- Analytics & Modeling - Digital Twin / Simulation
- Discrete Manufacturing
- Digital Thread
Field engineers and service teams often lack data and digital insights needed to assess, troubleshoot, and determine work scope for the large industrial assets in performing corrective and preventative maintenance activities. QA engineers many times need to understand why a particular problem in the part is happening recurrently or why parts from suppliers don’t stack up well in the assemblies due to mismatch. The root cause is usually hidden in design, manufacturing processes, supply chain logistics or production planning. But without the right data and digital insights, it's hard to pinpoint. GOAL To collect information in the design, manufacturing, service, supply-chain setup and provide access to and intelligent analytics for industrial manufacturing and performance data, to identify the root cause easier. Such insights can improve not only service and owner/operator productivity, but also provide critical feedback to the design engineering and manufacturing operations teams for continuous improvement.
*This is an IIC testbed currently in progress.* LEAD MEMBERS Infosys, GE MARKET SEGMENT Industrial Manufacturing, Discrete & Process Manufacturing, Automotive, Aerospace, High Tech FEATURES Study of design of the asset, its assembly structure, analysis data, production planning data, manufacturing data, service data, supply chain data. Development of a platform stack for real time data collection from the overall system. Big data and analytics. Phase 1 will focus only on manufacturing and service data. TESTBED INTRODUCTION The Industrial Digital Thread (IDT) testbed drives efficiency, speed, and flexibility through digitization and automation of manufacturing processes and procedures. Beginning at design, the seamless digital integration of design systems into manufacturing, leveraging the model-based enterprise, helps to enable virtual manufacturing before even one physical part is created. Sensor enabled automation, manufacturing processes, procedures, and machine data will enable optimization in operations and supply chain. Once the manufacturing process is complete, the digital ‘birth certificate’ (as built-signature) can then be compared to the as-designed engineering intention. This provides the opportunity for powerful big data analytics to enable service teams and field engineers to have better awareness, insights, and practical actions to improve the servicing and maintenance of critical assets. The Industrial Digital Thread is a complex and comprehensive concept and it will be implemented in multiple phases. Phase 1 focuses on assembling the software stack, establishing the architecture and connectivity, and addressing one use case around premature wear. In subsequent phases, this testbed will be able to support multiple use cases in design, manufacturing, services and supply-chain optimization.
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