بررسی سرمایه‌گذاری کلان داده در یک زنجیره ی ، تأمین سه سطحی: رویکرد نظریه بازی

نوع مقاله : پژوهشی

نویسندگان

1 دانشکده‌‌ مهندسی صنایع و سیستم‌ها، دانشگاه صنعتی اصفهان، اصفهان، ایران

2 دانشکده‌ مهندسی صنایع و سیستمها، دانشگاه صنعتی اصفهان، اصفهان، ایران

3 گروه مهندسی صنایع، دانشکده‌ علوم فنی، دانشگاه صنعتی قوچان، قوچان، ایران

چکیده

پیشرفت‌های شگرف تکنولوژی در دهه‌های اخیر توجه بسیاری را به سمت کلان‌داده‌ها و استخراج دانش و اطلاعات مفید از آن‌ها جلب کرده است. در پژوهش حاضر، یک زنجیره‌ی تأمین سه‌سطحی، شامل: خرده‌فروش، تولیدکننده، و تأمین‌کننده در ساختارهای مختلف قدرت ارزیابی شده است. اعضاء زنجیره‌ی تأمین سه‌سطحی در تجزیه‌وتحلیل کلان‌داده سرمایه‌گذاری می‌کنند و متناسب با آن از مزایای آن بهره می‌برند. به‌منظور بررسی شرایط شدنی و عوامل تأثیر‌گذار در سرمایه‌گذاری کلان‌داده در ساختارهای مختلف قدرت، مدل مسئله در دو حالت سرمایه‌گذاری کلان‌داده و بدون آن بررسی شده است. نتایج حاکی از تأثیر مستقیم میزان کارایی سرمایه‌گذاری و ضریب بهبود هزینه در حدود شدنی سرمایه‌گذاری کلان‌داده برای کل اعضاء زنجیره است. همچنین یافته‌ها نشان می‌دهند که سرمایه‌گذاری کلان‌داده موجب بهبود سود تعادلی اعضا، افزایش قیمت‌ تعادلی محصولات، افزایش سطح تعادلی نوآوری سبز و کیفیت محصولات در همه‌ی ساختارهای قدرت شده است.

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

Investigating Big Data Investment in a Three-Level Green Supply Chain: A Game Theoretic Approach

نویسندگان [English]

  • Zahra Esmaeeli 1
  • Naser Mollaverdi 2
  • Soroush Safarzadeh 3
1 Department of Industrial and Systems Engineering, Isfahan University of Technology, Isfahan, Iran
2 Department of Industrial and Systems Engineering, Isfahan University of Technology, Isfahan, Iran
3 Department of Industrial Engineering, Faculty of Engineering Science, Quchan University of Technology, Quchan, Iran
چکیده [English]

In recent decades, the rapid and remarkable advancements in technology have sparked significant interest in the potential of big data and the extraction of valuable insights and information from it. This has led to a surge in the utilization of big data analytics across various industries, including supply chain management. In this paper, our focus is on evaluating the dynamics of a three-level supply chain, which encompasses a retailer, manufacturer, and supplier, within different power structures. Notably, the members of this supply chain have made substantial investments in big data analytics and are reaping the benefits of these investments accordingly.
 
In our quest to gain a deeper understanding of the feasibility conditions and factors that impact big data investment within different power structures, we have meticulously examined the problem model in two distinct cases: one with big data investment and another without it. Our findings reveal that the direct impact of investment efficiency and cost improvement coefficient plays a significant role in determining the feasible limits of big data investment for all members of the supply chain. Furthermore, our research demonstrates that big data investment has a positive and far-reaching effect on the equilibrium profit of members. This leads to an increase in the equilibrium price of products, as well as an enhancement in the equilibrium level of green innovation and product quality across all power structures.

کلیدواژه‌ها [English]

  • Metallic yield damper
  • hysteresis behavior
  • hourglass pin
  • experimental study
  • nonlinear behavior

مراجع

1.    Ahmed, A. K., Senthilkumar, C. and Nallusamy, S., 2018. Study on environmental impact through analysis of big data for sustainable and green supply chain management. International Journal of Mechanical and Production Engineering Research and Development, 8, pp. 1245-1254
2. Hewage, T. N., Halgamuge, M. N., Syed, A. and Ekici, G., 2018. Big data techniques of Google, Amazon, Facebook and Twitter. J. Commun., 13, pp. 94-100.https://doi.org/10.12720/jcm.13.2.
3. Mukhtarov, I., 2023. Methodology of leveraging big data in financial services for enhanced decision-making. Финансовые рынки и банки, pp. 104-115
4. Zhong, R. Y., Newman, S. T., Huang, G. Q. and Lan, S., 2016. Big Data for supply chain management in the service and manufacturing sectors: Challenges, opportunities, and future perspectives. Computers and Industrial Engineering, 101, pp. 572-591. https://doi.org/10.1016/j.cie.2016.07.013
5. Madhani, P. M., 2022. Big Data Usage and Big Data Analytics in Supply Chain: Leveraging Competitive Priorities for Enhancing Competitive Advantages. IUP Journal of Supply Chain Management, pp. 19-20
6. Liu, P., 2021. Pricing rules of Green Supply Chain considering Big Data information inputs and cost-sharing model. Soft Computing, 25, pp. 8515-8531. https://doi.org/10.1007/s00500-021-05779-1
7. Tambuskar, D. P., Jain, P. and Narwane, V. S., 2023. An exploration into the factors influencing the implementation of big data analytics in sustainable supply chain management. Kybernetes. pp. 1-12. https://doi.org/10.1108/K-07-2022-1057
8. Liu, J., Chen, M. and Liu, H., 2020. The role of big data analytics in enabling green supply chain management: a literature review. Journal of Data, Information and Management, 2, pp. 75-83. https://doi.org/10.1007/s42488-019-00020-z
9. Mageto, J., 2021. Big data analytics in sustainable supply chain management: A focus on manufacturing supply chains. Sustainability, 13, pp. 7101. https://doi.org/10.3390/su13137101
10. Li, Q., Kang, Y., Tan, L. and Chen, B., 2020. Modeling formation and operation of collaborative green innovation between manufacturer and supplier: A game theory approach. Sustainability, 12, pp. 2209. https://doi.org/10.3390/su12062209
11. Ma, W., Zhang, R. and Chai, S., 2019. What drives green innovation? A game theoretic analysis of government subsidy and cooperation contract. Sustainability, pp. 11, 5584. https://doi.org/10.3390/su11205584
12. Tate, W. L., Ellram, L. M. and Dooley, K. J., 2012. Environmental purchasing and supplier management (EPSM): Theory and practice. Journal of Purchasing and Supply Management, 18, pp. 173-188. https://doi.org/10.1016/j.pursup.2012.07.001
13. Holt, D., 2004. Managing the interface between suppliers and organizations for environmental responsibility–an exploration of current practices in the UK. Corporate Social Responsibility and Environmental Management, 11, pp. 71-84. https://doi.org/10.1002/csr.55
14. Seuring, S. and Müller, M., 2008. From a literature review to a conceptual framework for sustainable supply chain management. Journal of Cleaner Production, 16, pp. 1699-1710. https://doi.org/10.1016/j.jclepro.2008.04.020
15. Nisar, Q. A., Haider, S., Ameer, I., Hussain, M. S., Gill, S. S. and Usama, A., 2022. Sustainable supply chain management performance in post COVID-19 era in an emerging economy: A big data perspective. International Journal of Emerging Markets. pp. 1-15. https://doi.org/10.1108/IJOEM-12-2021-1807
16. Al-Khatib, A. W., 2022. Big data analytics capabilities and green supply chain performance: Investigating the moderated mediation model for green innovation and technological intensity. Business Process Management Journal. pp. 1-10. https://doi.org/10.1108/BPMJ-07-2022-0332
17. Bag, S., Wood, L. C., Xu, L., Dhamija, P. and Kayikci, Y., 2020. Big data analytics as an operational excellence approach to enhance sustainable supply chain performance. Resources, Conservation and Recycling, 153, pp. 104559. https://doi.org/10.1016/j.resconrec.2019.104559
18. Fu, X., Liu, S. and Han, G., 2021. Supply chain partners' decisions with heterogeneous marketing efforts considering consumer's perception of quality. RAIRO-Operations Research, 55, pp. 3227-3243. https://doi.org/10.1051/ro/2021126
19. Zhu, G., Zhang, J., Xing, E. and Han, D., 2022. Pricing and quality decisions with conspicuous consumers. Transportation Research Part E: Logistics and Transportation Review, 165, pp. 102857. https://doi.org/10.1016/j.tre.2022.102857
20. Hosseini-Motlagh, S.-M., Nouri-Harzvili, M. and Zirakpourdehkordi, R., 2019. Two-level supply chain quality improvement through a wholesale price coordination contract on pricing, quality and services. International Journal of Industrial Engineering and Production Research, 30, pp. 287-312. https://doi:10.22068/ijiepr.30.3.287
21. Shoeleh, R., Seifbarghy, M. and Pishva, D., 2019. Modelling pricing, vertical co-op advertising and quality improvement in a non-cooperative three-echelon supply chain using game theory approach. RAIRO-Operations Research, 53, pp. 1937-1965. https://doi.org/10.1051/ro/2018109
22. Mohan, A., Karthika, V. and Simon, S., 2021. Application of Big Data Analytics in Enhancing the Supply Chain Management System. Asian Basic and Applied Research Journal, pp. 82-89.
23. Azeem, M., Haleem, A., Bahl, S., Javaid, M., Suman, R. and Nandan, D., 2022. Big data applications to take up major challenges across manufacturing industries: A brief review. Materials Today. Proceedings, 49, pp. 339-348. https://doi.org/10.1016/j.matpr.2021.02.147
24. Shi, C., Li, D. and Wu, T., 2021. Influencing factors of catering O2O customer experience: an approach integrating big data analytics with grounded theory. Tehnički vjesnik, 28, pp. 862-872. https://doi.org/10.17559/TV-20210124041130
25. Holmlund, M., Van Vaerenbergh, Y., Ciuchita, R., Ravald, A., Sarantopoulos, P., Ordenes, F. V. and Zaki, M., 2020. Customer experience management in the age of big data analytics: A strategic framework. Journal of Business Research, 116, pp. 356-365. https://doi.org/10.1016/j.jbusres.2020.01.022
26. Sanders, N. R., 2016. How to use big data to drive your supply chain. California Management Review, 58, pp. 26-48. https://doi.org/10.1525/cmr.2016.58.3.26
27. Liu, P., 2019. Pricing policies and coordination of low-carbon supply chain considering targeted advertisement and carbon emission reduction costs in the big data environment. Journal of Cleaner Production, 210, pp. 343-357. https://doi.org/10.1016/j.jclepro.2018.10.328
28. Liu, P. and Yi, S.-p., 2017. Pricing policies of green supply chain considering targeted advertising and product green degree in the big data environment. Journal of Cleaner Production, 164, pp. 1614-1622. https://doi.org/10.1016/j.jclepro.2017.07.049
29. Chen, S., 2021. Analysis of customization strategy for E-commerce operation based on big data. Wireless Communications and Mobile Computing, 2021, pp. 1-11. https://doi.org/10.1155/2021/6626480
30. Song, H., Shu, Y., Dai, Y., Zhou, L. and Li, H., 2022. Big data service investment choices in a manufacturer-led dual-channel supply chain. Computers and Industrial Engineering, 171, pp. 108423. https://doi.org/10.1016/j.cie.2022.108423
31. Liu, P. and Zhang, F.-j., 2022. Pricing strategies of dual-channel green supply chain considering Big Data information inputs. Soft Computing, pp. 1-19. https://doi.org/10.1007/s00500-021-06611-6
32. Liu, P., Long, Y., Song, H.-C. and He, Y.-D.,2020. Investment decision and coordination of green agri-food supply chain considering information service based on blockchain and big data. Journal of Cleaner Production, 277, pp. 123646. https://doi.org/10.1016/j.jclepro.2020.123646
33. Hu, H., Li, Y. and Li, M., 2022. Decisions and Coordination of Green Supply Chain Considering Big Data Targeted Advertising. Journal of Theoretical and Applied Electronic Commerce Research, 17, pp. 1035-1056. https://doi.org/10.3390/jtaer17030053
34. Agi, M. A., Faramarzi-Oghani, S. and Hazır, Ö., 2021. Game theory-based models in green supply chain management: A review of the literature. International Journal of Production Research, 59, pp. 4736-4755.
35. Liu, P. and Yi, S.-p., 2018. A study on supply chain investment decision-making and coordination in the Big Data environment. Annals of Operations Research, 270, pp. 235-253. https://doi.org/10.1007/s10479-017-2424-4
36. Wang, Y., Wang, X., Chang, S. and Kang, Y., 2019. Product innovation and process innovation in a dynamic Stackelberg game. Computers and Industrial Engineering, 130, pp. 395-403. https://doi.org/10.1016/j.cie.2019.02.042
37. Liu, P. and Yi, S.-p., 2016. Investment decision-making and coordination of supply chain: A new research in the big data era. Discrete Dynamics in Nature and Society, pp. 1-18. https://doi.org/10.1155/2016/2026715

فایل ها

هم رسانی

ارجاع به این مقاله

آمار