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Stream/Area:
-
Subject:
-
Paper ID:
IJSREM66451
Volume:
10 |
7 |
Jul 2026
Pages:
1-5
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.
MBA
Yashaswi's IIMS Chinchwad
MBA
ATSS's IICMR Institute Nigdi
The increasing scale of e-commerce operations has intensified the need for efficient and data-oriented logistics systems. This study investigates how analytical tools can enhance operational performance in a logistics aggregator environment. The research is based on shipment data collected over a defined period and applies a quantitative approach using spreadsheet-based processing and business intelligence dashboards. Key indicators, including delivery success rate, return-to-origin ratio, transit duration, and shipment cost, were examined to identify inefficiencies. Interactive dashboards were developed to improve monitoring and support decision-making. The results indicate that analytics-driven practices improve operational transparency, reduce dependency on manual reporting, and enable more effective carrier selection. Additionally, structured process documentation contributed to improved workflow consistency. The study highlights the role of analytics as a critical enabler for improving efficiency, reducing costs, and supporting strategic logistics management.
KEYWORDS
The study demonstrates that the application of analytics significantly enhances operational efficiency in logistics systems. Data-driven approaches enable organizations to improve performance monitoring, optimize resource utilization, and reduce operational costs. The use of visualization tools and structured processes further strengthens the ability to manage logistics operations effectively. The findings suggest that organizations adopting analytics-based strategies are better positioned to respond to dynamic market conditions and increasing customer expectations. Future research may focus on incorporating advanced analytical techniques such as machine learning and predictive modeling to further enhance logistics optimization and decision-making capabilities.
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Publication Date
8/1/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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Conflict of Interest
The authors declare no conflict of interest related to this research work.
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