Deploying Predictive Analytics Solutions in the Rail Industry and Seeing a Return on Investment

D e p l o y i n g P r e d i c t i v e A n a l y t i c s S o l u t i o n s

i n t h e R a i l I n d u s t r y a n d S e e i n g a R e t u r n

o n I n v e s t m e n t

R o b e r t M o r r i s , P h . D .

C o - F o u n d e r & C h i e f S c i e n c e O f f i c e r

P r e d i k t o , I n c .

S H O W I N G B U S I N E S S V A L U E F R O M

D E P L O Y E D P R E D I C T I V E A N A T Y I C S I S

I N D U S T R I A L B U S I N E S S E S H A V E B I G D A T A B U T

S T R U G G L E T O S E E V A L U E F R O M I T .

S O M E C H A L L E N G E S

1 )

I D E N T I F Y I N G T H E L O W E S T H A N G I N G F R U I T

2 )

M A X I M I Z E T H E U S E O F D E S P E R A T E D A T A

S O U R C E S

3 )

N E E D F O R A D A P T A T I O N T O T E M P O R A L A N D

S P A T I A L C O N T E X T C H A N G E S O N T H E F L Y

4 )

S C A L E A C R O S S A T R E M E N D O U S V O L U M E O F

U S E - C A S E S A N D B E I N G A C C U R A T E

5 )

S H O W I N G T H E V A L U E T O B U S I N E S S

6 )

C O M M U N I C A T I N G T H E V A L U E O F R E S U L T S

B U S I N E S S E S T E N D N O T T O K N O W W H A T

T H E Y W A N T O R C A N U S E

P R E D I C T F U T U R E E V E N T S ?

F O R E C A S T R E L I A B I L I T Y ?

I N S I G H T T O O E P R A T I O N A L D A T A ?

T R I A G E D M A I N T E N A C E ?

I T ’ S N O T J U S T A N A N A L Y S I S … I T ’ S A S O L U T I O N

A C C U R A C Y V S . U T I L I T Y

W E R E Y O U R I G H T O R W R O N G ?

H O W R I G H T I S R I G H T ?

H O W A C C U R A T E M U S T W E B E T O S H O W

V A L U E ?

H O W D O E S P R E D I K T O S C A L E ?

A U T O - D Y N A M I C

P R E D I C T I V E A N A L Y T I C S S O F T W A R E

G E T T I N G T O V A L U E W I T H T H E

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H O W P R E D I K T O S O F T W A R E D E L I V E R S

A C T I O N A B L E R E S U L T S

PREDIKTO ENTERPRISE PLATFORM

PREDIKTO INPUT API’S AUTO-DYNAMIC DATA TRANSFORM. ENGINE AUTO-DYNAMIC MACHINE LEARNING ENGINE PREDIKTO OUTPUT APIS & BI VIZ.

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A R C H I T E C T U R E D I A G R A M

Standard JSON

AutoDynamic

Feature Engineering

AutoDynamic

Feature Selection

ETL

Data Cleaning

Dimension

Reduction

AutoDynamic

Machine Learning

Ensemble

Post-Processing

and Calibration

Email

SMS

WebHook

UI Data Store

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R E D U C I N G B A D S T O P S

I N F R E I G H T R A I L R O A D

B U S I N E S S C H A L L E N G E

•

Class 1 railroad experiencing unscheduled train stops

due to false warnings from wheel monitoring sensors

(HBDs)

•

800 HBDs installed throughout 22,000 miles of track

•

Unscheduled railroad stops due to HBD failures costing

over $10M annually

S O L U T I O N

•

Use the Predikto platform to improve reliability, reduce

train delays and reduce maintenance costs

•

Automatically predict which HBDs are expected to fail

within the next 7 days. Allows maintenance teams to be

proactive and focus on the right equipment and tools

•

Dashboard with GIS maps showing bad actors

•

Asset health score to show worst performing HBDs

R E S U L T S

• Solution entered production June 2014 • 12.7% reduction in the number of bad stops • $1.5M impact in Year 1

• Predikto identifying 37% of all bad stops with 7 day advanced

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P R E D I C T I N G F A I L U R E S

I N C O M M U T E R T R A I N D O O R S

B U S I N E S S C H A L L E N G E

• Malfunctioning doors on commuter trains is a costly

problem for manufacturers, service providers, and passengers

• Manufacturers have Service License Agreements (SLAs) that

result in fines for service delays due to failed equipment

• When a door on a commuter train does not open properly,

passengers take longer to exit and board, resulting in delays, increased safety incidents, and potential for loss of revenue

S O L U T I O N

• Actionable and tailored prediction of whether a specific train

door will malfunction at some point in the future and allowed for enough time so that an actionable response to the problem could happen, but before delays occurred

• Malfunctions over the next 7 days delivered via the “Cloud” • The output from the Predikto’s automated solution were to

be interfaced with the customer’s existing data management to take action on immediately

R E S U L T S

• Predictions of whether and when a specific door in a “door set”

on a specific car on a specific train will fail, providing for a 7 day window.

• The precision (accuracy) of the “warnings” produced by

Predikto is in excess of 84 percent.

• The solution reduces delays caused by door failures, enhances

train travel velocity, expedites repair time, and most importantly, saves time and money.

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P R E D I C T I N G F A I L U R E S

I N R A I L R O A D T R A C K

B U S I N E S S C H A L L E N G E

•

Cracks or breakages in segments of track significantly

increases the likelihood for derailment.

•

Need for better methodology to prioritize track inspection.

•

Narrowing inspection to a small track segment.

S O L U T I O N

•

Analysis of historical track data, usage and weather to

identify risk track segments

•

Provide granular predictions to quarter mile segments.

•

Provide a lead-time to allow the customer to properly

schedule and procure for predicted failures, thus reducing

operational costs.

•

Algorithms focused on accurate detection.

R E S U L T S

• Actionable predictions on which half-mile segments of track

are most likely to experience a defect well in advance of it actually occurring at some point during the following 3 months with a 63% accuracy.

• Predictions on defects were based on data provided by the

customer including: ultrasonic readings, historical defects and track details, Class I Railroad company (train movements, tonnage, etc.), and data built in by Predikto (e.g., weather).

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P R E D I C T I N G D E L A Y S

I N B U L L E T T R A I N S

B U S I N E S S C H A L L E N G E

•

European railroad experiencing stops/delays due to

mechanical failure

•

Delays greater than 20 minutes including breakdowns

•

Cost to pull engine out of service very high

S O L U T I O N

•

Predikto providing alerts at train, car, component, and

sub-component levels

•

Provide top most likely error codes associated with failure

•

Algorithms configured for low false positive rate

•

Daily notification to operations team

T A R G E T R E S U L T S

• 100% Precision in Identifying 10% of Control Unit Delays • Identification of failures and delays greater than 20 minutes

1-4 days in advance.