Impetus webcast "Real-time Predictive Analytics in Manufacturing" available at http://lf1.me/hqb/ This Impetus webcast talks about: • The business value of predictive analytics • How real-time analytics is enabling ‘intelligent-data’ driven manufacturing • A Reference Architecture and real world examples based on the experiences of Impetus Big Data architects • A step-by-step guide for successfully implementing a predictive analytics solution

1. Real-Time Predictive Analytics
in Manufacturing
Vivek A. Ganesan, Principal Architect
Yue Cathy Chang, Sr. Director, Business Development
Impetus Technologies, Inc.

2. Big Data in Manufacturing

3. The Future of Manufacturing
INTELLIGENT DATA DRIVEN
MANUFACTURING
© 2013 Impetus Technologies
Recorded webinar is available at http://lf1.me/hqb/
For more Info contact bigdata@impetus.com

4. Quality of
Management Decisions
More Data, Better Quality
Intuition
Amount of Data Analyzed
© 2013 Impetus Technologies
Recorded webinar is available at http://lf1.me/hqb/
For more Info contact bigdata@impetus.com

5. Quality of
Management Decisions
More Data, Better Quality
Relevant
Data
Intuition
Amount of Data Analyzed
© 2013 Impetus Technologies
Recorded webinar is available at http://lf1.me/hqb/
For more Info contact bigdata@impetus.com

6. Quality of
Management Decisions
More Data, Better Quality
Accurate
Relevant
Big Data
Data
Intuition
Amount of Data Analyzed
© 2013 Impetus Technologies
Recorded webinar is available at http://lf1.me/hqb/
For more Info contact bigdata@impetus.com

7. Quality of
Management Decisions
More Data, Better Quality
Quick?
Accurate
Relevant
Big Data
Data
Intuition
Amount of Data Analyzed
© 2013 Impetus Technologies
Recorded webinar is available at http://lf1.me/hqb/
For more Info contact bigdata@impetus.com

8. We Have a Big Data Situation
When traditional information systems cannot …
Store
Process
Analyze
© 2013 Impetus Technologies
Volume
Velocity
Variety
COST
TIME
Recorded webinar is available at http://lf1.me/hqb/
For more Info contact bigdata@impetus.com

9. Big Data in Manufacturing
Volume
• Sensors
• Machine data
• “Internet of
Things”
© 2013 Impetus Technologies
Recorded webinar is available at http://lf1.me/hqb/
For more Info contact bigdata@impetus.com

10. Big Data in Manufacturing
Volume
• Sensors
• Machine data
• “Internet of
Things”
© 2013 Impetus Technologies
Velocity
• Drinking from
the fire hose!
• Consume or
collapse!!
• Analyze at the
speed of data?
Recorded webinar is available at http://lf1.me/hqb/
For more Info contact bigdata@impetus.com

11. Big Data in Manufacturing
Volume
• Sensors
• Machine data
• “Internet of
Things”
© 2013 Impetus Technologies
Velocity
• Drinking from
the fire hose!
• Consume or
collapse!!
• Analyze at the
speed of data?
Recorded webinar is available at http://lf1.me/hqb/
For more Info contact bigdata@impetus.com
Variety
• Dealing with
diagnostics
• Binary data +
log data
• Ability to
analyze variety
of data formats

12. Where is the Value in Big Data?

13. Big Data and the Fourth „V‟
 The fourth „V‟ is „Value‟
 The Value of Big Data in manufacturing is in Analytics
 Gartner defines four kinds of Analytics
 Descriptive

What? Who? How? Why?
 Diagnostic

What if? Why not? Who else?
 Predictive

What will happen when?
 Prescriptive

What can I do about it?
© 2013 Impetus Technologies
Recorded webinar is available at http://lf1.me/hqb/
For more Info contact bigdata@impetus.com

14. Big Data Predictive Analytics
The formula is simple:
1. Collect data at every stage of the manufacturing process
2. Store data on a Big Data store
• Economical, Accessible, Distributed, and Scalable
3. Process data
• Manage the variety and complexity of the data
4. Analyze data
• Apply mathematical models to make predictions
© 2013 Impetus Technologies
Recorded webinar is available at http://lf1.me/hqb/
For more Info contact bigdata@impetus.com

15. Predictive Analytics Context
D
A
T
A
Sensors
© 2013 Impetus Technologies
Control
Predictions
Instruments
I
N
G
E
S
T
Analytics
•
•
•
•
•
•
Fix
Throttle
Alert
Adjust
Optimize
Abort
Model
•
•
•
•
•
Represent
Learn
Predict
Iterate
Improve
 Batch
 Historical
 Iterative
 Real-Time
 Immediate
Feedback
Recorded webinar is available at http://lf1.me/hqb/
For more Info contact bigdata@impetus.com
 Instantaneous

16. Big Data, Big Value
 In manufacturing, the greatest value is in :
 Real-Time Predictive Analytics
 Prescriptive Analytics is possible but depends on :
 Good Predictions
 Fast Feedback Loop
 Real-Time Predictive Analytics is the first step towards :
 Intelligent Data-driven Manufacturing
© 2013 Impetus Technologies
Recorded webinar is available at http://lf1.me/hqb/
For more Info contact bigdata@impetus.com

17. Manufacturing Analytics Quadrant
REAL TIME
BATCH
 Historical
 "What happened"
 Hindsight
DIAGNOSTIC
© 2013 Impetus Technologies
PREDICTIVE
Recorded webinar is available at http://lf1.me/hqb/
For more Info contact bigdata@impetus.com

18. Manufacturing Analytics Quadrant
REAL TIME
BATCH
 Near-term
 "What is happening"
 Insight
 Historical
 "What happened"
 Hindsight
DIAGNOSTIC
© 2013 Impetus Technologies
PREDICTIVE
Recorded webinar is available at http://lf1.me/hqb/
For more Info contact bigdata@impetus.com

19. Manufacturing Analytics Quadrant
REAL TIME
BATCH
 Near-term
 "What is happening"
 Insight
 Historical
 "What happened"
 Hindsight
 Inferential
 "What may happen"
 Foresight
DIAGNOSTIC
© 2013 Impetus Technologies
PREDICTIVE
Recorded webinar is available at http://lf1.me/hqb/
For more Info contact bigdata@impetus.com

20. Manufacturing Analytics Quadrant
REAL TIME
BATCH
 Near-term
 "What is happening"
 Insight
 Influential
 "Make it happen"
 Intelligent
 Historical
 "What happened"
 Hindsight
 Inferential
 "What may happen"
 Foresight
DIAGNOSTIC
© 2013 Impetus Technologies
PREDICTIVE
Recorded webinar is available at http://lf1.me/hqb/
For more Info contact bigdata@impetus.com

21. Opportunities and Challenges
Business Opportunities
• Preventive Maintenance
• Real-time/near real-time actionable
response
• Improve Productivity/Margins
• Reduce wastes, improve efficiency
• Improve Yield
• High Ingestion Rates
• Sensor/tool data with subsecond ingestion requirements
• Millions of writes per second
• Complex Log Formats
• Semi-structured data
• Huge Amount of Data
• Supply Chain
• Optimize supply chain
© 2013 Impetus Technologies
Technical Challenges
• TB/PB of data storage for
deeper analytics
Recorded webinar is available at http://lf1.me/hqb/
For more Info contact bigdata@impetus.com

22. Let's Get Real!

23. Real-Time Predictive Architecture
Machine
Data
© 2013 Impetus Technologies
NoSQL +
Search
Recorded webinar is available at http://lf1.me/hqb/
For more Info contact bigdata@impetus.com

24. Real-Time Predictive Architecture
Machine
Data
© 2013 Impetus Technologies
NoSQL +
Search
Recorded webinar is available at http://lf1.me/hqb/
For more Info contact bigdata@impetus.com

25. Input Data: Raw Logs
• Meta-data about the prospective
product line is created at a
factory site.
– E.g., number of sensors emitting log
files or readings.
• Various log files are generated:
– Containing Text.
– Containing specific Sensor readings,
continuous as well as binary values.
– At each time-step, a specific pass/fail.
© 2013 Impetus Technologies
Recorded webinar is available at http://lf1.me/hqb/
For more Info contact bigdata@impetus.com

26. Input Data: Parsing
This data is parsed into a matrix
representation
• Columns representing sensor logs
• Rows representing the time
In our dataset:
• 590 attributes, approximately every
minute
Machine 1,
sensor 1-120
– Missing Data from logs
– Failure : success :: 1 : 15
• 50,000 time-steps, i.e., 29.5 million values
(“parsed”, not raw) in a month
© 2013 Impetus Technologies
Time
• Also has labels: {+1,-1} for failure/success
from for each time-step
Machine 12,
sensor 1-92

27. Interactive Visualization
Misalignment
© 2013 Impetus Technologies
Time: 12:00
Yield: 85%
Failure < 1%

28. Interactive Visualization
Machine 4, Tool 3, 4.
Input from Machine 2.
Misalignment
© 2013 Impetus Technologies
Time: 12:01
Yield: 86%
Failure < 2%

29. Interactive Visualization
Misalignment
© 2013 Impetus Technologies
Time: 12:02
Yield: 81%
Failure < 3%

30. Interactive Visualization
Alert: Machine 2,
Tool 3, 12, 14 are
not nominal
Misalignment
30%
70%
© 2013 Impetus Technologies
Time: 12:03

31. Interactive Visualization
Alert: Machine 2,
Tool 3, 12, 14 are
not nominal
Misalignment
90%
© 2013 Impetus Technologies
Time: 12:04

32. Interactive Visualization
Alert: Machine 8 and
machine 4 are failing.
Cause: Machine 2 has
voltage imbalance
Misalignment
© 2013 Impetus Technologies
Time: 12:05

33. Interactive Visualization
Alert: Machine 8 and
machine 4 are failing.
Cause: Machine 2 has
voltage imbalance
90%
Misalignment
80%
© 2013 Impetus Technologies
Time: 12:05

34. Interactive Visualization
Alert: Machine 8 and
machine 4 are failing.
Cause: Machine 2 has
voltage imbalance
90%
Misalignment
80%
© 2013 Impetus Technologies
Time: 12:05

35. Key Takeaways
•
•
•
•
Measure and Collect Everything
Process, Diagnose, and Predict
Get Real with Real-Time
Generate Actionable Intelligence
© 2013 Impetus Technologies
Recorded webinar is available at http://lf1.me/hqb/
For more Info contact bigdata@impetus.com

36. Talk to us about Impetus Solutions and
Services for Manufacturing Big Data
Assessment
Objectives &
Strategy
Model
Solution
Modeling
BUSINESS
PROCESS
MANAGEMENT
Analyze &
Optimize
Solution
Analysis
People,
Process,
Technology
Impact
Business Analytics
and Data Science
Solution Architecture, POC
and Production planning
Technology strategy, Use Case
development & Validation
bigdata@impetus.com
Big Data Platform Implementation
© 2013 Impetus Technologies
bigdata.impetus.com
Recorded webinar is available at http://lf1.me/hqb/
For more Info contact bigdata@impetus.com
Operations and Visualization

37. Thank You
bigdata@impetus.com
bigdata.impetus.com
Recorded version available at http://lf1.me/hqb/
© 2013 Impetus Technologies