Manufacturing Digital Twin

From aggregating complex data through various sources to limited root cause analysis, there are various points within the value chain where the manufacturing digital twin can help enterprises 10x their current capabilities.

Root cause analysis

Aggregating data from Manufacturing Execution System (MES), is complex and results in high downtime in root cause analysis.

Lack of part quality

There is a lack of part quality as technicians are unable to follow printed instructions due to complex nature of the assembly line process.

Inconsistency in output data

Critical data is not accessible for decision-making which results in inconsistency in output quality.

For discrete manufacturing, and some process manufacturing sectors, product rejection rates and overall equipment efficiency (OEE) remains stubbornly in the negative. Less than 1% data is used in decision making in enterprises. From aggregating complex data through various sources to limited root cause analysis, there are various points within the value chain where the manufacturing digital twin inside the metaverse can help enterprises 10x their current capabilities.
  • Low rejection rates
  • Part quality in precision engineering
Aggregate data and information from multiple systems, and take corrective actions with digital work instructions.
Identify and track changes in real-time across all devices.
Full view of all variables, time frames, data behavior and relationships as a 3D digital twin.

Higher level of clarity and readability than that of traditional electronic display systems.

Live connect between inspection and the production line engineers providing visibility to the operator on the quality status.

Digital twinning to eliminate non-revenue generating quality checks and through data aggregation and root cause analysis.

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