مدل راهبردی مدیریت موجودی انبار مبتنی بر یادگیری عمیق
محورهای موضوعی : مدیریت صنعتیحمیدرضا حاجعلی 1 , محمد علی افشار کاظمی 2 , عادل آذر 3 , عباس طلوعی اشلقی 4 , رضا رادفر 5
1 - دانشجوی دکتری گروه مدیریت صنعتی، واحد علوم و تحقیقات، دانشگاه آزاد اسلامی، تهران، ایران
2 - استاد گروه مدیریت صنعتی، واحد تهران مركزي، دانشگاه آزاد اسلامی، تهران، ایران (نویسنده مسئول)
3 - استاد گروه مدیریت صنعتی، دانشگاه تربیت مدرس، تهران، ایران
4 - استاد گروه مدیریت صنعتی، واحد علوم و تحقیقات، دانشگاه آزاد اسلامی، تهران، ایران
5 - استاد گروه مدیریت صنعتی، واحد علوم و تحقیقات، دانشگاه آزاد اسلامی، تهران، ایران
کلید واژه: کنترل موجودی, کلان داده, هوش مصنوعی, یادگیری عمیق, زنجیره تامین,
چکیده مقاله :
هدف:
ارائه یک مدل استراتژیک برای مدیریت موجودی با استفاده از یادگیری عمیق (LSTM) به منظور بهره‌برداری از داده‌های کلان صنعتی غیرخطی و کیفی که با روش‌های کلاسیک قابل استفاده نیستند.
روش‌شناسی پژوهش:
در این پژوهش، یک مدل یادگیری عمیق بر پایه حافظه بلندمدت کوتاه‌مدت (LSTM) طراحی شد. داده‌ها برای آموزش شبکه با استفاده از روش‌های سنتی مدیریت موجودی شامل مقدار اقتصادی سفارش (EOQ)، مقدار اقتصادی تولید (EPQ) و تحلیل ABC آماده‌سازی گردید.
یافته‌ها:
پس از ساخت و آموزش مدل، شبکه LSTM به دقت پیش‌بینی ۹۳ درصد دست یافت که نشان‌دهنده موفقیت ترکیب رویکردهای سنتی با هوش مصنوعی برای تحلیل داده‌های کلان در مدیریت موجودی است.
اصالت / ارزش‌افزوده علمی:
نوآوری این پژوهش در ترکیب روش‌های کلاسیک مدیریت موجودی با تکنیک‌های پیشرفته یادگیری عمیق نهفته است که راه‌حلی کاربردی برای چالش پیش‌بینی بر اساس داده‌های غیرخطی در زنجیره تأمین ارائه می‌دهد.
Objective: To present a strategic model for inventory management using deep learning (LSTM) to utilize non-linear and qualitative industrial big data that is unusable by classic methods.
Research Methodology: In this research, a Long Short-Term Memory (LSTM) deep learning model was designed. Data was prepared for training the network using the traditional methods of Economic Order Quantity (EOQ), Economic Production Quantity (EPQ), and ABC analysis.
Findings: After construction and training, the LSTM model achieved a prediction accuracy of 93%, demonstrating the success of integrating traditional approaches with AI for big data analysis in inventory management.
Originality/Scientific Value Added: The novelty of this research lies in combining classic inventory management methods with advanced deep learning techniques, providing a practical solution for the challenge of forecasting based on non-linear data in the supply chain.
[1]Amin, Motaharinejad, Mahdi and Memarzadeh, Mahdieh (2017). Big data dictionary. Publication of Dibagaran Art Cultural Institute of Tehran.
[2]Albayrak Ünal, Ö. Erkayman, B. & Usanmaz, B. Applications of Artificial Intelligence in Inventory Management: A Systematic Review of the Literature. Arch Computat Methods Eng 30, 2605–2625 (2023).
[3]Aro-Gordon, S., & Gupte, J. (2016). Review of modern inventory management techniques. Global Journal of Business & Management, 1(2), 1-22.
[4]Duan, W., Cao, Q., Yu, Y., & Levy, S. (2013), Mining online user-generated content: using sentiment analysis technique to study hotel service quality. In 2013 46th Hawaii International Conference on System Sciences (pp. 3119-3128). IEEE.
[5]Faizi Rad, Mohammad Ali, Pooya, Alireza, Naji Azimi, Zahra, & Amir Haeri, Maryam. (1400). Forecasting demand in university reservation systems with the aim of reducing food waste using neural networks with balanced error function. Industrial Management, 13(2), 193-170.
[6]Fan.x, Lyu.x, Xiao,F, Cia.T, Ding.c(2021), Research on Quick Inventory framwork Based on Deep Neural Network, Procedia Computer Science
[7]Fischer, T., & Krauss, C. (2018). Deep learning with long short-term memory networks
for financial market predictions. European Journal of Operational Research, 270(2), 654–669.
[8]García-Barrios (2021) A machine learning based method for managing multiple impulse purchase products: an inventory management approach. J Eng Sci Technol Rev 14(1):25–37
[9]Goodfellow, I., Bengio, Y., and Courville, A. (2016). Deep Learning. MIT Press.
[10]Goodfellow, Ian J et al. (2013). “Challenges in representation learning: A report on three machine learning contests”. In: International Conference on Neural Information Processing. Springer.
[11]Kim, S.W. (2022. An investigation on the direct and indirect effect of supply chain integration on firm performance.The International Journal of Production Economics, vol. 119(2). Pp. 328-346.
[12]Kumar,A. Shankar,R. Aljohani, N. (2020)A big data driven framework for demand-driven forecasting with effects of marketing-mix variables. https://doi.org/10.1016/j.indmarman.2019.05.003.
[13]Le, J. (2018). The 5 Computer Vision Techniques That Will ChLeCun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998). Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11), 2278-2324.
[14]Lee, J., Kao, H. A., & Yang, S. (2014), Service innovation and smart analytics for industry 4.0 and big data environment, Procedia Cirp, 16(1), 3-8.
[15]Marr, B. (2016), Why everyone must get ready for the 4th industrial revolution /2016/04/05/why everyone must get readyfor4thindustrial revolution/#26be9e2f3f90.
[16]Mauro, D. A., Greco, M., & Grimaldi, M. (2016). A Formal Definition of Big Data Based on its Essential Features. Library Review, 65(3), 122-135. Retrieved
[17]Mayer-Schoenberger, V., & Cukier, K. (2013). Big Data: A Revolution That Will TransformHow We Live, Work and Think. John Murray
[18]Nikolopoulos, K., Punia, S., Schafers, A., Tsinopoulos, C., & Vasilakis, C. (2020). Forecasting and planning during a pandemic: COVID-19 growth rates, supply chain disruptions, and governmental decisions. European Journal of Operational Research. https://doi.org/10.1016/j.ejor.2020.08.001.
[19]punia, S. P. Singh , J. K. Madaan, (2020), A cross-temporal hierarchical framework and deep learningforsupplychainforecasting,Computers&IndustrialEngineeringhttps://doi.org/10.1016/j.cie.202.
[20]Riyazi, Hamid, Doroudian, Mahmoud, Afshar Najafi, Behrouz. (1401). Inventory control of perishable goods based on shelf space and the effect of non-goods changes along with the commitment of minimum purchase of goods. Industrial Management, 14(1), 168-194.
[21]Sanaei, Ali (2016). Fourth Industrial Revolution, Isfahan: Academic Jihad, University of Isfahan.
[22]Scheutz, M., & T, M. (2016). Combining Agent-Based Modeling with Big Data Methods to Support Architectural and Urban Design. Springer International Publishing Switzerland, 18.
[23]Shao, L., Shum, H. P., & Hospedales, T. (2020). Special Issue on Machine Vision with Deep Learning. International journal of computer vision, 128(4), 771-772.
[24]Shi, Y. (2014). Big Data: History, Current Status, and Challenges Going Forward. The Bridge, The US National Academy of Engineering, 44(4), 6-11.
[25]Smith, M. L., Smith, L. N., & Hansen, M. F. (2021). The quiet revolution in machine vision-a state-of-the-art survey paper, including historical review, perspectives, and future directions. Computers in Industry, 130, 103472.
[26]Sultani, Maryam, Khatami Firouzabadi, Seyed Mohammad Ali, Amiri, Maqsood, & Hajian Heydari, Mojtabi. (1402). A hybrid approach to integrated omnidirectional channel demand forecasting using machine learning - time series clustering with dynamic time convolution algorithm and artificial neural networks. Articles in Production and Operations Management, 14(1), 121-140. doi: 10.22108/pom.2023.136202.1485
[27]Tarabian, Zahra, 2017, Development of the green supply chain model in the conditions of uncertainty of demand for perishable pharmaceutical goods using the meta-innovative method of colonial competition, National Conference of Industrial Management and Engineering of Iran, Isfahan, https:
[28]Toomey, J. W. (2000). Inventory management: principles, concepts and techniques (Vol. 12). Springer Science & Business Media.
[29]Wamba, S., Akter, S., Edwards, A., Chopin, G., Gnanzou, D., 2015. How ‘Big Data’ Can Make Big Impact: Findings. rom a Systematic Review and a Longitudinal Case Study. International Journal of Production Economics, In: Press, http://dx.doi.org/10.1016/j.ijpe.2014.12.031.
[30]Wazan, Milad (1401). Deep Learning: From Basics to Building Deep Neural Networks with Python. Tehran: Miyad Andisheh.
