Role of Analytics in Supply Chain Management Industry in Lithuania: Big Data Analytics & AI

Authors

DOI:

https://doi.org/10.55938/ijgasr.v2i4.65

Keywords:

Data Security, Logistics Industry, Supply Chain and Financial Outcomes, Data Analysis

Abstract

Supply chain managers face a variety of obstacles when preparing for the future, as change is bound to happen. The increase in the importance of "big data" and also the use of "analytics" to analyze this data are two significant changes in the past few years. The analysis of big data is extremely important because it has the potential to yield significant value, and it is essential for companies to make use of the wide range of information sources by carrying out a comprehensive and accurate examination.

Goal: The purpose of this article is to showcase the constantly changing nature of supply chain management practices, predict the future impact of big data and analytics in SCM, emphasize the potential benefits of these trends, and offer guidance to leaders in the field of SCM.

Approach/technique/procedure: It is emphasized how crucial it is to derive value from the vast quantity of data accessible in the field of supply chain management. Definition of "big data" and analytics, with explanation of how they affect SCM applications.

Outcomes: Instances demonstrate how the supply chain management domain can be influenced by these recent trends and advancements. These examples have effectively adopted, utilized, and put into practice analytics that rely on large volumes of data. The existence of big data is undeniable, and utilizing analytics to derive valuable insights from this information has the power to create a significant influence.

In summary, it can be stated that... It is important for supply chain managers to closely monitor these 2 trends because effectively incorporating "big data" analytics are able to keep them updated on advancements and alterations, ultimately enhancing their competitiveness.

Downloads

Download data is not yet available.

References

Ghobadi F., Rohani M. “Cost Sensitive Modeling of Credit Card Fraud using Neural Network strategy”, 2016 Signal Processing and Intelligent Systems, International Conference of pp. 1–5. IEEE. DOI: https://doi.org/10.1109/ICSPIS.2016.7869880

Hutter T., Haeussler S., Missbauer H., 2018. Successful implementation of an order release mechanism based on workload control: a case study of a make-to-stock manufacturer. International Journal of Production Research, 56, pp. 1565–1580. DOI: https://doi.org/10.1080/00207543.2017.1369598

Ji W., Wanga L., 2017. Big data analytics-based fault prediction for shop floor scheduling. Journal of Manufacturing Systems, Volume 43, pp. 187–194. DOI: https://doi.org/10.1016/j.jmsy.2017.03.008

Ketokivi M., Choi T., 2014. Renaissance of case research as a scientific method. Journal of Operations Management, 32, pp. 232–240. DOI: https://doi.org/10.1016/j.jom.2014.03.004

Kumar A., Shankar R., Choudhary A., Thakur L. S., 2016. A big data mapreduce framework for fault diagnosis in cloud- based manufacturing. International Journal of Production Research, 54, pp. 7060–7073 DOI: https://doi.org/10.1080/00207543.2016.1153166

Lindström J., Larsson H., Jonsson M., Lejon E., 2017. Towards intelligent and sustainable production: combining and integrating online predictive maintenance and continuous quality control. Procedia CIRP of The 50th CIRP Conference on Manufacturing Systems, Issue 63, pp. 443–448. DOI: https://doi.org/10.1016/j.procir.2017.03.099

Ramaswamy V., Gouillart F.J. The Power of Co- creation: Build it with them to Boost Growth, Productivity, and Profits, Simon and Schuster, Noida.

Rao A. M., rothstein m. A. How analytics is driving the supply chain innovation in north america., business & IT, 2022 DOI: https://doi.org/10.14311/bit.2022.01.19

SCHULTE R. “Application integration scenario: how the war is being won”, in Gartner Group, Application Integration – Making E-Business Work, Gartner Group, London.

SEUFERT A., Schiefer J. “Enhanced business intelligence- supporting business processes with real-time business analytics”, Proceedings of the 16th International Workshop on Database and Expert System Applications-DEXA’05, available at: www.ieee.org.

Tao F, Qi Q, Liu A, Kusiak A. Data-driven smart manufacturing. Int J Ind Manuf Syst Eng 2018;48:157–69. https://doi.org/10.1016/j.jmsy.2018.01.006. DOI: https://doi.org/10.1016/j.jmsy.2018.01.006

Zhang J. Multi-source remote sensing data fusion: status and trends. Int J Image Data Fusion 2010;1. https://doi.org/10.1080/19479830903561035 DOI: https://doi.org/10.1080/19479830903561035

Zhang Y, Ren S, Liu Y, Si S. A big data analytics architecture for cleaner manufacturing and maintenance processes of complex products. J Clean Prod 2017;142. https://doi.org/10.1016/j.jclepro.2016.07.123. DOI: https://doi.org/10.1016/j.jclepro.2016.07.123

Zhang Z , et al.. Pathologist-level interpretable whole-slide cancer diagnosis with deep learning. Nat Mach Intell 2019;1. https://doi.org/10.1038/s42256-019-0052-1. DOI: https://doi.org/10.1038/s42256-019-0062-z

Zhong RY, Newman ST, Huang GQ, Lan S. Big Data for supply chain management in the service and manufacturing sectors: challenges, opportunities, and future perspectives. Comput Ind Eng 2016;101. https://doi.org/10.1016/j.cie.2016.07.013. DOI: https://doi.org/10.1016/j.cie.2016.07.013

Zhu K, Li G, Zhang Y. Big data oriented smart tool condition monitoring system. IEEE Trans Ind Inform 2019;16:1. https://doi.org/10.1109/tii.2019.2957107 DOI: https://doi.org/10.1109/TII.2019.2957107

Published

2023-12-31

How to Cite

Mohammad Kuraishi, Z. (2023). Role of Analytics in Supply Chain Management Industry in Lithuania: Big Data Analytics & AI. International Journal for Global Academic & Scientific Research, 2(4), 13–22. https://doi.org/10.55938/ijgasr.v2i4.65

Similar Articles

<< < 1 2 3 4 5 

You may also start an advanced similarity search for this article.