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Global logistics enterprises generate vast transactional records that remain underutilised for strategic profitability management. This paper applies systematic Exploratory Data Analysis (EDA) and multi-dimensional feature engineering to a real-world operational dataset of 180,519 order records from APL Logistics (KWE Group), spanning 20,652 unique customers, 118 products across 50 categories, five global markets, and 23 order regions. The dataset encompasses total portfolio revenue of $36,784,734.31 and net profit of $3,966,902.97, yielding an overall profit margin of 10.78%. Critical findings include: 18.71% of orders (33,784 transactions) are loss-making; total discount expenditure ($3,730,378.40) constitutes 94.0% of net profit; the top 10% of customers (2,066 accounts) account for 49.1% of cumulative profit; a Pearson correlation of r = -0.0027 (p = 0.253) between discount rate and profit ratio — non-significant at the individual order level — coexists with a monotonic 25.2% aggregate-level margin decline from zero-discount to 21–25% discount bands; and a portfolio-wide late delivery rate of 54.83% imposes significant service-quality risk. An interactive six-module Streamlit dashboard is deployed to operationalise analytical outputs for real-time commercial decision support. The study advances the evidence base for profit-centric supply chain analytics and provides actionable recommendations for discount policy reform, customer value tiering, and shipping mode rationalisation