What preparation is required before implementing the Sight Machine platform?

  • Identify a potential list of specific projects.
  • Gain an understanding of the data sources on which Sight Machine will focus. Data sources include any databases, machines, and systems. You will also want to make note of information such as network locations and login credentials.

What level of access to shop floor systems is required?

  • Sight Machine needs read-only access to all relevant digital data sources.
  • Sight Machine can also work with indirect access (e.g., periodic query/file exports).

Will we need to change our existing processes or systems to work with Sight Machine?

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.

What support is needed from Infrastructure and Network teams to set up the Sight Machine platform?

Infrastructure and Network roles include:

  • Manufacturing point of contact (Operational Technology, or OT)
  • IT point of contact

Infrastructure and Network responsibilities include facilitating troubleshooting during the setup.

Do we need to develop any custom APIs or custom software to collect data from shop floor systems in order to work with the Sight Machine platform?

  • No. There are no requirements for custom API access or custom software.
  • Sight Machine can work:
    • With existing data sources’ APIs.
    • From direct database access.
    • From file/log exports.

What are the infrastructure requirements on the shop floor, and any other on-premises or cloud requirements?

  • Sight Machine is a Software as a Service (SaaS), hosted solution.
  • On-premises data collection is provided by a Sight Machine appliance or in a virtual machine (VM) environment.
  • If you select a VM environment, hardware requirements include:
    • 2 CPUs
    • 16 GB of RAM
    • 250 GB of disk space
  • Users interact with the software through a standard Web-browser.
  • Bandwidth to transmit data is required, although Sight Machine’s data acquisition technology is optimized to mitigate typical bandwidth and network reliability challenges associated with production facilities.

What sort of tool is Sight Machine?

  • Sight Machine provides a platform that focuses on data capture, contextualization, and operationalization of data and analytics for manufacturing.
  • Extract, Transform, and Load (ETL) is a key component of Sight Machine and the Sight Machine version has been specifically built for manufacturing.
    • The patented ETL engine (the AI Data Pipeline) allows Sight Machine to transform, clean up, and place data into pre-built manufacturing data models that are ready for analysis. These data models represent real-world parts, materials, batches, machines, operations, and their interactions.
    • The ability to configure the AI Data Pipeline quickly and incorporate data from machine sensors (PLC), enterprise resource planning (ERP), Manufacturing Execution System (MES), Maintenance, Quality, and other systems is a key differentiator from other analytic tools that only do trend analysis and exploration with time-series machine data.
  • Sight Machine has an edge connectivity layer called FactoryTX that captures information from disparate data sources at different locations and centralizes it within the Sight Machine (or your private) cloud.
    • The Sight Machine product includes endpoint management, data security, and data compression.
    • It is configurable through a Web browser and is extensible if new or custom plugins (e.g., proprietary data formats) need to be developed. This can be done by you, Sight Machine, or a third-party.
    • Other products typically provide an API endpoint with which you must integrate, or are local point solutions that do not allow for comparison across multiple facilities and the inherent scalability of the cloud.

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