Bringing IoT to Production Digital Shadows Task-specific Digital - - PowerPoint PPT Presentation

bringing iot to production digital shadows
SMART_READER_LITE
LIVE PREVIEW

Bringing IoT to Production Digital Shadows Task-specific Digital - - PowerPoint PPT Presentation

Bringing IoT to Production Digital Shadows Task-specific Digital Shadow for wide- Task-specific Digital Shadow for mid- Task-specific Digital Shadow for in- range analyses (exemplary application: range analyses (exemplary application: depth


slide-1
SLIDE 1 1 Physical World Development Virtual World Production Usage 01 1 01 1 01 01 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

Bringing IoT to Production
 Digital Shadows

Data aggregation and refinement Task-specific Digital Shadow for wide- range analyses (exemplary application: Optimization of value stream) Task-specific Digital Shadow for in- depth analyses (exemplary application: Optimization of cutting tool) Task-specific Digital Shadow for mid- range analyses (exemplary application: Optimization of machine processing) 1 1 1 1 1 1 1 1 1 1 1 1 1 1 01 1 1 1 1 01 01 01 1 1 11 1 1 1 1 1 1 1 1 1 1 1 1 11 1 1 1 1 01 1 1 1 1 01 01 01 0 1 1 1 0 1 0 1 0 1 1 1 11 1 1 1 1 1 1 1 1 1 1 1 1 Digital Shadows R a w D a t a Analytics Reduced Models
slide-2
SLIDE 2 2

Central Infrastructural Approach
 Infrastructure of the Internet of Production

Development Cycle Production Cycle User Cycle Model 
 Reduction Cluster Algorithms Multi-Modal Information Access Data Integration Data Model Data Storage and Caching Management of data access to proprietary systems Aggregation and Synchronization Learning Algorithms Meta Heuristics Context-sensitive Processing Customer Data CRM Machine Data MES Product Data PLM Test Data CAQ Process Data ERP/ SCM CAD Data CAD Simulation Data FEM Feedback Data BDE Data Provision and Access Event-driven Decisions A Autonomous Actions Adaptive Processes Smart Expert Middleware+ Smart Data 
 Analytics Digital Shadow Application Software Raw Data Decision Support Agent
slide-3
SLIDE 3 3 3. Systematic Learning

Challenges for the IoP
 Highly Iterative Innovation

How can cross-domain and lifecycle-wide collaboration for highly iterative innovation be enabled?

2. Multi Lateral Access and Usability 1. Digital Perception 4. Realtime Acting

IoP Pote

5. Highly Iterative Innovation Product Maturity t 36 month Concept Functional Prototype Technical Prototype highly iterative Change: Steel to Alu Change: L7E to M1 Change: High-voltage Development
 Cycle Production
 Cycle User
 Cycle