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Presentation Slide Deck on www.senturus.com
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Introductions
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Why this topic?
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Predictive Maintenance and Quality (PMQ) Overview
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Customer Examples
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PMQ Architecture Overview
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Q & A
Today’s Agenda
4
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Architectures & Data Transformation
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Solutions
•
Tools
•
Methodologies & Techniques
•
People & Processes
Senturus Webinar Series Topics
5
Copyright 2013 Senturus, Inc. All Rights Reserved.
Our Approach:
Our webinars are delivered by Senturus team members
and industry experts (guest speakers)
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Architectures & Data Transformation
•
Solutions
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Predictive Analytics applied to Maintenance & Quality
•
Tools
•
Methodologies & Techniques
•
People & Processes
Today’s Webinar
6
Copyright 2013 Senturus, Inc. All Rights Reserved.
Our Approach:
Our webinars are delivered by Senturus team members
and industry experts (guest speakers)
John Peterson
CEO,
Senturus
Anuj Marfatia
IBM Program Director,
Solutions Marketing
Who we are
Senturus: Business Analytics Consultants
Business
Intelligence
Enterprise
Planning
Predictive
Analytics
Our Team:
Business depth combined with technical expertise.
Former CFOs, CIOs, Controllers, Directors
700+ Clients, 1400 Projects, 13 Years
10
•
Senturus has been a IBM Cognos “
Premier
” Partner for
12 years (One of 10 such Partners in North America)
•
Senturus has won a number of IBM and Cognos
awards
over those years
•
Senturus sits on the
board
of the Cognos User Group of
Northern California
•
A number of Senturus consultants are
former IBM
Cognos
employees
•
IBM Cognos professional services has
often staffed
their consulting engagements with Senturus people
•
We have delivered over 1,000
successful
projects using
IBM technologies
Why now?
The Evolution of Business Analytics
Operational Reports
Dashboards & Scorecards
SQL Reports
OLAP & Ad-hoc
Forward
Looking
Integrated Planning &
Forecasting
Advanced Visualization
Predictive Analytics
Rear
View
Mirror
Present
Future
Past
Today’s Situation
•
Organizations have spent over a
Trillion
dollars on
operational systems (ERP, CRM, etc.) which contain
massive amounts of valuable digital data
•
Organizations have spent
Billions
of dollars on Business
Intelligence systems and data marts/warehouses
•
New IP-based devices are throwing off
Billions
of records
(“data exhaust”) daily
•
I.E. – The Data Is There!
Today’s Situation (cont.)
•
Moore’s law has driven
massive increases
in computing
power, storage space, and network bandwidth.
•
…while
reducing cost
•
Thus, driving esoteric
applications into the mainstream
.
•
I.E. The Power and Software is There!
Today’s Situation (cont.)
•
BUT, most BI systems still simply pump out
canned reports showing
past
activity
•
AND, expect humans to sift through it all in
order to make impactful business decisions and
take appropriate action for the
future
Shifting from Reactive to Proactive
Poor asset
performance
• Lack of visibility intoasset health • High costs of unscheduled maintenance • Inability to accurately forecast asset downtime and costs
• Resultant
unnecessary process proliferation • Aging assets pushed
to limits to meet consumer needs
Limited process
integration
• Lack of visibility of predictors across organizational silos • Difficulty synchronizing demand and supply • Too many manualprocesses and information sources • Losses in processes have become normal • Resource complexity makes it harder to respond to changing needs Raw-material price volatility Compliance and scrutiny Aging workforce Complex supply chains Customer demands
Lean
operations
Predictive and Business Intelligence Predictive Maintenance and Quality
Number of sensors by 2015
1Estimated price of average
passive sensor by 2021,
representing a 66 percent
decrease in eight years
2Percent of CIOs with mandates
to transform the business who
are looking to simplify key
internal processes
499%
#1
Failure of critical assets was
the top risk stated by
executives as having the
biggest impact on operations
31 trillion,
USD0.03
Interconnected growth,
lower data-capture cost
Focus on operational
processes
Risk of asset failure
1Making Markets:Smarter Planet. IBM Investor Briefing, 2012
2 Big Data-Startups, “The Great Sensor-Era: Brontobytes Will Change Society,” April 16, 2013.
3 Aberdeen Group, Asset Management: Using Analytics to Drive Predictive Maintenance, March 19, 2013. 4 IBM, The Essential CIO: Insights from the Global Chief Information Officer Study, May 2011.
Predictive and Business Intelligence Predictive Maintenance and Quality
IBM Predictive Maintenance and Quality
Accelerate time to value
• Real-time capabilities
• Big data, predictive and
advanced analytics
• Quicker and more-accurate
decision making
• IBM Maximo
®integration
• Open architecture
• Business intelligence
Improve
asset productivity
Increase
process efficiency
Reduce
Operational
costs
• Helps monitor, maintain and optimize
assets for better availability, utilization
and performance
• Helps predict asset failure to better
optimize quality and supply chain
processes
• Reduces guesswork during the
decision-making process
PMQ Enables Better Business Outcomes
Combined with out-of-the-box models,
dashboards, reports and source connectors
PMQ offers business value for organizations
BUSINESS USE CASES BUSINESS VALUE
Predict asset failure
•
Assess failure based on usage
and wear characteristics
•
Use individual-component information,
environmental information or both
•
Help identify conditions that can lead to
high failure
Predict poor quality parts/components
• Help detect anomalies within processes
• Compare parts against a master
• Conduct in-depth, root-cause analysis
Estimate and extend component life
Increase return on assets
Improve maintenance, inventory and
resource schedules
Improve quality and reduce recalls
Reduce time to identify issues
Improve customer service
Israel Electric Increases Grid Reliability
20% cost reduction
by avoiding the expensive
process of reinitiating a power
station after an outage
USD80,000 savings
per turbine on petrol combustion
costs by avoiding malfunctions of
turbine components
Increased
efficiency
of preventive maintenance
schedules, costs and resources,
resulting in fewer outages and
higher customer satisfaction
Business problem: The company’s research institute is charged with
improving the safety and reliability of power generation and transmission while fueling innovation. That includes planning for disruptive events such
as solar storms, making improvements in transmission efficiency, incorporating new sources of renewable energy into the grid and analyzing growing volumes of data from an increasingly smart grid.
Solution: This energy provider uses powerful predictive analysis to understand when and why outages occur so it can take steps to prevent
them.
Honda R&D Co., Ltd Uses Predictive Analytics
50% reduction
in carbon dioxide emissions by
commercializing EV technology
Business challenge: Because all-electric vehicles (EVs) do not use gasoline as do traditional or hybrid cars, they rely entirely on their batteries for power.
Honda R&D Co., Ltd., a division of Honda Motor Co., Ltd., wanted to better understand what factors had the greatest effect on battery performance and
longevity.
The smarter solution: Honda R&D can now gather and analyze near-real-time battery data from Fit EVs on the road in Japan and the United States. Analysis can identify which operating factors, such as road conditions, charging patterns and trip length, have the greatest effect on battery life. Further analysis can help the automaker predict when batteries need to be replaced so it can alert owners
in advance.
“Data gathered from the real-world operation of our vehicles is critical to predict the longevity of current batteries and greatly influences future product design.”
—Senior chief engineer, Automobile R&D Center
Boosts confidence
and customer satisfaction with
EVs by improving performance
Improves design
by analyzing massive amounts
of operating data
#1
Asset performance
Process integration
Collect and integrate data
Structured and unstructured, streaming and at rest
Generate predictive and statistical models
Attain analytical insights
Display alerts and recommend
actions
Act upon insights
#2
#3
#4
#5
Predictive
Maintenance
and Quality
• Data agnostic
• User-friendly model creation
• Interactive dashboards
• Enables faster decisions
PMQ Analyzes Data from Multiple Sources
PMQ uses data from raw format to action
Telematics, manufacturing execution systems, existing databases, distributed control systems High-volume streaming data Enterprise asset management systemsIBM Predictive Maintenance and Quality
End user reports,dashboards, drill downs
With a proven architecture
• Advanced analytics powered by IBM SPSS
and Cognos software • Data integration
provided by IBM Integration Bus and
IBM InfoSphere®
Master Data Management Collaborative Edition software, which feeds a
prebuilt, data schema based on IBM DB2®
software
• Process integration with automatic work-order
generation from Maximo software • Data models, message
flows, reports, dashboards, business rules, adapters and key
performance indicators Telematics, manufacturing execution systems, existing databases, distributed control systems High-volume streaming data Enterprise asset management systems
Predictive
analytics
Decision
management
Business
intelligence
Analytic data store
(Prebuilt data schema for storing quality, select machine and production data, and configuration)
Integration bus
(Prebuilt data schema for storing quality, select machine and production data, and configuration)
End user reports, dashboards, drill
downs
Converges Asset Management & Analytics Capabilities
Analytical insights Asset lifecycle manage-ment Facilities operation Staff planning Supply chain processes• Better maintenance
windows to reduce
operating expense
• More efficient assignment
of labor resources
• Enhanced capital
forecasting plans
• Enhanced spare parts
inventory
• Automated analytical
techniques, including
anomaly detection for
assets and sensors
• Improved reliability and
uptime of assets
• Asset maintenance history
• Condition monitoring and
historical meter readings
• Inventory and purchasing
transactions
• Labor, craft, skills,
certifications and calendars
• Safety and regulatory
requirements
Enterprise asset
management
Predictive Maintenance
and Quality
=
Better outcomes
+
Maximo integration
Real-time capabilities
Big data, predictive and
advanced analytics
Accelerated
time to value
Quicker, more accurate
decision making
Open architecture
Business intelligence
PMQ Key Features
Infrastructure
activities
• Program and project management • Setup and installation
• Hardware • Software • Specialists • Hosting
Analytical
activities
• Solution impact assessment • Business case development • Use case definition• Data integration • Information modeling • Predictive modeling
Specialized
skills
• Integration skills • Business consulting • Industry skills • Maintenance experts • Maximo specialists • Industry expertise • Scientists and mathematiciansProf. services
Vendor software
Vendor research
Vendor systems and technology
Client value
PMQ is a Comprehensive Solution
Business value
assessment
Align business capabilities with business strategy, and
recommend a road map for improved
value.
Solution workshop
Lay out the path ahead, from immediate improvements to a common future vision.
Proof of concept
Prove the path forward, starting small and
scaling up.
1
Visioning workshop
Whether via web seminar, at your facility
or in an IBM solution center, we can begin
charting a course.
2
3
4
Let’s Get Started with Better Business Outcomes
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