A Conceptual Model for Real-Time Route Optimization: Algorithmic Pathways to Delivery Profitability and Logistics Cost Reduction
TL;DR
The paper argues that profitability-aware, continuously learning routing constitutes a distinct organisational capability rather than a software feature, and that this distinction explains persistent performance dispersion among otherwise comparable distributors.
Abstract
Last-mile and line-haul distribution now absorb a disproportionate share of total logistics expenditure, yet the algorithms that govern vehicle routing in most operating environments remain calibrated to static, distance-minimising objectives that ignore the financial heterogeneity of orders, customers, and time windows. This paper develops a conceptual model that reframes route optimization as a continuously updated profitability decision rather than a one-time combinatorial cost-minimisation problem. The model integrates three interacting components: a real-time data and sensing layer that converts telematics, demand signals, and traffic conditions into decisionrelevant state variables; a margin-aware optimization engine that embeds order-level contribution, cost-to-serve, and service penalties directly into the objective function; and a governance and learning layer that aligns algorithmic decisions with enterprise cost-control, resilience, and sustainability goals. Drawing on dynamic capabilities theory, the resource-based view, the theory of constraints, and complex adaptive systems thinking, the paper synthesizes evidence from supply chain analytics, predictive demand sensing, cost-to-serve accounting, autonomous fleet coordination, and digital supply chain governance to derive a set of testable propositions linking real-time optimization to measurable margin and cost outcomes. The framework is validated theoretically against established constructs and confronted with mixed empirical evidence, including studies reporting that analytical sophistication does not automatically translate into financial gain. Operationalisation guidance specifies data requirements, decision cadence, and key performance indicators, while explicit boundary conditions identify where the model is expected to hold and where it is likely to fail. The paper argues that profitability-aware, continuously learning routing constitutes a distinct organisational capability rather than a software feature, and that this distinction explains persistent performance dispersion among otherwise comparable distributors.
