No. Sight Machine takes a data-first approach to solving customer needs. This means that the platform does not typically require any process changes for customers to derive value. The platform pulls information from your existing Manufacturing Execution System (MES), historian, enterprise resource planning (ERP), and other production systems.
Infrastructure and Network roles include:
Infrastructure and Network responsibilities include facilitating troubleshooting during the setup.
The more relevant data that you can provide, the better. That being said, the type of data typically acquired consists of:
Typically, the data can be streamed from the plant and processed in near real-time. However, this is dependent on a variety of factors, such as edge device processing power and plant networking bandwidth capacity.
Yes. Data is fed into the AI Data Pipeline and then onto Sight Machine’s patented manufacturing-specific data models (i.e., the plant Digital Twin).
The data is stored in the cloud and includes:
Yes. Sight Machine’s analytics service is extensible and allows for the deployment of customized algorithms back into the platform. A typical customer-produced analytic is first developed using the Sight Machine Python SDK. Then, customers work with Sight Machine’s Data Science Team to deploy the analytic on the platform.