Supply Chain Management Optimization Based on Bigdata Analysis

Research Article
Open access

Supply Chain Management Optimization Based on Bigdata Analysis

Weida Ruan 1*
  • 1 University of California, Davis    
  • *corresponding author weiruan@ucdavis.edu
Published on 13 September 2023 | https://doi.org/10.54254/2754-1169/23/20230359
AEMPS Vol.23
ISSN (Print): 2754-1177
ISSN (Online): 2754-1169
ISBN (Print): 978-1-915371-89-8
ISBN (Online): 978-1-915371-90-4

Abstract

Contemporarily, in the context of big data, enterprises need to continuously optimize and innovate the supply chain, change the traditional supply chain management process pattern as well as optimization the whole supply processes. These actions are able to strengthen the actual effect level of supply chain process operation and achieve more benefits for the enterprise. Under the background of big data, more information management technologies are widely used, and supply chain management needs to clarify the deficiencies in its own operation, and reengineer and adjust the deficiencies. This paper will systematically discusses the implementation of the state-of-art bigdata analysis techniques in SCM. According to the analysis and evaluations of the measures, the usage of advanced big data technology will help increase the cultivation and establishment of management talent resources, so as to optimize the supply chain management process. Overall, these results shed light on guiding further exploration of supply chain management optimization.

Keywords:

bigdata, supply chain, smart logistics

Ruan,W. (2023). Supply Chain Management Optimization Based on Bigdata Analysis. Advances in Economics, Management and Political Sciences,23,96-101.
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References

[1]. Nguyen, T., Li, Z. H. O. U., Spiegler, V., Ieromonachou, P., Lin, Y.: Big data analytics in supply chain management: A state-of-the-art literature review. Computers & Operations Research, 98, 254-264 (2018).

[2]. Nguyen, T., Li, Z. H. O. U., Spiegler, V., Ieromonachou, P., Lin, Y.: Big data analytics in supply chain management: A state-of-the-art literature review. Computers & Operations Research, 98, 254-264 (2018).

[3]. Zhong, R. Y., Newman, S. T., Huang, G. Q., Lan, S.: Big Data for supply chain management in the service and manufacturing sectors: Challenges, opportunities, and future perspectives. Computers & Industrial Engineering, 101, 572-591 (2016).

[4]. Waller, M. A., Fawcett, S. E.: Data science, predictive analytics, and big data: a revolution that will transform supply chain design and management. Journal of Business Logistics, 34(2), 77-84 (2013).

[5]. Raman, S., Patwa, N., Niranjan, I., Ranjan, U., Moorthy, K., Mehta, A.: Impact of big data on supply chain management. International Journal of Logistics Research and Applications, 21(6), 579-596 (2018).

[6]. Fisher, D., DeLine, R., Czerwinski, M., Drucker, S.: Interactions with big data analytics. interactions, 19(3), 50-59 (2012).

[7]. Bi, Z., Cochran, D.: Big data analytics with applications. Journal of Management Analytics, 1(4), 249-265 (2014).

[8]. Aceto, G., Ciuonzo, D., Montieri, A., Persico, V., Pescapé, A.: Know your big data trade-offs when classifying encrypted mobile traffic with deep learning. In 2019 Network traffic measurement and analysis conference (TMA) pp. 121-128 (2019).

[9]. Aydın, O., Akdoğan, N.: Cost Controlling System “Just-in-Time (JIT)” Amidst the Covid-19 Pandemic: An Advantage or Disadvantage in the Digital Era? Conceptual Framework. In Auditing Ecosystem and Strategic Accounting in the Digital Era: Global Approaches and New Opportunities pp. 385-401 (2021).

[10]. Yamamoto, K., Lloyd, R. A.: The role of big data and digitization in just-in-time (JIT) information feeding and marketing. American Journal of Management, 19(2), 126-133 (2019).

[11]. Ptiček, M., Vrdoljak, B.: Big data and new data warehousing approaches. In Proceedings of the 2017 International Conference on Cloud and Big Data Computing pp. 6-10 (2017).

[12]. Ramos, C. M., Martins, D. J., Serra, F., Lam, R., Cardoso, P. J., Correia, M. B., Rodrigues, J. M.: Framework for a hospitality big data warehouse: The implementation of an efficient hospitality business intelligence system. International Journal of Information Systems in the Service Sector (IJISSS), 9(2), 27-45 (2017).

[13]. Jukić, N., Sharma, A., Nestorov, S., Jukić, B.: Augmenting data warehouses with big data. Information Systems Management, 32(3), 200-209 (2015).

[14]. Molka-Danielsen, J., Engelseth, P., Olešnaníková, V., Šarafín, P., Žalman, R.: Big data analytics for air quality monitoring at a logistics shipping base via autonomous wireless sensor network technologies. In 2017 5th international conference on enterprise systems (ES) pp. 38-45 (2017).

[15]. Zhang, N., Zheng, K.: Research and design of the architecture of the marine logistics information platform based on big data. Journal of Coastal Research, 106(SI), 628-632 (2020).

[16]. Ayed, A. B., Halima, M. B., Alimi, A. M.: Big data analytics for logistics and transportation. In 2015 4th international conference on advanced logistics and transport (ICALT) pp. 311-316 (2015).

Cite this article

Ruan,W. (2023). Supply Chain Management Optimization Based on Bigdata Analysis. Advances in Economics, Management and Political Sciences,23,96-101.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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About volume

Volume title: Proceedings of the 2023 International Conference on Management Research and Economic Development

ISBN：978-1-915371-89-8(Print) / 978-1-915371-90-4(Online)
Editor：Javier Cifuentes-Faura, Canh Thien Dang
Conference website: https://2023.icmred.org/
Conference date: 28 April 2023
Series: Advances in Economics, Management and Political Sciences
Volume number: Vol.23
ISSN：2754-1169(Print) / 2754-1177(Online)

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References

[1]. Nguyen, T., Li, Z. H. O. U., Spiegler, V., Ieromonachou, P., Lin, Y.: Big data analytics in supply chain management: A state-of-the-art literature review. Computers & Operations Research, 98, 254-264 (2018).

[2]. Nguyen, T., Li, Z. H. O. U., Spiegler, V., Ieromonachou, P., Lin, Y.: Big data analytics in supply chain management: A state-of-the-art literature review. Computers & Operations Research, 98, 254-264 (2018).

[3]. Zhong, R. Y., Newman, S. T., Huang, G. Q., Lan, S.: Big Data for supply chain management in the service and manufacturing sectors: Challenges, opportunities, and future perspectives. Computers & Industrial Engineering, 101, 572-591 (2016).

[4]. Waller, M. A., Fawcett, S. E.: Data science, predictive analytics, and big data: a revolution that will transform supply chain design and management. Journal of Business Logistics, 34(2), 77-84 (2013).

[5]. Raman, S., Patwa, N., Niranjan, I., Ranjan, U., Moorthy, K., Mehta, A.: Impact of big data on supply chain management. International Journal of Logistics Research and Applications, 21(6), 579-596 (2018).

[6]. Fisher, D., DeLine, R., Czerwinski, M., Drucker, S.: Interactions with big data analytics. interactions, 19(3), 50-59 (2012).

[7]. Bi, Z., Cochran, D.: Big data analytics with applications. Journal of Management Analytics, 1(4), 249-265 (2014).

[8]. Aceto, G., Ciuonzo, D., Montieri, A., Persico, V., Pescapé, A.: Know your big data trade-offs when classifying encrypted mobile traffic with deep learning. In 2019 Network traffic measurement and analysis conference (TMA) pp. 121-128 (2019).

[9]. Aydın, O., Akdoğan, N.: Cost Controlling System “Just-in-Time (JIT)” Amidst the Covid-19 Pandemic: An Advantage or Disadvantage in the Digital Era? Conceptual Framework. In Auditing Ecosystem and Strategic Accounting in the Digital Era: Global Approaches and New Opportunities pp. 385-401 (2021).

[10]. Yamamoto, K., Lloyd, R. A.: The role of big data and digitization in just-in-time (JIT) information feeding and marketing. American Journal of Management, 19(2), 126-133 (2019).

[11]. Ptiček, M., Vrdoljak, B.: Big data and new data warehousing approaches. In Proceedings of the 2017 International Conference on Cloud and Big Data Computing pp. 6-10 (2017).

[12]. Ramos, C. M., Martins, D. J., Serra, F., Lam, R., Cardoso, P. J., Correia, M. B., Rodrigues, J. M.: Framework for a hospitality big data warehouse: The implementation of an efficient hospitality business intelligence system. International Journal of Information Systems in the Service Sector (IJISSS), 9(2), 27-45 (2017).

[13]. Jukić, N., Sharma, A., Nestorov, S., Jukić, B.: Augmenting data warehouses with big data. Information Systems Management, 32(3), 200-209 (2015).

[14]. Molka-Danielsen, J., Engelseth, P., Olešnaníková, V., Šarafín, P., Žalman, R.: Big data analytics for air quality monitoring at a logistics shipping base via autonomous wireless sensor network technologies. In 2017 5th international conference on enterprise systems (ES) pp. 38-45 (2017).

[15]. Zhang, N., Zheng, K.: Research and design of the architecture of the marine logistics information platform based on big data. Journal of Coastal Research, 106(SI), 628-632 (2020).

[16]. Ayed, A. B., Halima, M. B., Alimi, A. M.: Big data analytics for logistics and transportation. In 2015 4th international conference on advanced logistics and transport (ICALT) pp. 311-316 (2015).

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