Supply Chain Optimization through Real-Time Data Analysis and Visualization with Power BI

Muralidhar; Banda Krishna Vaishnavi; Harish Knikhil S; Magizhan C B; Naveen Balaji S

doi:10.48001/jodpba.2024.1121-30

Authors

Muralidhar Department of Production Engineering, PSG College of Technology, Coimbatore, Tamil Nadu, India
Banda Krishna Vaishnavi Department of Production Engineering, PSG College of Technology, Coimbatore, Tamil Nadu, India
Harish Knikhil S Department of Production Engineering, PSG College of Technology, Coimbatore, Tamil Nadu, India
Magizhan C B Department of Production Engineering, PSG College of Technology, Coimbatore, Tamil Nadu, India
Naveen Balaji S Department of Production Engineering, PSG College of Technology, Coimbatore, Tamil Nadu, India

DOI:

https://doi.org/10.48001/jodpba.2024.1121-30

Keywords:

Data analytics, Data visualization, Optimization, Power BI, Supply chain

Abstract

Supply chain optimization is a critical aspect of modern business operations, aiming to enhance efficiency, reduce costs, and improve customer satisfaction. In today's dynamic business environment, supply chains face increasing complexity and uncertainty. The ability to access and analyse data in real-time is paramount for organizations to stay competitive and responsive to market dynamics. Effective management requires timely access to accurate data and actionable insights. By harnessing the power of real-time data and integration of real-time data analysis techniques, coupled with visualization and Business Intelligence tools like Power BI, organizations can make informed decisions promptly, leading to agile and responsive supply chain management. Raw data has been transformed into meaningful insights through interactive dashboards and reports by using Power BI.

Downloads

Download data is not yet available.

References

Abdul Rahman, A. M., Abu Bakar, N. A., Ab Rahim, N. Z., & Sallehudin, H. (2020, July). Data-driven inventory management solution for procurement and supply chain of utility company. In The 1st International Conference on Information Technology & Business ICITB2020.

http://dx.doi.org/10.2139/ssrn.3660529.

Anitha, P., & Patil, M. M. (2018). A review on data analytics for supply chain management: A case study. International Journal of Information Engineering and Electronic Business, 10(5), 30.

https://doi.org/10.5815/ijieeb.2018.05.05.

Fragkiskaki, K. (2023). Big data and analytics in enhancing procurement and supply chain efficiency: A literature and empirical study. Digital Library and Institutional Repository,

https://dspace.lib.uom.gr/handle/2159/29578?locale=en.

Hallikas, J., Immonen, M., & Brax, S. (2021). Digitalizing procurement: The impact of data analytics on supply chain performance. Supply Chain Management: An International Journal, 26(5), 629-646.

https://doi.org/10.1108/SCM-05-2020-0201.

Handfield, R., Jeong, S., & Choi, T. (2019). Emerging procurement technology: data analytics and cognitive analytics. International Journal of Physical Distribution & Logistics Management, 49(10), 972-1002.

https://doi.org/10.1108/IJPDLM-11-2017-0348.

Liu, J., Hwang, S., Yund, W., Neidig, J. D., Hartford, S. M., Ng Boyle, L., & Banerjee, A. G. (2020). A predictive analytics tool to provide visibility into completion of work orders in supply chain systems. Journal of Computing and Information Science in Engineering, 20(3), 031003.

https://doi.org/10.1115/1.4046135.

Nabil, D. H., Rahman, M. H., Chowdhury, A. H., & Menezes, B. C. (2023). Managing supply chain performance using a real time microsoft power BI dashboard by action design research (ADR) method. Cogent Engineering, 10(2), 2257924.

https://doi.org/10.1080/23311916.2023.2257924.

Perumalsamy, R., & Natarajan, J. (2010, July). Predictive analytics using genetic algorithm for efficient supply chain inventory optimization. In 2010 Second International Conference on Computing, Communication and Networking Technologies (pp. 1-8). IEEE.

https://doi.org/10.1109/ICCCNT.2010.5591607.

Raman, S., Patwa, N., Niranjan, I., Ranjan, U., Moorthy, K., & Mehta, A. (2018). Impact of big data on supply chain management. International Journal of Logistics Research and Applications, 21(6), 579-596. https://doi.org/10.1080/13675567.2018.1459523.

Reddy, S. S. S., Mamatha, C. H., Chatterjee, P., & Nagarjuana Reddy, S. (2017). Contemporary supply chain and inventory data management using data analytics. International Journal of Mechanical Engineering and Technology, 8(12), 290-294.

https://shorturl.at/hqyI1.

Tan, M. H., & Lee, W. L. (2015). Evaluation and improvement of procurement process with data analytics. International Journal of Advanced Computer Science and Applications, 6(8), 70. https://doi.org/10.14569/IJACSA.2015.060809.

Wang, G., Gunasekaran, A., Ngai, E. W., & Papadopoulos, T. (2016). Big data analytics in logistics and supply chain management: Certain investigations for research and applications. International Journal of Production Economics, 176, 98-110.

https://doi.org/10.1016/j.ijpe.2016.03.014.