1) Beet Analytics and Soliton Technologies presented machine learning techniques to PAW Manufacturing to optimize manufacturing cycle times. 2) Their process visibility system collects data from machines to determine what is currently wrong, what could go wrong, and where hidden capacity exists. 3) They demonstrated how their system visualizes machine cycle data to detect performance issues before breakdowns and presented a phased approach to apply advanced analytics like predictive failure modeling and root cause analysis.

1. Presented to: PAW Manufacturing
The information depicted or described herein are exclusive property of BEET, LLC., and Soliton Technologies, Inc, and are
submitted in confidence. Permission to use or reproduce in any way this proprietary information is expressly withheld.
Applying Machine Learning to
Optimize Manufacturing Operation Cycle Time
Presented by:
Girish Rao, Beet Analytics
Anish Mathews, Soliton Technologies, Inc.

2. Presented to: PAW Manufacturing
(C) Beet LLC., and Soliton Technologies Inc. All rights reserved.
Who Are We?
Process
Visibility
System
answers 3 questions in manufacturing process –
 What is wrong NOW?
 What MIGHT go wrong?
 Where is the HIDDEN CAPACITY?

3. Presented to: PAW Manufacturing
(C) Beet LLC., and Soliton Technologies Inc. All rights reserved.
Customers and Partners
Customers
Potential New Customers
Partners & Resellers
Potential New Partners

4. Presented to: PAW Manufacturing
(C) Beet LLC., and Soliton Technologies Inc. All rights reserved.
Human Heartbeat
Envision - Process EKG
• Visual (Invisible to Visible)
• Easy to Interpret
• Life Saving (Prevent Heart Attack)
• Visual (Invisible to Visible)
• Easy to Interpret/Immediate Actionable
• Keep the line Running (Prevent Downtime)
Over 12 Awarded & Pending Patents covering the GUI, Data
Collection, Processing and Analytics
“Machine” Heartbeat

5. Presented to: PAW Manufacturing
(C) Beet LLC., and Soliton Technologies Inc. All rights reserved.
Machine Heartbeat
Single cycle sequence
(35.2 seconds) for a large
CNC cutting machine,
displayed by ENVISION,
the time losses are clearly
visualized
Warning (fix now) Watch (pay attention)Good (under baseline)Baseline

6. Presented to: PAW Manufacturing
(C) Beet LLC., and Soliton Technologies Inc. All rights reserved.
SEQ1
SEQ4
SEQN
SEQ1
SEQ4
SEQN
Duration
TimeOne Machine Cycle
SEQ1
SEQ4
T1 T2
Duration
TimeOne Machine Cycle T1 T2
Cycle Time = T1 Cycle Time = T2
Duration
Time
SEQ 4 Historian
SEQ4
SEQ4
SEQ4
SEQ4
SEQ4
SEQ4
SEQ4
SEQ4
SEQ4
SEQ4
SEQ4
SEQ4
SEQ4
SEQ4
SEQ4
SEQ4
SEQ4
SEQ4
SEQ4
SEQ4
SEQ4
SEQ4
Fault
SEQ4
SEQ4
SEQ4
SEQ4
SEQ4
SEQ4
SEQ4
SEQ4
Duration
Time
SEQ4
Fault
SEQ4
Factory Information System
(ActivPlant based)
ENVISION captures every motion event, and delivers actionable information
at the Point of Event
ENVISIONtm
Captures Device Level
Performance Erosion
before a breakdown
The Breakdown may
have been prevented
with ENVISION
Visualize What Is Failing Before It’s Too Late

7. Presented to: PAW Manufacturing
(C) Beet LLC., and Soliton Technologies Inc. All rights reserved.
• Cycle time of each individual motion element, and comparison against established
baseline
• Overall cycle time of motion groups and assets
• Machine state data – Auto, Blocked, Starved
• Fault and warning message data
• Notes and comments regarding machine issues or fixes
Data available for Analytics

8. Presented to: PAW Manufacturing
(C) Beet LLC., and Soliton Technologies Inc. All rights reserved.
Use Case for Advanced Analytics
• Envision data from an OEM’s robotic assembly line
• The line has about 900 motion elements with an average cycle time of 48 secs
• The line generates approximately 1.3 million cycle records a day

9. Presented to: PAW Manufacturing
(C) Beet LLC., and Soliton Technologies Inc. All rights reserved.
Cycle Time Deterioration
ENVISION history screen capture shows
• Negative trend starts on 4/22, ≈ .3 sec. to .5 sec. over baseline
• As of 5/14, the plant is working on a fix
• As of 5/17 cycles are mostly “Watch/Yellow”, .3-.5 sec over cycle
time

10. Presented to: PAW Manufacturing
(C) Beet LLC., and Soliton Technologies Inc. All rights reserved.
 How to best set control limits for individual motions?
 Can we predict how long before the unplanned downtime is going to happen?
 Can we define patterns to identify the root cause of the issues?
Questions Regarding a Motion

11. Presented to: PAW Manufacturing
(C) Beet LLC., and Soliton Technologies Inc. All rights reserved.
Phased Approach with Soliton
Phase 1
• Develop data distribution and control
limits algorithm
Phase 2
• Develop prediction model to predict
potential failures
Phase 3
• Develop model to determine root cause

12. Presented to: PAW Manufacturing
(C) Beet LLC., and Soliton Technologies Inc. All rights reserved.
Setting Control Limits for Individual Motions
Envision
• Envision (Web App) calls the C# DLL with the
data for calculating the Mean and the
tolerance limits
Soliton’s
DLL
• Developed in C# .NET
• Call Python distributable and sends the result
back
Python
• Fit the data to multiple distributions
• Pick the best distribution that fits
• Return the result

13. Presented to: PAW Manufacturing
(C) Beet LLC., and Soliton Technologies Inc. All rights reserved.
Distribution Details
Source: Wikipedia

14. Presented to: PAW Manufacturing
(C) Beet LLC., and Soliton Technologies Inc. All rights reserved.
Picking the closest fit distribution
• Log likelihood test
• Modified KS Stat Test
http://www.weibull.com/hotwire/issue71/relbasics71.htm
http://www.itl.nist.gov/div898/handbook/eda/section3/eda35g.htm

15. Presented to: PAW Manufacturing
(C) Beet LLC., and Soliton Technologies Inc. All rights reserved.
Evaluation of DLL
• Generated 2500 test data set - 500 each for Uniform,
Normal, Lognormal, Weibull and Exponential distributions

16. Presented to: PAW Manufacturing
(C) Beet LLC., and Soliton Technologies Inc. All rights reserved.
Evaluation of DLL

17. Presented to: PAW Manufacturing
(C) Beet LLC., and Soliton Technologies Inc. All rights reserved.
Current Status & Next Steps
• Data distribution module integration testing with Envision
• Phase 2
• Identification of right data parameters for building predictive models for
workstation down
• Develop predictive model and filter out false positives
• Phase 3
• Root cause and variability analysis

18. Presented to: PAW Manufacturing
(C) Beet LLC., and Soliton Technologies Inc. All rights reserved. 18
1ST SPAC 2ND SPAC
LAST SPAC

19. Presented to: PAW Manufacturing
(C) Beet LLC., and Soliton Technologies Inc. All rights reserved. 19
Trend and Variance
- Visually SPAC RIGHT (all 3) seem
to exhibit two distinct
characteristics
- A clear upward trend (non-
stationarity)
- A change in variance (around
a mean trend) in the
event_length
- We need to statistically test both
of these characteristics

20. Presented to: PAW Manufacturing
(C) Beet LLC., and Soliton Technologies Inc. All rights reserved. 20
Trend Analysis
- Key Question:
- How to avoid false
positives/negatives?
- Window 1 would extrapolate to
an earlier upward trend (false
positive)
- Window 2 would extrapolate to
a downward trend (false
negative)
- We could develop weighting
factors based on event_length
magnitudes

21. Presented to: PAW Manufacturing
(C) Beet LLC., and Soliton Technologies Inc. All rights reserved.
Thank You!
www.beet.com
www.solitontech.com