IJSMT Journal

International Journal of Science, Strategic Management and Technology

An International, Peer-Reviewed, Open Access Scholarly Journal Indexed in recognized academic databases · DOI via Crossref The journal adheres to established scholarly publishing, peer-review, and research ethics guidelines set by the UGC

ISSN: 3108-1762 (Online)
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LOGISTICS AND SUPPLY CHAIN PERFORMANCE DASHBOARD

AUTHORS:
Priyanka Behera
Mentor
Smruti Ranjan Swain
Affiliation
Department of Master of Computer Applications GIFT Autonomous, Bhubaneswar, Odisha, India
CC BY 4.0 License:
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Abstract
The rapid growth of global trade, e-commerce, and transportation systems has significantly increased the complexity of logistics and supply chain operations. Organizations generate large volumes of logistics data from shipment records, inventory updates, supplier information, transportation costs, and delivery activities. Efficient analysis of this data is essential for improving operational visibility, reducing delays, optimizing inventory management, and supporting data-driven business decisions. This paper presents a Logistics and Supply Chain Performance Dashboard developed using the Databricks Lakehouse Platform and Medallion Architecture for scalable logistics data processing and analytical reporting.

 

The proposed system processes logistics datasets through Bronze, Silver, and Gold layers to improve data quality, perform transformation, and generate meaningful business insights. Interactive dashboards and visualization techniques are used to monitor key performance indicators (KPIs) such as delivery performance, transportation costs, supplier efficiency, inventory levels, and order fulfillment status through charts, KPI cards, and analytical reports. The developed dashboard helps organizations identify operational bottlenecks, improve supply chain visibility, and support strategic decision-making. This research demonstrates how modern big data technologies, Medallion Architecture, and dashboard visualization tools can be effectively utilized for intelligent logistics analytics and supply chain performance management.
Keywords
Logistics Management; Supply Chain Analytics; Databricks; Medallion Architecture; Dashboard Visualization; Data Analytics; KPI Monitoring.
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Behera, P. (2026). Logistics and Supply Chain Performance Dashboard. International Journal of Science, Strategic Management and Technology, 02(6). https://doi.org/10.55041/ijsmt.v2i6.064

Behera, Priyanka. "Logistics and Supply Chain Performance Dashboard." International Journal of Science, Strategic Management and Technology, vol. 02, no. 6, 2026, pp. . doi:https://doi.org/10.55041/ijsmt.v2i6.064.

Behera, Priyanka. "Logistics and Supply Chain Performance Dashboard." International Journal of Science, Strategic Management and Technology 02, no. 6 (2026). https://doi.org/https://doi.org/10.55041/ijsmt.v2i6.064.

References
[1] A. Gandomi and M. Haider, “Beyond the Hype: Big Data Concepts, Methods, and Analytics,” International Journal of Information Management, vol. 35, no. 2, pp. 137–144, 2015.

[2] K. Kambatla, G. Kollias, V. Kumar, and A. Grama, “Trends in Big Data Analytics,” Journal of Parallel and Distributed Computing, vol. 74, no. 7, pp. 2561–2573, 2014.

[3] M. Zaharia, M. Chowdhury, M. J. Franklin, S. Shenker, and I. Stoica, “Spark: Cluster Computing with Working Sets,” in Proceedings of the 2nd USENIX Conference on Hot Topics in Cloud Computing, 2010, pp. 1–7.

[4] M. Zaharia et al., “Apache Spark: A Unified Engine for Big Data Processing,” Communications of the ACM, vol. 59, no. 11, pp. 56–65, 2016.

[5] I. A. T. Hashem, I. Yaqoob, N. B. Anuar, S. Mokhtar, A. Gani, and S. U. Khan, “The Rise of Big Data on Cloud Computing: Review and Open Research Issues,” Information Systems, vol. 47, pp. 98–115, 2015.

[6] S. Chopra and P. Meindl, Supply Chain Management: Strategy, Planning, and Operation, 6th ed. Pearson Education, 2016.

[7] M. Christopher, Logistics and Supply Chain Management, 5th ed. Pearson Education, 2016.

[8] A. Gunasekaran, T. Papadopoulos, R. Dubey, et al., “Big Data and Predictive Analytics for Supply Chain and Organizational Performance,” Journal of Business Research, vol. 70, pp. 308–317, 2017.

[9] R. Dubey, A. Gunasekaran, S. J. Childe, T. Papadopoulos, and K. Wamba, “World Class Sustainable Supply Chain Management: Critical Review and Further Research Directions,” International Journal of Logistics Management, vol. 28, no. 2, pp. 332–362, 2017.

[10] H. Stadtler, “Supply Chain Management and Advanced Planning Basics, Overview and Challenges,” European Journal of Operational Research, vol. 163, no. 3, pp. 575–588, 2005.
Ethics and Compliance
✓ All ethical standards met
This article has undergone plagiarism screening and double-blind peer review. Editorial policies have been followed. Authors retain copyright under CC BY-NC 4.0 license. The research complies with ethical standards and institutional guidelines.
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