More Related Content Similar to Predictive Analytics: Extending asset management framework for multi-industry applications leveraging HP’s Big Data HAVEn platform (20) Predictive Analytics: Extending asset management framework for multi-industry applications leveraging HP’s Big Data HAVEn platform2. © Copyright 2014 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice.2
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Session BB3061 Speaker James Redlinger, Steffin Harris
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3. © Copyright 2014 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice.
Predictive Analytics
Extending asset management framework for multi-industry
applications leveraging HP’s Big Data HAVEn platform
James Redlinger – HP Global Alliance CTO
Steffin Harris – North American Big Data Leader, Capgemini
June, 2014
4. © Copyright 2014 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice.4
Welcome to HP HAVEn
Approaching Big Data with an intelligent software architecture
Social Media IT/OT ImagesAudioVideo
Transactional
dataMobile Search engineEmail Texts
Catalog massive
volumes of
distributed data
Hadoop/
HDFS
Process and
index all
information
Autonomy
IDOL
Analyze at
extreme scale
in real time
Vertica
Collect and
unify machine
data
Enterprise
security
Powering
HP software
and your apps
nApps
Documents
HAVEn
5. © Copyright 2014 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice.5
Capgemini nApps in Action
Commercial Insurance
Risk Analytics (CIRA)
Predictive Asset
Management (PAM)
Operating
Efficiency
Piecemeal
Data
Unified
Information
Operate at frontiers of
efficiency &
productivity
Claim Cost
Reduction
Generic
Approach
Identify the
Higher Risks
Reducing the cost to
settle claims
Managing Risk
Account Level
Management
Individual Risk
Level Management
Mitigate risk &
improve compliance
Customer
Experience
One Size
Fits All
Able to Reward
The Good Risks
Improved customer
satisfaction
Cost Management
Missed
Opportunities
Lower Loss
Ratios
Increase profitability &
improve use of capital
6. © Copyright 2014 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice.6
Webinar Speaker Introduction
Practice: NA BIM
Level: Principal
Office: South San Francisco
“Capgemini is helping to revolutionize
the Big Data Industry by developing
industry specific partner enabled
solutions to solve our customers most
challenging problems
- HP Discover Barcelona 2013
Consulting Competencies
Big Data Strategy – Technologies, Platforms, Analytics
Project Mgmt, Governance, IT Strategy, Performance Mgmt
EDW Architecture, BI/DW Strategy, Design, Implementation, Run
HP Vertica, SAP, Oracle, Teradata, Hadoop, BI/BW/BOBJ, OBIEE,
SSAS/RS,
ERP Design, Implementation & Delivery
Energy, Retail Consumer Products, High-Tech, Manufacturing, Telco.
Business Experience / Education
20 years of Management Consulting Services
Global CTO Board Member
Big data platforms, Analytics, Services
Master Data Management, Knowledge Management, KPI Development
Business Intelligence (BI) Strategy, Enterprise Performance Management,
Sales Performance Management, Infrastructure & IT Portfolio
Optimization, KPI Development
Business Intelligence Solution Services (Arch, Technology & Process)
Education: Bachelor of Science, Airway Science, Masters, Information
Systems Management.
”
7. © Copyright 2014 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice.7
Who is interested in this solution?
What should my strategy be to
ensure that we take full advantage
of Big Data Analytics and remain
relevant in the marketplace. How do
I become the leader in the world of
everything being connected?
CEO
We are helping client to improve
reliability and availability of plant and
equipment whilst attempting to
reduce operational cost by using
predictive asset analytics.
COO
Understanding the value drivers,
real profitability through enterprise
performance analytics is changing
business strategies the focus is on
revenue leakage management,
working capital, expenses and
controlling analysis and
optimization of Assets.
Client is talking Innovation, Big
Data, Market is Talking Internet of
Things – How can I be more
effective in enabling the enterprise
to support an IOT strategy?
CFO
CIO
We are using detailed analysis of
activities locations and schedules to
optimize workforce, supply
chains and logistics – achieving
substantial performance
improvements.
COO
Risk is a complex area, analytics
(and increasing the use of big data)
is helping our client to quantify and
manage those risks in a proactive
manner.
Risk Manager
8. © Copyright 2014 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice.8
Big Data & Analytics Industry Outlook (II/IOT)
Asset Intensive Industry Revolution powered by
Industrial Internet, Internet of Things
Source: GE’s Jeff Immelt’s vision on IoT
9. © Copyright 2014 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice.9
Predictive Asset Analytics
Advanced Analytics to monitor, predict and optimize asset performance in an increasingly connected world
Industrial Internet, Internet of Things
Data Insights from Asset Analytics End Users
Asset Register
Sensors/ Telemetry
– usage, operations settings,
logs, events / alarms, …
Asset Management
Guidelines
Performance Benchmarks
Failure/ Warranty Claims
Asset Service History
Field/Technical
Inspection Notes
Asset & Services Catalog
Asset Financials
Social Media /
Call Center Data
Engineering, R&D
Manufacturing
Technical Services
Quality
Logistics, Sourcing
Field Services
Finance & HR
Suppliers & Customers
3rd Party Service Providers
Reliability Metrics
Event Sequence
Plant Optimization.
Optimize
Equipment
Reliability
Performance Patterns
Metrics/ KPIs & Drivers
Alerts & Control.
Benchmarking
Segmentation/Clustering
Business Rules.
Root Cause Analysis
Drivers & Time to failure
Failure Prediction.
Warranty & Claims Mgmt
Parts/ Field optimization
Fraud Detection.
Operations
Control
Performance
Management
Service
Optimization
Predictive
Asset
Maintenance
10. © Copyright 2014 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice.10
Maintenance – The Maturity Model
Reactive
[fix when
something
fails]
Preventative
[OEM
prescriptions,
observations by
technicians]
Condition Based
[rule based monitoring
at real time to track
anomalies]
Predictive
[analyze usage
patterns, trends in
behavior,
maintenance
history to predict
failure well ahead
of time]
Business Drivers
Prevent Unplanned
Outages
Improved MTBF, MTTR,
MTTF
Reduced Maintenance
Improved budgeting &
forecasting
Improved decision
making
Minimize repeat repairs
Unplanned Outages cost
The Oil & Gas Industry
$100B/Annually
Unplanned Outages in
The Utilities Industry can
Cost the business $55K/Hr
11. © Copyright 2014 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice.11
Solution Overview – Asset Analytics
Monitor, predict and optimize asset performance
Joint Solution Developed with HP
Reliability Metrics
Event Sequence
Plant Optimization.
Benchmarking
Segmentation & Business
Rules.
Usage Pattern
Health Monitoring
Alarms & Control.
Failure Prediction
Root Cause Analysis.
Predictive warranty
Parts optimization
Fraud Detection.
Optimize
Equipment
Reliability
Performance
Management
Operations
Control
Predictive
Asset
Maintenance
Service
Optimization
Scale
Hadoop/
HDFS
Source
Autonomy
IDOL
Speed
Vertica
Secure
Enterprise
Security
Powering
HP Software
+ your apps
nApps
Social media Video Audio Email Texts Mobile
Transactional
data Documents IT/OT
Search
engine Images
HAVEn
12. © Copyright 2014 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice.12
Performance Monitoring & Control
Actionable insights for Energy Management
The Client, a major Energy & Utility
equipment manufacturer, launched a strategic
program to design eco-friendly energy utilization solutions
that define new global standards.
As part of the program, smart energy meters and
environment sensors were installed in key areas of
selected facilities. This data was made available for
analysis
The Mandate: Analyze energy utilization and efficiency in
a facility and extract insights to support actionable energy
management strategies.
Overview
Quality and Variations in data
• Statistical analysis to test quality of data from
the meters/sensors
• Measures and reports to flag and alert on any deviations
from expected standards
Energy Consumption Analysis
• Analyze patterns and trends in energy consumption – by
location, time, day, etc.
• Relationships between energy consumption and facility
use and facility comfort levels (light, heat, ventilation).
Data volume and quality was a limiting
factor for much of the analytics
The data was from very different types of facilities and
did not support comparative analysis or benchmarking
These data issues resulted in the project being designed
as a proof of concept on “what is possible” using
advanced analytics.
Challenge/Issue
Used limited data to showcase the potential
of analytics in energy management
Client Testimonial
“…testify that the team was able, in three month’s time,
to deliver six analytical frameworks on top of our data, in
spite of the data’s relatively poor quality. As a conclusion,
this project was a success and demonstrated the team’s
skills”.
Benefits
Exploratory cross tabulations
including frequency counts,
histograms etc.
Alerts the energy manager to actively
identify deviations
‘What if’ simulation for key Decisions.
Solution
13. © Copyright 2014 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice.13
Predictive Asset Analytics Industry Solutions
Metrics/ KPI Definition by
Department
Reports, Dashboards & Trends.
Warranty Management for Indian
Auto Manufacturer
Prediction of warranty claims and
cost by vehicle & part.
Warranty Claims Forecasting for US
Auto Manufacturer
Prediction of out-of-Stock, Over-
Stock Conditions
Market Product Penetration &
Customer Satisfaction.
Retail /Consumer Products
Analytics to understand drivers of
energy consumption and derive
control parameters.
Energy Management for E&U
Solution Provider
Analyze installed asset base and
map to VAS to drive revenue.
Service Opportunity Identification
for E&U Equipment Manufacturer
Predict Network Demand
Failure prediction
Workforce Management.
Telecom Network Analytics
Performance Monitoring
& Control for HVAC Equipment
14. © Copyright 2014 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice.14
Performance Monitoring & Control
Chillers Plant Operational Efficiency
Challenge/Issue
Large data storage (~1TB for medium size builders),
unable to analyze in near real time.
Unable to know usage of the equipment &
performance behavior.
What are the variables that controls equipment
performance.
What will be control limits on various usage scenario
KPI Description
kwPerTon It is a measure of Chiller Efficiency – it is measured as utilization of kw energy to
cool one ton of air inside the building at 40F. Lesser the better
Chiller Supply
Load Overrun
(%)
It is a measure to understand the gap between the chiller cooling load supply
and building cooling requirement .
• If it is positive => excess chiller cooling load supply => Energy saving
opportunity
• If it is negative => less chiller load supply => Customer dissatisfaction due to
not meeting the right ambient temperature inside the building
15. © Copyright 2014 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice.15
Demo
16. © Copyright 2014 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice.16
Use cases by Industry by Asset Value Chain
Industry Asset Manufacturing Asset Service Provider Asset Owner
Energy & Utilities •Asset Reliability Analysis
• Maintenance & Warranty
Limits
• Asset Failure Identification &
Prediction
• Labor Planning & Scheduling
• Asset Reliability
Analysis, RCM
• Consumption Theft
Modeling
Oil, Gas &
Petrochemical
•Asset Reliability Analysis
• Maintenance & Warranty
Limits
• Asset Failure Identification &
Prediction
• Labor Planning & Scheduling
• Asset Reliability
Analysis, RCM
• Consumption Theft
Modeling
Heavy
Engineering &
Construction
•Asset Reliability Analysis
• Maintenance & Warranty
Limits
• Asset Failure Demand
Prediction
• Service Planning & Scheduling
• Asset Reliability
Analysis, RCM
Aerospace &
Defense
• Asset Reliability Analysis
• Maintenance & Warranty
Limits
• Asset Failure Demand
Prediction
• Service Planning & Scheduling
• Asset Reliability
Analysis, RCM
Heavy Vehicles &
surface
Transportation
• Asset Reliability Analysis
• Maintenance & Warranty
Limits
• Asset Failure Demand
Prediction
• Service Planning & Scheduling
• Asset Reliability
Analysis, RCM
17. © Copyright 2014 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice.17
Use cases by Industry by Asset Value Chain cont’d
Industry Asset Manufacturing Asset Service Provider Asset Owner
Communication
Service Provider
• Asset Reliability Analysis
• Maintenance & Warranty
Limits
• Asset Failure Demand
Prediction
• Service Planning & Scheduling
• Asset Usage Analysis,
Tracking
Household Assets
–
Electrical/Electron
ic/Mechanical
• Asset Reliability Analysis
• Maintenance & Warranty
Limits
• Asset Failure Demand
Prediction
• Service Planning & Scheduling
• Asset Usage Analysis,
Tracking
Light Vehicles • Asset Reliability Analysis
• Maintenance & Warranty
Limits
• Asset Failure Demand
Prediction
• Service Planning & Scheduling
• Asset Usage Analysis,
Tracking
Medical Devices • Asset Reliability Analysis
• Maintenance & Warranty
Limits
• Asset Failure Demand
Prediction
• Service Planning & Scheduling
• Asset Usage Analysis,
Tracking
Building HVAC
Assets
• Asset Reliability Analysis
• Maintenance & Warranty
Limits
• Asset Failure Demand
Prediction
• Service Planning & Scheduling
• Asset Usage Analysis,
Tracking
18. © Copyright 2014 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice.18
Big Data challenges that are often overlooked
1. Did you just call my baby
ugly?
2. That’s not what we used
before!
3. It’s just commodity hardware,
right?
4. Is your solution sustainable?
19. © Copyright 2014 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice.19
Wrap Up/Q&A
For Additional Information Contact:
James Redlinger
HP Global Alliance CTO ,
James.redlinger@hp.com
Steffin Harris
NA Big Data Leader, Capgemini
Steffin.Harris@capgemini.com
@SHarrisSFO
20. © Copyright 2014 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice.
Thank you
21. © Copyright 2014 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice.
Editor's Notes Talking Points: S Harris
Industrial machines have always issued early warnings, but in an inconsistent way and in a language that people could not understand. The advent of networked machines with embedded sensors and advanced analytics tools has changed that reality. For the first time in history, remotely distributed machines across the globe – from MRIs to wind turbines to aircraft engines – can be monitored in real time, unlocking the language of machines and opening tremendous benefits. Add Examples to this slide by Industry Predictive Model
Benchmark Performance
Predict Efficiency
Online Dashboard
Dynamically monitor and control deviations Expected ROI/ROI examples 18