By Mr. Prashant Jain & Mr. Wang Zixin,Student, Master of Technology in Enterprise Business Analytics Programme

1. Application of Analytics in
Manufacturing Sector
Mr. Prashant Jain
Mr. Zixin Wang
NUS-ISS, 13 July 2018
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HP Inc. Singapore

2. AGENDA
• Business Understanding
• Think Approach
• Process Understanding
• Data Understanding and Exploration
• Modelling Results and Conclusion
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3. Business Understanding
Solving a problem is useless if it carries no business value.
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4. Let’s talk business @ HP INDIGO
• Electro-Ink Manufacturing
Facility for CMYK
• The Indigo Electro-Ink are
used in HP Indigo Digital
Presses.
• HP has proprietary,
patented technology
and a business model
which sells both presses,
consumables and
services.

5. Let’s talk business @ HP INDIGO
1.
•Typical batch process is several hours long
•The batch processes are highly repeatable
2.
•Several raw materials are added in a tank
•Lot of process operations are carried out
3.
•Finally, batch quality is checked at the end.
•Batch is unloaded and packaged.
• Electro-Ink Manufacturing
Facility for CMYK
• The Indigo Electro-Ink are
used in HP Indigo Digital
Presses.
• HP has proprietary,
patented technology
and a business model to
sells both presses,
consumables and
services.

6. Characteristics
of Batch
Manufacturing
GOALS
Produce maximum yield
Best product quality
Minimum amount of waste
CHALLENGES
Highly complex process
Hidden interlinked attributes
Huge amount of data
Easily miss atypical process
variation relationships
WHY
PREDICTIVE
ANALYTICS
?
Summarize all SPC charts
End-of-Batch Quality
Prediction
Fault Prediction

7. Think Approach
Come up with logical approach
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8. 8
Source: https://www.youtube.com/watch?v=onKK-a1-zRY

9. Comparing Approaches at hand
UNIVARIATE OR SPC
•Doesn't take into account interactions
among inputs
•Interactions between process variables
and output quality characteristics
MULTIVARIATE SPC (MSPC)
•Compiles many acceptable batches
•Accounts for process relationships
•Allows a much more accurate analysis
Understanding
Golden Batch
Understanding
Golden Profile
VS

10. Process Understanding
Domain knowledge is essential to any data analytics project
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11. Understanding Ink production Process
Agitator
Cooling
water
in
Cooling
water
out
Material-1
Material-2
Material-3
GRINDING TANK
RECIRCULATION
• The Ink grinding process inside Attritor is several
hours long with many stages before transferring.
• Pain Point: If batch-end Ink quality is poor, we
won’t know until the last hour.
• Objective: To come up with a predictive model to
predict the quality of batch half way through the
process so that suitable action can be taken when we
still have time to improve the batch.
Parameter Type Data points per batch
Process
(every min logging)
800-1000
Quality
(end of batch)
1-data point

12. 12
7 hrs midway
5 stages
passed
Stage-1
Stage-2
Stage-3
Stage-4
Stage-5
Stage-6
Stage-7
Stage-8
Stage-9
Parameter-1

13. Data Understanding and
Exploration
Data collection, Integration, Preparation and Transformations
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14. Data collection and SUCCESS Criteria
Started with Magenta Color for Modelling:
• Process data (sample shown in the table) is collected for 4
months.
• Ink Quality has various parameters and is very subjective in itself.
• Process Team chose ‘DE’ to be the dependent parameter for
modelling.
• Quality depends on properties of Raw Materials also – 3rd data
set.
Success Criteria:
• Predictive modeling for DE(Quality parameter) with at least 90%
Model accuracy.

15. Dependent
variable
Distribution
• 4 months of data is
collected
• Dependent Variable
histogram looks
Normal.
• Some outlier
batches removed.
• Shapiro test: NHT
accepted, data is
normally distributed.
15

16. Correlation analysis
Correlation close to +1 –> Positively
correlated
Correlation close to -1 –>
Negatively correlated
• Carried out correlation analysis
and discovered that process
parameters have strong
correlation with corresponding
setpoints.
Insights:
Certain parameters have high
correlation with other parameters
and techniques that tend to
combine highly correlated
parameters into single parameter
can be used – Dimension reduction

17. Predictive Modelling workflow
Input parameters:
• 4 months of Magenta
batches
• 900 data points per
batch
• 16 variables per
batch
Output parameters data:
• 14 quality related
variables
Raw Material properties
data:
• RM properties data
• Total 46 variables
Batch Data
Validation
Batch Wise
Unfolding
Data
Integration
Dimension
Reduction
Modelling Evaluation
Prediction

18. Batch Data validation – Golden profile
Batch Categories
Green:
• Perfect batch
• Process attributes are within limits
• Quality parameters are fine
Yellow:
• Imperfect batch
• Some process attributes have
variations
• Quality parameters are fine
Red:
• Failed batch
• Process attributes are out of limits
• Quality parameters are out of
limits
18
0
20
40
60
80
100
120
140
160
180
Batch Categories of 4
months of data
Red Green Yellow

19. PROCESS DATA – 3D FORMAT
19
Batch Data
Not of uniform length
& 3D Format
Raw
Materials
data (46
variables
)
Output
paramete
rs
(1-
variable)
Input
Paramete
rs (16
variables)
Process data - 3D format can’t be modelled directly.

20. Batch alignment - Dynamic Time Warp
• Sometimes operator and event-initiated processing halts and restarts are part
of the batch process design, such as adding a special ingredient.
• However, the batch data used in model development must be somehow
aligned in order to facilitate data analysis.
• To achieve uniform batch length. the data at a certain time in the batch could be
simply chopped off or compressed.
• Better results may be achieved by applying a newer technique known as
dynamic time warping (DTW).
• DTW aligns batch data with the reference trajectories by minimizing total
distance between them.
• The batch with median time duration can be used as an initial DTW reference
batch.
20

21. 21
• Process data is logged at every
minute.
• 3D data format can’t be
modelled directly. It needs to be
unfolded into 2D format.
• Used Batch-wise unfolding
technique to change to 2D
format.
• Resulting data is a flat matrix with
over 16000 columns.
• For POC, need not take data at
every minute.
• Sampling at every 10min of first
500 data points (approx. 8.3
hours) to get 830 columns.
BATCH-WISE
UNFOLDING

22. Data integration
Input parameters
Approx. 830 variables
Primary key = Batch ID
Output parameters
1 variable –
Dependent (y)
Primary key = Batch ID
Raw Material Data
46 variables
Primary key = Batch ID
Batch-ID
Primary Key
(150 batches)
Batch-wise
Unfolded Input
parameters
(sampling at
every 10min –
830 variables)
Raw Material
Data
46 variables
DE (Selected
Output
Parameter)
Final 2D Integrated Dataset
Still 800+ columns, need to compress

23. Dimension reduction
with principal component analysis
• Reduced 815 variables to just
100 Principal components.
• Selected on the basis of screen
plot eigen value criteria which
explain 99.6% of the total
variance.
• Modeling with PCs – Neural
Nets, Decision Trees, Random
Forest, Gradient Boost
Machines, MLR & etc.
3 dimensions >>> 2
dimensions

24. Modelling Results and Conclusion
Model selection and discussion
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25. Multiple Linear regression –
Best models
• Divide dataset into 70:30 for training and testing.
• Built model on Training data and checked %
error on Test data.
25
Model
No.
Variables Adj.R2 MAPE on
test
M1 All PC (100) 0.75 9.07%
M2 35 significant PC
from M1 summary
0.74 9.76%
M3 35 PC + Removed
2 outliers using
Cook’s plot
0.77 10.1%
M4 33 significant PC
from M3 summary
0.76 10.4%
M5 26 significant PC 0.71 10.8%
Best
Models

26. y
Prediction
interval at 95%
confidence
26
Base Model
 Prediction is Average y
 9 points out of prediction
interval.
 Base Model Prediction
accuracy is 80%.
 Does not accounts for
parameter variations.
Regression Model
 3 points out of prediction
interval
 Model Prediction accuracy
is 92.8%.
 Advantage: Can predict
variations in data better
than base model.
Base Model

27. CONCLUSION
• This approach, when applied to analyse the impact of processing
conditions on final-product quality, can provide operators with
continuous and accurate predictions of end-of-batch quality
parameters.
• A successful application of batch analytics may result in minimizing
batch variations and improving batch quality, validated for a batch
process.

28. Thank You!
prashant.jain@u.nus.edu
Zixin.wang@u.nus.edu
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