened susceptibility to input constraints, where any disruption in its supply chain triggers significant price spikes, thus unsettling the market. This amplifies the sensitivity of supply chain management for steel. Consequently, a systemic and dynamic approach is essential for market regulation policies, raw material supply, transportation strategies, and warehousing considerations. It should be noted that the use of intelligent approaches and machine learning can play a significant role in coordinating such issues.
 
Conclusion
Considering that Stackelberg's approach was used in the current research, the sequence of players' entry into the game holds significance with respect to the Nash equilibrium. The development of market entry monitoring rules and regulations in this industry should be investigated because the steel industry is one of the industries that face high entry and exit costs. As a result, Policymakers and industry managers should monitor the entry and exit of players within this sector. They should endeavor to establish norms and regulations governing interactions among market participants to foster a structured and well-defined competitive environment.

کلیدواژه‌ها [English]

  • Game theory
  • Optimal price
  • Neural network
  • Steel industry

مراجع

منابع
دری، محسن؛ جعفری، ‌میثم و چهارسوقی، کمال (1398). انتخاب خط‌مشی سفارش هماهنگ‌شده در زنجیره تأمین دو سطحی: رویکرد نظریه بازی. تحقیقات مدرن در تصمیم‌‌گیری، 4(3)، 47- 73.
سلطانی تهرانی، الهه و دایی کریم‌زاده، سعید (1394). پیش‏بینی قیمت فولاد با استفاده از مدل سری زمانی. کنفرانس بین‌المللی مدیریت و اقتصاد در قرن بیست‌و‌‌‌یکم، تهران.
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مدیریت صنعتی
دوره 15، شماره 3
1402
صفحه 478-507

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