Smart Manufacturing: Advanced Sensing, Control and Platforms … · 2014. 12. 1. · Distribution...
Transcript of Smart Manufacturing: Advanced Sensing, Control and Platforms … · 2014. 12. 1. · Distribution...
https://smartmanufacturingcoalition.org http://smartmanufacturing.com
Smart Manufacturing: Advanced Sensing, Control and Platforms for
Manufacturing California-France Forum on Energy Efficiency Technologies 2014
Smart Manufacturing Leadership Coalition (SMLC) Jim Davis, Vice Provost – IT & CTO, UCLA
Smart Manufacturing
• Smart Manufacturing, Data & Analytics – Walk through illustrative examples and then generalize
• What is Smart? – Generalize problem patterns and how we can think about them
• Technical and Business Drivers for Platform Infrastructure – Map into architectural considerations of Advanced Sensing Control
and Platform Technologies
• The Case for an Open Architecture Smart Manufacturing Platform
• Public Private Partnerships - Advanced Manufacturing Partnership 2.0 Recommendations
Machining Operation
General Dynamics Scranton, PA
http://afc-holcroft.com/products/continuous/roller-hearth
Holcroft Continous Roller Hearth
http://afc-holcroft.com/userfiles/file/pdf/ConveyorFurnace_Brochure.pdf
General Dynamics Scranton
Hot Forging Heat Treatment
General Dynamics Scranton, PA
General Mills Networked-Based Manufacturing
Supply Chain Distribution Center
Customer
Business Systems, ERP
Smart Grid
Smart Factory
Recipe Management Mapping formula into operating recipes
EDI transaction & quality certifications
FDA Tracking & traceability Green Light
Analyze - to put into production Make – right ingredients – confirmation on recipe Release – meet requirements to release
Mapping SAP information Into operation
Graphics courtesy of Rockwell Automation
Check status
SMR High Fidelity Model Validation
Web portal Specify parameters; submit
workflow
Display results; validate New parameters; validate Update operations
Evaluate new operating modes
Collect data; validate model New temperature conditions Estimate time
Octave
Reduced Order Model
Sensors
Smart Manufacturing’s Commitment to a Comprehensive Approach
Manufacturing Methods from Macro View
An Industry-Driven Open Architecture
Shared Infrastructure
SMLC Partnerships “Information that drives the next century’s structural shift in manufacturing.”
Smart Manufacturing
Smart Manufacturing Leadership Coalition (SMLC) – 501c (6) Making real-time info available: • when it is needed, • where it is needed • and in the form it is needed
throughout the Manufacturing ecosystem
Test Beds - General Dynamics, General Electric, General Mills, General Motors, Praxair, Corning, Pfizer, NETL, Alcoa, Owens Corning, Advanced Manufacturing Partnership SoCal, Southwest Research Institute Design/Manufacturing Platform Providers – JPL/NASA, UCLA, Rockwell, Honeywell, Emerson, Schneider Electric, Nimbis, OSISoft, Savigent Modeling & Simulation Materials, Design, Manufacturing – Caltech/JPL, UCLA, UT Austin, Tulane, NCSU, CMU, Purdue Smart Manufacturing/Smart Grid – EPRI Global Metrics/Outreach – AIChE, ASQ, AMT, ACEEE, NCMS, MESA, MT Connect, Society of Manufacturing Engineers (SME), Sustainable Solutions, Spitzer & Boyes Agency partners – DOE, NIST, NSF Regional Partners –Center for Advanced Technology Systems/RPI, Wisconsin Manufacturing Institute, AMP SoCal, Association of State Energy Research & Technology Transfer Institutions (ASERTTI), National Association of State Energy Officials (NASEO)
What is SMART
Test Bed Smart Systems Smart Machine Line Operations
In-Production High Fidelity Modeling Dynamic Decisions
Enterprise and Supply Chain
Decisions
Design, Planning and Model Development
Integrated process machine and product management
Enhanced management complex behaviors
Performance management global integrated decisions
Variability reduction
Design models in production
Benchmarking machine-product interactions
Rapid qualification components products materials
Untapped enterprise degrees of freedom in efficiency, performance, and time
In situ measurement and integrated value chains
Product/material in-production ability
Machine-power manage management
Integrated computational materials engineering (ICME)
Enterprise analytics and business operational tradeoff decisions
Tracking, traceability and genealogy
New product, material technology insertion
Adaptable machine configurations
Configurable data and analyses for rapid analytics and model development
External partner integration and interoperabilty
Achievable Meaningful Use Goals and Magnitude of Impact
• Demand-driven efficient use of resources and supplies in more highly optimized plants and supply
– 25% reduction in safety incidents – 25% improvement in energy efficiency – 10% improvement in overall operating efficiency – 40% reduction in cycle times – 40% reduction in water usage
• Product safety – Product tracking and traceability throughout the supply
• Sustainable production processes for current and future critical industries
– 10x improvement in time to market in target industries – 25% reduction in consumer packaging – Process Intensification
• Maintain and grow existing U.S. industrial base – Environment for broad innovation – 25% revenue in adjacent industries – 25% revenue in new products and services – 2x current SME’s addressing total market – More highly skilled sustainable jobs created
• ROI constrained or prohibitive - Requires broader infrastructure investment to scale - Incremental investment difficult - Requires IT investment with 70% of cost non-value - Depends on other companies - supply chain - Need 80% reduction in cost of implementing
modeling and simulation - 10x reduction in the cost of sensors and sensor
infrastructure • ROI opportunity comprehensive
– Multiple systems – Integrated global performance metrics – Aggregating data
• Installed base of serviceable manufacturing facilities – $60 B in IT investment – Retrofit
• Risk – Major change & New business model – Uncertain about technology, security & IP
• Organization – IT capability lacking or IT not talking to operations – Workforce skills – Collaboration
A Set of Issues Beyond Individual Company
Technical and Business Drivers for Shared Infrastructure
Practitioner Adoptions • Frost & Sullivan 2013 Survey of 347 Manufacturing CEOs
– 56% accelerate evaluation of transformative manufacturing technologies within 24 mos.
– Only 20% accept risks with early implementation • ASQ 2014 Survey 700 Respondents
– 80% not knowledgeable of Smart Manufacturing • 37% don’t see a need; 29% cost barrier; 14% management resistance
– 20% knowledgeable • 73% Great interest to do more • 13% actually doing something
– 82% increased efficiency – 49% fewer product defects – 45% increased customer satisfaction – 33% applied at plant level; 29% across all levels
Practitioner Market Challenges McKinsey Data
• US SME 19% of manufacturing sales & 13% of exports - 60% low to medium technology
• US SME’s have languished in adopting technology - value add per employee relative to large companies – 80% of large companies in 1960s – 60% of large companies in 1990s
Dept/SMM Small & Medium
Enterprises Manufacturing
Consortia
Modeling & Interoperability
Protocols IT Providers
Key Development
Resources Universities, SMM’s
Integrators, Labs
SMLC Industry-Driven Integrated
Performance Metrics
Micro, Meso, Macro
SMLC Industry-Academic-Government Platform Collaboration Model
Marketplace
Real-time Data & Modeling Workflow & Metric Toolkit/ App Development
SM Platform Open-architecture
Cloud-based
Test Bed Manufacturer
& Supplier Crosslinking
Engagements UCLA
Apps & Toolkits
CommunityResources
& Data Services
Machine Product Management
In Production High Fidelity Modeling Design and Planning Dynamic Decisions
Enterprise & Supply Chain Decisions
Benchmarking Rapid Qualification
Variability Management Real-time Plan Passes
Performance Management Tracking, Traceability
ICME
Composable Workflows
Apps & Toolkits
Advanced Sensing, Control & Automation, Platform Modeling & Dynamic Orchestration of Manufacturing Intelligence in
Heterogeneous Manufacturing Enterprises defined by SEAMs ASCPM
• Location where two or more parts of a manufacturing enterprise or supply chain are joined together
• Parts differ by time horizon, lexicon, technology, human in loop, culture, business drivers, vendors, or priorities.
System 1 System 2
Seam
SM Platform enabled virtual connection
Interfaces
Smart Manufacturing: Multi-Layered Seams, Time, Data & Action
Machines – People - Materials Dynamic Manufacturing Ecosystem
Design Data
Product Manufacturing
Materials & Process Tech Prototype Qualification In
Service
Macro Layer
Meso Layer
Micro Layer 1000s control loops Time - minutes
100s control loops Time -hours
10s control loops Time – days
Focus: 10x Multiple Pass Variability
Reduction; Supply Chain Information
Busi
ness
Sys
tem
s
Cont
rol &
Aut
omat
ion
Focus: 100x Event Variability/Tradeoff
Adjustment; Dynamic Performance Mgmt.;
Integrated Metrics
Focus: Insertion, Qualification, ICME, High
Fidelity Dynamic Operations
Smart Manufacturing based on ISA 95
Smart Manufacturing & Vendor Products
SM
Architect for Scalability, Cost, Extensibility, Entrée, Security & Multi Vendor Orchestrate function, data and time
Compose heterogeneous environment workflows at User Level Provision secure IT on as needed basis
Smart Manufacturing Open Architecture Platform
Composabilty
Control & Automation Propriety Optimized
Automation Workflows
Data Partnership Workflow
Orchestration
Sensor Data
Market Place Data collection,
modeling & Synchronization multi-scale time requirements embedded
Data collection, modeling & synchronization defined by workflow
No single scale time requirement in workflow
Decision
Design to manufacturing Workflow libraries
Separate Data & apps
Data to apps paradigm
Workflow (WfaaS)
Data-based Workflow Orchestration
Proprietary Workflow
Provisioning
State Tasking
Orchestration
Security Provenance
Application Instances
Selectable Apps
SM Platform Architecture
Provisioning
Sensor Network
Proprietary Platform
SM Platform Secure Data Interface and Workflow Architecture
OEM 1 Analysis BPM/Kepler Workflows
Sensor Network
Proprietary Platform
OEM 1
DOE Tesbed Marketplace
SM Apps & Applications
Dash Boards
UCLA/Nimbis Admin
User & Role Authentication
Browser and Remote Desktop Access
Supplier 2
Platform Historian 2
Visualization Workflows
Metric APP Model Visualize Commercial viewer(s)
Platform Historian 1
DOE SM Cloud Platform
Visualize APP
Open Data Model
DMZ service
DMZ server
Https:
FTP to PI
SM DMZ
SM Connect Supplier 2 BPM/ Kepler Workflows
Secure I, P and SaaS
Secure Data Highways
Data & Device Integration & Orchestration
Smart Factory Manufacturing
Smart Enterprise Manufacturing
Manufacturing Health & Sustainability
Collective Innovation &
Practice
Converting Data to Information
Converting Information to Knowledge
Converting Knowledge to Wisdom
Open Architecture
Data Valuation Collective vs. Proprietary
Practice Valuation Collective vs. Proprietary
The Business Model of Data
IoT
Big Data
Smart
Collective Wisdom
The Business of Open Architecture Market, Valuation of Data & Innovation
Performance Metrics with Platform
Year 1 Year 2 Year 3 Year 4 Year 5 notes
Decrease R & D cost/risk
25% 30% 35% 40% 50% Compared to no platform baseline; 1st year decrease - data & modeling infrastructure; 2nd year progressive decrease - building on shared research
Accelerated pace of R & D outcomes
2 years to 1 year
+5% faster +10% faster +15% faster
+20% faster 1st year reduced duration - flexibility with modeling infrastructure; 2nd year progressive - more interface apps, better use standards, better modeling tools, consolidated experience
Decrease in replication/deployment cost/risk & accelerated pace
60% 1 – 2 replications
65% multiple replications
70% multiple replications
80% Multiple replications
1 year shift because R & D faster; Year 2 cost progression; consolidated experience, more interface apps, better standards, better modeling tools; pace accelerated multiple parallel replications
Retrofit modeling - waste heat minimization/energy productivity - base no advanced technology (See DOE proposal)
20% 30% 35% 40% 50% Advance control and low hanging fruit practices 20 – 25%; advanced real time modeling & integration next 20%; sophisticated integrated modeling next Calculated for control and IT retrofits for existing process – measurements, new, dynamic operating conditions; does not account for potential for new equipment
1 year shift
Low hanging
fruit
1st phase energy R & D
2nd phase energy R & D
Platform Energy Economics 25
What is Smart Manufacturing
Internal & External Value Chain Networked-Based Manufacturing
Supply Chain Distribution Center
Customer
Business Systems, ERP
Smart Grid
Smart Factory
Configurable high fidelity/statistical modeling and analytics Dynamic plant configuration and readiness Dynamic product component/material configuration Faster changeovers & more variable order sizes Dynamic inventory minimization & management
EDI transaction lot & quality certifications
Tracking & traceability
More customer freedom To customize
Graphics courtesy of Rockwell Automation
Minimum just in case inventory & corrective actions thru entire system
Chain of custody
Order adjustment Reduce premium shipments
Use Cases Mike Yost Mesa
AMP 2.0 Recommendations