Product teams have to work with data that spans their engineering design tools, manufacturing software, connected equipment, and maintenance records. The lack of a unified process creates islands of information about a product that can be challenging to govern and apply effectively.
How can companies benefit from using product lifecycle management (PLM) to connect artificial intelligence (AI), the IoT, and digital-thread capabilities?
AI and IoT Enable Better Connections and Decisions in PLM
AI can help engineering and manufacturing teams make better decisions about a product by analyzing relevant information, looking for patterns, and automating processes. When used with PLM, AI tools can help manage design reviews, requirements, quality investigations, maintenance procedures, and more.
For instance, engineering teams could use AI to examine historical quality reports and identify common issues and their root causes. Then, they may want to investigate the cause and whether any changes are required to improve product quality or performance.
On the other hand, recommendations resulting from AI processing should always be evaluated by trained personnel. This is especially important for quality investigations, regulatory approvals, and changes that impact product design, operation, safety, and other essential factors. Similarly, reliable results and effective governance depend on accurate information and proper change control.
Internet of Things (IoT) Devices Allow Companies to Add Real-World Data About How Their Products Are Used
IoT sensors and devices create an opportunity to relay additional information about how products function in the real world. This data can augment design reviews, quality investigations, and other PLM processes. It is vital to assess what information is necessary for a given use case and ensure the connected devices are reliable.
For example, machinery manufacturers may want to receive vibration data about how their industrial equipment is operating. If the IoT sensors identify unusual vibrations, maintenance personnel may want to investigate the problem and take corrective actions. At the same time, engineering professionals might use the same information to find design or performance issues that may need to be resolved during the next revision of the equipment.
This is an illustration of a broader concept of “closing the loop” between a product’s design, manufacturing, and maintenance. One of the main challenges that companies face when trying to analyze this information is having the right tools that connect these data sets to product design and PLM records. Similarly, organizations must ensure that the data coming from IoT devices is reliable and secure.
Digital Threads Help Make Connections Between Various Pieces of Information About a Product Easier to Govern and Apply
A digital thread is a unified record that contains necessary information about a product’s design, manufacturing, service, performance, and other parameters in a connected and accessible format. By analyzing this information, product teams can identify patterns, trace problems, and make better design or maintenance decisions.
In practice, this means that designers and engineers can look at a given aspect of the manufactured product (e.g., a particular component or its failure mode) and see all relevant information that may impact it. For instance, if there is a question about a certain aspect of a product design, all connected records, including drawings, manufacturing instructions, test results, and service records, should be available for review. This helps different departments (e.g., design, manufacturing, and maintenance) collaborate and make decisions based on the same information. In practice, this is about making sure that the proper people can see the required information and that the data retrieved from it is accurate.
It is essential to note that a digital thread is not necessarily a single software platform. In practice, companies adopt a collaborative approach that involves integrating disparate systems and tools, assigning unique identifiers to connected records, and ensuring that all participants can see and update relevant information.
Why Product Lifecycle Management Customization Is So Important
It is important for companies to realize that different industries have varying requirements for PLM software, and customized solutions are vital for ensuring that individual organizations can maximize benefits. At the same time, the most useful customizations are those designed around business requirements and are not dependent on a given company’s ability to implement complex and costly software solutions.
Before initiating PLM customization, businesses must also consider these factors:
• Can different software platforms (including engineering design, PLM, and product lifecycle intelligence tools) exchange information?
• Does the company have high-quality records of manufactured products?
• Are there appropriate security measures to ensure that product design and operational data are protected?
• Can the customized solution scale and support the company’s growing needs?
• Will the proposed customizations allow the company to adapt its processes and tools when new regulations, software versions, or other factors are introduced?
• Are there adequate governance practices and change control mechanisms in place?
• Does the proposed solution adequately address business needs?
By understanding how a given organization can benefit from connected PLM tools and processes, it becomes easier to customize these solutions to meet industry specific, individual requirements.
Bring Everything Together to See How Connected Product Information Improves Your Business
When implementing connected product lifecycle management solutions, companies have to remember that the combined application of AI, IoT, and digital threads is much more powerful than each tool individually.
In practice, IoT devices generate additional data about a product’s performance, which, when combined with other information, becomes instrumental in identifying patterns and potential problems. Meanwhile, AI helps analyze this data and find trends that require further review. Finally, the digital thread concept ensures that all this information is connected and can be accessed and utilized by different departments.
For example, manufacturers can ensure that they fully consider equipment performance by combining data from IoT devices and maintenance records, analyzing this information together with other data about a product, and reviewing the findings in context. Notably, such a review is always done by human analysts who investigate the trends identified by AI and make decisions about necessary changes to connected product records.
At the same time, companies must ensure that they only collect relevant data, have the appropriate tools to analyze it, and have the proper governance mechanisms in place. Only by taking such steps, businesses will be able to maximize the advantages of connected PLM tools.
Companies that are considering how to leverage connected technologies in industrial settings can learn more about industrial AI, IoT, and digital threads at Aperture Venture Studio .
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