The future of manufacturing is iterative, collaborative and data-driven
Another key digital transformation practice is integrating artificial intelligence (AI) and machine learning (ML) to automate, or at least streamline and simplify, product development. At Raytheon, teams leverage model-based engineering to predict complex fluid, structural, and thermal interactions in missile systems so engineers can better understand how a product will operate at hypersonic speeds. But any industry can use AI and ML to help teams make better and smarter design choices and, ultimately, better products and experiences for customers and end users.
Natural language processing technology, a branch of AI that trains machines to understand human speech and writing, for example, can improve everything from customer support operations to e-commerce product descriptions. AI and ML can also be used to streamline and automate warehouse operations and data entry and processing. During the early days of the pandemic, some banks relied on AI-powered robotic process automation to respond to the massive influx of Paycheck Protection Program applications and rapidly file submissions to the U.S. government.
A central goal of many digital transformations is connecting the data stored throughout the organization, making it easier to discover, access, and leverage. This often is achieved via a federated data model, an approach to data management that creates a centralized view of the organization’s data, though it resides in disparate locations. By aligning data that was formerly siloed, while storing it at its source, data federation provides simple access to up-to-date data, allowing diverse teams to collaborate throughout the development, manufacturing, and testing processes.
At companies like Raytheon, where security and safety are a top concern, the data must be organized and firewalled to ensure that only those with proper access can view classified information. But once implemented, a modern data architecture has also helped Raytheon’s teams navigate a global supply chain that continues to face challenges caused by the pandemic and ongoing political strife.
“We had data stored in multiple systems. There was data in our procurement system and data in our risk management system. There was data stored in each program’s master schedule,” says Gundrey. “Now, our supply chain team is able to pull all of this data together and use AI and ML to better predict material lead times and help us better plan our program activities.”
These transformative technologies and all-important data can all be linked via “digital thread,” a communications architecture that runs through the manufacturing process. By capturing and streaming data throughout the product lifecycle, the digital thread integrates disparate digital technologies in a holistic view. People and process, of course, remain central to this transition. As Gundrey says, “I want folks to know that as we’re building out this digital thread, it’s all about the people and the work processes that come along with it.”
The benefits of digital transformation
For today’s companies, the benefits of digital transformation are extensive. In addition to connecting and speeding up the product development cycle and giving teams richer and more relevant data, it can also help reduce risk.
At an individual business level, this could mean reducing the risk of disappointing an end user because the agile methodology helped teams identify potential issues early in the design process. Using these iterative and collaborative approaches gives teams the ability to tackle complex design issues early on, preventing the risk of costly reworking later, and gives managers more foresight about how separate components will work together.