Like any branch of engineering, manufacturing is subject to optimization. With increasing digitization, modeling of plants is possible before their construction. One of the optimizations is already present when choosing the structure of the model itself. By choosing a graph as such a structure, we encompass an enormous number of different plants whose structure significantly resembles a graph.
Every factory strives to optimize its plant. Through graphs, it is possible to create a state network that simulates a particular plant. By analyzing such plants and eliminating potential risks before construction itself, it is possible to save millions of dollars.
For example, measures of centrality can detect which units are most likely to fail. Strengthening such units prevents the possibility of plant unavailability.
Modeling knowledge with graphs within companies with large factory plants can lead to a variety of indirect benefits. With the structure of the graph, it is possible to show how the domain areas relate to each other. Also, it can enable workers to more easily understand the working structures of their colleagues. It can also give field researchers a concise description of the area, after which they could make a more focused decision to continue their research.
Monitoring experiments and processes can give a headache to one who analyzes such data. Therefore, by organizing such experiments into graphs, it is possible to make a detailed analysis that would not be possible in relational systems due to the high structure of the data and the numerous table joins.
Where to next?
This text is a summary of one area that fits perfectly with the application of graphs. Therefore, we would like to have you with us when implementing some of these solutions. Share opinions, experiences and problems you encounter when working with Memgraph on our Discord server. We are here for you and we will help you along the way.