Business demands have forced manufacturers to be more agile. Smart manufacturers adopt technology, including digital twins , to move faster and facilitate automation. Using a digital twin in a plant can fast-track the discovery of production bottlenecks, drive efficiencies, lower costs, reduce environmental footprint and help manage risk. However, mystery still surrounds the use of digital twins for manufacturing.
In this blog post, we clear things up while exploring the key strategies driving success in manufacturing environments that represent the vanguard of smart manufacturing/industry 4.0.
Digital twins’ keys to success
Smart manufacturers provide a wealth of guidance on using digital twins. Here, we document the keys to success experienced by early adopters of digital twins for manufacturing. 1. Pursue for business, not just innovation A digital twin excels when serving business needs. However, being viewed as a shiny object for a digital transformation program can result in wasted spending.
Defined use cases and key performance can guide manufacturers to tangible value.
2. Address multiple use cases
Manufacturers typically have multiple use cases compounded by feeding the many use cases into one canonical data model. This is very complex and time consuming. A better approach is to thin slice the use cases, each use case will demand a specific lens, a canonical data model, often required to comply with specific standards. As more canonical data models are built, manufacturers improve the digital twin’s visual representation. Thus, organizations can use the right lenses for decision making, whether the focus is on safety management, productivity improvements, asset management, energy reduction, process optimization, carbon and emissions management or most likely a combination of all to help prioritize the decisions.
3. Cleanse and catalog data
Digital twin data comes in different types and from multiple sources. It’s time-series, transactional, structured, or unstructured data. It comes from a historian, a control system, smart sensors, enterprise systems or external sources. Often using different terminologies and context to refer to the same assets. All that disparate data needs to be cleaned and organized.
4. Turn data into insights
Analyzing asset data unified in a data lake environment can produce insights into shop floor operations. You can put insights to work, improving product quality, addressing production processes, measuring environmental impact, understanding yield losses, identifying risks on asset reliability, or any number of other initiatives.
5. Unify data in the cloud for user actions
Unifying data in the cloud provides scalability and granularity to support new user interactions from 3D engineering tools to geospatial environments, and advanced analytics that improve operations. By leveraging data for insights, you can also infuse your manufacturing plant’s preventive maintenance program with predictive capabilities as an example.
6. Beware of the “one size fits all” digital twin
No single platform can serve the digital twin needs of a manufacturer. The era we’re in now demands flexibility, which means assembling the best components per use case. The trick is to ensure that these components promote integration with Open APIs. This enables organizations to accelerate implementation of digital twin strategies by leveraging marketplace ecosystems such as the one provided by Microsoft Azure Marketplace.
7. Keep current with aging assets and maintenance records
Assets degrade over time and require ongoing maintenance. Whether a repair, revamp, or replacement, your digital twin must keep up with asset lifecycle changes. It’s why asset performance management is a common use case. For process manufacturers seeking agility, digital twins that apply the digital world to reality on the shop floor can help, all while solving business problems. As a virtual representation of the physical world, digital twins must respond to real-life challenges of the physical assets inside a manufacturing plant.
Many organizations use 3D models for digital twins, but they’re not for everyone. For example, a manager may require a digital twin expressed as a dashboard, while an operator will need an interface fit for the factory floor. Digital twins can transform operations, accelerate a holistic understanding of an entire entity or process, drive optimal decision-making due to test-run scenarios, and result in proactive action. Once it works digitally, the decision will more likely generate the same result in reality. Having asset data unified in a cloud environment opens the door to asset performance management and fuels advanced industrial analytics currently pursued by smart manufacturers.