The IoT Inc Business Meetup Silicon Valley
Opening remarks and guest presentation
Bruce Sinclair (Organizer): [email protected]
Target of Meetup
For business people selling products and services into IoT
but of course everyone else is welcome: techies, end-users, …
Focus of presentations and discussions:
© Iot-Inc. 2015
Type of Products
All incremental value in an IoT product comes from transforming its data into useful information
• Trinity of value: model, app and analytics
Analytics transforms data into value
© Iot-Inc. 2015
Coming Up
© Iot-Inc. 2014
Next Meeting, Thursday, May 5, 2016
Mega Meetup2 will be in July
Presentation, recording of Meetup and announcements for today’s meeting will be sent in one week to everyone who provided their email upon signup for this meeting or any other past Meetup
Send announcements to me: My email: [email protected]
Feel free to upload pictures and tweet with hashtag #iotbusiness
Reviews would be great!
Sponsor spots still available!
Food & Drink is $600 per meeting x 10 meetings a year = $6,000
• Gold = $2,000 and Bronze = $500
We have Sponsors!
© Iot-Inc. 2014
Gold Bronze Shelter
Predictive AnalyicsIndustrial Internet of Things for discreet manufacturing
IOT for BusinessApril 7, 2016
Berkeley,CA|Chennai,India1
William SobelChief Strategy OfficerSystem Insights
MTConnect Chief Architect
VIMANA by SystemInsights Copyright © 2016 System Insights, All Rights Reserved
About Myself…
2
• ChiefStrategyOfficer–SystemInsights• ChiefArchitectoftheMTConnectStandardforthelast9years• ChairoftheMTConnectTechnicalSteeringCommittee
– OriginalAuthoroftheStandard• Co-ChairIICIndustrialAnalyticsTaskGroup
• MyBackground– FinancialSystemsArchitecture– Real-TimeAnalytics– DistributedArchitecture&FaultTolerance
VIMANA by SystemInsights Copyright © 2015 System Insights, All Rights Reserved
System Insights
3
The predictive analytics platform for manufacturing intelligence
2009
Berkeley, CA
2010
India
40+
Customers
60+
Sites
VIMANA by SystemInsights Copyright © 2015 System Insights, All Rights Reserved
IIOT for Manufacturing
4
Industrial Internet of Things
VIMANA by SystemInsights Copyright © 2016 System Insights, All Rights Reserved
Internet of Things
5
• Industrial vs. Everything Else – Highest potential Impact – $1.2 - 3.7T
• Areas – Operations Optimization – Predictive Maintenance – Inventory Optimization – Health and Safety
McKinsey Global Institute THE INTERNET OF THINGS: MAPPING THE VALUE BEYOND THE HYPE JUNE 2015
VIMANA by SystemInsights Copyright © 2015 System Insights, All Rights Reserved
New Economies
6
Present
Emergent
5-10 Years
10 - 25 Years
Manufacturing Services & Data Monetization
Outcome Based Economy
Autonomous On-Demand & Distributed
Operational Efficiency
World Economic Forum: Industrial Internet of Things: Unleashing the Potential of Connected Products and Services, Jan 2011
VIMANA by SystemInsights Copyright © 2016 System Insights, All Rights Reserved
Intelligence Hierarchy
7
← 95% of shopsPrimitive - No software or control software only
Monitored - Data collection and backward looking reports
Real-Time Analytic - Determines why a process failed or productivity was lost
Proactive - Detect problems before they happen using CEP and learning
Self Optimizing - Learning models optimize processes
Predictive - Problems are solved before they impact process
Pres
ent
Disr
uptiv
e
Intelligence
VIMANA by SystemInsights Copyright © 2015 System Insights, All Rights Reserved
Metal Cutting
9
Milling
Turning
TraditionalLaser
Water Jet
Wire EDMNon Traditional
Additive
HybridAdditive
VIMANA by SystemInsights Copyright © 2015 System Insights, All Rights Reserved
Digital Thread
10
010010011
VIMANA by SystemInsights Copyright © 2015 System Insights, All Rights Reserved
Case Study: Predictive Analytics for Process Manufacturing
Karthik Ranganathan
12
VIMANA by SystemInsights Copyright © 2016 System Insights, All Rights Reserved
Predictive Quality and Yield
13
• Complex multi-stage high-speed manufacturing process• Process is manually configured and adjusted• Utilize legacy data collection of low-level sensor data• Data Cleansing and Semantic Transformation
– Multidimensional Data Modeling• Contextualize Data with Device, Part and Process
VIMANA by SystemInsights Copyright © 2015 System Insights, All Rights Reserved
Multi-dimensional data
14
1. StoreEverything–Can’tcreatedata2. Distributedanalysis:Slicedataacrossany
plane,including:time,machineorganization,parts–Findpatternsandcorrelations
3. Everythingmusthavecontext
VIMANA by SystemInsights Copyright © 2016 System Insights, All Rights Reserved
Data Set
15
• Source– 33Tablesand800+variables/columns– Size:250+GB
• Challenges– Datawasorganizedper-operation,notacrossalloperations– Variedparametersacrossoperationsandtypeofequipment– Potentialmissingdataanddataentryerrors
• Thedatahadtobepreppedforanalysis,includingCleanupandContextualization
VIMANA by SystemInsights Copyright © 2016 System Insights, All Rights Reserved
Data Cleansing
16
• Outliers– Observationswhichdeviatesignificantlyfromthe
norm,withsuspectveracity– Canremovetopreventmodelcorruption
• ProbableCauses– DataEntryErrors– MissingData– DifferingUnits– Measurementflaws
• Examples– UTS/LYSvalues>1000MPa,<10MPa– Outputslabweight>Inputslabweight(~1.85%)
VIMANA by SystemInsights Copyright © 2015 System Insights, All Rights Reserved
Process-based Quality Prediction
17
Mean Deviation 2.0%
R-squared 81%
Actual Quality Parameter
Pred
icte
d Q
ualit
y Pa
ram
eter
Build process-based model to predict final quality
VIMANA by SystemInsights Copyright © 2016 System Insights, All Rights Reserved
Quality Prediction
18
• Expectedqualityparametersprovidedbycustomer• However,itwasseenthattheseboundsarenotstrictlyfollowedwhileattributinggrade• Wedetectedthetruequalitylimitsautomaticallyfromthedata• Reductionoflowerboundaryby10~20MPaforbothLYSandUTS
VIMANA by SystemInsights Copyright © 2016 System Insights, All Rights Reserved
Predicting Grade
19
• Predictgradebasedontheinputparameters
• Analysis:– 5GradesAnalyzed– TotalObservations–5008– TrainingData–80%
Production of Coils that match Expected Grade
92 +/- 1%
Matched Plant Predictions 99 +/- 0.5% Improvement on Missed Predictions 45 +/- 10%New Prediction Methodology 95 +/- 1%
VIMANA by SystemInsights Copyright © 2016 System Insights, All Rights Reserved
Predictive Services
20
• Usingthemodels,wecanpredicttheUTSwithto95%Accuracybasedonchemistry
• Derivedfromempiricalanalysisofmanufacturingprocessanddataanalytics
VIMANA by SystemInsights Copyright © 2016 System Insights, All Rights Reserved
Value
21
• Reducecostofquality– Controlqualitywithcriticalparameters– Understandprocessparameterstocontroltohavebiggestimpactonpartquality– Setparameterlimits–highergranularityandcontroloverprocess
• CaptureTribalKnowledge– Captureandquantifyknow-howofthe“humansintheloop”– Improveknowledgetransferandmanagementoversight
• Targetareasforprocessimprovement– Significantprocessvariation–identifyareaswhereithassignificantimpactonquality
VIMANA by SystemInsights Copyright © 2016 System Insights, All Rights Reserved
Market
22
• GlobalSteelIndustry– Production(2015)–1690milliontonnes– Market–$1.3Trillion
• OneCompanyinStudy– Market:$3billion– Operationalprofit:$700million
• EstimatedSavings:– Basedona3-5%reductioninoperationalcosts– $150millioninpotentialprofit– $250billioninpotentialprofitforentiremarket
VIMANA by SystemInsights Copyright © 2015 System Insights, All Rights Reserved
Present: Feed - Forward Processes
24
• Part • Machine • Tool • Processes
• Best Practices • Improvement Opportunities • Deviations from Plan
• Was the part produced to plan? – Faster or slower? – Why?
• Part Program • Setup Sheet • Work Instructions
• Tool Breakage • Poor Surface Finish • Productivity Gains
No Robust Feedback mechanism to planning functions.
Programmer
Operator
• Process Actuals – Feed/Speed – Cycle Time – Operator Feedback
VIMANA by SystemInsights Copyright © 2015 System Insights, All Rights Reserved
Current Feedback
25
Machining VerificationEngineeringDesign
VIMANA by SystemInsights Copyright © 2015 System Insights, All Rights Reserved
Digital Manufacturing Feedback
26
Machining VerificationEngineeringDesign
VIMANA by SystemInsights Copyright © 2015 System Insights, All Rights Reserved
Crowd Sourcing for Manufacturing
27
FacilityCommunity
• Knowledge-Based Recommendations – Optimized Conditions – Optimized Asset Selection – Optimized Throughput
Community Instructions, Plans
Process Data
Programs Instructions, & Plans
• Rules • Analytics
& Process Data
Analytics = Data → Knowledge
VIMANA by SystemInsights Copyright © 2015 System Insights, All Rights Reserved
Tool and Process Analytics
28
Analyze process parameters and tooling usage to reduce tool costs and improve tool life
Detailed understanding of cycle time and process parameters Automatically identify best practices for process planning
VIMANA by SystemInsights Copyright © 2015 System Insights, All Rights Reserved
Increases in Efficiency
29
Conservative Estimates:10% Increase in Production Efficiency + 10% Reduction of Cycle Time
Total Time: 100%
Producing: 40%
ICT: 60% of Prod.
Total Time: 100%
Producing: 50%
ICT: 70% of Producing
Total Time: 100%
Producing: 50%
ICT: 70% of Producing
CT Reduction: 10% Effectively: 62% higher production
5000 Hours
5000 Hours
5000 Hours
2000 Hours
1200 Hours
2500 Hours
1750 Hours
2500 Hours
1750 Hours
1950 Hours
Baseline These improvements are what was referenced in the report – remember?
1.2 - 3.7 Trillion
VIMANA by SystemInsights Copyright © 2015 System Insights, All Rights Reserved
Predictive Analytics
30
Why would we do anything else?
VIMANA by SystemInsights Copyright © 2016 System Insights, All Rights Reserved
Semantic Transformation
31
0 10
20
30
40 50 60 70 80 90 100
010100100a = 5000b = 12.43c = 87.22d = 2
Device Linear X Position: 196.54mm Load: 12.43% Rotary C Rotary Velocity: 87.22 RPM Controller Execution: ACTIVE
Meaning
Syntax SemanticsRaw Data
Propriatary Structure Wisdom
Bad
Good
:01 :02 :03 :04 :05 :06
V Mill 1
Lathe 1
Robot
CMM
Cell
Day
Line
Part 1Part 2
Part 6
......Batch
Execution: ACTIVELoad[X]: 120%Feedrate[Override]: 80%
Time
Equi
pmen
t
Prod
uct
Analysis Predictive
Prognositcs
Value
Information
Data Enrichment
part XY32 – part AB56 –
Producing part AB56at avg. SS of 2500 RPM
Producingunknown part
Producing part XY32at avg. SS of 2160 RPM
–
Not Producing Producing
Producing State – Device: When All Paths Producing
Path 1
Path 2
MULTI-PATH Device
Not Producing Producing
Producing State – Device: When Any Paths Producing
VIMANA by SystemInsights Copyright © 2016 System Insights, All Rights Reserved
Continuous Improvement
32
Continuous Improvement
Baseline
Analyze
Evaluate
Production
Implement
Execution optimization Process Improvement Workforce Training
Tooling Optimization Predictive Quality Manufacturing Strategy Design for Manufacturing