Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.14 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI)
Efficient logistics and supply chain management are critical for ensuring timely product delivery and maintaining customer satisfaction. This study analyzes factory-to-customer shipping route efficiency using the Nassau Candy Distributor dataset. Data analytics techniques were applied to evaluate delivery times, regional shipping performance, product sales distribution, and transportation efficiency. Python libraries such as Pandas, Matplotlib, and Seaborn were used for data processing and visualization, while an interactive dashboard was developed using Streamlit to present key logistics insights. The analysis identifies shipping delays, high-performing regions, and product demand patterns. The findings demonstrate how data analytics can support better logistics planning, optimize shipping routes, and improve overall supply chain efficiency.
"FACTORY-TO-CUSTOMER SHIPPING ROUTE EFFICIENCY ANALYSIS", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2456-3315, Vol.11, Issue 3, page no.a48-a54, March-2026, Available :http://www.ijrti.org/papers/IJRTI2603008.pdf
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2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator