Introduction
Data in manufacturing environments is increasingly complex, encompassing everything from equipment diagrams, safety and maintenance logs to sensor readings and production schedules. Managing and extracting insights from this data is challenging, especially when it exists in multiple formats and lacks clear structure. Traditional databases struggle to handle these flexible, interconnected relationships. Graph technology, however, provides a way to represent and work with these complex connections, making it possible to uncover valuable insights from unstructured data.
In this article, I’ll explore what graph technology is, how it applies to manufacturing, and how it can lay the foundation for artificial intelligence (AI) and predictive analytics. For leaders and specialists in control, automation, and software development, understanding graphs can be important, opening up new ways to manage data, optimise processes, and make informed decisions.
What is a Graph?
In the context of data, a graph is a way to model relationships between various pieces of information. A graph consists of “nodes” (points representing entities like equipment, people, or data) and “edges” (lines representing relationships or connections between nodes). Think of a social network where each person is a node and each connection (e.g., coworker, family member) is an edge. This framework is particularly useful in contexts where relationships are just as important as the individual data points.
In manufacturing, graphs allow us to represent complex relationships in data that are difficult to handle in a standard table format. Instead of forcing every piece of data into rows and columns, graphs make it easy to track and query relationships that cross many different data types and sources.
Why Graphs Are Ideal for Unstructured Data
Manufacturing data often includes unstructured elements like equipment specifications, procedural documentation, and sensor data. Traditional relational databases like SQL Server, which organise data in rows and columns, struggle to capture the nuanced relationships between these types of data. For example, how does a pump’s maintenance history and instruction manual relate to its real-time sensor readings? Or how are certain operating procedures connected to a piece of equipment’s performance over time? Graphs provide a flexible way to represent these connections and query them in ways that reveal hidden insights.
Graphs answer complex questions, such as:
- What components are interconnected within a system?
- What relationships exist between equipment faults and maintenance history?
- How do various environmental factors affect equipment performance over time?
By capturing both data points and their relationships, graphs help to analyse complex data landscapes and provide new perspectives on system behaviour.
Practical Applications of Graphs in Manufacturing
In manufacturing, graphs can streamline data management and improve decision-making by providing a clear, interconnected view of operational data. Below are some examples of how graphs are used in manufacturing settings:
Linking Piping and Instrumentation Diagrams (P&IDs) to Equipment: Each component in a P&ID (such as pumps, valves, or sensors) can be a node in a graph. These nodes connect to related technical documents, equipment specifications, and model data. This allows engineers to move seamlessly from a high-level system view to detailed equipment information, all within a graph-based framework.
Connecting Equipment to Procedures and Maintenance Logs: Maintenance and operating procedures can be linked to each equipment node. For instance, a node representing a cooling valve might connect to associated maintenance schedules, troubleshooting guides, and compliance requirements. When technicians access the graph, they see an organised view of all procedures associated with the component, ensuring they have the most relevant information for accurate and efficient repairs.
Integrating Real-Time and Historical Data: Graphs can link equipment to real-time sensor data and historical performance records. This approach helps engineers identify performance patterns, which can be instrumental in developing predictive maintenance programs. By analysing relationships over time, engineers can detect early warning signs of equipment failure and take preventative action.
Tracing Impact Across Systems: When a component undergoes maintenance or an operational adjustment, graph connections reveal potential impacts on interconnected systems. For example, if a pump is scheduled for servicing, the graph can show other processes or equipment affected by this downtime, helping managers schedule maintenance without disrupting production.
How Graphs Enable AI in Manufacturing
For manufacturers looking to leverage AI, graphs provide the foundational data structure that AI needs to generate valuable insights. AI algorithms rely on context-rich data to make accurate predictions, optimise processes, and augment decision-making. Graphs model these relationships in a way that mirrors real-world complexities, such as connections between equipment performance and maintenance logs. Without these contextual relationships, AI lacks the necessary depth to make informed predictions.
With a graph database in place, AI models can examine patterns across interconnected data points—such as the relationship between ambient temperature, machine load, and equipment wear. This allows AI to predict potential failures, optimise operational schedules, and suggest adjustments based on historical and real-time data.
To implement AI in manufacturing, the first step is to build a data graph that accurately captures all relevant relationships. This involves identifying key entities (like equipment, procedures, and sensor data) and mapping out how they relate to each other. Initially, this graph may only cover core components and maintenance records, but it can be strengthened over time by adding more data, such as detailed environmental readings or operator logs. As this graph grows and becomes more refined, AI models gain a richer, more reliable foundation to work with, which increases the accuracy and effectiveness of predictions and optimisations. While this sounds simple enough, in practice this can be a difficult process and time consuming. Commercial platforms designed specifically for manufacturing environments can make this process more streamlined and effective.
Getting Started with Graph Technology
For technical leaders interested in graph technology, here’s a straightforward roadmap to begin exploring:
Understand the Basics of Graph Theory: Familiarise yourself with basic graph theory concepts, such as nodes, edges, and paths. There are several learning resources available to you, from simple YouTube video explainers to full courses. Most people will need a high level conceptual understanding and this can be learned in a few hours by taking an introductory online course.
Experiment with Graph Databases: Take a look at Azure CosmosDB. At the time of writing, this is an excellent entry point and is free for small databases and light workloads, ideal for learning. Microsoft provide plenty of online resources to help you learn. If you are a C# programmer looking to get your hands dirty (the best way to learn), check out Tim Coreys YouTube video on the subject. Neo4j, is another of the more popular graph databases, offers a sandbox environment with sample datasets and free getting started tutorials. This makes it easy to get started and explore graph technology and experiment with Cypher, Neo4j’s query language; but note that in order to explore the full capabilities you will need to purchase a subscription for your business.
Explore Other Graph Technologies: Beyond CosmosDB and Neo4j fundamentals, you can explore alternatives like Amazon Neptune (a managed graph database on AWS) and Apache TinkerPop (an open-source framework for working with graphs). These provide alternatives that in some cases may integrate better with your existing infrastructures.
- Explore resources from Commercial Vendors: As you learn about graphs, and particularly about the manufacturing use-cases, you can start to investigate commercial platforms that are designed for manufacturing environments. One such vendor is Cognite which appears to have an interesting offering for process manufacturing and heavy industrial environments. If you are seriously researching use-cases for your businesses, you will find the vendors very willing to help you get started.
Conclusion
Graphs are transforming how manufacturing companies manage and analyse complex, interconnected data. From linking equipment to its maintenance and performance history to providing the backbone for AI-driven insights, graphs offer a powerful and flexible approach to handling unstructured data. By creating a foundational graph of relationships within your data, you pave the way for predictive maintenance, optimised processes, and a more responsive manufacturing operation.
By understanding and implementing graph technology, technical leaders can harness a powerful tool that turns complex data into actionable insights, improving operational efficiency and setting the stage for AI-driven innovation in manufacturing.
