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Real-world IoT for
Enterprises Abhimanyu Prabhavalkar
VP, PaaS Development, Oracle
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Internet of Things is Here (and everywhere)
The Opportunity is Big ….. Especially in Manufacturing, Logistics/Transportation, Healthcare, Utilities etc.
$3 Trillion Market
50 Billion
Devices
8 Zeta
bytes of data
By 2020
By 2018
Today
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Service Based Economy
Ownership Service Break/Fix Preventative
Central Service
Self/Guided Service
Static Analytics
Real-time Analytics
Today Tomorrow Today
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Business Impact of IoT on Service & Warranty
WARRANTY COSTS
REMOTE SERVICE
MANAGEMENT
Let’s consider a news story
Challenges in delivering IoT Solutions
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The OT-IT Chasm
Business Applications
IoT Devices
Manufacturing, Supply Chain, Asset Mgmt
Customer Relationship Mgmt, Sales, Service
Vertical Apps – Utilities, Healthcare, Retail
Current processes
Today, many conditions are manually detected, and then manually entered in the business applications
Manual Processing
Reactive Controls
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Lack of Clarity in Value Generation
• Plenty of offerings without clear value
• Smart light switch, smart appliance etc.
• Mere Remote Control?
• Digitize the building, Energy savings/design improvement – Much Greater Value
Lack of interoperability • Proprietary platforms
• Works best when confined to a limited set of data sources
• Costs money and long term engagement with vendor
• Open platforms
• Free
• Need effort for developing all the inter-operation on your own
How do I collect data from intelligent devices? Abstract complexity associated with device connectivity
Standardize integration of devices with enterprise
Building an IoT application - Key Challenges
How do I analyze IoT data? Reduce noise and detect business events at real-time
Enable historical big-data analysis
How do I integrate IoT data & events with enterprise infrastructure? Make enterprise processes IoT friendly
Allow enterprise & mobile applications to control devices
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Best Practices
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Opportunity for the IoT Ecosystem
DEVICE OEM
SYSTEM INTEGRATOR
SOLUTION PROVIDER
ISV SERVICE PROVIDER
CUSTOMER
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• Understand your horizon but pick a sandbox
• Where to start? - Predictive Maintenance OR Where are my assets?
Start small, think big…
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Asset Tracking Application
Dashboard that shows asset locations, performance KPIs and Incidents
Asset Diagnostics
Operational Monitoring • Equipment Health and Diagnostics • Asset Utilization • Location Tracking
IoT Deployment Phases
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Bu
sin
es
s V
alu
e
Time to Value
Connected Assets • Remote monitoring • Business validation
0-3 Months
Predictive Analytics
• Proactive decisions • Improved products
3-6 Months
Service Excellence
• IoT blended into enterprise applications
• Differentiation through customer experience
6-12 Months
• IoT is the ultimate Big Data
• Data in Motion and Data at Rest are necessary to drive business applications
• Easy aggregation and visualization
• Identify patterns to enable predictive decisions
Analytics, analytics, analytics…
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• Man
• Who was operating Machines when faulty treadmills were produced?
• Were they trained appropriately? • Machine
• Which robots were used in production of these treadmills?
• Were these machines operating properly?
• Method
• What inspections were performed on these treadmills?
• Were appropriate SOP procedures followed?
• Materials
• Which raw material batches were used during the production?
• Which suppliers were used? • Measurements
• What were the test results? • Were the machines calibrated?
Man Machine
Method
Materials
Measurements
CAUSE
The 5-Ms Analysis
• Man
• Who was operating the machines on which faulty treadmills were produced?
• Were they trained appropriately? • Machine
• Which robots were used in production of these treadmills?
• Were these machines operating properly?
• Method
• What inspections were performed on these treadmills?
• Were appropriate SOP procedures followed?
• Materials
• Which raw material batches were used during the production?
• Which Suppliers were used? • Measurements
• What were the test results? • Were the machines calibrated?
Man Machine
Method
Materials
Measurements
CAUSE
Reactive Analysis
IoT Data Data in Motion
IoT Data Data in Motion
Big Data Data at Rest
Big Data Data at Rest
Big Data Data at Rest
Both Data in Motion and Data at Rest are necessary
Predictive Analytics
Time for IoT Opportunities in Mfg Are there patterns of events that cause the equipment to fail ? Is there a correlation between machine parameters and product quality ? What are the top factors/influencers that affect product yield ? Can we predict the likelihood of certain product defects ? What’s the downstream impact of yield change or defective parts ?
Manufacturing Use Case The 3 voices that need to be heard…
Solution for BestFitnessEquipments
An alternative news story
• Think distributed
• Architect for open-ness
• Empower the edge
Embrace intelligence at the edge…
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Integrate don’t isolate…
Business Applications
IoT Devices
Manufacturing, SCM, EAM
CRM, Sales, Service
Vertical Apps – Utilities, Healthcare, Retail
IoT Cloud Service
Connect Analyze Integrate/Act
Embrace synergies from digital…
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Architecture
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Connected Assets
Predictive and Preventive Analytics
Service Excellence
Field Service Use Case
Smart Asset Maintenance with ERP
Smart Asset Maintenance with ERP
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World-wide leading manufacturer of valves,
automation components and other products
for high-tech process industry
Challenges
• Increase lifetime and reliability of components
used in safety and health-critical chemical
processes
Solution components
• Real-time filtering and processing of events
from equipment deployed worldwide
• Integration with CRM and service ticket system
Benefit
• Proactive and timely parts replacement avoids
production downtime
• Increased knowledge of product usage
improves product quality and functionality
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Summary • Identify use cases that drive business value
• Data in Motion and Data at Rest are necessary to drive
Business applications
• Analytics and Enterprise Integration are key to realizing value of IoT data
• Embark on a phased approach
Real-world IoT for
Enterprises Abhimanyu Prabhavalkar
VP, PaaS Development, Oracle
