IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v14y2022i6p3486-d772487.html
   My bibliography  Save this article

Supply Chain Management Optimization and Prediction Model Based on Projected Stochastic Gradient

Author

Listed:
  • Mohammed Alkahtani

    (Industrial Engineering Department, College of Engineering, King Saud University, P.O. Box 800, Riyadh 11421, Saudi Arabia)

Abstract

Supply chain management (SCM) is considered at the forefront of many organizations in the delivery of their products. Various optimization methods are applied in the SCM to improve the efficiency of the process. In this research, the projected stochastic gradient (PSG) method was proposed to increase the efficiency of the SCM analysis. The key objective of an efficient supply chain is to find the best flow patterns for the best products in order to select the suppliers to different customers. Hence, the focus of this research is on developing an efficient multi-echelon supply chain using factors such as cost, time, and risk. In the convex case, the proposed method has the advantage of a weakly convergent sequence of iterates to a point in the set of minimizers with probability one. The developed method achieves strong sequence convergence to the unique optimum, with probability one. The SCM dataset was utilized to assess the proposed method’s performance. The proposed PSG method has the advantage of considering the holding cost in the profit analysis of the company. The results of the developed PSG method are analyzed according to the product’s profit, stock, and demand. The proposed PSG method also provides the prediction of demand to increase profit.

Suggested Citation

  • Mohammed Alkahtani, 2022. "Supply Chain Management Optimization and Prediction Model Based on Projected Stochastic Gradient," Sustainability, MDPI, vol. 14(6), pages 1-14, March.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:6:p:3486-:d:772487
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/14/6/3486/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/14/6/3486/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Maozhu Jin & Hua Wang & Qian Zhang & Yucheng Zeng, 2020. "RETRACTED ARTICLE: Supply chain optimization based on chain management and mass customization," Information Systems and e-Business Management, Springer, vol. 18(4), pages 647-664, December.
    2. Zhou, Wei & O'Neill, Eoghan & Moncaster, Alice & Reiner, David M. & Guthrie, Peter, 2020. "Forecasting urban residential stock turnover dynamics using system dynamics and Bayesian model averaging," Applied Energy, Elsevier, vol. 275(C).
    3. Pourya Pourhejazy & Oh Kyoung Kwon, 2016. "The New Generation of Operations Research Methods in Supply Chain Optimization: A Review," Sustainability, MDPI, vol. 8(10), pages 1-23, October.
    4. Goldbeck, Nils & Angeloudis, Panagiotis & Ochieng, Washington, 2020. "Optimal supply chain resilience with consideration of failure propagation and repair logistics," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 133(C).
    5. Mustufa Haider Abidi & Usama Umer & Muneer Khan Mohammed & Mohamed K. Aboudaif & Hisham Alkhalefah, 2020. "Automated Maintenance Data Classification Using Recurrent Neural Network: Enhancement by Spotted Hyena-Based Whale Optimization," Mathematics, MDPI, vol. 8(11), pages 1-33, November.
    6. Seyed Mohsen Mousavi & Ardeshir Bahreininejad & S. Nurmaya Musa & Farazila Yusof, 2017. "A modified particle swarm optimization for solving the integrated location and inventory control problems in a two-echelon supply chain network," Journal of Intelligent Manufacturing, Springer, vol. 28(1), pages 191-206, January.
    7. Jörn Grahl & Stefan Minner & Daniel Dittmar, 2016. "Meta-heuristics for placing strategic safety stock in multi-echelon inventory with differentiated service times," Annals of Operations Research, Springer, vol. 242(2), pages 489-504, July.
    8. Hui Xia, 2020. "Improve the Resilience of Multilayer Supply Chain Networks," Complexity, Hindawi, vol. 2020, pages 1-9, January.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Flavia Fechete & Anișor Nedelcu, 2022. "Multi-Objective Optimization of the Organization’s Performance for Sustainable Development," Sustainability, MDPI, vol. 14(15), pages 1-20, July.
    2. Shu-Chu Liu & Quan-Ying Jian & Hsien-Yin Wen & Chih-Hung Chung, 2022. "A Crop Harvest Time Prediction Model for Better Sustainability, Integrating Feature Selection and Artificial Intelligence Methods," Sustainability, MDPI, vol. 14(21), pages 1-13, October.
    3. Basim Aljabhan & Muath A. Obaidat, 2023. "Privacy-Preserving Blockchain Framework for Supply Chain Management: Perceptive Craving Game Search Optimization (PCGSO)," Sustainability, MDPI, vol. 15(8), pages 1-23, April.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Matthias Klumpp & Dominic Loske, 2021. "Sustainability and Resilience Revisited: Impact of Information Technology Disruptions on Empirical Retail Logistics Efficiency," Sustainability, MDPI, vol. 13(10), pages 1-20, May.
    2. Jing Wang & Yuchen Zhang & Mark Goh, 2018. "Moderating the Role of Firm Size in Sustainable Performance Improvement through Sustainable Supply Chain Management," Sustainability, MDPI, vol. 10(5), pages 1-14, May.
    3. Preil, Deniz & Krapp, Michael, 2022. "Bandit-based inventory optimisation: Reinforcement learning in multi-echelon supply chains," International Journal of Production Economics, Elsevier, vol. 252(C).
    4. Zhanwei Tian & Guoqing Zhang, 2021. "Multi-echelon fulfillment warehouse rent and production allocation for online direct selling," Annals of Operations Research, Springer, vol. 304(1), pages 427-451, September.
    5. Dmitry Ivanov, 2022. "Viable supply chain model: integrating agility, resilience and sustainability perspectives—lessons from and thinking beyond the COVID-19 pandemic," Annals of Operations Research, Springer, vol. 319(1), pages 1411-1431, December.
    6. Ghanei, Shima & Contreras, Ivan & Cordeau, Jean-François, 2023. "A two-stage stochastic collaborative intertwined supply network design problem under multiple disruptions," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 170(C).
    7. Essam Kaoud & Mohammad A. M. Abdel-Aal & Tatsuhiko Sakaguchi & Naoki Uchiyama, 2020. "Design and Optimization of the Dual-Channel Closed Loop Supply Chain with E-Commerce," Sustainability, MDPI, vol. 12(23), pages 1-21, December.
    8. Antonio Zavala-Alcívar & María-José Verdecho & Juan-José Alfaro-Saiz, 2020. "A Conceptual Framework to Manage Resilience and Increase Sustainability in the Supply Chain," Sustainability, MDPI, vol. 12(16), pages 1-38, August.
    9. Dekkers, Rob & de Boer, Ronald & Gelsomino, Luca Mattia & de Goeij, Christiaan & Steeman, Michiel & Zhou, Qijun & Sinclair, Scott & Souter, Victoria, 2020. "Evaluating theoretical conceptualisations for supply chain and finance integration: A Scottish focus group," International Journal of Production Economics, Elsevier, vol. 220(C).
    10. Clavijo-Buritica, Nicolás & Triana-Sanchez, Laura & Escobar, John Willmer, 2023. "A hybrid modeling approach for resilient agri-supply network design in emerging countries: Colombian coffee supply chain," Socio-Economic Planning Sciences, Elsevier, vol. 85(C).
    11. Ana Esteso & M. M. E. Alemany & Angel Ortiz & Shaofeng Liu, 2022. "Optimization model to support sustainable crop planning for reducing unfairness among farmers," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 30(3), pages 1101-1127, September.
    12. Yang, Qihui & Scoglio, Caterina M. & Gruenbacher, Don M., 2021. "Robustness of supply chain networks against underload cascading failures," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 563(C).
    13. Fan Liu & Ning Ma, 2019. "Multicriteria ABC Inventory Classification Using the Social Choice Theory," Sustainability, MDPI, vol. 12(1), pages 1-19, December.
    14. Mustufa Haider Abidi & Muneer Khan Mohammed & Hisham Alkhalefah, 2022. "Predictive Maintenance Planning for Industry 4.0 Using Machine Learning for Sustainable Manufacturing," Sustainability, MDPI, vol. 14(6), pages 1-27, March.
    15. Kannan Govindan, 2016. "Evolutionary algorithms for supply chain management," Annals of Operations Research, Springer, vol. 242(2), pages 195-206, July.
    16. Xiangshuo He & Jian Zhang, 2018. "Supplier Selection Study under the Respective of Low-Carbon Supply Chain: A Hybrid Evaluation Model Based on FA-DEA-AHP," Sustainability, MDPI, vol. 10(2), pages 1-17, February.
    17. Sasan Harifi & Madjid Khalilian & Javad Mohammadzadeh & Sadoullah Ebrahimnejad, 2021. "Optimization in solving inventory control problem using nature inspired Emperor Penguins Colony algorithm," Journal of Intelligent Manufacturing, Springer, vol. 32(5), pages 1361-1375, June.
    18. Li, Guo & Xue, Jing & Li, Na & Ivanov, Dmitry, 2022. "Blockchain-supported business model design, supply chain resilience, and firm performance," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 163(C).
    19. Avelina Alejo-Reyes & Erik Cuevas & Alma Rodríguez & Abraham Mendoza & Elias Olivares-Benitez, 2020. "An Improved Grey Wolf Optimizer for a Supplier Selection and Order Quantity Allocation Problem," Mathematics, MDPI, vol. 8(9), pages 1-24, August.
    20. Juanjuan Qin & Yuhui Zhao & Liangjie Xia, 2018. "Carbon Emission Reduction with Capital Constraint under Greening Financing and Cost Sharing Contract," IJERPH, MDPI, vol. 15(4), pages 1-32, April.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:14:y:2022:i:6:p:3486-:d:772487. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.