General Electric Case Studies IIC Industrial Digital Thread (IDT) Testbed

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	IIC Industrial Digital Thread (IDT) Testbed General Electric

IIC Industrial Digital Thread (IDT) Testbed

General Electric

Technology Category	Analytics & Modeling - Digital Twin / Simulation
Applicable Functions	Discrete Manufacturing
Use Cases	Digital Thread
Challenge	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. Read More
Customer Name	undisclosed Read More
Solution	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. Read More Log in to view content
Data Collected	Downtime, Maintenance Requirements, Maintenance Work Orders, Operation Performance, Production Efficiency Read More Log in to view content
Contents

Technology Category

Analytics & Modeling - Digital Twin / Simulation

Applicable Functions

Discrete Manufacturing

Use Cases

Digital Thread

Challenge

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.

Customer Name

undisclosed

Solution

*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.

Data Collected

Downtime, Maintenance Requirements, Maintenance Work Orders, Operation Performance, Production Efficiency

Impact #1	[Efficiency Improvement - Productivity] Improvement in overall efficiency of the manufacturing setup. Saving of resources.
Impact #2	[Efficiency Improvement - Production Uptime] Reduction in failed parts. Reduction in downtime of the asset in service. Improvement in bottom-line.
Impact #3	[Efficiency Improvement - Quality Assurance] Improvement in quality. Flexibility in manufacturing.

Impact #1

[Efficiency Improvement - Productivity]
Improvement in overall efficiency of the manufacturing setup. Saving of resources.

Impact #2

[Efficiency Improvement - Production Uptime]
Reduction in failed parts. Reduction in downtime of the asset in service. Improvement in bottom-line.

Impact #3

[Efficiency Improvement - Quality Assurance]
Improvement in quality. Flexibility in manufacturing.

Partners	Industry IoT Consortium (IIC) Infosys

Partners

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Overview

IIC Industrial Digital Thread (IDT) Testbed

Operational Impact

Technology