The IoT Inc Business Meetup Silicon Valley
Opening remarks and guest presentation
Bruce Sinclair (Organizer): bruce@iot-inc.com
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: bruce@iot-inc.com
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
© Iot-Inc. 2015
Predictive Analyics

Industrial Internet of Things for
discreet manufacturing

IOT for Business

April 7, 2016
Berkeley,	CA	|	Chennai,	India
1
William Sobel
Chief Strategy Officer
System Insights
MTConnect Chief Architect
VIMANA by System
Insights Copyright © 2016 System Insights, All Rights Reserved
About Myself…
2
• Chief	Strategy	Officer	–	System	Insights	
• Chief	Architect	of	the	MTConnect	Standard	for	the	last	9	years	
• Chair	of	the	MTConnect	Technical	Steering	Committee	
– Original	Author	of	the	Standard	
• Co-Chair	IIC	Industrial	Analytics	Task	Group	
• My	Background	
– Financial	Systems	Architecture	
– Real-Time	Analytics	
– Distributed	Architecture	&	Fault	Tolerance
VIMANA by System
Insights 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 System
Insights Copyright © 2015 System Insights, All Rights Reserved
IIOT for Manufacturing
4
Industrial Internet of Things
VIMANA by System
Insights 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 System
Insights 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 System
Insights 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
PresentDisruptive
Intelligence
VIMANA by System
Insights Copyright © 2015 System Insights, All Rights Reserved
Steel Production
8
VIMANA by System
Insights Copyright © 2015 System Insights, All Rights Reserved
Metal Cutting
9
Milling
Turning
Traditional
Laser
Water Jet
Wire EDM
Non Traditional
Additive
Hybrid
Additive
VIMANA by System
Insights Copyright © 2015 System Insights, All Rights Reserved
Digital Thread
10
010010011
VIMANA by System
Insights Copyright © 2015 System Insights, All Rights Reserved
Products
11
VIMANA by System
Insights Copyright © 2015 System Insights, All Rights Reserved
Case Study: Predictive Analytics for
Process Manufacturing
Karthik Ranganathan
12
VIMANA by System
Insights 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 System
Insights Copyright © 2015 System Insights, All Rights Reserved
Multi-dimensional data
14
1. Store	Everything	–	Can’t	create	data	
2. Distributed	analysis:	Slice	data	across	any	
plane,	including:	time,	machine	organization,	
parts	–	Find	patterns	and	correlations	
3. Everything	must	have	context
VIMANA by System
Insights Copyright © 2016 System Insights, All Rights Reserved
Data Set
15
• Source	
– 33	Tables	and	800+	variables/columns	
– Size:	250+	GB	
• Challenges	
– Data	was	organized	per-operation,	not	across	all	operations	
– Varied	parameters	across	operations	and	type	of	equipment	
– Potential	missing	data	and	data	entry	errors	
• The	data	had	to	be	prepped	for	analysis,	including	Cleanup	and	
Contextualization
VIMANA by System
Insights Copyright © 2016 System Insights, All Rights Reserved
Data Cleansing
16
• Outliers	
– Observations	which	deviate	significantly	from	the	
norm,	with	suspect	veracity	
– Can	remove	to	prevent	model	corruption	
• Probable	Causes	
– Data	Entry	Errors	
– Missing	Data	
– Differing	Units	
– Measurement	flaws	
• Examples	
– UTS/LYS	values	>	1000	MPa,	<	10	MPa	
– Output	slab	weight	>	Input	slab	weight	(~1.85%)
VIMANA by System
Insights Copyright © 2015 System Insights, All Rights Reserved
Process-based Quality Prediction
17
Mean Deviation
2.0%
R-squared
81%
Actual Quality Parameter
PredictedQualityParameter
Build process-based model to predict final quality
VIMANA by System
Insights Copyright © 2016 System Insights, All Rights Reserved
Quality Prediction
18
• Expected	quality	parameters	provided	by	customer	
• However,	it	was	seen	that	these	bounds	are	not	strictly	followed	while	attributing	grade	
• We	detected	the	true	quality	limits	automatically	from	the	data	
• Reduction	of	lower	boundary	by	10	~	20	MPa	for	both	LYS	and	UTS
VIMANA by System
Insights Copyright © 2016 System Insights, All Rights Reserved
Predicting Grade
19
• Predict	grade	based	on	the	input	parameters	
• Analysis:	
– 5	Grades	Analyzed	
– Total	Observations	–	5008	
– Training	Data	–	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 System
Insights Copyright © 2016 System Insights, All Rights Reserved
Predictive Services
20
• Using	the	models,	we	can	predict	
the	UTS	with	to	95%	Accuracy	
based	on	chemistry	
• Derived	from	empirical	analysis	of	
manufacturing	process	and	data	
analytics
VIMANA by System
Insights Copyright © 2016 System Insights, All Rights Reserved
Value
21
• Reduce	cost	of	quality	
– Control	quality	with	critical	parameters	
– Understand	process	parameters	to	control	to	have	biggest	impact	on	part	quality	
– Set	parameter	limits	–	higher	granularity	and	control	over	process	
• Capture	Tribal	Knowledge	
– Capture	and	quantify	know-how	of	the	“humans	in	the	loop”	
– Improve	knowledge	transfer	and	management	oversight	
• Target	areas	for	process	improvement	
– Significant	process	variation	–	identify	areas	where	it	has	significant	impact	on	quality
VIMANA by System
Insights Copyright © 2016 System Insights, All Rights Reserved
Market
22
• Global	Steel	Industry	
– Production	(2015)	–	1690	million	tonnes	
– Market	–	$1.3	Trillion		
• One	Company	in	Study	
– Market:	$3	billion	
– Operational	profit:	$700	million	
• Estimated	Savings:	
– Based	on	a	3-5%	reduction	in	operational	costs	
– $150	million	in	potential	profit	
– $250	billion	in	potential	profit	for	entire	market
VIMANA by System
Insights Copyright © 2015 System Insights, All Rights Reserved
Big Data Services
23
VIMANA by System
Insights 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 System
Insights Copyright © 2015 System Insights, All Rights Reserved
Current Feedback
25
Machining VerificationEngineeringDesign
VIMANA by System
Insights Copyright © 2015 System Insights, All Rights Reserved
Digital Manufacturing Feedback
26
Machining VerificationEngineeringDesign
VIMANA by System
Insights Copyright © 2015 System Insights, All Rights Reserved
Crowd Sourcing for Manufacturing
27
Facility
Community
• 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 System
Insights 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 System
Insights 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 System
Insights Copyright © 2015 System Insights, All Rights Reserved
Predictive Analytics
30
Why would we do anything else?
VIMANA by System
Insights Copyright © 2016 System Insights, All Rights Reserved
Semantic Transformation
31
0102030
40 50 60 70
8090100
010100100
a = 5000
b = 12.43
c = 87.22
d = 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 1
Part 2
Part 6
......
Batch
Execution:ACTIVE
Load[X]: 120%
Feedrate[Override]: 80%
Time
Equipment
Product
Analysis Predictive
Prognositcs
Value
Information
Data Enrichment
part XY32 – part AB56 –
Producing part AB56
at avg. SS of 2500 RPM
Producing
unknown part
Producing part XY32
at 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 System
Insights 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
VIMANA by System
Insights Copyright © 2015 System Insights, All Rights Reserved
Questions?
33

Predictive Analytics and the Industrial Internet of Manufacturing Things with Will Sobel

  • 1.
    The IoT IncBusiness Meetup Silicon Valley Opening remarks and guest presentation Bruce Sinclair (Organizer): bruce@iot-inc.com
  • 2.
    Target of Meetup Forbusiness 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
  • 3.
    Type of Products Allincremental 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
  • 4.
    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: bruce@iot-inc.com Feel free to upload pictures and tweet with hashtag #iotbusiness Reviews would be great!
  • 5.
    Sponsor spots stillavailable! 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
  • 6.
  • 7.
    Predictive Analyics Industrial Internetof Things for discreet manufacturing IOT for Business April 7, 2016 Berkeley, CA | Chennai, India 1 William Sobel Chief Strategy Officer System Insights MTConnect Chief Architect
  • 8.
    VIMANA by System InsightsCopyright © 2016 System Insights, All Rights Reserved About Myself… 2 • Chief Strategy Officer – System Insights • Chief Architect of the MTConnect Standard for the last 9 years • Chair of the MTConnect Technical Steering Committee – Original Author of the Standard • Co-Chair IIC Industrial Analytics Task Group • My Background – Financial Systems Architecture – Real-Time Analytics – Distributed Architecture & Fault Tolerance
  • 9.
    VIMANA by System InsightsCopyright © 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
  • 10.
    VIMANA by System InsightsCopyright © 2015 System Insights, All Rights Reserved IIOT for Manufacturing 4 Industrial Internet of Things
  • 11.
    VIMANA by System InsightsCopyright © 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
  • 12.
    VIMANA by System InsightsCopyright © 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
  • 13.
    VIMANA by System InsightsCopyright © 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 PresentDisruptive Intelligence
  • 14.
    VIMANA by System InsightsCopyright © 2015 System Insights, All Rights Reserved Steel Production 8
  • 15.
    VIMANA by System InsightsCopyright © 2015 System Insights, All Rights Reserved Metal Cutting 9 Milling Turning Traditional Laser Water Jet Wire EDM Non Traditional Additive Hybrid Additive
  • 16.
    VIMANA by System InsightsCopyright © 2015 System Insights, All Rights Reserved Digital Thread 10 010010011
  • 17.
    VIMANA by System InsightsCopyright © 2015 System Insights, All Rights Reserved Products 11
  • 18.
    VIMANA by System InsightsCopyright © 2015 System Insights, All Rights Reserved Case Study: Predictive Analytics for Process Manufacturing Karthik Ranganathan 12
  • 19.
    VIMANA by System InsightsCopyright © 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
  • 20.
    VIMANA by System InsightsCopyright © 2015 System Insights, All Rights Reserved Multi-dimensional data 14 1. Store Everything – Can’t create data 2. Distributed analysis: Slice data across any plane, including: time, machine organization, parts – Find patterns and correlations 3. Everything must have context
  • 21.
    VIMANA by System InsightsCopyright © 2016 System Insights, All Rights Reserved Data Set 15 • Source – 33 Tables and 800+ variables/columns – Size: 250+ GB • Challenges – Data was organized per-operation, not across all operations – Varied parameters across operations and type of equipment – Potential missing data and data entry errors • The data had to be prepped for analysis, including Cleanup and Contextualization
  • 22.
    VIMANA by System InsightsCopyright © 2016 System Insights, All Rights Reserved Data Cleansing 16 • Outliers – Observations which deviate significantly from the norm, with suspect veracity – Can remove to prevent model corruption • Probable Causes – Data Entry Errors – Missing Data – Differing Units – Measurement flaws • Examples – UTS/LYS values > 1000 MPa, < 10 MPa – Output slab weight > Input slab weight (~1.85%)
  • 23.
    VIMANA by System InsightsCopyright © 2015 System Insights, All Rights Reserved Process-based Quality Prediction 17 Mean Deviation 2.0% R-squared 81% Actual Quality Parameter PredictedQualityParameter Build process-based model to predict final quality
  • 24.
    VIMANA by System InsightsCopyright © 2016 System Insights, All Rights Reserved Quality Prediction 18 • Expected quality parameters provided by customer • However, it was seen that these bounds are not strictly followed while attributing grade • We detected the true quality limits automatically from the data • Reduction of lower boundary by 10 ~ 20 MPa for both LYS and UTS
  • 25.
    VIMANA by System InsightsCopyright © 2016 System Insights, All Rights Reserved Predicting Grade 19 • Predict grade based on the input parameters • Analysis: – 5 Grades Analyzed – Total Observations – 5008 – Training Data – 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%
  • 26.
    VIMANA by System InsightsCopyright © 2016 System Insights, All Rights Reserved Predictive Services 20 • Using the models, we can predict the UTS with to 95% Accuracy based on chemistry • Derived from empirical analysis of manufacturing process and data analytics
  • 27.
    VIMANA by System InsightsCopyright © 2016 System Insights, All Rights Reserved Value 21 • Reduce cost of quality – Control quality with critical parameters – Understand process parameters to control to have biggest impact on part quality – Set parameter limits – higher granularity and control over process • Capture Tribal Knowledge – Capture and quantify know-how of the “humans in the loop” – Improve knowledge transfer and management oversight • Target areas for process improvement – Significant process variation – identify areas where it has significant impact on quality
  • 28.
    VIMANA by System InsightsCopyright © 2016 System Insights, All Rights Reserved Market 22 • Global Steel Industry – Production (2015) – 1690 million tonnes – Market – $1.3 Trillion • One Company in Study – Market: $3 billion – Operational profit: $700 million • Estimated Savings: – Based on a 3-5% reduction in operational costs – $150 million in potential profit – $250 billion in potential profit for entire market
  • 29.
    VIMANA by System InsightsCopyright © 2015 System Insights, All Rights Reserved Big Data Services 23
  • 30.
    VIMANA by System InsightsCopyright © 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
  • 31.
    VIMANA by System InsightsCopyright © 2015 System Insights, All Rights Reserved Current Feedback 25 Machining VerificationEngineeringDesign
  • 32.
    VIMANA by System InsightsCopyright © 2015 System Insights, All Rights Reserved Digital Manufacturing Feedback 26 Machining VerificationEngineeringDesign
  • 33.
    VIMANA by System InsightsCopyright © 2015 System Insights, All Rights Reserved Crowd Sourcing for Manufacturing 27 Facility Community • 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
  • 34.
    VIMANA by System InsightsCopyright © 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
  • 35.
    VIMANA by System InsightsCopyright © 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
  • 36.
    VIMANA by System InsightsCopyright © 2015 System Insights, All Rights Reserved Predictive Analytics 30 Why would we do anything else?
  • 37.
    VIMANA by System InsightsCopyright © 2016 System Insights, All Rights Reserved Semantic Transformation 31 0102030 40 50 60 70 8090100 010100100 a = 5000 b = 12.43 c = 87.22 d = 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 1 Part 2 Part 6 ...... Batch Execution:ACTIVE Load[X]: 120% Feedrate[Override]: 80% Time Equipment Product Analysis Predictive Prognositcs Value Information Data Enrichment part XY32 – part AB56 – Producing part AB56 at avg. SS of 2500 RPM Producing unknown part Producing part XY32 at 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
  • 38.
    VIMANA by System InsightsCopyright © 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
  • 39.
    VIMANA by System InsightsCopyright © 2015 System Insights, All Rights Reserved Questions? 33