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2Copyright © 2017 Uptake23-Mar-17Brad Nicholas – Chicago IoT March 2017.pptx
AGENDA
01 3 minutes about Uptake02 Some key considerations03 The 3 patterns04 Manufacturing discussion / Q&A
3Copyright © 2017 Uptake23-Mar-17Brad Nicholas – Chicago IoT March 2017.pptx
Uptake at a glance
AEROSPACE AGRICULTURE CONSTRUCTION ENERGY
104Mpredictions generated to date
2014founded in Chicago
82%across Data Science & Engineering
700 Employees
Uptake has developed partnerships in:
HEALTHCARE MINING RAIL RETAIL
Uptake selected as the hottest startup of 2015 – beating out Uber and Slack. – Dec 2015
Uptake’s Industry Thought Leaders featured in:
4Copyright © 2017 Uptake23-Mar-17Brad Nicholas – Chicago IoT March 2017.pptx
Our platform is purpose-built to deliver actionable insights and recommendations into workflow, empowering people to create value
Raw Data Data Ingestion Platform Apps
Data Science Engines
Data Integrity
Software Development Kit
Failure PredictionAnomaly Detection
RecommendationsEvent / Alert Filtering
Data Operations CenterNormalization & Cleansing
End to end visibilityEncryption in transit and at rest
API PortalDeveloper Content Mgmt.
App Store Tools
Assets
Customers
ERP
Contextual
• Weather
• Social Media
• 3rd party
Sample Apps:
• Condition-Based Monitoring
• Supply Chain Optimization
• Fuel and Energy Management
• Performance Optimization
Workflow Integration
Examples:
• Automated locomotive re-routing
• Automated parts ordering
• Automated maintenance scheduling
END-TO-END CYBER, INFORMATION, AND OPERATIONAL SECURITY
5Copyright © 2017 Uptake23-Mar-17Brad Nicholas – Chicago IoT March 2017.pptx
About Me
I run the IoT team at Uptake
bradn www.linkedin.com/in/bradn
Automotive, Manufacturing, Consulting, Telecom, Startups
EE MBA
Fun fact: I “OEM+” hack & restore German cars
6Copyright © 2017 Uptake23-Mar-17Brad Nicholas – Chicago IoT March 2017.pptx
We’re hiring.
https://boards.greenhouse.io/uptakeCome see me if you’re interested in IoT, device management, embedded programming, crypto
8Copyright © 2017 Uptake23-Mar-17Brad Nicholas – Chicago IoT March 2017.pptx
Digitization is lagging in many industry sectors that need IIoT
MGI Industry Digitization Indexhttp://www.mckinsey.com/industries/high-tech/our-insights/digital-america-a-tale-of-the-haves-and-have-mores
• Quasi-public and/or highly localized sectors are lagging in digitization
• Labor-intensive sectors need digital tools for the workforce
• Knowledge-intensive sectors are already highly digitized
• Capital-intensive sectors have high IoT potential
• Service sectors can digitize customer transactions
• B2B sectors can benefit from expanded digital engagement
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9Copyright © 2017 Uptake23-Mar-17Brad Nicholas – Chicago IoT March 2017.pptx
3 essential elements to IIoT value creation
Data Ingestion“Sense”
Analytics“Infer”
Workflow“Act”
10Copyright © 2017 Uptake23-Mar-17Brad Nicholas – Chicago IoT March 2017.pptx
IIC’s reference model for industrial analytics covers most of the bases
Multi-tiered approachSensing vs ActuatingDifferent time horizonsOpen vs Closed loop
Source: Industrial Internet Consortium IIRA http://www.iiconsortium.org/IIRA.htm
11Copyright © 2017 Uptake23-Mar-17Brad Nicholas – Chicago IoT March 2017.pptx
Where you compute affects many things
There is no one architecture that will address everything.But there are certainly some common questions to answer
Proximity Response Time
Node Computing Capacity
Bandwidth Consumed
Focal Points Exceptions
Sense Act
12Copyright © 2017 Uptake23-Mar-17Brad Nicholas – Chicago IoT March 2017.pptx
How you are able to connect also affects what you can do
Latency, bandwidth, cost and complexity are usually not as optimal as you want them to be
MobileLocal IndividualSite
13Copyright © 2017 Uptake23-Mar-17Brad Nicholas – Chicago IoT March 2017.pptx
Other key IIoT needs, beyond strong security & viable economics
Separation of Concerns is essentialKey to managing complexity, achieving maintainability and resiliencehttps://effectivesoftwaredesign.com/2012/02/05/separation-of-concerns/
IP protection is crucialData rights management for both original and derived data, at rest and in flight, all nodes, including authorized usehttps://motherboard.vice.com/en_us/article/why-american-farmers-are-hacking-their-tractors-with-ukrainian-firmware
Heterogeneity is unavoidableComputing environmentsNode stateMobility vs fixed locationNetworking options and node availabilityDomain responsibility
IT/OT barrier is literally a real thingOperational control comes firstSkills/expertise is very differentMost capital equipment is decades old and relies on physical securityhttp://blog.iiconsortium.org/2016/08/it-vs-ot-for-the-industrial-internet-two-sides-of-the-same-coin.html
15Copyright © 2017 Uptake23-Mar-17Brad Nicholas – Chicago IoT March 2017.pptx
3 patterns seem to address most IIoT deployment scenarios
Physical EdgeOn-device IoT node
Platform & Applications
Cloud Edgereverse CDN for the physical web
Edge Gateway“On location”
connectivity node
16Copyright © 2017 Uptake23-Mar-17Brad Nicholas – Chicago IoT March 2017.pptx
The Cloud Edge is effectively the ‘virtual physical web’
A hybrid node that serves as a “concentrator” or “reverse CDN” for the physical web. It can isolate IoT traffic and service cloud-based applications with anything they need from the physical web
Concentrates physical web data streamsInteracts with Edge Gateways and higher end Physical Edge nodesServes web APIs to cloud applications
You can train ML using the data on this node. You could continually train ML given sufficient compute capacity and data.You can distribute its contents via CDN, subject to data rights management
17Copyright © 2017 Uptake23-Mar-17Brad Nicholas – Chicago IoT March 2017.pptx
The Physical Edge interacts directly with IIoT data sources
Protects the OT layer and hosts specialized, “high interaction” IoT processes
Serves as a direct data extraction point for physical web data generated by a machine or processProtects machine / process operation at all costs, even if data extraction compromisedRuns on-machine / on-process analytics functionsProtects OEM and machine owner IP by enforcing data rights management at the source, under terms suitable to the IP owners
Must be designed and deployed in collaboration with machine / process OEMs and operatorsProvides much richer data access capabilities
18Copyright © 2017 Uptake23-Mar-17Brad Nicholas – Chicago IoT March 2017.pptx
The Edge Gateway
Resides in proximity to physical web nodes and handles connectivity gapsManages “inter physical web” IoT interactions that aren’t needed to control things
Primary function is to monitor physical web machines / processesEliminates the need for physical web devices to interact with the Cloud Edge directlyQueues on premise when backhaul connectivity is unavailable, restricted due to cost or otherwise unusableSpeaks local machine dialects
19Copyright © 2017 Uptake23-Mar-17Brad Nicholas – Chicago IoT March 2017.pptx
The 3 edge patterns can be implemented flexibly
Physical Edge nodes deployed on advanced machines with excellent connectivity can connect directly to Cloud Edge nodes – without an Edge Gateway
Cloud Edge nodes could be deployed anywhere connectivity to other edge nodes and “data center quality” bandwidth is available
• A very high end physical web machine or process
• At a fixed location like an airport terminal
Edge Gateway nodes could be co-deployed with Physical Web nodes as long as suitable backhaul connectivity to a Cloud Edge node is available
21Copyright © 2017 Uptake23-Mar-17Brad Nicholas – Chicago IoT March 2017.pptx
OEE - Overall Equipment EffectivenessTotal Productive MaintenanceSeiichi Nakajima 1982-1984
www.AMTonline.orghttp://capstonemetrics.com/files/whitepaper-oeeoverview.pdf
OEE = Availability x Performance x QualityTEEP = Loading x OEE
22Copyright © 2017 Uptake23-Mar-17Brad Nicholas – Chicago IoT March 2017.pptx
OEE & TEEP let you keep score, but that’s about it.
• They’re useful, but reactive, not predictive• What are the historical causes of poor OEE? Are they clearly
understood? Are they static or do things change over time?• Are there ways to recognize patterns in historical data that can provide
advanced indication of those causes developing?• Can you act on those causes?• How much time would you have to act?• What would you need to improve your ability to predict issues and act
on those predictions?
23Copyright © 2017 Uptake23-Mar-17Brad Nicholas – Chicago IoT March 2017.pptx
DiscussionSome potential improvements - beyond normal operations
Physical Web node functionsVibration analytics for rotational and reciprocating machineryAdditional process quality instrumentationDetailed / granular OEE data collection via SCADA and machine control integrations
Physical Web and Cloud Edge node functionsEvent correlation analytics
24Copyright © 2017 Uptake23-Mar-17Brad Nicholas – Chicago IoT March 2017.pptx
This is the tip of the iceberg
A lot of critical questions have been left unanswered here. Great discussion topics!
Greenfield vs Brownfield (factory fit vs retrofit)Remote device managementCompute capacitySystem operations
Additional material:McKinsey Analytics Reporthttp://www.mckinsey.com/business-functions/mckinsey-analytics/our-insights/the-age-of-analytics-competing-in-a-data-driven-world?cid=analytics-alt-mgi-mgi-oth-1612
Peter Levine – The End of Cloud Computinghttp://a16z.com/2016/12/16/the-end-of-cloud-computing/
Frank Chen – Deep Learning and Machine Learning Primerhttp://a16z.com/2016/06/10/ai-deep-learning-machines/