Rather than treating locations as static points, the Supply Chain Designer evaluates them as economic decision nodes. It reveals which sites should carry volume, where capacity limits constrain growth, and how fixed and variable costs influence network performance. By modeling products with different logistics requirements and flow restrictions, companies can reflect real operational and regulatory constraints and understand their cost and service impact before making structural decisions.
Complementing this design perspective, the Shipment Flow Optimizer focuses on how shipments should move through an existing network. Optimizing across time and space dimensions, the app determines which shipments should go direct, which should be consolidated, and how flows should be routed to minimize total logistics cost while respecting service commitments, time windows, and capacity limits.
The Shipment Flow Optimizer evaluates all shipment movements together, enabling consistent routing decisions across large shipment volumes and clear identification of when consolidation adds value. Warehouses and hubs are treated as active decision points, helping companies detect bottlenecks, manage peaks, and understand the cost impact of capacity, handling, and storage constraints. Transport decisions are evaluated based on service selection, capacity availability, and cost structures, giving companies clear insight into why specific routing and service options are chosen and how they affect overall cost and feasibility.
With this segment, companies can compare different network usage and shipment strategies, quantify the cost of current practices versus optimized alternatives, and test scenarios before implementation.
To support faster execution of these advanced analyses, Log-hub also introduces the Supply Chain Apps Agent, an AI-powered assistant that runs optimization studies based on user-defined objectives and uploaded data. Users can describe their analytical goals and scenarios in natural language, and upload their data, after which the agent prepares the analysis, executes the models, and returns results in the form of data outputs, maps, and dashboards within the platform. The agent is expected to boost analyst productivity by running analyses in the background, allowing project teams to save time and focus on higher-value tasks.
In addition, Log-hub has introduced AI Data Sanity Check, a new AI-based feature that supports data preparation by identifying anomalies and inconsistencies. This helps reduce manual effort in data cleaning and improves the reliability of analytical results.
With this expanded advanced analytics segment, Log-hub brings together optimization and AI support in a single environment, enabling companies to design their supply chains, analyze operational flows, and run complex scenarios more efficiently, with greater transparency and confidence.
