1
Department of Decision Support, Faculty of Computers and Informatics, Zagazig University, 44519, Zagazig, Egypt
(wafaahmed503@gmail.com)
2
Computers and Systems Department, Electronics Research Institute, Cairo 11843, Egypt
(sherine.abdelkader28@hotmail.com)
3
Department of Decision Support, Faculty of Computers and Informatics, Zagazig University, 44519, Zagazig, Egypt
(HRAbdelhady@zu.edu.eg)
4
Department of Decision Support, Faculty of Computers and Informatics, Zagazig University, 44519, Zagazig, Egypt
(amgawad2001@yahoo.com)
Abstract :
The integration of the Internet of Things (IoT) and Big Data Analytics (BDA) has brought about a revolution in Green Supply Chain Management (GSCM). In particular, it has enabled the optimization of many aspects of the supply chain (SC), including transportation, inventory management, and customer service. The application of BDA in IoT-enabled GSCM is receiving a lot of attention because it has the capacity to assist businesses become more cost-effective and environmentally sustainable to make more informed decisions. By identifying the inefficiencies in the supply chain and take corrective action. With the advent of the IoT, businesses are now able to get a great deal of information from sensors that are installed in different parts of their SC, including transportation vehicles, warehouses, and factories. This data can be leveraged for a variety of purposes, including optimizing the SC for sustainability and reducing its environmental impact. There are also challenges associated with BDA in IoT-enabled GSCM. The volume of data that needs to be processed presents the biggest obstacles. This requires specialized tools and expertise in data management and analytics. Despite these difficulties, technology has the power to completely alter how firms conduct their operations. This paper presents an overview about BDA in IoT-enabled GSCM. The review highlights the benefits and challenges in adopting BDA in IoT-enabled GSCM, the key technologies involved, and the various applications of BDA in IoT-GSCM. Finally, provides insights into the future directions of research in this area.
Keywords :
Big Data Analytics (BDA); Internet of Things (IoT); Green Supply Chain Management (GSCM).
References :
[1] Z. Ning et al., “Mobile Edge Computing Enabled 5G Health Monitoring for Internet of Medical Things: A Decentralized Game Theoretic Approach,” IEEE J. Sel. Areas Commun., vol. 39, no. 2, pp. 463–478, 2021, doi: 10.1109/JSAC.2020.3020645.
[2] I. A. Zualkernan, M. Rashid, R. Gupta, and M. Alikarar, “Smart Home Big Data,” vol. 63, no. 4, pp. 426–434, 2017.
[3] M. L. Tseng, M. S. Islam, N. Karia, F. A. Fauzi, and S. Afrin, “A literature review on green supply chain management: Trends and future challenges,” Resour. Conserv. Recycl., vol. 141, no. June 2018, pp. 145–162, 2019, doi: 10.1016/j.resconrec.2018.10.009.
[4] N. Hassan, S. Gillani, E. Ahmed, I. Yaqoob, and M. Imran, “The Role of Edge Computing in Internet of Things,” IEEE Commun. Mag., vol. 56, no. 11, pp. 110–115, 2018, doi: 10.1109/MCOM.2018.1700906.
[5] X. Sun and N. Ansari, “EdgeIoT: Mobile Edge Computing for the Internet of Things,” IEEE Commun. Mag., vol. 54, no. 12, pp. 22–29, 2016, doi: 10.1109/MCOM.2016.1600492CM.
[6] Y. Sun, H. Song, A. J. Jara, and R. Bie, “Internet of Things and Big Data Analytics for Smart and Connected Communities,” IEEE Access, vol. 4, pp. 766–773, 2016, doi: 10.1109/ACCESS.2016.2529723.
[7] S. Singh, M. K. Gandhi, and A. Kumar, “Scope of big data analytics in green supply chain management: a review,” Cardiometry, no. 22, pp. 306–312, 2022, doi: 10.18137/cardiometry.2022.22.306312.
[8] M. M. Rathore, A. Paul, W. H. Hong, H. C. Seo, I. Awan, and S. Saeed, “Exploiting IoT and big data analytics: Defining Smart Digital City using real-time urban data,” Sustain. Cities Soc., vol. 40, pp. 600–610, 2018, doi: 10.1016/j.scs.2017.12.022.
[9] S. Madakam, R. Ramaswamy, and S. Tripathi, “Internet of Things (IoT): A Literature Review,” J. Comput. Commun., vol. 03, no. 05, pp. 164–173, 2015, doi: 10.4236/jcc.2015.35021.
[10] M. Haghi Kashani, M. Madanipour, M. Nikravan, P. Asghari, and E. Mahdipour, “A systematic review of IoT in healthcare: Applications, techniques, and trends,” J. Netw. Comput. Appl., vol. 192, no. May, p. 103164, 2021, doi: 10.1016/j.jnca.2021.103164.
[11] I. H. Sarker, A. I. Khan, Y. B. Abushark, and F. Alsolami, “Internet of Things (IoT) Security Intelligence: A Comprehensive Overview, Machine Learning Solutions and Research Directions,” Mob. Networks Appl., no. 0123456789, 2022, doi: 10.1007/s11036-022-01937-3.
[12] S. B. Rane, S. V. Thakker, and R. Kant, “Stakeholders’ involvement in green supply chain: a perspective of blockchain IoT-integrated architecture,” Manag. Environ. Qual. An Int. J., vol. 32, no. 6, pp. 1166–1191, 2021, doi: 10.1108/MEQ-11-2019-0248.
[13] V. K. Quy et al., “IoT-Enabled Smart Agriculture: Architecture, Applications, and Challenges,” Appl. Sci., vol. 12, no. 7, 2022, doi: 10.3390/app12073396.
[14] A. I. Awad, M. M. Fouda, M. M. Khashaba, E. R. Mohamed, and K. M. Hosny, “Utilization of mobile edge computing on the Internet of Medical Things: A survey,” ICT Express, no. xxxx, 2022, doi: 10.1016/j.icte.2022.05.006.
[15] W. Shi, J. Cao, Q. Zhang, Y. Li, and L. Xu, “Edge Computing: Vision and Challenges,” IEEE Internet Things J., vol. 3, no. 5, pp. 637–646, 2016, doi: 10.1109/JIOT.2016.2579198.
[16] M. Goudarzi, H. Wu, M. Palaniswami, and R. Buyya, “An Application Placement Technique for Concurrent IoT Applications in Edge and Fog Computing Environments,” IEEE Trans. Mob. Comput., vol. 20, no. 4, pp. 1298–1311, 2021, doi: 10.1109/TMC.2020.2967041.
[17] Y. Chen, “IoT, cloud, big data and AI in interdisciplinary domains,” Simul. Model. Pract. Theory, vol. 102, no. January, 2020, doi: 10.1016/j.simpat.2020.102070.
[18] B. Varshini, H. Yogesh, S. D. Pasha, M. Suhail, V. Madhumitha, and A. Sasi, “IoT-Enabled smart doors for monitoring body temperature and face mask detection,” Glob. Transitions Proc., vol. 2, no. 2, pp. 246–254, 2021, doi: 10.1016/j.gltp.2021.08.071.
[19] M. Alshamrani, “IoT and artificial intelligence implementations for remote healthcare monitoring systems: A survey,” J. King Saud Univ. - Comput. Inf. Sci., no. xxxx, 2021, doi: 10.1016/j.jksuci.2021.06.005.
[20] M. Liyanage, P. Porambage, A. Y. Ding, and A. Kalla, “Driving forces for Multi-Access Edge Computing (MEC) IoT integration in 5G,” ICT Express, vol. 7, no. 2, pp. 127–137, 2021, doi: 10.1016/j.icte.2021.05.007.
[21] K. Zhan, “Sports and health big data system based on 5G network and Internet of Things system,” Microprocess. Microsyst., vol. 80, no. November 2020, pp. 1–6, 2021, doi: 10.1016/j.micpro.2020.103363.
[22] X. Li, S. Liu, F. Wu, S. Kumari, and J. J. P. C. Rodrigues, “Privacy preserving data aggregation scheme for mobile edge computing assisted IoT applications,” IEEE Internet Things J., vol. 6, no. 3, pp. 4755–4763, 2019, doi: 10.1109/JIOT.2018.2874473.
[23] M. Abbasi, E. Mohammadi-Pasand, and M. R. Khosravi, “Intelligent workload allocation in IoT–Fog–cloud architecture towards mobile edge computing,” Comput. Commun., vol. 169, no. December 2020, pp. 71–80, 2021, doi: 10.1016/j.comcom.2021.01.022.
[24] H. Elazhary, “Internet of Things (IoT), mobile cloud, cloudlet, mobile IoT, IoT cloud, fog, mobile edge, and edge emerging computing paradigms: Disambiguation and research directions,” J. Netw. Comput. Appl., vol. 128, no. November 2018, pp. 105–140, 2019, doi: 10.1016/j.jnca.2018.10.021.
[25] S. Garg, K. Kaur, G. Kaddoum, P. Garigipati, and G. S. Aujla, “Security in IoT-Driven Mobile Edge Computing: New Paradigms, Challenges, and Opportunities,” IEEE Netw., vol. 35, no. 5, pp. 298–305, 2021, doi: 10.1109/MNET.211.2000526.
[26] N. Kumar, R. K. Kaushal, and S. N. Panda, “IoT Based Smart and Portable System for Remote Patient Monitoring and Drug Delivery,” J. Phys. Conf. Ser., vol. 1950, no. 1, 2021, doi: 10.1088/1742-6596/1950/1/012017.
[27] S. Tuli et al., “HealthFog: An ensemble deep learning based Smart Healthcare System for Automatic Diagnosis of Heart Diseases in integrated IoT and fog computing environments,” Futur. Gener. Comput. Syst., vol. 104, pp. 187–200, 2020, doi: 10.1016/j.future.2019.10.043.
[28] K. M. Hosny, A. I. Awad, M. M. Khashaba, and E. R. Mohamed, “New Improved Multi ‑ Objective Gorilla Troops Algorithm for Dependent Tasks Offloading problem in Multi ‑ Access Edge Computing,” J. Grid Comput., 2023, doi: 10.1007/s10723-023-09656-z.
[29] M. G. R. Alam, M. S. Munir, M. Z. Uddin, M. S. Alam, T. N. Dang, and C. S. Hong, “Edge-of-things computing framework for cost-effective provisioning of healthcare data,” J. Parallel Distrib. Comput., vol. 123, pp. 54–60, 2019, doi: 10.1016/j.jpdc.2018.08.011.
[30] M. Sookhak, M. R. Jabbarpour, N. S. Safa, and F. R. Yu, “Blockchain and smart contract for access control in healthcare: A survey, issues and challenges, and open issues,” J. Netw. Comput. Appl., vol. 178, no. December 2020, p. 102950, 2021, doi: 10.1016/j.jnca.2020.102950.
[31] M. Marjani et al., “Big IoT Data Analytics: Architecture, Opportunities, and Open Research Challenges,” IEEE Access, vol. 5, pp. 5247–5261, 2017, doi: 10.1109/ACCESS.2017.2689040.
[32] M. S. Gaur, S. Kumar, N. K. Gaur, and P. S. Sharma, “Persuasive Factors and Weakness for Security Vulnerabilities in BIG IOT Data in Healthcare Solution,” J. Phys. Conf. Ser., vol. 2007, no. 1, 2021, doi: 10.1088/1742-6596/2007/1/012046.
[33] I. Ahmed, M. Ahmad, G. Jeon, and F. Piccialli, “A Framework for Pandemic Prediction Using Big Data Analytics,” Big Data Res., vol. 25, p. 100190, 2021, doi: 10.1016/j.bdr.2021.100190.
[34] A. Aryal and B. Li, “The emerging big data analytics and IoT in supply chain management : a systematic review,” 2018, doi: 10.1108/SCM-03-2018-0149.
[35] S. Ketu and P. K. Mishra, “Internet of Healthcare Things: A contemporary survey,” J. Netw. Comput. Appl., vol. 192, no. August, p. 103179, 2021, doi: 10.1016/j.jnca.2021.103179.
[36] N. Mehta and A. Pandit, “Concurrence of big data analytics and healthcare: A systematic review,” Int. J. Med. Inform., vol. 114, no. January, pp. 57–65, 2018, doi: 10.1016/j.ijmedinf.2018.03.013.
[37] A. Tahsin and M. Manzurul Hasan, “Big data & data science: A descriptive research on big data evolution and a proposed combined platform by integrating R and Python on Hadoop for big data analytics and visualization,” ACM Int. Conf. Proceeding Ser., pp. 4–5, 2020, doi: 10.1145/3377049.3377051.
[38] F. Mannering, C. R. Bhat, V. Shankar, and M. Abdel-Aty, “Big data, traditional data and the tradeoffs between prediction and causality in highway-safety analysis,” Anal. Methods Accid. Res., vol. 25, p. 100113, 2020, doi: 10.1016/j.amar.2020.100113.
[39] J. Ranjan and C. Foropon, “Big Data Analytics in Building the Competitive Intelligence of Organizations,” Int. J. Inf. Manage., vol. 56, no. February 2020, p. 102231, 2021, doi: 10.1016/j.ijinfomgt.2020.102231.
[40] T. Wahyuningsih, “Problems, Challenges, and Opportunities Visualization on Big Data,” J. Appl. Data Sci., vol. 1, no. 1, pp. 20–28, 2020, doi: 10.47738/jads.v1i1.8.
[41] R. D. Raut, S. K. Mangla, V. S. Narwane, M. Dora, and M. Liu, “Big Data Analytics as a mediator in Lean, Agile, Resilient, and Green (LARG) practices effects on sustainable supply chains,” Transp. Res. Part E Logist. Transp. Rev., vol. 145, no. November 2020, p. 102170, 2021, doi: 10.1016/j.tre.2020.102170.
[42] T. M. Choi, S. W. Wallace, and Y. Wang, “Big Data Analytics in Operations Management,” Prod. Oper. Manag., vol. 27, no. 10, pp. 1868–1883, 2018, doi: 10.1111/poms.12838.
[43] G. Palareti et al., “Comparison between different D-Dimer cutoff values to assess the individual risk of recurrent venous thromboembolism: Analysis of results obtained in the DULCIS study,” Int. J. Lab. Hematol., vol. 38, no. 1, pp. 42–49, 2016, doi: 10.1111/ijlh.12426.
[44] Y. Wang, L. A. Kung, S. Gupta, and S. Ozdemir, “Leveraging Big Data Analytics to Improve Quality of Care in Healthcare Organizations: A Configurational Perspective,” Br. J. Manag., vol. 30, no. 2, pp. 362–388, 2019, doi: 10.1111/1467-8551.12332.
[45] B. Roßmann, A. Canzaniello, H. von der Gracht, and E. Hartmann, “The future and social impact of Big Data Analytics in Supply Chain Management: Results from a Delphi study,” Technol. Forecast. Soc. Change, vol. 130, no. September 2017, pp. 135–149, 2018, doi: 10.1016/j.techfore.2017.10.005.
[46] J. P. Cabrera-Sánchez and Á. F. Villarejo-Ramos, “Acceptance and use of big data techniques in services companies,” J. Retail. Consum. Serv., vol. 52, no. July 2019, p. 101888, 2020, doi: 10.1016/j.jretconser.2019.101888.
[47] “Tools for big data analysis,” 2018.
[48] J. Yao, H. Shi, and C. Liu, “Optimising the configuration of green supply chains under mass personalisation,” Int. J. Prod. Res., vol. 0, no. 0, pp. 1–19, 2020, doi: 10.1080/00207543.2020.1723814.
[49] D. Sugandini, M. Muafi, C. Susilowati, Y. Siswanti, and W. Syafri, “Green supply chain management and green marketing strategy on green purchase intention: SMEs cases,” J. Ind. Eng. Manag., vol. 13, no. 1, pp. 79–92, 2020, doi: 10.3926/jiem.2795.
[50] A. Jayant, B. F. Environment, and A. Jayant, “Impact of Green Supply Chain Management Practices in India,” no. July, pp. 1–14, 2017.
[51] S. M. Diab, F. A. AL-Bourini, and A. H. Abu-Rumman, “The Impact of Green Supply Chain Management Practices on Organizational Performance: A Study of Jordanian Food Industries,” J. Manag. Sustain., vol. 5, no. 1, pp. 149–157, 2015, doi: 10.5539/jms.v5n1p149.
[52] J. Morana, Sustainable supply chain management. 2013. doi: 10.4018/ijsda.2019070102.
[53] S. Luthra, V. Kumar, S. Kumar, and A. Haleem, “Barriers to implement green supply chain management in automobile industry using interpretive structural modeling technique-an Indian perspective,” J. Ind. Eng. Manag., vol. 4, no. 2, pp. 231–257, 2011, doi: 10.3926/jiem.2011.v4n2.p231-257.
[54] R. D. Raut, B. Narkhede, and B. B. Gardas, “To identify the critical success factors of sustainable supply chain management practices in the context of oil and gas industries: ISM approach,” Renew. Sustain. Energy Rev., vol. 68, no. June 2016, pp. 33–47, 2017, doi: 10.1016/j.rser.2016.09.067.
[55] M. N. Shafique, A. Rashid, I. S. Bajwa, R. Kazmi, M. M. Khurshid, and W. A. Tahir, “Effect of IoT capabilities and energy consumption behavior on green supply chain integration,” Appl. Sci., vol. 8, no. 12, pp. 1–18, 2018, doi: 10.3390/app8122481.
[56] J. S. Chou, N. T. Ngo, W. K. Chong, and G. E. Gibson, Big data analytics and cloud computing for sustainable building energy efficiency. Elsevier Ltd, 2016. doi: 10.1016/B978-0-08-100546-0.00016-9.
[57] J. C. Ho, M. K. Shalishali, T. L. Tseng, and D. Ang, “Opportunities in Green Supply Chain Management,” Coast. Bus. J., vol. 8, no. 1, pp. 18–31, 2009.
[58] A. Banik, H. M. M. Taqi, S. M. Ali, S. Ahmed, M. Garshasbi, and G. Kabir, “Critical success factors for implementing green supply chain management in the electronics industry: an emerging economy case,” Int. J. Logist. Res. Appl., vol. 25, no. 4–5, pp. 493–520, 2022, doi: 10.1080/13675567.2020.1839029.
[59] A. Banik, H. M. M. Taqi, S. M. Ali, S. Ahmed, M. Garshasbi, and G. Kabir, “Critical success factors for implementing green supply chain management in the electronics industry: an emerging economy case,” Int. J. Logist. Res. Appl., vol. 25, no. 4–5, pp. 493–520, 2022, doi: 10.1080/13675567.2020.1839029.
[60] T. Debicki and C. Guzman, Integration of Information Flow for Greening Supply Chain Management, no. July. 2020. [Online]. Available: http://dx.doi.org/10.1007/978-3-030-24355-5_16%0Ahttp://link.springer.com/10.1007/978-3-030-24355-5
[61] P. R. C. Gopal and J. Thakkar, “Analysing critical success factors to implement sustainable supply chain practices in Indian automobile industry: a case study,” Prod. Plan. Control, vol. 27, no. 12, pp. 1005–1018, 2016, doi: 10.1080/09537287.2016.1173247.
[62] F. Testa and F. Iraldo, “Shadows and lights of GSCM (green supply chain management): Determinants and effects of these practices based on a multi-national study,” J. Clean. Prod., vol. 18, no. 10–11, pp. 953–962, 2010, doi: 10.1016/j.jclepro.2010.03.005.
[63] J. J. Assumpção, L. M. S. Campos, J. A. Plaza-Úbeda, S. Sehnem, and D. A. Vazquez-Brust, “Green Supply Chain Management and business innovation,” J. Clean. Prod., vol. 367, no. June, 2022, doi: 10.1016/j.jclepro.2022.132877.
[64] S. Bag, L. C. Wood, L. Xu, P. Dhamija, and Y. Kayikci, “Big data analytics as an operational excellence approach to enhance sustainable supply chain performance,” Resour. Conserv. Recycl., vol. 153, no. May 2019, p. 104559, 2020, doi: 10.1016/j.resconrec.2019.104559.
[65] R. Zhao, Y. Liu, N. Zhang, and T. Huang, “An optimization model for green supply chain management by using a big data analytic approach,” J. Clean. Prod., vol. 142, pp. 1085–1097, 2017, doi: 10.1016/j.jclepro.2016.03.006.
[66] M. K. Lim, J. Wang, C. Wang, and M. L. Tseng, “A novel method for green delivery mode considering shared vehicles in the IoT environment,” Ind. Manag. Data Syst., vol. 120, no. 9, pp. 1733–1757, 2020, doi: 10.1108/IMDS-02-2020-0078.
[67] J. J. Hathaliya and S. Tanwar, “An exhaustive survey on security and privacy issues in Healthcare 4.0,” Comput. Commun., vol. 153, no. February, pp. 311–335, 2020, doi: 10.1016/j.comcom.2020.02.018.
[68] V. Agrawal, R. P. Mohanty, S. Agarwal, J. K. Dixit, and A. M. Agrawal, “Analyzing critical success factors for sustainable green supply chain management,” Environ. Dev. Sustain., no. 0123456789, 2022, doi: 10.1007/s10668-022-02396-2.
[69] L. Liu, Z. Chang, X. Guo, S. Mao, and T. Ristaniemi, “Multiobjective Optimization for Computation Offloading in Fog Computing,” IEEE Internet Things J., vol. 5, no. 1, pp. 283–294, 2018, doi: 10.1109/JIOT.2017.2780236.
[70] J. Ruan et al., “A Life Cycle Framework of Green IoT-Based Agriculture and Its Finance, Operation, and Management Issues,” IEEE Commun. Mag., vol. 57, no. 3, pp. 90–96, 2019, doi: 10.1109/MCOM.2019.1800332.
| Style | # |
|---|---|
| MLA | Wafaa A. Saleh, Sherine M. Abdelkader, Heba Rashad, Amal Abdelgawad. "The Role of Big Data Analytics in IoT-enabled Green Supply Chain Management: Architecture, challenges, and future perspectives." American Journal of Business and Operations Research, Vol. 10, No. 1, 2023 ,PP. 25-40 (Doi : https://doi.org/10.54216/AJBOR.100103) |
| APA | Wafaa A. Saleh, Sherine M. Abdelkader, Heba Rashad, Amal Abdelgawad. (2023). The Role of Big Data Analytics in IoT-enabled Green Supply Chain Management: Architecture, challenges, and future perspectives. Journal of American Journal of Business and Operations Research, 10 ( 1 ), 25-40 (Doi : https://doi.org/10.54216/AJBOR.100103) |
| Chicago | Wafaa A. Saleh, Sherine M. Abdelkader, Heba Rashad, Amal Abdelgawad. "The Role of Big Data Analytics in IoT-enabled Green Supply Chain Management: Architecture, challenges, and future perspectives." Journal of American Journal of Business and Operations Research, 10 no. 1 (2023): 25-40 (Doi : https://doi.org/10.54216/AJBOR.100103) |
| Harvard | Wafaa A. Saleh, Sherine M. Abdelkader, Heba Rashad, Amal Abdelgawad. (2023). The Role of Big Data Analytics in IoT-enabled Green Supply Chain Management: Architecture, challenges, and future perspectives. Journal of American Journal of Business and Operations Research, 10 ( 1 ), 25-40 (Doi : https://doi.org/10.54216/AJBOR.100103) |
| Vancouver | Wafaa A. Saleh, Sherine M. Abdelkader, Heba Rashad, Amal Abdelgawad. The Role of Big Data Analytics in IoT-enabled Green Supply Chain Management: Architecture, challenges, and future perspectives. Journal of American Journal of Business and Operations Research, (2023); 10 ( 1 ): 25-40 (Doi : https://doi.org/10.54216/AJBOR.100103) |
| IEEE | Wafaa A. Saleh, Sherine M. Abdelkader, Heba Rashad, Amal Abdelgawad, The Role of Big Data Analytics in IoT-enabled Green Supply Chain Management: Architecture, challenges, and future perspectives, Journal of American Journal of Business and Operations Research, Vol. 10 , No. 1 , (2023) : 25-40 (Doi : https://doi.org/10.54216/AJBOR.100103) |