Can Graph Databases Be the Future for R&D Productivity?

In the fast-paced world of manufacturing, efficiency, cost-effectiveness, and innovation are paramount. Manufacturers are constantly seeking new ways to optimize their processes and stay ahead of the competition.

Case Study: Manufacturer A's Journey with Graph Technology

Let's dive into the story of Manufacturer A, a company that produces insulated cables for various applications, including electric power, railways, navy vessels, automobiles, airports, and urban communication. They needed a way to cut down development costs and time while improving their Design of Experiment (DoE) success rate.

The solution?

A system that could effectively manage and recommend formulation recipes based on their extensive data

Challenges Faced

Manufacturer A faced three major hurdles:

Reliance on Expertise: Without a proper data management system, researchers relied heavily on their intuition and experience. This meant valuable knowledge could be lost if a key researcher retired

Competitive Pressure: The high competition in the cable manufacturing industry required Manufacturer A to reduce development costs. When certain materials were unavailable or discontinued, finding cost-effective substitutes was essential

Data Management Inefficiencies: The lack of standardized data from various experiments led to numerous manual processes and errors

Analyzing correlations between materials and outcomes using traditional tables was inefficient

The Graph Database Solution

To tackle these challenges, Manufacturer A turned to a graph database optimized for relationship-driven data. They implemented an Insulation Prescription System comprising two main components: a graph database and machine learning.

Apache AGE served as the platform for integrating all their collected and tested data. This graph database management extension on a relational database system provided a flexible schema for adding and storing relational models, playing a crucial role in visualizing the analyzed data.

Implementation Steps

Data Ingestion: All collected data was seamlessly transitioned into the graph database.

Data Processing: Codes were extracted from the stored data using the graph database's algorithms. Tools like Python were used to visualize ingredient data and characteristic features.

Machine Learning: The aggregated data was used to predict a DoE with a high probability of producing insulation material with specific attributes.

Manufacturer A's experience showcases the potential of graph databases to enhance R&D efficiency and innovation in manufacturing. By leveraging advanced technology and data-driven insights, they achieved unprecedented efficiencies and competitiveness.

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