Logistics

The Logistics industry has an array of unique needs that can be greatly improved through quantifiable data solutions. Advanced data analytics along with leaner spatial analytical tools are transforming the industry and making industry players more efficient, effective and profitable. Our data scientists with spatial expertise, and partnerships with companies like Mapbox, help our clients implement innovative logistics solutions that keep them ahead of competition and thriving.

Real-Time Vehicular Data Collection

Each vehicle can be equipped with sensors that collect important data in real-time as the vehicle is operated. GPS position, vehicle speed and events are combined with external data sources — like traffic conditions — and displayed in easy to comprehend dashboards and visualizations that reveal anomalies and patterns. Layering machine learning on top reveals and suggests possibilities for improvement and predicts possible future issues so action can be taken before they become a problem. This solution conveys the big picture and enables users to drill down into the data to find root causes and opportunities to improve performance and make real-time data-backed decisions.

Route Planning

Effective data-based route planning can drastically improve overall performance. Continuously collecting data from vehicles and external sources enables routes to be optimized. Routing can also be optimized based on the shipment, vehicle type, weather, road condition, restrictions and other factors. The accuracy and holistic view that this solution provides allow the user to maximize efficiency and avoid delays and fines. Commodity-based route planning keeps drivers on the fastest route while avoiding any restrictions.

Driver Behavior Analytics

Capturing and optimizing driving behavior is a crucial risk management tool for any transportation company. By capturing driving data such as heavy braking, strong acceleration and cruise control usage provides insights that can be used to create effective and custom coaching plans. The data also reveals and enables the company to minimize risk by accurately identifying dangerous driving habits for which precautionary measurements or corrective actions can be taken. In the case of unforeseen events, the data provides crucial facts.

Unmoved Vehicles

Capturing data of unmoved vehicles can be extremely useful. An unmoved vehicle is often not obvious through regular reporting and can become invisible. By identifying these invisible assets, downtime can be kept to a minimum, and rest area planning can be used to optimize travel routes.

Fuel Consumption Tracking

Fuel is one of the biggest cost factors when it comes to any transportation business. Combining real-time fuel consumption data with other relevant factors allows fuel efficient plans that maximize output and identify driving habits that lead to fuel inefficiency to be created and implemented.

Technologies

Rising parcel quantities and the international coordination of deliveries placed considerable demands on the fragmented ERP and BI systems of Hungary's leading delivery service provider. To ensure the necessary insights for decision-makers, our client turned to Starschema to create a unified operational framework that would ensure the rapid, uninterrupted flow and reportability of business-critical information.

Case Study

Using Data to Improve Global Supply Chains

Over 60% of the world’s global seaborne trade is shipped using intermodal freight containers, and the ports that manage them serve as central points for supply chains – over 90% of global trade is conducted through ports.

White Paper

Opening the Black Box - Learn how to think about AI and thrive in data-driven cultures of today and tomorrow

Machine Learning models can be so complex that they seem like a magical black box with inputs and outputs, but little understanding of how the outputs are derived. In this white paper,

Eszter Windhager-Pokol, Starschema head of data science, explains the concept of interpretability and several methods to address the problems posed by black box models.