This document discusses how artificial intelligence can enhance smart manufacturing. It begins by providing background on previous industrial revolutions and defining key concepts like Industry 4.0, smart manufacturing, and the goals of connecting all aspects of manufacturing. It then discusses that while smart manufacturing relies on data, connectivity, and robotics/automation, AI is needed to truly optimize production. The document outlines how AI goes beyond industrial robots by doing tasks like driving trucks and working in call centers. It emphasizes the importance of data, machine learning techniques like classification, and involving domain experts in developing AI for manufacturing. Finally, it provides examples of how AI can improve technician experience in vehicle repair.

1. CAN A.I. MAKE SMART
MANUFACTURING SMARTER?
PAW MANUFACTURING
JUNE 21, 2017

2. WHEN DID THE INDUSTRIAL REVOLUTION BEGIN?
A. 1780’S
B. 1870’S
C. 1910’S
D. NONE OF THE ABOVE

3. WHAT IS INDUSTRY 4.0?

4. A NEW INDUSTRIAL REVOLUTION
• WHAT WAS THE PRIMARY DRIVER OF GROWTH IN THE PREVIOUS 3 INDUSTRIAL
REVOLUTIONS?
• CAPITAL
• LABOR
• PRODUCTIVITY

5. PRODUCTIVITY GAINS IN EACH INDUSTRY LEVEL
0
50
100
150
200
250
300
Industry 1.0 Industry 2.0 Industry 3.0 Industry 4.0
% Productivity jump

6. WHAT IS SMART MANUFACTURING?

7. GOAL OF SMART MANUFACTURING
• SMART MANUFACTURING IS THE INTENSIFIED APPLICATION OF ADVANCED INTELLIGENCE
SYSTEMS ON DATA TO
• ENABLE RAPID MANUFACTURING
• PROVIDE DYNAMIC RESPONSE TO PRODUCT DEMAND
• AND ALLOW REAL-TIME OPTIMIZATION OF PRODUCTION
• CONNECT ALL ASPECTS OF MANUFACTURING:
• FROM INTAKE OF RAW MATERIALS
• TO THE DELIVERY OF FINISHED PRODUCTS TO MARKET.

8. CORE OF SM
Source: Smart Manufacturing Leadership Council (2016)

9. WILL SM SUCCEED WITH ONLY
• DATA?
• CONNECTIVITY?
• ROBOTICS/AUTOMATION?

10. AI FOR MANUFACTURING: GOING BEYOND ROBOTS …

11. IMPACT OF “BRAINLESS” INDUSTRIAL ROBOTS
• BIGGEST FACTOR IN LOSS OF MFG. JOBS  INCREASE IN PRODUCTIVITY
• 88% OF MANUFACTURING JOBS WERE LOST TO ADVANCEMENTS IN TECHNOLOGY AND
AUTOMATION (BALL STATE UNIVERSITY STUDY)
• INDUSTRIAL ROBOTS ALONE HAVE ELIMINATED UP TO 670,000 JOBS BETWEEN 1990 AND 2007
• NUMBER OF INDUSTRIAL ROBOTS WILL QUADRUPLE IN THE NEXT DECADE

12. A.I. DOING WHAT ROBOTS DID (TO MANUFACTURING)
• CALL CENTERS  CHAT BOTS: TODAY
• CHECK OUT CLERKS  AMAZON GO: NEXT 1-2 YEARS
• TRUCK DRIVERS  DRIVER LESS VEHICLES: NEXT 2-3 YEARS

13. WHAT IS AI?
• NEED TO UNDERSTAND WHAT AI REALLY IS:
• AI != MACHINE > HUMANS
• AI != JUST A BETTER ALGORITHM
• AI == DATA + MACHINE LEARNING + HUMANS (SUBJECT MATTER EXPERTS)
Source: Crowdflower: Essential CIO guide to AI (2017)

14. 1. DATA
WHAT’S THE BIG DEAL?

15. What is different this time?
5745 TB/day! Manufacturing IoT: Opportunities and challenges
Rockwell Automation, October 2014
1
16
500
5745
NYSE TWITTER FACEBOOK MANUFACTURERS
IN COMPARISON …
TB/day
HOW MUCH DATA IN INDUSTRIAL SECTOR?

16. What is different this time?
But is that all the data?
25
250
51,200
FULLY INSTRUMENTED CAR SELF DRIVING CAR FULLY INSTRUMENTED JET
ENGINE
GB/HR
What about data from “connected” manufactured products … ?

17. DATA
• ONLY ABOUT 0.5% OF ALL DATA IS CURRENTLY ANALYZED
• “YOU HAVE TO START WITH A QUESTION AND NOT WITH THE DATA,”
• ANDREAS WEIGEND, FORMER CHIEF SCIENTIST OF AMAZON

18. 2. MACHINE LEARNING
“IF YOU WILL TELL ME PRECISELY WHAT IT IS THAT A MACHINE CANNOT DO, THEN I CAN
ALWAYS MAKE A MACHINE WHICH WILL DO JUST THAT!”
- JOHN VON NEUMANN (1948)

19. CORNERSTONE IDEAS IN ML/DL
• ALMOST ALL OF AI TODAY IS MACHINE LEARNING, FROM A COMPUTING PERSPECTIVE
• ALMOST ALL OF THAT ML IS SUPERVISED LEARNING
• ALMOST ALL OF THAT SUPERVISED LEARNING IS CLASSIFICATION

20. CLASSIFICATION IN ML
• CLASSIFICATION INVOLVES PREDICTING WHICH OF THE TWO OR MORE CLASSES, A GIVEN
SAMPLE MUST BELONG TO WHEN MULTIPLE (100S OR 1000S) FEATURES OR INDEPENDENT
VARIABLES ARE KNOWN

21. A BRIEF HISTORY OF ML
1950 1960s 1970s 1980s 1990 2000s 2010s
Statistical methods including LR
Today
2017
Minsky paper on MLP
describing weaknesses
1969
AI “winter”
2001 2006
2001, Brieman
introduces
Random Forests
2006, Hinton
introduces Deep
Learning
Simple neural
networks
2011
2011, IBM Watson
wins Jeopardy!

22. DEEP LEARNING
• DEEP LEARNING IS A DEEP AND WIDE STACKING OF MAPPINGS BETWEEN INDEPENDENT
VARIABLES AND THE PREDICTED OUTCOME

23. DEEP LEARNING IN ACTION
1 layer 3 layers

24. DEEP LEARNING: RECOGNIZING A FACE
Source: Michael Nielsen, http://neuralnetworksanddeeplearning.com/chap1.html

25. AI !=ALGORITHMS

26. 3. HUMANS

27. WHY DO WE NEED A HUMAN HERE?

28. WHY DO WE NEED A *QUALIFIED* HUMAN HERE?

29. WHY DO WE NEED A HUMAN HERE?
Source: Crowdflower: Essential CIO guide to AI (2017)

30. TWO TYPES OF HUMAN HELP
• SOMEONE WHO UNDERSTANDS THE DOMAIN (SME)
• SOMEONE WHO UNDERSTANDS THE TECHNOLOGY (DATA SCIENTIST)

31. THE CHALLENGE
100 M lines of code
in the average
modern vehicle
Complex
diagnostics
Difficult access to
repair information
Technician
Turnover
Repair information
available across
multiple systems
Technician Turnover is
> 30%

32. TECHNICIAN SERVICE EXPERIENCE
• Requires years of training and experience
• Prone to turnover resulting in lost knowledge
• Larger learning curve as industry and vehicles continue to
evolve
• Knowledge spread out among different technicians
• Begins with a database of existing knowledge and
information
• Continues to learn and add content
• Keeps pace with current model year knowledge
• Information accessible and all in one place
Current State:
Technician Centric
Solution:
Cloud accessible

33. PROCESS OF ANALYSIS AND KEY INPUTS
A.I.
Internal Knowledge
WebForums
Warranty
Claims
Repair
Orders
Service
History
Location CRM Forms
Service Library
Dealer/Field Notes
Collaboration Sources Customer Data
Based on technician input, Watson
starts selecting potential root
causes
The analytics engine ranks the
causes identified based on
historical data
Watson narrows
down the likely
causes by asking
further questions
Once the cause is identified, repair
instructions are made available
through direct access to the service
library
Feedback is collected on the
outcome of the repair to allow
Watson to close the loop and learn
from previous experience
Ranking of possible
root causes
Technician
Input
Deep
Dive
Technical
Procedure
Self
Improvement

34. WHERE AI CAN TAKE SM
Source: Maurice Conti TEDx https://www.youtube.com/watch?v=aR5N2Jl8k14

35. THANK YOU!
“Worrying about sentient robots is similar to worrying about
overpopulation on Mars” Andrew Ng, 2016