The value of Process Mining in Manufacturing
Joris Keizers Ph.D. – Group Operations Manager
2017
1April 10,10-4-2017
A member of SPGPrints Group
A member of SPGPrints Group
INTRODUCTION
 Group Operations Manager at Veco, world leader in high
precision metal solutions: electroforming, etching, laser
cutting
 Passion for improving the transparency and performance of
business processes, using People and (Big) Data
 First experience with process mining in 2014, by the MOOC
“Process Mining: Data Science in Action”
 Received “Process Miner of the Year award 2016” for proven
lead time reduction in manufacturing environment
 Today I present two challenges we have solved using process
mining and six sigma: improving production lead time for
proven products and improving time to market for new
(to be engineered) products
2
10-4-2017
A member of SPGPrints Group
TODAY’S AGENDA
Turning Veco’s ambition into challenges for
our Operations Department
The world before Process Mining…
How Process Mining has unlocked hidden process
knowledge and enabled us to improve our lead times
How Process Mining has helped us improving our time to market:
combining administrative and manufacturing processes
3
10-4-2017
A member of SPGPrints Group
Micro-precision parts reimagined.
We take the creation of micro-precision parts
to unprecedented levels.
4
10-4-2017
A member of SPGPrints Group
What we are here to do.
To transform a design challenge into
a micro-precision part that goes
beyond our customers’ imagination.
We inspire designers and product developers to go
further, push the boundaries and dare to innovate
even more.
5
10-4-2017
A member of SPGPrints Group
SHORT PRODUCTION LEAD TIMES ARE KEY AS FINAL QUALITY NOT
KNOWN UNTIL INSPECTION
Lithography
Electroforming
Coating
Inspection
6
10-4-2017
A member of SPGPrints Group
TODAY’S AGENDA
Turning Veco’s ambition into challenges for
our Operations Department
The world before Process Mining…
How Process Mining has unlocked hidden process
knowledge and enabled us to improve our lead times
How Process Mining has helped us improving our time to market:
combining administrative and manufacturing processes
7
10-4-2017
A member of SPGPrints Group
OUR IMPROVEMENTS ARE BASED ON THE SIX SIGMA METHODOLOGY
Six Sigma methodology
Formulating the practical
problem
Implementing practical
solution
Developing a statistical
solution
Measuring and validating
the current capability
Changing to a statistical
problem
8
10-4-2017
A member of SPGPrints Group
ANALYSE PHASE: BASED ON OUR KNOWLEDGE, OUR PEOPLE SKETCH THE PROCESS
FLOWS, BRAINSTORM ON HYPOTHESES AND PERFORM ANALYSES USING MINITAB
Analyse Phase
Probability Plot of Nr. Of Reschedules
99
95
90
Percent
80
70
60
50
40
30
20
10
POLineNr
1
2
3
4
5
1
0,1
-1
0
1
2
3
4
5
Nr. Of Reschedules
6
7
SalesPartorder
entry
code
Partial delivery
Mean StDev N
AD
P
1,308 1,601 13 1,258 <0,005
0,7692 0,9268 13 1,133 <0,005
1,714 1,496 7 0,245 0,635
2,143 1,345 7 0,967 0,006
8
Order confirmation
Warehouse
Goods receipt
Probability Plot of Created OTD
(Only standards, no repairs)
99
95
90
First confirmed
70
60
50
40
30
20
Leadtime
0-30
30-60
60+
10
5
1
Mean StDev N
AD
P
4,923 5,590 13 0,773 0,033
8,053 28,85 19 3,732 <0,005
-6,75 19,82 24 0,812 0,030
-50
0
Created OTD
50
Requested delivery date
Probability Plot of Created OTD
Normal - 95% CI
0
99
-100
100
95
90
Description
80
Percent
Percent
80
70
60
50
40
30
20
POLineNr
1
2
3
4
10
5
1
-50
-25
0
25
Created OTD
Mean StDev N
AD
P
5,2 24,53 35 3,468 <0,005
0 11,65 13 1,083 <0,005
1,5 17,83 8 0,773 0,025
-8 20,38 7 0,585 0,078
50
75
Actual
Call-off date Final confirmed
Purchase order entry
Probability Plot of Created OTD
Normal - 95% CI
99
Tcard entry
95
90
Requested quantity
Probability Plot of Created OTD
Goods issue
Standard / Repair
Normal - 95% CI
99
90
99
70
60
50
40
30
10
Deliv ery date in
both sy stems e
10
5
-50
-25
0
25
Created OTD
90
Mean StDev N
AD
P
12,48 22,76 25 4,143 <0,005
-4,579 17,31 38 2,243 <0,005
50
75
80
100
70
60
50
40
30
20
Jaar
10
2011
2012
5
1
10-4-2017
-100
-50
0
50
OTD tecan
Difference
Same
1
0,1
95
Call off
article?
(y/n)
n
y
20
Percent
Percent
20
On time delivery
Normal - 95% CI
80
9
70
60
50
40
30
5
Probability Plot of OTD tecan
95
1
Percent
80
Mean StDev N AD
P
*
* 0
*
42,93 41,12 15 0,241 0,727
11,57 16,94 7 0,841 0,015
100
150
200
A member of SPGPrints Group
-60
-40
-20
0
Created OTD
20
40
60
ANALYSE PHASE: FOR EACH IDENTIFIED BOTTLENECK WORKFLOW, WE GENERATE A
PROBABILITY PLOT OF THE THROUGHPUT TIME AND FORMULATE HYPOTHESES
Probability plots
Possible target leadtime with
reduced mean and reduced variance
Hypothesis 1: variation is due to production shift
Hypothesis 2: variation is due to product type
Each dot is a production order,
with the lead time on the x-axis
25% of all production orders have a
lead time of maximum 8,5 days
The steeper the curve, the lower
the variance
Sample consists of 200
production orders
50% of all production orders have a
lead time of maximum 11,7 days
10
10-4-2017
A member of SPGPrints Group
ANALYSE PHASE: BASED ON IDENTIFIED HYPOTHESES, MINITAB SHOWS US THE IMPACT OF – FOR
EXAMPLE – SHIFT NUMBER ON LEAD TIME…
Probability plots
Hypothesis 1: variation is due to production shift
Conclusion: “Shift” is no root cause for
variation in throughput time
11
10-4-2017
A member of SPGPrints Group
ANALYSE PHASE:… OR IMPACT OF PRODUCT CODE ON LEAD
TIME
Probability plots
Hypothesis 2: variation is due to product type
Conclusion: “Product” is a root
cause for difference in variation
Next question: how does Product B impact the throughput
time and how can this variation be reduced?
Our analyses have proven to be effective, however the process of analysing is not very efficient and it
is hard to get all colleagues with process knowledge on board
12
10-4-2017
A member of SPGPrints Group
TODAY’S AGENDA
Turning Veco’s ambition into challenges for
our Operations Department
The world before Process Mining…
How Process Mining has unlocked hidden process
knowledge and enabled us to improve our lead times
How Process Mining has helped us improving our time to market:
combining administrative and manufacturing processes
13
10-4-2017
A member of SPGPrints Group
END OF 2014, WE STARTED WITH PROCESS MINING…
14
10-4-2017
A member of SPGPrints Group
PROCESS MINING HAS CHANGED THE WAY PEOPLE CAN APPLY KNOWLEDGE AND
INSIGHTS TOWARDS SUBSTANTIAL PROCESS IMPROVEMENTS
Process Mining
My
ambition
“Reduce lead times from 4 weeks to 2 weeks”
Before
process mining
After
process mining
“We can give you lead
time of 3 weeks”
“We can give you lead time
between 1 and 2 weeks”
Response of
Operations Team
15
10-4-2017
A member of SPGPrints Group
PART 1 OF AN EXPERIMENT WHERE WE COMPARE LEAD TIME IMPROVEMENT BASED ON
MINITAB ANALYSES VERSUS PROCESS MINING
Experiment
1
Solely based on
Minitab Analysis
2 Combine Minitab and Disco
+ Process Mining
16
10-4-2017
A member of SPGPrints Group
Before process mining
BEFORE PROCESS MINING, I HAD TO EXPORT 30000 EVENTS TO EXCEL, RUN PARETO
ANALYSES AND SELECT “SUSPECTED ROUTING-ARCS” FOR FURTHER ANALYSIS
17
1 Get eventlog
2 Select high traffic paths
3 Build probability plots
10-4-2017
Select candidates to
4
improve
A member of SPGPrints Group
AFTER THE INITIAL ANALYSES I WAS ABLE TO PRESENT MY TEAM 11 PROBABILITY PLOTS
AND TO ASK THEM: “WELL…. WHAT DO YOU THINK WE HAVE TO DO?”
Normal - 95% CI
Normal - 95% CI
99,9
04
95
0,01 99,9
-20
50
20
-10
99
95
80
Percent
1
95
0
5
80
20
99
5
95
1
Probability
Plot of Path
08
50
Normal - 95% CI
20
10
5
Path
1
20
30
0,01
01
40
-10
-5
0,01
80
70
60
50
40
30
20
-10
0,01
0 -10
-5 10 0
Path 02
5A D
P-Value
1
95
90
5 20
10
Path 04
5
10
Percent
10
80
70
60
50
40
30
20
Mean
95
StDev
N90
A80
D
P-Value
70
2,763
3,219
734
44,687
<0,005
15
30
20
0,01
6,180
<0,005
60
50
40
30
20
0,1
-5
99,9
99
80
70
60
Mean50
40
StDev
30
N
1020
5
AD
Path P-Value
06 10
1
0
Mean
StDev
N
AD
P-Value
4,532
3,519
269
15
4,892
<0,005
10
20
0,1
Path 05 -5
10-4-2017
5
Path 08
10
150,1
Mean
StDev
N
AD
P-Value
CI
6,177
2,805
215
4,492
<0,005
0,1
Mean
StDev
N
AD
P-Value
Normal - 95% CI
20
99,9
25
95
90
-30
Mean
StDev
N
AD
P-Value
5
80
70
Path 09
-20
-10 60
50
10
0
40
30
20
15
10
20
Path 07
30
40
50
60
10
5
-10
20
-5
0
5
Path 10
10
15
A member of SPGPrints Group
12,12
11,74
123
6,093
<0,005
Probability Plot of Path 21
30
1
0
1
0
P-Value
<0,005
Normal
- 95%
99
5
-5
1,046
1,894
1534
187,577
<0,005
5
1
-10
4,647
220
Probability
Plot of Path 07
AD
2,450
95
90
50
25
-10
N
95
90
10
-10
StDev
Normal - 95% CI
99
80
70
60
50
Probability Plot of Path 10
50
5
Normal
95%
CI
40
Mean
4,824
5
10
15
20
25
20StDev
30
Path3,789
03
1
20
N
284
0
Probability Plot ofMean
Path 09
6,805
99,9
10
99,9
1
2,795
2,894
980
41,681
<0,005
80
99
95
90
Mean
StDev
N
AD
P-Value
50
- 95% CI
Percent
5
99
Normal - 95% CI
Percent
20
99
99,99
80
2,832
Normal
2,676
770
29,696
<0,005
Probability Plot of Path 05
99
Percent
50
Percent
80
99,99
Percent
99,99
Percent
95
Mean
StDev
N
AD
P-Value
Percent
Mean
6,787
StDev
5,906
N
789
99
Probability Plot
Probability
of Path 02
of16,918
Path
APlot
D
P-Value
Normal - 95% CINormal
- 95%<0,005
CI
99
Percent
Probability Plot of Path 06
99,99
99,99
0,1
18
Probability Plot of Path 03
Probability Plot of Path 01
Percent
Before process mining
Probability plots
1
0,1
-20
-10
0
10
Path 21
20
30
40
13,29
7,297
59
1,535
<0,005
CHALLENGE: HOW TO LEVERAGE ALL THE PROCESS KNOWLEDGE OF LINE
MANAGEMENT AND OPERATORS WHEN STARING AT THESE ABSTRACT MINITAB PLOTS?
Before process mining
Probability plots
19
Purchasing manager: hired for his
capability to reduce supply risk and source
against good terms and conditions
Maintenance manager: hired for his capability to
manage the maintenance crew and develop
maintenance concepts in high tech environment
Inspection manager: hired for his
capability to deliver requested quantities
and qualities within time and budget
Supply Chain manager: hired for his capability to
balance demand and supply and roll out supply
chain management towards our customers
10-4-2017
A member of SPGPrints Group
Production manager: hired for his
capability to deliver requested quantities
and qualities within time and budget
PART 2 OF AN EXPERIMENT WHERE WE COMPARE LEAD TIME IMPROVEMENT BASED ON
MINITAB ANALYSIS VERSUS PROCESS MINING
Experiment
1
Solely based on
Minitab Analysis
2 Combine Minitab and Disco
+ Process Mining
20
10-4-2017
A member of SPGPrints Group
WITHIN MINUTES, WE NOW GET A DYNAMIC OVERVIEW OF OUR PROCESS
CAPABILITIES
After process mining
Process Mining
21
Process conformity is not an issue at Veco, but lead time
reduction is an issue for Veco
High traffic process step: Always ensure sufficient capacity
and uptime for this step
Product Line 1: High costs, relatively long lead times and least
stable process. So selected for further research via “Filtering”
10-4-2017
A member of SPGPrints Group
AFTER FILTERING, PROCESS MINING HAS HELPED US TO DO A VERY POWERFULL EXPLORATORY
DATA ANALYSIS WHICH LEVERAGED THE PROCESS KNOWLEDGE OF ALL DEPARTMENTS INVOLVED
After process mining
Process Mining
22
Production manager: “I see daily pushes, but by doing
this more clever, I can drastically reduce lead time and
free up capacity”
Production manager: “This bottleneck in capacity can
be eliminated for only a few thousand euro’s”
Daily
push
Maintenance manager: “If you free up capacity, I have more
flexibility in scheduling preventive maintenance and I will cause
less interruptions for production”
Weekly
push
Inspection Manager: “We need to balance the
demand for inspection capacity versus the
availability of inspection capacity”
10-4-2017
Purchasing manager and supply chain manager: “For this
subcontracting step, moving towards a daily push pattern
seems to be more important than transportation costs. We
need to resolve this with our suppliers”
A member of SPGPrints Group
THE IMPROVEMENTS IN THE PROCESS HAVE LED TO A PRODUCTION LEAD TIME WHICH NOW HAS
A SUBSTANTIALLY LOWER VARIATION AND LOWER MEDIAN
After process mining
Production lead time
75-th percentile
Median
25-th percentile
Rush orders
23
10-4-2017
A member of SPGPrints Group
TODAY’S AGENDA
Turning Veco’s ambition into challenges for
our Operations Department
The world before Process Mining…
How Process Mining has unlocked hidden process
knowledge and enabled us to improve our lead times
How Process Mining has helped us improving our time to market:
combining administrative and manufacturing processes
24
10-4-2017
A member of SPGPrints Group
IMPROVING OUR ENGINEER-TO-ORDER PROCESS APPEARED TO BE
KEY FOR OUR GROWTH AMBITIONS
Our challenge
25
10-4-2017
Vrijgegeven
proces
Productie
Manufacturing
FAIR
A member of SPGPrints Group
Keuren
MTS order
Application
Engineering
Artikel,
Proceslijn en
CAD
beschikbaar
MTO order
ETO order
Engineering
(product /process)
Our comfort zone
Gereed
Product
beschikbaar
Delivery
PROCESS MINING HAS HELPED IN 2 WAYS: MINING THE ENGINEERING PROCESS
AND DETECTING FREQUENT PATHS FOR SAMPLES
Example of a
routing
We succeeded in combining eventlogs with
administrative and manufacturing processes
26
10-4-2017
We have used process mining to detect routings which
are often used for sample production
A member of SPGPrints Group
LESSONS LEARNED
•
Process mining is a valuable tool within the
DMAIC-approach; mainly in the Analyse-phase
•
Process mining has helped us to increase the
efficiency of doing our analyses
•
Process mining has helped us to involve more
people (and thus more possible solutions) in
the Analyse and Improve phase of our six
sigma projects
•
We strongly believe that process mining has
the biggest impact if it is being launched by
the business leaders, rather than Quality, IT,
Finance,…
•
It is always fun to work with new leading-edge
techniques ….
27
10-4-2017
A member of SPGPrints Group
Thank you for your attention
April 10,10-4-2017
2017
28
A member of SPGPrints Group
A member of SPGPrints