With the fourth industrial revolution, the value of 'data' becomes even more. Smart Factory technology is expanding for industrial automation, which is gaining traction as the core industry of the fourth industrial revolution and a new growth engine to enhance the competitiveness of the manufacturing industry.With the development of the Smart Factory, producers have collected and evaluated various data utilizing network or sensor devices rather than workers assigned to each line, resulting in an automated production system.

Manufacturers can increase the competitiveness of the manufacturing business by optimizing production processes to meet changing environmental conditions.As the smart factory trend grows, manufacturers are inventing new ways to manage the massive amounts of data such gadgets generate. Companies' needs are also developing to manage 'Data Lineage', which refers to how processing data is formed, the processes they go through, and the outcomes of the influence.

What solution can effectively address these needs and provide corporate value? Graph Database is the solution!

It can collect all types of data created in real time from the various IoT sensors on production sites, achieve 'high processing efficiency' through data analysis required for decision-making, and derive actual commercial value.In other words, in order to correctly develop and operate a smart factory, it is necessary to understand what types of values may be generated through data classification and analysis rather than simply collecting large amounts of data.

Customers of AgensGraph benefit from extremely creative services since AgensGraph is revolutionizing the basic technology behind data collection, storage, and processing.

Benefits

**_‘Big data analytics’ is the core technology for the smart factory_**.
Adopting the AgensGraph solution allows for the analysis of massive amounts of data at a rapid manufacturing rate, as well as the viewing and use of vast amounts of data. Processing issues in production sites can be discovered and addressed ahead of time using data-driven decision-making and 'relationship' analysis.

Furthermore, this approach enables the collection of'structured' and 'unstructured' data generated outside the process, as well as higher-dimensional analysis that goes beyond process data-centered analysis.

As a result, AgensGraph effectively combines and organizes data from different sources, providing customers with the business value of cost savings and increased productivity, as well as assisting them in finding the best solutions.