In the rapidly evolving landscape of compound development for manufacturing companies, the quest for efficiency, cost-effectiveness, and innovation is relentless. Manufacturers are constantly seeking innovative approaches to optimize their processes and maintain a competitive edge in the industry.
Let's take a look at manufacturer A's experience showcasing how a new graph technology can revolutionize not only data management but also the very essence of product innovation. Manufacturer A produces insulated cables that can be used in various applications like electric power, railways, navy vessels, automobiles, airports, and urban communication. The manufacturer conducted numerous tests and experiments to meet the requirements of their end-users. To cut down on both development costs and time while enhancing the Design of Experiment (DoE) success rate, they aimed to find a system that could effectively oversee and recommend formulation recipes based on the extensive material combination data they had gathered.
The manufacturer faced three major challenges when adopting the insulation prescription system.
The work advancement heavily relied on the researchers' expertise. In the absence of a data storage and management system, the researchers would have to depend on their intuition, which had been developed through numerous tests. If one of these researchers were to retire, it would result in the loss of the valuable knowledge they had accumulated over the years.
Competition from other cable manufacturing companies As a result of the high competition, the manufacturer had to reduce the overall cost during the compound development process. In certain situations where a required material became unavailable or was no longer in production, researchers had to seek alternative, cost-effective materials as substitutes.
Lack of standards in the data generated from various experiments Manufacturers had to undertake numerous manual processes, often resulting in a comparable number of errors due to inefficient data management. Arranging a lengthy compilation of compound data into tables proves to be an inefficient approach when analyzing the correlations between materials and the resulting outcomes.
As a result, the manufacturer found a graph database that was optimized for storing relationship-driven data.
The Insulation Prescription System model comprises two key components: a graph database and machine learning. The manufacturer opted for a graph database to expedite data processing and enhance the automation of the analytical model.
Apache AGE, a graph database management extension on a relational database system, serves as the chosen platform for integrating all the manufacturer's collected and tested data. This transition to a graph database is seamless, thanks to its adaptable schema, which facilitates the addition and storage of relational models. It plays a crucial role in visualizing the analyzed data.
After the data ingestion phase, the subsequent step involves extracting codes from the stored data using the graph database's algorithms. These codes are then processed using analysis tools like Python to visualize ingredient data and characteristic features. The machine learning model aggregates all this data and predicts a Design of Experiment (DoE) with a high probability of producing insulation material with specific attributes.
Manufacturer A showcases the potential of graph databases in improving the R&D efficiency and innovation in manufacturing. The fusion of advanced technology and data-driven insights opens doors to unprecedented efficiencies and competitiveness.
If you are interested in learning more about the R&D innovation derived with a graph database extension technology, we have a perfect opportunity!Head to the link below to register for our webinar, taking place this Thursday, September 14th from 10AM PDT. This is your opportunity to discover how this transformative technology can reshape your industry!