We live in a time when getting the right information can make huge differences. In the modern digital world, data is the most important asset and, therefore, it can help manufacturing companies to make the right decision. How to manage data in the most efficient way and how to provide access to the data in a way that can help engineers and everyone in a manufacturing company is a very important question.
At OpenBOM we believe product knowledge graphs, networks, and graph databases can make that difference for our customers. The data is powerful, so bringing data in the middle of the decision can make a huge difference.
Check my five-year-old presentation at the PI PLMx event in Hamburg, which speaks about the PLM innovation path from data control to intelligence. As we move forward, past the pandemic, and explore a variety of options on how manufacturing companies can innovate in the modern digital world, it is becoming obvious that “networks” and “data intelligence” are among the two most powerful paradigms that are taking the manufacturing industry by storm.
In the past manufacturing companies were focused on applications that can help them to operate and run business processes. While it is still true that companies need to have enterprise software to operate, it is quickly becoming obvious that data is a new platform and in the extremely connected manufacturing world, focus on data, data availability, data quality, and the ability to use data for making important decisions – those are things that will define the future of data in PLM.
To bring a new perspective on the way OpenBOM can manage product information, connect pieces of data, and provide intelligent insight, we developed a data management technology that provides a superior level of data management flexibility and also brings graph technologies to OpenBOM.
In this article, we will delve into the reasons behind OpenBOM’s adoption of a graph database, the importance of knowledge graphs and graph data science, the problems that can be solved using graph data science, and the upcoming release of OpenBOM Graph Navigation. Ultimately, we will highlight the significance of graph navigation for enhancing user experience and unlocking valuable data insights.
Why OpenBOM is Using Graph Database?
OpenBOM’s decision to use a graph database is driven by its ability to efficiently model and manage complex relationships between data entities. Traditional relational databases fall short when it comes to capturing intricate connections, resulting in performance bottlenecks and limited flexibility. A graph database, on the other hand, excels at representing and querying relationships, making it ideal for managing product structure with all dependencies and structures. By leveraging a graph database, OpenBOM ensures that users can navigate and analyze their BOMs seamlessly, improving overall performance and productivity. Read more about it in our previous article – Why OpenBOM is Using Graph Database?
Importance of Knowledge Graphs and Graph Data Science
Knowledge graphs serve as a powerful tool for representing and connecting vast amounts of structured and unstructured data. OpenBOM utilizes knowledge graphs to capture the relationships between different BOM elements, facilitating intelligent navigation and data exploration. With the help of graph data science techniques, OpenBOM can unlock valuable insights hidden within the complex web of product data. By analyzing patterns, identifying clusters, and predicting trends, graph data science enables users to make informed decisions and optimize their product development and manufacturing processes.
What Problems Can Graph Data Science Solve?
Graph data science offers a range of capabilities that address significant challenges faced by businesses in various domains. For OpenBOM users, graph data science can solve problems such as identifying component reuse opportunities, optimizing supply chain management, detecting potential bottlenecks, and improving overall company efficiency. By harnessing the power of graph algorithms and analytics, OpenBOM empowers users to uncover critical insights that were previously hidden or difficult to obtain, enabling them to streamline operations, reduce costs, and enhance collaboration.
Read more about how OpenBOM incorporates graph data models as part of OpenBOM technology. In the picture below you can see a conceptual representation of the product graph and dependent objects.
Let’s focus on 3 examples of how graph data science algorithms can help manufacturing companies.
Centrality Algorithm (Importance)
Centrality uncovers the roles of individual nodes in the graph and their impact. Centrality algorithms identify influential nodes based on their position in the network. These algorithms infer group dynamics, such as credibility, rippling vulnerability, and bridges between groups.
In the context of product data, you can think about making an analysis of relationships between nodes representing suppliers (contactors) and parts to understand what are important suppliers or, the opposite, to understand what components have a single supplier.
Community detection allows you to identify nodes with a significant number of interactions (or connections) These algorithms find communities where members have more significant interactions. These connections reveal tight clusters, isolated groups, and structures. This information helps predict similar behavior or preference (eg. preferred supplier), or to identify connections, for example, to find dependent nodes in change impact analysis when looking for objects with dependent changes.
These algorithms employ set comparisons to look at how alike individual nodes are. The properties and attributes of nodes are used to score the likeness between nodes. This approach is used in applications, such as personalized recommendations and developing categorical hierarchies. For product structure and BOM management, similarity can help to find similar parts with the same characteristics to support part (or supplier) re-use.
OpenBOM Graph Navigation Is Coming
In the coming release, OpenBOM will introduce Graph Navigation (GN), which will be a special mark in the OpenBOM platform evolution. This new feature will enable users to visually explore and navigate their BOMs through an intuitive graph-based interface. Users will be able to traverse the interconnected relationships between parts, assemblies, suppliers, and more, gaining a comprehensive understanding of their BOM structures.
With Graph Navigation, users can easily identify dependencies, detect potential issues, and make informed decisions faster than ever before. Graph Navigator also will provide an easy way to navigate between informational pieces managed by OpenBOM (BOMs, Items, Purchase Orders, etc.)
OpenBOM’s Graph Navigation improves user experience and data insight within the product data management landscape. By adopting a graph database and harnessing the power of knowledge graphs and graph data science, OpenBOM empowers users to navigate, analyze, and derive actionable insights from their data effortlessly. With improved visibility into complex relationships, users can optimize their processes, reduce errors, and accelerate time-to-market.
The utilization of graph databases, knowledge graphs, and graph data science, enables users to unlock the full potential of their data, enhancing productivity, collaboration, and decision-making. With Graph Navigation, OpenBOM is changing the way users interact with their product data, providing an intuitive and powerful tool for navigating complex relationships. Get ready for that.
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