Jubair
Ahamed
I'm an MBA graduate focused on supply chain analytics, with a special interest in port operations and trade facilitation. I go beyond tracking cargo on paper to analyze how systems like single-window platforms measurably reduce processing time — combining data analysis, research, and an on-the-ground understanding of dock operations.
Download Resume ↓Logistics Mind. Supply Chain Vision.
I'm Jubair Ahamed, an MBA candidate specializing in Logistics and Supply Chain Management at Dr. M.G.R. Educational and Research Institute, Chennai. My journey is driven by a passion for building smarter, technology-enabled supply chains that improve the movement of goods and create value across global trade.
With a strong academic foundation in Commerce (B.Com) and advanced studies in logistics, I have developed a keen interest in trade facilitation, port operations, maritime logistics, and digital transformation. I believe that the future of supply chains lies in the seamless integration of technology, data, and operational excellence.
A defining milestone in my journey was my internship at Chennai Port Authority, where I conducted research on the impact of the National Logistics Portal – Marine (NLP-Marine) on trade efficiency. The study was based on primary data collected from 30 professionals across port and logistics organizations, providing valuable insights into how digital platforms are transforming India's maritime logistics ecosystem.
Alongside my logistics career, I have cultivated a strong interest in financial markets and long-term investing. To strengthen my financial knowledge, I earned certifications through the NSE Academy Fin-PRO Program and successfully completed the SEBI Investor Awareness Test (NISM). These experiences have enhanced my analytical thinking, risk assessment, and data-driven decision-making — skills that complement my work in supply chain management.
I am continuously exploring emerging technologies, industry trends, and innovative solutions that are reshaping global logistics. Whether it's supply chain optimization, digital trade platforms, or sustainable logistics practices, I am committed to learning, growing, and contributing to the future of the industry.
This portfolio represents more than my academic achievements — it reflects my curiosity, professional experiences, projects, certifications, and my ambition to build a meaningful career at the intersection of technology, trade, and supply chain innovation.
7.79/10
7.50/10
74.15%
Internships.
- Researched the impact of the National Logistics Portal (Marine) on trade facilitation efficiency, surveying 30+ port stakeholders and applying chi-square analysis.
- Analyzed EXIM documentation workflows and digital trade platform adoption across G2G, G2B, and B2B stakeholder models.
- Gained practical exposure to port operations and maritime single-window systems.
- Completed a hands-on internship in Supply Chain & Operations at a Chennai-based trade house.
- Built foundational understanding of procurement coordination, documentation, and operational support functions.
Research & projects.
A data-driven study into how NLP-Marine's single-window platform reshapes trade facilitation at Chennai Port — surveying 30+ stakeholders across the port ecosystem to measure real change in processing efficiency, not just adoption intent.
- Applied chi-square statistical analysis (χ² = 0.032) linking platform adoption to processing efficiency.
- Mapped digital single-window systems supporting G2G, G2B, and B2B trade facilitation models.
- Findings pointed to a 40% reduction in processing downtime among platform users.
Built an end-to-end Power BI dashboard analyzing inventory across 303 SKUs, using ABC-XY classification to segment items by value contribution and demand variability.
- Designed dynamic DAX-driven thresholds and measures (CALCULATE, SUMX) enabling real-time reclassification as filters changed.
- Applied Pareto (80/20) analysis to identify high-priority SKUs driving the majority of inventory value.
- Created drill-through views for SKU-level diagnostics, improving inventory prioritization and stockout/overstock visibility.
Built a linear regression model in Python mapping historical price-demand data to a price-response curve, then simulated demand, revenue, cost, and profit across a full pricing range to locate the profit-maximizing price point.
- Fit a scikit-learn LinearRegression model on price/demand pairs to derive the demand function (demand ≈ 694.9 − 11.7 × price).
- Simulated expected demand, revenue, cost, and profit across 49 price points using pandas.
- Identified the profit-maximizing price at $32/unit, yielding ≈$8,627 in simulated profit — the peak of the profit curve.
