Using data from multiple sources, a digital twin continuously learns and updates itself to represent the current working condition of the object or process.
Decision-makers gain deep understanding, which they apply to improve and optimize the performance of the modeled asset and the larger systems it interacts with.
The Two Models of
Manufacturing Digital Twins
Operational Digital Twins
This far-reaching innovation expands the concept of digital twins to provide an integrated understanding of production as a whole. An Operational Digital Twin blends and correlates real-time streaming IoT data together with other inputs. It then applies machine learning, AI, and advanced modeling techniques to create a dynamic virtual representation of the entire plant. For the first time, manufacturers gain full visibility into the manifold and multi-layered interdependencies among assets, processes, and operations.
These unprecedented insights unlock the full business value of manufacturing analytics, enabling:
- Scalability to address the full range of production use cases and opportunities
- Actionable intelligence to significantly reduce downtime, dramatically improve plant productivity and efficiency, and avert problems before they happen
Operational Digital Twins are extremely complex and challenging to create and refine. Unlike Asset Twins, they require the blending of thousands of data sources that come in myriad formats, including real-time streaming input. The only way to ingest, correlate, and integrate such diverse datasets at scale is with AI and machine learning — techniques that have only lately attained the right level of maturity for the job.
Asset Digital Twins
These are vendor-specific models of a single asset or machine, which tap into operational data for the purpose of asset optimization.
They can be used for:
- Enhancing performance and reducing operating costs
- Field management of a large number of assets, such as trains or jet engines
- Predictive maintenance
While asset twins provide a window into single components, they offer no visibility into the intricate and all-important relationships among machines, workflows, and parts or batches.
To date, the absence of these foundational insights has prevented manufacturing analytics from delivering more than a fraction of its potential production impact.
This limitation has only recently been overcome, through a groundbreaking advance in digital twin technology.
How Does Sight Machine
Create Operational Digital Twins?
The Sight Machine platform is a pioneering system that is purpose-built to create Operational Digital Twins.
It leverages AI to automate the process of digitally representing any manufacturing machine, line, facility, supplier, part, or batch.
Our patented AI Data Pipeline integrates algorithms, expert-systems learning, and continually advancing techniques for ingesting, transforming, and combining streaming data from thousands of sources and assets.
The outcome is a digital twin that delivers profound actionable insight into all layers of the manufacturing environment, from individual sensors to entire supply chains.
Sight Machine’s Digital Twin Advantage
Enabled by pre-configured manufacturing-specific data models
AI and machine learning quickly create digital twins from unstructured data
Real-time streaming data ingestion, processing, and transformation, fully optimized for manufacturing
Out-of-the-box manufacturing analysis and visualization tools for unlocking the value of your Operational Digital Twins
Sight Machine Enables Operational Intelligence with the Digital Twin
For more information, download this white paper from ARC Advisory Group on enabling operational intelligence with Sight Machine-generated digital twins.