Supply Chain & Logistics Overview

Analysis of unstructured data has become core to smart inventory management, forecasting, and transportation logistics, and this type of data is growing exponentially. Unlike legacy systems, Graph database systems excel at connecting complex and unstructured data that can reveal better insights in the short term - rather broad, quarterly or annual views - as well as incorporate new data and evolve with changing analytical needs within the supply chain.

Common Use Cases

Asset Management

For market leaders, asset management is more than monitoring stock levels and movement through the network.

To maximize revenue & minimize costs, sales leaders are turning to graph databases, which can add new relationships & unstructured data without compromising performance.

Risk & Impact Analysis

Intelligent risk analysis requires adapting to ever-changing, exponentially growing, unconnected data.

While legacy systems were created to store & analyze well-defined data, graph databases can easily connect large, complex, and varied data to predict potential threats.

Resource & Capacity Planning

As with risk analysis, resource & capacity planning requires a multi-dimensional approach using billions of data points.

Graph-based systems can easily incorporate rich metadata, such as location, time, weather and connected events, to better forecast real-world scenarios.