Connecting the unseen gaps between warehouse velocity, fleet aerodynamics, and demand elasticity — one semantic data point at a time.

In the era of hyperconnected supply chains, most platforms offer isolated visibility: a warehouse dashboard here, a fleet tracking screen there. LogiFleet Pulse breaks that paradigm. It is a multi-modal logistics intelligence engine that fuses warehousing micro-operations, fleet telemetry, inventory aging curves, and external macroeconomic signals into a unified semantic layer. Think of it as the brainstem of your logistics nervous system — not just reporting what happened, but predicting where friction will emerge before it turns into a bottleneck.

Built on a custom multi-fact star schema with time-phased dimensions, LogiFleet Pulse leverages MS SQL Server for transactional integrity and Power BI for adaptive visual storytelling. The platform automatically reconciles cross-fact KPIs (e.g., "dwell time per SKU vs. fleet idling cost per route") using composite aggregation bridges. Decision-makers see one version of the truth — not a fragmented mosaic.

Imagine a city’s traffic control center merged with an ant colony’s pheromone trails. That’s LogiFleet Pulse. It ingests data from:

• Warehouse Management Systems (receiving, putaway, picking, packing, shipping)
• Telematics and GPS feeds (vehicle location, fuel consumption, driver behavior)
• Supplier portals and procurement logs (lead times, defect rates, batch numbers)
• External weather and traffic APIs (delayed as correlated risk factors)
• Customer order history (demand seasonality, return patterns, delivery preference)

These streams are harmonized through a time-aware dimension table (snapshots at 15-minute granularity) and a bridge table for many-to-many relationships between routes and storage zones. The result? A single query can answer: “Show me all shipments delayed due to weather that originated from cold-storage zones with more than 72 hours of dwell time in the last quarter.”

Standard tools keep warehouse and fleet metrics separate. LogiFleet Pulse uses composite shared dimensions (e.g., time, geography, product hierarchy) to mathematically link inventory turnover with fuel burn rates. Example: “If SKU A moves from slow-mover to fast-mover bin, how does that affect optimal delivery route density?”

Using weighted scoring based on real-time telemetry (engine diagnostics, tire pressure, load weight), the platform generates a proactive maintenance queue that doesn’t just flag issues — it ranks them by potential revenue impact. A tire pressure drop on a truck carrying high-margin perishables gets priority over one carrying bulk cardboard.

A novel spatial dimension that maps storage areas not by aisle number but by gravitational pull — combining pick frequency, item fragility, and replenishment lead time. High-gravity zones (fast-moving, high-value items) are placed closer to shipping docks. The system auto-recommends rearrangements when pattern drift is detected.

Beyond static dashboards, LogiFleet Pulse runs time-phased simulation scenarios: “What happens to fleet utilization if we switch from 80% to 95% warehouse capacity?” It uses historical multi-fact data to generate probabilistic outcomes, not simple linear extrapolations.

Multiple users across departments can tag, annotate, and verify anomalies (e.g., sudden spike in dwell time during a supplier strike). The platform learns from these human-in-the-loop corrections and improves its prediction precision over time.

The repository includes the complete SQL schema and Power BI template (.pbit) for:

• FactWarehouseOperations (putaway cycles, pick rates, packing times, dwell spans)
• FactFleetTrips (route segments, fuel consumption, idle time, loading/unloading events)
• FactCrossDock (transfers between inbound and outbound without long-term storage)
• DimTime (15-minute buckets, hour-of-day, day-of-week, fiscal periods)
• DimGeography (hierarchical: continent → country → region → warehouse/route node)
• DimProductGravity (assigned gravity score based on velocity, value, and fragility)
• DimSupplierReliability (lead time variance, defect percentage, compliance score)

All scripts are annotated with implementation notes and best practices for indexing and partitioning.

1. Deploy the SQL schema onto your MS SQL Server instance (2019+ recommended). The script creates tables, relationships, views, and stored procedures for incremental loading.
2. Configure your data sources — update the connection strings in config_sample.json to point to your WMS, telemetry API, and ERP feeds. The platform uses polybase and external tables for streaming ingestion.
3. Import the Power BI template — open LogiFleet_Pulse_Master.pbit and connect to your SQL server. The model automatically detects the fact tables and builds the relationships.
4. Set up alerts — use the provided automated alerting stored procedure to define thresholds (e.g., fleet idling time > 15% of trip duration).
5. Onboard your team — assign roles through the security table. Dashboards adapt dynamically to user permissions.

• A 3PL operator reduced cross-dock dwell time by 22% after identifying that certain product categories were being assigned to the wrong gravity zone.
• A retail chain cut fuel costs by 14% by routing high-priority deliveries through more direct paths, learned from fleet idle time correlations with specific road segments.
• A food distributor avoided $340k in spoilage by triggering an alert when a freezer unit’s temperature drifted outside the tolerance window for more than 20 minutes — tied directly to the affected shipment’s ETA.

LogiFleet Pulse is designed to coexist with:

• Microsoft Power Platform (Power Automate for workflow triggers)
• Azure Synapse Analytics (for external big data enrichment)
• Tableau (via ODBC bridge, though Power BI is the primary visualization layer)
• Any WMS or TMS that exports via SQL, REST API, or flat files

This project is licensed under the MIT License — you are free to use, modify, and distribute it for commercial or non-commercial purposes. See the LICENSE file for full terms.

Contributions are welcome. To maintain consistency:

• Fork the repository
• Create a feature branch (feature/your-idea)
• Submit a pull request with a detailed description of changes and the business context

Please ensure any new SQL scripts include:

• Indexing strategy recommendations
• Row-level security annotations
• EXAMPLE query showing business value

LogiFleet Pulse is a data modeling and visualization template — it does not collect, store, or transmit any personal data. The schema and dashboards are provided "as is" without warranty of any kind. Users are responsible for:

• Ensuring compliance with local data protection regulations (GDPR, CCPA, etc.)
• Validating the accuracy of external data sources (weather APIs, traffic feeds, etc.)
• Performing regular backups of their Power BI reports and SQL databases

The predictive algorithms used for bottleneck detection and fleet triage are decision support tools — they do not replace human judgment. Always verify critical logistics decisions with on-the-ground personnel.

• Logistics intelligence platform
• Multi-fact star schema data modeling
• Real-time fleet optimization dashboard
• Cross-modal supply chain analytics
• Power BI logistics visualization
• MS SQL Server data warehousing for logistics
• Warehouse gravity zone optimization
• Predictive bottleneck detection
• Temporal elasticity modeling for supply chain
• End-to-end logistics KPI harmonization

Think of LogiFleet Pulse less as a tool and more as a telescope for your logistics universe. Most systems let you see the stars, but they don’t show you the gravitational forces bending the light between them. LogiFleet Pulse reveals those forces — the hidden relationships between a fork truck’s route and a delivery truck’s idle time, between a supplier’s delay and a customer’s satisfaction score.

It’s not just data. It’s the invisible architecture of movement made visible.