Pursuing the digital railroad ken donnelly

1. © 2015 IBM Corporation
Pursuing the Digital Railroad
Ken Donnelly
IBM
Global Systems Integrator Sales Leader
March 2015

2. © 2015 IBM Corporation
Agenda
Pursuing the Digital Railroad
– Introduction and Overview
– Creating interconnected, instrumented and intelligent systems
– Improving performance by reducing unscheduled outages, predicting service demand and
increasing efficiency with predictive technology.
– Money talks – how much could your railway be saving by effectively leveraging sensor technology?

3. © 2015 IBM Corporation
DRIVERS OF CHANGE CHALLENGES STRATEGIC IMPERATIVES
Economic Growth
Global economy is expanding and citizens are getting
wealthier. Rail providers will need to expand capacity to
keep up with freight and passenger growth
Passenger Growth
As the number and size of cities grows, pressure on
rail system to move people between and within those
cities grows.
Globalization
The growing interconnectedness
of the world is driving growth in demand, with an expectation
of improved service
Technology Improvements
Technology now enables the capture and analysis of real-
time information about the status, location and condition
of rail operations
Capacity and congestion
Meet the growing, changing demand efficiently,
consistently and profitably?
Empowered customers
Deliver choices for ticket purchase, changes in
travel plans, and presenting information in the
way that passengers value
Efficient, green operations
Reduce cost and dependency on scarce resources while
reducing environmental impact.
Safety and security
Increase the safety of operations, with less impact on
customers and reduce exposure to security risks
Predict demand and optimize
capacity and assets.
Dramatically improve the end-to-
end traveler experience.
Improve rail operational efficiency
while reducing environmental
impact.
Assure safety and security of rail
Global Rail Business Drivers and Trends

4. © 2015 IBM Corporation4
Something to Think About:
Is this how we approach asset management and optimization today?
Assets need to enable the services that maximize the profits of an enterprise.
The enterprise should manage asset maintenance and deployment to maximize profits, not to minimize asset
costs.
IBM Asset Optimization moves asset maintenance and deployment from a low cost paradigm to an
investment paradigm.
The investment paradigm means that maintenance and deployment costs are incurred at the
level necessary for the enterprise to operate at its profit-maximizing level.
4

5. © 2015 IBM Corporation
ARC Report – The Internet of Things (IoT)

6. © 2015 IBM Corporation
Agenda
Pursuing the Digital Railroad
– Introduction and Overview
– Creating interconnected, instrumented and intelligent systems
– Improving performance by reducing unscheduled outages, predicting service demand and
increasing efficiency with predictive technology.
– Money talks – how much could your railway be saving by effectively leveraging sensor technology?

7. © 2015 IBM Corporation
Intelligent Sensors Growth Rate
7
1 - Sources:
a. Berg, iSuppli, and ABI M2M forecast average for 2005-2011 extrapolated at 2010-2011 growth rate.
b. FierceWireless “AT&T moves closer to embedded wireless vision” May 8, 2009

8. © 2015 IBM Corporation
The project used data mining, machine learning and predictive modeling to predict impending failure/alarm of critical rail car
components. The prediction drives proactive inspection and repairs, reducing operational equipment failure
Machine Vision Detector
Optical Geometry Detector
Truck Performance Detector
Wheel Impact Load Detector
Acoustic Bang Detector
Hot Box Detectors
Warm Bearing Detectors
Hot Wheel / Cold Wheel Detectors

9. © 2015 IBM Corporation
Intelligent
A more intelligent transportation infrastructure will put all this new data to work.
– Mobile monitoring systems will provide railroads with more intelligence through continuous real-time data
capture and analysis, such as the health of rolling stock and operational data from manifest verifications
to freight condition and intrusion detection.
– Sensors on cars will trigger messages based on decision modeling and analytics.
– Autonomic routines will then dispatch services, order parts, schedule maintenance and perform remote
diagnostics.
– Eventually, such technologies could reduce the need for fixed infrastructure along the wayside and give
railroads the flexibility and responsiveness they need to optimize crew schedules, and integrate
passenger and freight transport more seamlessly, with far fewer delays.
Locomotive
Health Score
Locomotive
Life Span
Derived
Measurements
Primary
Predictors
Predicted
Monitored
Equipment
(Diesel Loco)
- Alternator
- Engine
Alternator
Amperage
Alternator
Temp
Engine
Temperature
LUBE
Quality
Engine
RPM
Alternator
Amperage
Engine
Temperature
Predictive Model
Used
Linear Regression
Model
Cox Regression
Model
others..

10. © 2015 IBM Corporation
Agenda
Pursuing the Digital Railroad
– Introduction and Overview
– Creating interconnected, instrumented and intelligent systems
– Improving performance by reducing unscheduled outages, predicting service demand and
increasing efficiency with predictive technology.
– Money talks – how much could your railway be saving by effectively leveraging sensor technology?

11. © 2015 IBM Corporation
Total Cost of Ownership
End of Life Replacement Costs
Warranty Tracking Inability
Manufacturer TCO Differences
Unknown factors that reduce TCO
Uncertain Inventory Requirements
No Closed Loop Measures/Metrics
Maintenance Inefficiencies
High Backlog Confusion
Crew, Tool, and Resource Utilization
Inadequate Response Time
Inadequate level of Maintenance
Emergency vs. Routine Redundancy
Complex Work Planning
Customer Behavior Uncertainty
Asset Utilization Fluctuations
Customer Satisfaction Impacts
Loss of Revenue
Service Level Agreements not met
Reliability Consequences
Inability to Predict Failure
Effect of Usage and Environment
Restoration times are uncertain
Root Cause Failures not understood
Risk Factors not Clear
Enterprise Assets
Typical Pain points

12. © 2015 IBM Corporation
What if you could accurately predict which
characteristics tend to lead to an increased
frequency of failures?
What if, when an asset is scheduled for maintenance, you
could predict what parts are likely to fail in the near
future?
What if you could identify the characteristics that tend to
increase ownership cost and downtime over the life of a
system?
What if you could replace those parts
that have not yet failed and avoid
further unscheduled downtime?
What if you could quickly mine the thousands of logs that describe the
maintenance performed on systems and determine what important
observations are being logged by the maintenance team?
What if you could unearth patterns in maintenance operations
over time that could point to opportunities for improvements?
Are you facing such challenges?
And/or other ones?

13. © 2015 IBM Corporation
Predictive Analytics is essential to answering these question ….
Captured
Detected
Inferred
Use Structured Data &
Unstructured Data
Descriptive
Analytics
Prescriptive
Analytics
Predictive
Analytics
Simplified to be consumable and
accessible to everyone,
optimized for their specific
purpose, at the point of impact,
to deliver better decisions and
actions through:
What trends will
continue?
Forecasting
How can we achieve the best
outcome and address
variability? Stochastic
Optimization
What happened?
What exactly is the
problem?
How many, how
often, where?
What actions are
needed?
What could happen
if? Simulation
How can we achieve the best
outcome? Optimization
What will happen
next if? Predictive
Modelling
Analytics Sophistication
• From multiple (valuable) sources
Sensors (temp, PSI,..)
Maintenance Records
R&M, OMS, Warranty,..
Operating Conditions
Multiple
Data Sources
• From multiple (valuable) sources
Multiple
Data Sources
Use Structured Data &
Unstructured Data
• From multiple (valuable) sources
Multiple
Data Sources
Use Structured Data &
Unstructured Data
• From multiple (valuable) sources
Multiple
Data Sources

14. © 2015 IBM Corporation
Agenda
Pursuing the Digital Railroad
– Introduction and Overview
– Creating interconnected, instrumented and intelligent systems
– Improving performance by reducing unscheduled outages, predicting service demand and
increasing efficiency with predictive technology.
– Money talks – how much could your railway be saving by effectively leveraging sensor technology?

15. © 2015 IBM Corporation
“Gentlemen, we have run out of
money. Now we must think!”
- Sir Winston Churchill
A First Thought ……

16. © 2015 IBM Corporation
An Example
Increase reliability 30% improvement by 2015
Cost savings of £2.2B
Annual passenger growth since start of the 2008 recession 3.8%
700% safety improvement
40% asset reliability performance improvements
£400 million saving due to asset life extension
£4.6 million saving due to mobilization of inspection processes
Improvement in reliability (MDBF) 478% across specific asset classes
This is more than just technology and process, it is about
people and organizational culture. For the business transformation to deliver the results of world class this has to be
led from the very top of an enterprise.

17. © 2015 IBM Corporation
Organizational Challenges
Probably the most important and challenging aspect of an a program is addressing the needs of people within
the organization.
Because asset management is holistic, it depends upon comprehensive coordination and communication. Most
organizations are functionally segregated.
The challenge is to help the people in the organization understand and appreciate the benefits of the process
from the perspective of the entire organization rather than the viewpoint of their individual units.
Another challenge is building organization-wide commitment to change. Creating buy-in at both the executive
and operations levels of the organization is critical to success.
Ad hoc
Foundational
Competitive
Differentiating
Break away
•Spreadsheets and extracts
•Data warehouses and
reporting
•Contextual business rules and
pattern recognition
•Content analytics of
unstructured data
•Master data
•Managing structured Data
•Metrics, dashboards, scorecards
•Predictive, real time
analytics
Big Data Value Proposition

18. © 2015 IBM Corporation
The Digital Railroad

19. © 2015 IBM Corporation
Summary
The World is changing rapidly ….. Intelligent Devices and the ability to effectively harvest the right data
and convert it to meaningful Information is now more crucial than ever.
Technology is easy …. Organizational changes are CHALLENGING
– Information Silo’s must be eliminated.
Paper Records are archaic … Do you have a strategy to capture data real time?
Seconds count to delivering a Safe, Reliable, and Profitable Service.