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First Pass Yield Analysis and Improvement at a Low
Volume, High Mix Semiconductor Equipment
Manufacturing Facility
by
Shaswat Anand
Bachelor of Engineering in Mechanical Engineering
Delhi College of Engineering, University of Delhi, 2012
Submitted to the Department of Mechanical Engineering
in partial fulfillment of the requirements for the degree of
Master of Engineering in Advanced Manufacturing and Design
at the
MASSACHUSETTS INSTITUTE OF TECHNOLOGY
September 2016
c○ Massachusetts Institute of Technology 2016. All rights reserved.
Author . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Department of Mechanical Engineering
August 10, 2016
Certified by. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Dr. Stanley Gershwin
Senior Research Scientist
Thesis Supervisor
Accepted by . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Rohan Abeyaratne
Quentin Berg Professor of Mechanics
Chair, Committee of Graduate Students
2
First Pass Yield Analysis and Improvement at a Low Volume,
High Mix Semiconductor Equipment Manufacturing Facility
by
Shaswat Anand
Submitted to the Department of Mechanical Engineeringon August 10, 2016, in partial fulfillment of the
requirements for the degree ofMaster of Engineering in Advanced Manufacturing and Design
Abstract
"Improve quality, you automatically improve productivity" - W. Edwards Deming
Quality is the heart and soul of any manufacturing unit. Quality metric stagnationat a high mix semiconductor equipment manufacturing facility was the motivation forthis project.
An analysis was done to understand the working and importance of the qualitymetrics, First Pass Yield and Quality Notifications per Module, to understand thereasons for its stagnation over the past couple of years at the assembly plant. Alsomodule specific study was done to understand the trends in the quality improvementand the improvements achieved on different modules assembled at the facility.
As per scientific method, a hypotheses tree was laid out with a view to ascertainthe reasons behind the plateauing of the quality metrics. Further these metrics weretested using data from the ERP software (SAP), other tailor made software packagesand from discussions and interviews with assembly floor people and the manufacturingand quality engineers.
As a result of this work shortages of critical parts was found out to be a crucialcontributor to the quality issues arising on the shop floor because of the extra exposuretime of the assemblies and building the assemblies out of procedure in such a case.Various alternative strategies are suggested to improve service levels along with theeconomical impact these strategies shall have.
Finally, invaluable data collections suggestions are a part of this work which shallact as enablers in the continuous journey of quality improvement.
Thesis Supervisor: Dr. Stanley GershwinTitle: Senior Research Scientist
3
4
Acknowledgments
First and foremost I would like to thank my parents for all that I am. None of this
would have been possible without them. A sincere thanks to all my family members
for their invaluable contributions.
I thank my adviser, Dr Stanley Gershwin for extending his valuable time and
guiding and motivating me throughout this work. It was in the discussions with him
that we always found new ways to approach the complex of problems. This project
would not have been a success without the numerous valuable insights and suggestions
of Dr. Gershwin.
I would like to sincerely thank Dan Martin at Applied Materials for he gave us the
freedom to explore different aspects of the project. I also sincerely thank the entire
FPY team and all Manufacturing and Quality Engineers at Applied Materials who
guided throughout the project and made the stay a memorable one.
Next, I extend my hearty thanks to Professor David Hardt and Jose Pacheco for
their support and guidance throughout the program.
Thanks to my friends and teammates Sean and Elyud, who I worked with at
Applied Materials. Thanks for the wonderful learning atmosphere.
Last but not the least I would like to thank my wonderful friends who made life
awesome. Thanks Meenakshi(Queen), Karthik(GK), Anshul(Single), Srinivas(Tsunami)
and Rohith. Thanks a lot for all the memories!
5
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6
Contents
1 Introduction 13
2 First Pass Yield Program 17
2.1 An Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
2.2 Quality Notification . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
2.3 First Pass Yield (FPY) Metric . . . . . . . . . . . . . . . . . . . . . . 19
2.4 FPY Sample Calculation . . . . . . . . . . . . . . . . . . . . . . . . . 19
2.4.1 FPY Sample Data: December 2015 and January 2016 . . . . . 20
2.5 A Secondary Metric: QNs per Module . . . . . . . . . . . . . . . . . 20
2.6 FPY and QNs/module: A Historical Look . . . . . . . . . . . . . . . 24
2.7 Addressing QNs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
2.7.1 The Bucketing Approach . . . . . . . . . . . . . . . . . . . . . 25
2.8 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
3 FPY Stagnation: Analysis and Proposed Improvements 29
3.1 FPY and Expected Error Rate . . . . . . . . . . . . . . . . . . . . . . 30
3.1.1 Setting QNs per module target . . . . . . . . . . . . . . . . . 33
3.2 Hypotheses Tree . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36
3.2.1 Complexity of Modules . . . . . . . . . . . . . . . . . . . . . . 36
3.2.2 Analysis of High FPY Module . . . . . . . . . . . . . . . . . . 38
3.3 Experience of Employees . . . . . . . . . . . . . . . . . . . . . . . . . 39
3.4 MIT Critical Path Project . . . . . . . . . . . . . . . . . . . . . . . . 41
3.5 Shortage of Critical Parts . . . . . . . . . . . . . . . . . . . . . . . . 42
7
3.5.1 Critical Shorts . . . . . . . . . . . . . . . . . . . . . . . . . . . 44
3.5.2 Relating Critical Shorts to QNs . . . . . . . . . . . . . . . . . 44
3.5.3 Part Routes . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45
3.5.4 The 2-bin Kanban System . . . . . . . . . . . . . . . . . . . . 47
3.5.5 Current Bin Sizing Method for KC Parts . . . . . . . . . . . . 47
3.5.6 Gold Squares . . . . . . . . . . . . . . . . . . . . . . . . . . . 50
3.5.7 Inventory Levels Restructuring: Results and Potential Benefits 51
3.6 Re-Bucketing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53
3.7 FPY Metric Benchmarking . . . . . . . . . . . . . . . . . . . . . . . . 56
3.8 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57
4 Results 59
4.1 Data Collection: Improvements and Suggestions . . . . . . . . . . . . 59
4.1.1 Critical Shortages Data . . . . . . . . . . . . . . . . . . . . . . 60
4.1.2 SMKT Gold Square Shortages Data . . . . . . . . . . . . . . . 61
4.1.3 Flagging Procedural Changes . . . . . . . . . . . . . . . . . . 63
4.1.4 ERP (SAP) QN Updates . . . . . . . . . . . . . . . . . . . . . 64
4.1.5 Capturing the MIT Rebucketing Approach . . . . . . . . . . . 64
4.2 QN Feedback . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65
4.3 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66
5 Conclusions, Recommendations and Future Work 67
5.1 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67
5.2 Recommendations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68
5.3 Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70
A Discussion on Distributions of Demand 73
A.1 Demand Characterization . . . . . . . . . . . . . . . . . . . . . . . . 73
A.1.1 Curve Fits . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74
A.1.2 Weekly Demand: Negative Binomial Distribution . . . . . . . 74
8
List of Figures
2-1 Quality Notification - Categories and Buckets . . . . . . . . . . . . . 18
2-2 FPY and QNs per module: 2011 - Jan 2016 . . . . . . . . . . . . . . 25
3-1 Exponential Fits . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32
3-2 Relationship betwen QNs per module and FPY . . . . . . . . . . . . 34
3-3 Probability Distribution of QNs per module, UES - FY 2012 and FY
2015 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35
3-4 Histograms for QNs per module, UES - FY 2012 and FY 2015: The
mean(red line) QNs/module value has been slowly shifting towards 1.0 35
3-5 Historial Trends for FPYs of the two most complex modules: UES and
90 Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37
3-6 Relating Experience of Assemblers to # of QNs . . . . . . . . . . . . 40
3-7 Impact of MIT Critical Path Project on # of QNs . . . . . . . . . . . 42
3-8 Relating Short Counts to # of QNs . . . . . . . . . . . . . . . . . . . 45
3-9 Relating Shortage Occurrences to # of QNs . . . . . . . . . . . . . . 46
3-10 2-Bin Kanban System . . . . . . . . . . . . . . . . . . . . . . . . . . . 48
3-11 Shorts of various procurement types . . . . . . . . . . . . . . . . . . . 49
A-1 Example daily demand distributions and curve fits - Three different
represntative TRIDENT KC parts. [3] . . . . . . . . . . . . . . . . . 75
A-2 Probability mass function and Cumulative distribution function of a
geometric distribution. . . . . . . . . . . . . . . . . . . . . . . . . . . 76
A-3 Probability mass function and Cumulative distribution function of a
geometric distribution. . . . . . . . . . . . . . . . . . . . . . . . . . . 77
9
A-4 Weekly demands for three representative parts showing negative bino-
mial distribution [2]. . . . . . . . . . . . . . . . . . . . . . . . . . . . 78
10
List of Tables
2.1 FPY Sample Calculation. . . . . . . . . . . . . . . . . . . . . . . . . 21
2.2 FPY: Module wise for the month of December 2015. . . . . . . . . . . 22
2.3 FPY: Module wise for the month of January 2016. . . . . . . . . . . . 23
3.1 KC Parts bin sizing cost analysis at different service levels . . . . . . 51
3.2 KC Parts predicted shortage analysis . . . . . . . . . . . . . . . . . . 51
3.3 Gold Square sizing cost analysis . . . . . . . . . . . . . . . . . . . . . 52
3.4 Gold Square sizing predicted shortage analysis . . . . . . . . . . . . . 52
3.5 Re-Bucketing of QNs for the period Jan.-June 2016: Current Method
vs. the MIT Method (continued on next page). . . . . . . . . . . . . 55
3.6 Re-Bucketing of QNs for the period Jan.-June 2016: Current Method
vs. the MIT Method (continued). . . . . . . . . . . . . . . . . . . . . 56
11
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12
Chapter 1
Introduction
This thesis project has been carried out at the manufacturing facility of Applied
Materials, Inc. (Nasdaq: AMAT) located in Gloucester, MA. Applied Materials,
Inc. is the global leader in providing innovative equipment, services and software
to enable the manufacture of advanced semiconductor, flat panel display and solar
photovoltaic products. Applied Materials purchased Varian Semiconductor and their
ion implantation equipment manufacturing facility in Gloucester, MA in 2011. The
Varian division of Applied Materials, located in Gloucester, MA, produces a variety
of product lines all involved with ion implantation.
Ion implantation is the most common process of doping semiconductors in the
manufacturing of semiconductors in the present day. This process of doping a silicon
wafer involves presenting the wafer to a focused and filtered ion beam. The beam
begins as an ionized gas and is focused through a beamline of magnets that filters the
gas to only the desired ions by the time the beam hits the wafer. This beamline equip-
ment is complex, and as a result the equipment to refine this beam is manufactured
in a series of modules. These modules are manufactured and shipped as individual
units, and are tested only at the module level. They are not usually assembled and
tested as a complete build unit until deployed at the customer site. This makes it
imperative to test each and every module at the end of its build so that they work in
perfect harmony at the customer site.
In this direction, as a part of their continuous improvement program, the man-
13
ufacturing facility at Gloucester implemented a "First Pass Yield (FPY)" quality
program in 2011. This program is mainly aimed at reducing the number of quality
defects per module and thereby minimizing the rework caused because of these qual-
ity defects. At the heart of all these is the reduction of cost to build a module by
putting less number of man hours on it and provide products of superior quality to
its customers. FPY is a measure of the percentage of modules manufactured without
manufacturing-related defects. The scope of the project deals with defects arising on
the shop floor which are attributable to workmanship errors. This does not include
errors arising out of a defective part from a supplier or because of any inherent design
or procedural issue.
This quality project carried out by Applied Materials resulted in significant reduc-
tion in number of defects per module across all the modules in the initial few years,
although the rate of reduction has not been the same for all of them. It increased
from around 55% across modules in the fiscal year 2011 to about 80% in 2013, but has
stagnated since then. One of the primary goals of this thesis project was to ascertain
reasons for this stagnation of FPY, critique the current FPY program and present
Applied Materials with a methodology that improves yield in the future. Another
area of concern for the FPY team has been goal setting. Goal setting for FPY is not
scientific currently and is mostly a management-selected benchmark that the team
believes they can reach for the year. In view of the stagnated FPY numbers for the
past two years, the team is facing difficulty in creating a new higher goal to push the
program further. So this thesis also aims to come up with a scientific method towards
finding an eventual improvement goal for FPY. It hopes to answer the question for a
theoretical limit for yield as well.
Looking into the FPY program as is, Chapter 1 explains the two most important
metrics, FPY and Quality Notifications per Module, and the way they are handled at
Applied Materials. Further the First Pass Yield Program and its current methodology
along with its advantages and disadvantages is detailed in Chapter 2. The ideas
behind bucketing of Quality Notifications is also discussed in this chapter. Chapter 3
develops a mathematical relation between the two metrics: FPY and QNs per module
14
and how they should be viewed together to get a holistic picture. It also describes the
scientific methodology followed by the MIT team to analyze the FPY stagnation issue.
Here various hypotheses are made and then tested using data and improvements are
suggested in those specific directions. Suggestions on the lines of shortage reduction
is one of the main contributions of this thesis. In Chapter 4, various issues that
the team faced because of poor quality of data, which hindered their quest to make
sound conclusions on various hypotheses, are entailed. The suggestions in these areas
to improve the quality of data and how it shall help going forward in the quality
journey are also enumerated in the chapter. Chapter 5 summarizes all the gains
achieved and suggestions for further implementation and improvement of the quality
in general and the quality metrics in particular at Applied Materials. This chapter
also ties together the work done by the entire MIT team at Applied Materials in the
direction of Total Quality Management(TQM).
15
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16
Chapter 2
First Pass Yield Program
2.1 An Introduction
This program was brought into effect to improve quality on the shop floor at Ap-
plied Materials. The mission of this program is to improve the overall quality of the
products from the facility. Some of the enablers to this are reducing waste, reducing
rework, addressing vendor concerns, improving design, carrying out poka-yokes etc.
At the heart of it the program is intended to carry out organization-wide efforts to fos-
ter continuous improvements so that they deliver high-quality products and services
to customers.
2.2 Quality Notification
The manufacturing process at Applied Materials Gloucester unit is mainly a hand
assembly process. All the elementary parts for the assemblies/sub-assemblies are
procured from outsourced suppliers. Many sub-assemblies are built in the supermar-
ket/SMKT area. SMKT is a designated area where assemblies are assembled which
are then used in the module assembly process or sold directly to any customer who
wants it. Any quality issue found out during the process of build or testing of a
module, using the individual parts from the supplier or the sub-assemblies built in-
house or at a contracted location, is logged in as a Quality Notification/QN in the
17
Figure 2-1: Quality Notification - Categories and Buckets
ERP/SAP system. Generally one of the persons working on the module enters the
QN in the online system. While entering the QN details like time to diagnose the
issue and time to rectify it are mentioned. Also, the person entering the QN assigns
it a category depending on the perceived reason of the QN. The QN also has first
hand issue description where text is entered and is of value in the long run.
Any QN can be classified into either of these three categories : Manufacturing
(Workmanship), Supplier or Design. Manufacturing QNs are logged for all quality
issues originating on the shop floor like a misplaced connection, over-tightening a
screw, breaking a graphite part during installation, faulty water connection, leakage
in the assembled chamber, swapped fiber optics cables etc. Typical Supplier QNs
have broken or out of specification parts received from the supplier. Design QNs
arise because of quality issues which are rooted in bad design of a part. These QNs
are logged when a design flaw crops up during the assembly or when a faulty part
from a supplier reaches the assembly floor. In all the discussions in this thesis our
metrics are related only to the Manufacturing QNs only. Consequently FPY refers
to Manufacturing FPY in all the mentions in the thesis. Further, the Manufacturing
FPY is classified in one of the four buckets namely Parts, Harnessing, Connections
and Vacuuum as shown in the Figure 2-1. This classification shall be detailed in this
chapter later on.
As mentioned above, each QN is looked up at by the manufacturing as well as
18
quality engineers to ensure that it is in the proper bucket. Each of the four buckets
has a manufacturing engineer as its bucket leader. Each bucket leader is responsible
for analyzing the quality issue and take steps to ensure that a recurrence of the event
does not happen. The mitigation steps after the root cause analysis can be intro-
ducing poka-yoke,making procedural changes, making design changes, disseminating
knowledge among the workforce etc. or a combination of the above.
2.3 First Pass Yield (FPY) Metric
This is one of the most important metrics for the manufacturing division at Applied
Materials, MA. It is the percentage of the modules that pass the final testing without
any quality issue logged against it. In this work, only quality issues caused as a
result of workmanship issues, on the shop floor, are considered here. Any quality
defect arising out of a defective part from a supplier or because of an inherent design
issue will not be counted towards the calculation of the FPY. Also issues arising
out of critical errors in Standard Operating Procedures (SOPs) are excluded from
calculations for this metric.
A quality defect arising on a module is logged as a QN as described in the previous
section. Any QN is counted against a module depending on the part number it is
logged against. Any module that has a QN logged against it affects the FPY for that
module.
2.4 FPY Sample Calculation
The FPY for any particular module for a time period is defined as the ratio of the
number of modules built without any quality defect (or QN) to the total number of
modules built in that period. It is generally expressed as a percentage rather than
a ratio. However in Table 2.1 and Equations 2.1 and 2.2 it is expressed as a ratio
and can be multiplied by the number 100 to get the percentage values for FPY.
2.1 illustrates how the manufacturing FPY is calculated for a period from the total
19
number of modules built and the number of modules built without any QN logged
against it.
Extending this definition, we calculate the FPY for the manufacturing unit as
the weighted average of the individual modules’ FPYs as shown by Equation 2.2 as
described in the following section.
2.4.1 FPY Sample Data: December 2015 and January 2016
Two tables showing all the modules manufactured in the months of Decemeber 2015
and January 2016 are shown below. They show how each module’s FPY is affected
by any QN. Another important metric worth noting is the tables below is QNs per
module and its interplay with the FPY metric which shall be further delved into in
the following sections.
Another way of representing FPY formula in (2.1) can be:
FPY =Σ(Module FPY× No. of modules)
Total number of all modules(2.2)
The above Equation 2.2 or the Equation 2.1 and the Tables 2.2 and 2.3 can be used
to calculate the FPY for any particular time period. As is very evident from the
table that some modules have defects in both months and some particular ones are
devoid of any quality issues in either of the months. There are various reasons for
this behavior which the thesis shall delve into in the upcoming sections. Further all
these QNs in each module and the number of modules in the tables are utilized to
find the FPY for a period.
2.5 A Secondary Metric: QNs per Module
At Applied Materials, it is the FPY metric that is paid the most attention and is
the metric of choice for reporting higher up in the organization. However, the FPY
numbers can be quite misleading at times because of the way it is calculated. Another
20
ModuleType
Nos.of
ModulesBuilt
Nos.of
ModuleswithoutQNs
ModuleFPY(asafraction)
A𝑥
𝑢𝑢 𝑥
B𝑦
𝑣𝑣 𝑦
C𝑧
𝑤𝑤 𝑧
Table2.1:
FPYSam
pleCalculation.
FPY
=
(𝑢 𝑥×
𝑥)
+(𝑣 𝑦
×𝑦)
+(𝑤 𝑧
×𝑧)
𝑥+𝑦
+𝑧
=𝑢
+𝑣
+𝑤
𝑥+𝑦
+𝑧
(2.1)
21
Location
Total
Build
Passed
#Defects
%FPY
Average
QNs/M
odule
55/70ModAssy
/Test
98
189
0.1190
ModAssy
/Test
96
867
0.89Facilities
ModAssy
/Test
99
0100
0.00Gas
Box
ModAssy
/Test
99
0100
0.00MCTerm
Assy
/Test
22
0100
0.00MCBLAssy
/Test
22
0100
0.00UESModAssy
/Test
112
1518
1.36Final
Assem
bly/Shipping
1010
0100
0.00Final
Test
33
0100
0.00Buffer
1010
0100
0.00
Table2.2:
FPY:Modulewise
forthemonth
ofDecem
ber
2015.
22
Location
Total
Build
Passed
No.
ofDefects
%FPY
Average
QNs/Module
55/70ModAssy/T
est
1412
386
0.21
90ModAssy/T
est
146
1243
0.86
FacilitiesModAssy/T
est
1414
0100
0.00
Gas
Box
ModAssy/T
est
1414
0100
0.00
MCTerm
Assy/T
est
64
267
0.33
MCBLAssy/T
est
66
0100
0.00
UESModAssy/T
est
206
2530
1.25
Final
Assem
bly/Shipping
2020
0100
0.00
Final
Test
44
0100
0.00
Buffer
2020
0100
0.00
Table2.3:
FPY:Modulewiseforthemonth
ofJanuary2016.
23
metric that always needs to accompany the FPY numbers, since it paints a better
picture of quality, is the QNs/Module metric. This is owing to the fact that a single
QN on a module affects its FPY metric as badly as multiple QNs on the same module.
Also FPY numbers can be inflated if the number of modules built is less. So looking
only at FPY numbers can be misleading. The alternative metric, QNs per module, is
therefore always presented along with FPY. The expression for QNs/module is shown
in Equation 2.3.
QNs/Module =Total number of QNs
Total number of modules(2.3)
It gives us an average number of quality errors made in any specific module type.
The lower the QNs per Module metric the better it is. Lower number of QNs per
module mean lower quality issues and so less rework and consequently lower costs
incurred.
2.6 FPY and QNs/module: A Historical Look
The Figure 2-2 shows how the two metrics have done since the inception of the
program and the motivation for this thesis. The higher the FPY metric the better it
is. Conversely for the QNs/module metric, the lower the better.
As is evident in the Figure 2-2, the initial years show a great improvement in the
FPY as well as the QNs per module metric. It was primarily because of the new
focus on quality and led to the addressing of a lot of easier to solve issues. Once these
"low-hanging fruits" were over, the metrics pretty much plateaued. This flattening
of the curves is the prime motivation for this work.
2.7 Addressing QNs
A quality issue on the shop floor is to be entered in to the ERP system as a QN
which has all the details of the issue. A quality issue can be found out as soon as
it happens during the build, at any time further during the build or at the testing
24
Figure 2-2: FPY and QNs per module: 2011 - Jan 2016
stage. The QN can be logged into the system by the person diagnosing the issue, the
person rectifying it or the team lead. The QN details include time to diagnose the
problem, time to rectify it, the afflicted part number and a text entry which details the
quality issue. Besides these each QN is assigned a group or bucket which is basically
a method to segregate different types of QNs. The current approach of bucketing the
QNs at Applied Materials, along with its advantages and disadvantages, is described
in the following subsections. Any QN entered in the ERP system is assigned to a
Quality/Manufacturing Engineer who takes care of rooting out the problem.
2.7.1 The Bucketing Approach
The way the quality notifications are handled at Applied right now is by segregating
them into buckets or groups. This has been the approach since the program started.
All the quality issues, once logged in the ERP as QNs, are segregated into the following
four buckets:
25
1. Connections: Any QN logged against an issue that is a mechanical or a pneu-
matic connection shall be put into this category. A typical defect can be a water
leak at an elbow joint which was detected when the module was being tested.
2. Harnessing: QNs where there is a fault with the fiber optics (also referred to
as light links at Applied Materials) fall in this bucket. Fiber optics are used
for communications within a particular module as well as between modules.
A typical fault of this category can be a kinked fiber optic cable which might
render the cable useless or a faulty connection made within or between modules.
3. Vacuum: There are many sections of the modules built here that need to be
in vacuum to perform the function of doping of semiconductors effectively. All
these chambers are sealed by means of air-tight vacuum seals. Any leakage in
these seals leads to air ingress and this is reported as a QN. All such QNs will
fall under this category. These QNs are mostly found during the testing of the
module.
4. Parts: This takes into account all such workmanship errors which result in
parts being broken or damaged. These parts can be salvaged at times and at
other times can only be discarded. Wrongly assembled parts also fall in this
category.
Each of these buckets is led by a manufacturing engineer responsible for analyzing
the issues of the corresponding bucket. A root cause analysis of these issues is done
and steps are taken to minimize chances of recurrence of the defect. The analyses
may point towards some potential design changes, supplier issues, procedural issues,
workmanship mistakes etc. This analysis is then presented by each bucket leader at
the weekly FPY meeting to disseminate the learnings to all manufacturing engineers.
More importantly the findings are conveyed verbally to people working on the shop
floor.
26
Advantages
One of the main advantages of bucketing is the fact that it allocates responsibility
to a specific individual to root out issues in a particular bucket. It also helps in
goal setting and comparing how each bucket is doing. Moreover, monitoring their
contribution to the FPY or the QNs per module metric helps the management focus
on specific buckets.
Disadvantages
This is the approach followed for the FPY program since the time of its commence-
ment. Gains were achieved in the initial years mainly because there were lots of
"low-hanging fruits" to be plucked. This led to numerous improvements in all the
buckets which had a collective positive impact on the FPY.
However, this method has not been successful to reduce the FPY numbers for
the past couple of years. This method binds the team to think in a particular way.
Right now, the current method at times ends up putting quality issues emanating out
of similar root causes into different buckets. Also since the current approach is not
helping, a completely new approach is needed which was taken by the team of Anand,
Daigle and Ismail and has been discussed in detail in Chapter 3 and in Ismail’s [3]
work.
2.8 Summary
The FPY program has well served the quality effort at Applied Materials since it
began. The QN logging system in response to any quality issue and the the way it is
dealt with afterwards was detailed in this chapter. Also, the two important metrics
of FPY and QNs per module were explained since these terms will be frequently used
in this work. Also, the way QNs are segregated has been introduced which shall be
detailed in later chapters. This has built a foundation to explain the analyses of the
potential causes of quality failures in the next chapter. Also an understanding of
27
the two metrics in this chapter shall help understand the interplay between the two
metrics and give equal, if not more, weightage to one of them.
28
Chapter 3
FPY Stagnation: Analysis and
Proposed Improvements
In this chapter, we shall delve in to the various supposed reasons of FPY stagnation
over the past few years. The approach followed here is that of building a hypotheses
tree where a number of hypotheses are proposed and are then tested. All this testing is
done on data from ERP system at Applied Materials by employing various statistical
methods. These hypotheses are arrived at by looking at ERP data and on the basis
of the visits and discussions with various engineers and assemblers on the shop floor.
It is a matter of fact that there will be an element of subjectivity in how people
associate reasons to low FPY. This makes it imperative to test all hypotheses by
using statistical methods and make sound conclusions accordingly. The causality
attribution by this process shall help us work in particular areas which shall have a
positive impact on FPY. However, lack of sound data often hinders deriving concrete
conclusions, when efforts have been made to do the best possible analysis with the
available data and use personal experiences of people on the assembly floor. This
chapter shall also delve into how different modules have fared on their FPYs and
which ones need to be focused to gain improvements on the overall FPY metric. It
also discusses the interplay between the two metrics.
FPY stagnation can be attributed to many reasons such as inexperience of new
assemblers, lack of attention to detail among assemblers, improper procedures, not
29
following procedures strictly, complexity of modules, unavailability of parts on time
(shortage of parts) etc. All these supposed factors contributing to FPY stagnation
shall be looked in detail in this chapter. Finally various methods of improvements
have been suggested along these different dimensions on the basis of the analyses
carried out.
3.1 FPY and Expected Error Rate
There are different types of modules being assembled at the Applied Materials manu-
facturing facility. They vary a lot from each other in terms of design, complexity, time
of build, assembler experience on it etc. This means that each of the modules has
different numbers of failure opportunities and consequently different expected failure
rates. Historical data reveal that modules like 90 Module and UES have a very low
FPY or correspondingly a high QNs per module count.
At Applied Materials, the metric that is given the most importance is the FPY.
However, it is not directly under control and it depends on the number of modules
passing the final test without any quality issues and the total number of modules
tested. Direct monitoring of number of quality issues and its impact on the QNs
per module metric is easy but QNs per module is somehow not given that high an
importance at Applied Materials. Therefore an effort has been made to relate the
two metrics: FPY and QNs per module. Consequently a mathematical model has
been developed to relate FPY and expected number of failures, which is a substitute
for the metric - QNs per module in this section.
Mathematical Model
Some of the assumptions considered in this model are:
1. Each module will have many opportunities for failure and it shall be assumed
that the probability of failure for each opportunity for a module is the same.
This will be not be true in reality since certain failure modes are repetitive
which implies that they have a higher probability of occurrence.
30
2. It shall be assumed that the product of the number of opportunities for failure
and the number of modules built is a very large number, which is very close to
reality.
�̄� = E(𝑛) = 𝑄×𝑁
where,
�̄� = Expected number of failures
n = Number of failures in a given time
Q = Probability of failure per opportunity
N = Opportunities per failure
So the Probability of having ZERO failures (which shall essentially be equivalent
to the FPY of the module) on a module can be written as:
𝑃 = (1 −𝑄)𝑁
which is equivalent to
𝐹𝑃𝑌𝑚𝑜𝑑 = (1 − �̄�
𝑁 ×𝑚)𝑁×𝑚 (3.2)
Also, the equation (3.2) can be simplified as (considering 𝑁 ×𝑚 is large):
𝐹𝑃𝑌𝑚𝑜𝑑 = lim𝑁×𝑚→∞
(1 − �̄�
𝑁 ×𝑚)𝑁×𝑚 = 𝑒−�̄� (3.3)
To verify our model we construct scatter plots of FPY for a particular module
versus QNs per module. Three of these scatter plots with a curve fit to the trend is
shown in Figure 3-1. All these curves point to the fact that the relation is exponential
in nature. It is not exactly exponential owing to the assumptions that were made in
the coming up with the mathematical model.
31
(a) 70 Module Scatter (b) Facilities Module Scatter
(c) Beamline Module Scatter (d) UES Module Scatter
Figure 3-1: Exponential Fits
32
3.1.1 Setting QNs per module target
FPY is a somewhat deceiving metric. Even if the quality efforts lead to lessening in
the number QNs/module it may not necessarily have any impact on the FPY of a
module or the total FPY. This is due to the fact that only one error on a module is
enough to affect its FPY whereas it is quite possible that the QNs/module metric has
improved over time. More than deceiving, it will be very difficult to obtain gains on
FPY of complex modules because they have very high number of opportunities for
failure. In a complex module it is quite possible that substantial progress in the field
of quality is made but even then it is far from eliminating all the errors each time.
Even one such quality issue will end up hampering any improvement in the FPY
metric. So using the mathematical model above a target is proposed for the QNs per
module so as to see any developments in the FPY metric. At the same time, FPY
metric must always be accompanied by QNs per module metric. Having proved that
the relation between the two metrics is exponential in nature, it is proposed that the
critical value of QN per module is 1, to have any noticeable improvements in the FPY
of the module. Figure 3-1 shows scatter for monthly FPY of four modules versus their
QNs/module count. Clearly the scatter for modules like the Beam Line and Facilities
show that their QNs per module metric is below 1.0 for most of the months. This
reflects on their high FPY numbers as well. On the contrary the QNs/module values
for 90 Module and UES are above 1.0 most of the time which also leads to poor FPY
numbers. Figure 3-2 illustrates symbolically this interplay between the two metrics
namely the QN/module and the FPY metric. In the Figure 3-2 it is shown that
as the QNs/module value keeps on going down, the increase in the FPY numbers
is very slow unless the QNs/module value reaches a critical point. In the symbolic
illustration this QNs/module number is shown as 1.0 where the value of FPY takes
a sudden jump.
The historical data of FPY reveals that 90 Module and UES have the poorest
FPY as well as the highest QNs per module. These are the modules which hurt the
overall FPY the most as well. So it is imperative to improve these modules more than
33
Figure 3-2: Relationship betwen QNs per module and FPY
any other to see any further gains in the overall FPY numbers. Hence, this model
proposes the QNs per module number to be brought to below the value of 1.0 for the
the modules for which it is higher to move over the current plateau. This has been
the case with all the modules that have now achieved QNs per module count of less
than 1.0 and FPY numbers touching 100%. A model shows the journey of modules
making this transition from QNs per module metric from above 1 to below 1 in the
Figure 3-3. Figure 3-3 shows the probability distributions of a hypothetical module
such that the mean value of QNs per module metric makes a transition from higher
than 1.0 value in Figure 3-3 (a) to a lower than 1.0 value in Figure 3-3 (c). The
vertical dashed red line shows the 1.0 QNs/module mark. It is after this transition
of the QNs/module number from higher than 1.0 to a value lower than 1.0 that the
FPY values for the modules breaks through a barrier and these modules no longer
hurt the overall FPY as much.
Following on the above proposed hypothesis, the modules with high QNs per
module value are analyzed and a case for UES has been made. In Figure 3-4, the
histogram of the QNs/module has been shown. In this figure the red vertical line
shows the mean value of QNs/module for the duration. As is illustrated in the Figure
3-4, the mean QNs/module is shifting towards the "magic" number of 1.0 but is still
over it. The UES FPY as well as overall FPY metric will further improve only when
the mean QNs per module reaches below 1.0 values for the modules having QNs per
module value higher than 1.0.
34
(a) FY 2012 Distribution (b) FY 2015 Distribution
(c) FY 2015 Distribution
Figure 3-3: Probability Distribution of QNs per module, UES - FY 2012 and FY2015
(a) FY 2012 Distribution (b) FY 2015 Distribution
Figure 3-4: Histograms for QNs per module, UES - FY 2012 and FY 2015: Themean(red line) QNs/module value has been slowly shifting towards 1.0
35
3.2 Hypotheses Tree
The previous sections talked about understanding the interplay between FPY and
QNs per module metric and when a big jump in FPY can be envisaged. This however
does not tell us the reason of FPY stagnation or how to tackle this quality challenge.
This section will cover the hypotheses that were drawn to account for the reasons of
plateaued FPY and reject or accept their claim.
3.2.1 Complexity of Modules
The way FPY is defined places equal weight on all modules. However, the truth
is that the modules are different in their complexities. This complexity lies in the
number of operations needed to be done in assembling them, number of parts to be
joined together, different kind of connections to be made, different types and numbers
of seals to be made etc. All this lends different number of failure opportunities to
each module. Also the probabilities of occurrences of all these different possibilities,
even on one type of module, are widely different. Consequently, even though we want
each module to be impeccable as far as quality goes, they should not be treated the
same. We might need to have different strategies to have the same level of quality in
each of them.
For example, the modules like Gas Box and Facilities have very few opportunities
for error and consequently have higher FPY numbers and lower QNs per Module
numbers as opposed to other complex modules like 90 Module or UES.
Relating Complexity and Quality
Having said that complexity of modules directly affects quality, it would be a good
idea to make a mathematical model describing this relation. This would help in
understanding how to reduce the number of opportunities to below some threshold
value by improving design, introducing poka-yokes etc. to achieve significant gains
in the quality metrics. Also knowing the number of opportunities for failure for
various modules shall help assign weightage to modules and come up with a better
36
(a) FPY trend: 90 Module
(b) FPY trend: UES
Figure 3-5: Historial Trends for FPYs of the two most complex modules: UES and90 Module
representative FPY metric (FPY𝑤𝑒𝑖𝑔ℎ𝑡𝑒𝑑).
A look at the monthly FPYs of these two modules in the Figure 3-5 for the past
five years clearly tells that the metric has not shown any significant improvement over
the past few years.
However, the team did not go out to find the number of opportunities for failure
for each module given the time it would take, but it certainly is not an intractable
problem. The key takeaway from this knowledge and the previous section on FPY
QNs per modules interplay is to direct specific attention to the complex modules
like UES and 90 Module which have higher than 1.0 QNs per module value. The
37
current approach at Applied Materials does not pay any special attention to these
critical modules. Focus can be directed by forming teams that specifically look into
the quality issues of these critical modules. We expect that this dedicated effort shall
work wonders in improving their FPY rather than a generic approach towards all the
modules.
3.2.2 Analysis of High FPY Module
Different modules have different ranges of FPYs when considered by module as in
Tables (2.1) and (2.2). This can be attributed to various factors, the primary among
which shall be the complexity of the modules. A hypothesis put forward was that
there could be other factors at play as well and so an analysis was done to understand
what it takes to have a low QN count on a module.
To understand this, the assembly process of a Facilities module was thoroughly
followed up. This module has consistently shown improvements in the FPY numbers
as well as the QNs per module data which is also shown in Figure 3-1 (b).
Delving into the Facilities module, the following reasons were identified for its
sustained improvements:
1. Low Complexity: The module has a low complexity as compared to modules
like 90 module or the End Station.
2. Fixed Workforce: The module has a fixed group of three people who work on
it. This non variability of workforce brings certain positives as well as provides
avenues for errors. The pros and cons for this are as under:
(a) Pros: The experience keeps on building and the employees know the whole
assembly inside out. This fixed workforce also ensures that they take
ownership of issues in the modules assembly and go to lengths to get those
rectified.
Also, there is a very high probability that the employees develop acumen
on the assembly and develop ingenious ways to do it. But the important
38
thing here shall be to keep the Standard Operating Procedure/SOP always
updated with these latest developments so that it is easy for even a new
person to assemble correctly if need be.
(b) Cons: Employees working on the module are so used to it that they
remember every part of the SOP and may tend to not look carefully at it
while assembling. This can be of concern after any SOP revision when any
important improvement can be easily missed and lead to a quality defect.
Another issue that has not been noticed but is quite possible in such a
case is that employees might have a tendency to rectify any quality issue
without logging a QN in the ERP system for it. This is possible since the
same people will be one identifying as well as rectifying the issue. This
should be avoided at all costs and all issues should be logged into the
system so that correct data can be generated and proper mitigation steps
can be taken.
3.3 Experience of Employees
Before going any further, it is made clear that the words "assembler(s)", "employee(s)"
and "worker(s)" have been used interchangeably in the following text. It seems
straightforward that experience of assemblers on the assembly process will dictate
the quality or the number of QNs. This point comes into play here at Applied Mate-
rials because they have a mix of permanent and contractual employees. Contractual
employees work in cycles and are employed only for a maximum permissible dura-
tion, which is twelve months, after which their contract expires and someone else
takes their place. At many times contractual employees whose contract has expired
come back again after being away from the assembly process for some time. All these
varied types of employees may have some effect on quality. It is also possible that
the duration of employment does not have any effect at all. Another hypothesis is
that only the experience on a particular type of assembly counts towards how well an
assembler does in terms of quality.
39
Figure 3-6: Relating Experience of Assemblers to # of QNs
To validate this hypothesis UES module was picked. In doing so, an assumption
was made on the basis of discussions with manufacturing engineers it was taken for
granted that assembly experience on a specific type of assembly matters more than
assembly experience in general. Next all assemblers on the UES assembly line are
segregated into two categories, experienced or inexperienced; which is arrived at by
looking if they have worked on it for more than 3 months as well as getting a feedback
from the team leads, who are the leading assemblers on the line in different shifts.
All this leads us to the proportion of inexperienced hours on a tool which is then
plotted against the number of QNs on the tool in Figure 3-6.
The results are very widespread and do not lead us to any clear conclusion. How-
ever, one of the conclusions derived from this study is that QN has nothing to do
with the type of assembler, whether contractual or permanent. A lower threshold
of 3 to 6 months is a necessary but not a sufficient condition for a assembler to be
called experienced and adept at delivering his duties. The learnings are not always
transferable from one particular assembly to another since the level of complexity
is significantly different. In Figure 3-6, the spread of QNs is pretty much the same
everywhere. Common sense dictates that there should have been more QNs when the
number of inexperienced hours is on the higher side. However, a possible explana-
40
tion for the inconclusive results observed is the confounding of responses since only
one factor has been considered. The confounding can be because of new employees
starting at different times throughout the year. The inflow of new (generally inexpe-
rienced ) contractual workers and outflow of experienced contractual workers is done
in a phased manner which manifests itself in different levels of worker experience. The
positive thing here is that Applied Materials recognizes this fact and tries to have
minimum variation of assemblers on complex modules. It is however to be noted that
most of the manufacturing engineers seem to be not very happy with this approach
in general since they believe that it hampers productivity and quality.
3.4 MIT Critical Path Project
One of the projects that caught the attention of the MIT team of Anand, Daigle and
Ismail was the work done by a previous MIT team [1, 4] , which worked on reducing
the lead time on the assembly of the UES module. The crux of the project was to
allow for faster assembly of the modules by completely breaking down the assembly
steps and having as many as possible parallel steps in addition to eliminating some
wastes from the then existing assembly process. The project had shown a marked
decrease in the assembly time for the module.
The current team was interested to see if this complete reorganization of the
assembly process had any effect on the quality of the assembly being carried out. To
look into this, the team looked into the QN count before and after implementation of
the project. The 3-7 shows that there was a decrease in the normalized monthly QN
count before and after the implementation of the project. A possible explanation to
this is the higher degree of standardization of the build procedure as a result of the
project that led to reduction of the number of failure opportunities or reduction of
the probabilities of these failure occurrences or both.
The above hypotheses, if trues shows, that working on improvement of procedures
can have a significant impact on quality. Just like a procedure can be optimized to
minimize lead times, it can be optimized to minimize opportunities for failures and
41
Figure 3-7: Impact of MIT Critical Path Project on # of QNs
probabilities of these failures on assemblies.
3.5 Shortage of Critical Parts
Any module assembly consists of piece parts or sub-assemblies that are assembled
in the supermarket/SMKT or come directly from outside vendors. The supermarket
is an internal assembly area where sub-assemblies are assembled which will go into
the modules being assembled. Individual parts for the super market assembled sub-
assemblies also come from outside vendors. These parts or sub-assemblies form a very
important part of the assembly process of a module and finally the tool.
The issue of shortages was brought to notice by workers on the shop floor as well
as manufacturing engineers while discussing quality issues with them. Whenever a
module is laid down at the start of the build it needs a number of piece parts or sub-
assemblies to start the assembly process of the modules and progress further. Many
times, one or more of these piece parts, supposed to come from outside vendors, or
sub-assemblies, supposed to come from the internal supermarket, are delayed and
42
the assembly process of module is affected. In this situation two possible scenarios
exist; Delay the assembly of the module until the part arrives or start building with
whatever is available and fit in parts as and when they arrive. The later method is
the one mostly employed since the commitment to the customer is of prime concern
and holding on the complete module for a specific part may end up delaying the tool.
In this employed method it becomes necessary to undo some assembly work when the
missing parts arrive.
However, this approach potentially exposes the assembly to the various quality
issues. The main drawback of this method is that the assembly is now built in a way
that does not match with the procedure, which in general is the path of least errors
and is designed to ensure ease of assembly. Building a module and accommodating
sub-assemblies at later points as compared to what the procedure says, exposes the
module to several risks. The primary risk is that of missing or damaging something
because of lack of accessibility or building around missing parts and hence work
completely out of procedure. At the very least, the increased build time increases the
risk of cropping of quality issues and hence QNs due to a greater exposure time of
the parts in assembly process.
This is not a direct visible effect of shortages as a QN is generally attributed to
various reasons other than shortage or delay of parts. However, it is important to
note that in such cases, the root cause of the QN is the shortage and this should be
mentioned in the ERP QN entry going forward. This kind of data is not present now
which makes it difficult to assess the impact of shortages on quality. This suggestion
to attribute QNs to shortage of parts has been made later in this work as well to
account more specifically for the shortage related QNs.
This issue has led to many such defects where assemblies have gone wrong, har-
nessing has been done wrongly, leaks have emerged etc. As a result, focus was drawn
on the reasons for shortage of parts and how it can be improved to have a positive
impact on quality.
43
3.5.1 Critical Shorts
Not all parts in a module assembly that are short can lead to QNs. Some parts are
more critical to the quality of the module than others and such modules have been
referred in this work as critical parts, the absence of which, when needed, leads to
a critical short. Applied Materials does not classify parts as critical parts or their
shortages as critical shortages but an effort has been made in this work to understand
which are the critical parts if they exist at all.
To understand such parts, first all the parts that shorted in the period January -
June 2016 were listed. This was done using crossdock information, which is essentially
pulled from the ERP system and details the parts that went directly to the shop floor
rather than a storage location. This is the modus operandi at Applied Materials
whenever a part that has been shorted arrives. Further an assessment of parts in this
list was done, with inputs from quality engineers, to segregate the critical shorts and
thus come up with a measure of how many parts are critically shorted on average.
Independent copies of all shorts were looked into by different manufacturing engineers
to decide which parts they considered as critical. A positive sign from this study was
that all the engineers agree to a great extent as to what shall be considered a critical
short. This confirms the teams hypotheses that something like "Critical Shorts"
exists.
3.5.2 Relating Critical Shorts to QNs
Anand, Daigle and Ismail analyzed the ERP data to link shortages to QNs despite
the shortcomings of the data available which shall be further discussed in the ensuing
chapters and sections of this work. Using various ERP screens, information was
collected on the number of shortages, the number of shortage occurrences and the
number of QNs on a tool. This was done to test the hypothesis that the higher
the number of shortage occurrences, the higher the number of QNs logged. Here a
shortage occurrence is defined as the absence of a part number when an assembler
wants it. It does not take into account as to how many pieces of it were needed by
44
Figure 3-8: Relating Short Counts to # of QNs
the assembler.
As depicted in Figure 3-8, it is clear that the shorts can be directly related to the
the number of QNs on a tool and therefore every additional short brings down the
quality metric of FPY.
Also, the following analysis was done on the main tool of the assembly line, the
Trident, since it is the biggest contributor to revenue as well as profit for Applied
Materials. This also helps avoid complexity since there are various different types of
tools that are built over the course of time and they are very different from each other
in their complexities.
Another effort was made to present the effect of shortages on QNs in Figure 3-9.
For all of these Trident tools made in the last one year period, groupings of tools are
made on the basis of the number of shorts they experienced which is plotted against
the QNs on the tool. Figure 3-9 shows that there exists a strong correlation between
shortages and QNs.
This further led Anand, Daigle and Ismail to delve into the reasons for material
shortages which is detailed in the following sections.
3.5.3 Part Routes
At Applied Materials, materials are procured in a variety of ways. This is important
to understand before looking into the route which a shorted part followed. Also, it
45
Figure 3-9: Relating Shortage Occurrences to # of QNs
gives insight into which routes are critical from a shortage point of view.
The modes of procurement relevant from this work’s point of view are:
1. Purchase Order (PO)
2. Purchase Order of part designed by Varian
3. KC parts, which are 2-bin Kanban parts
4. KB parts, which are large Kanban parts
The first two order types are treated on a part to part basis and the orders are
released through MRP. KC and KB parts are delivered by vendors as per an agreement
with vendors to fulfill demand within 5 days for most of these parts. KC parts are
internal kanban which means that the bins are located in Applied Materials shop
floor. This is different from a KB part where the bins are located with the vendor.
46
3.5.4 The 2-bin Kanban System
KC parts run on a 2-bin Kanban system, where the current design in intended to
hold inventory for two weeks of demand. After the depletion of one bin, the vendor
is supposed to fill it within the next five days and in the meanwhile the second bin
shall serve the demand. During these five days, if the the cumulative demand exceeds
the bin size, a shortage is encountered, assuming that one bin was completely full
when the order was released. The bin sizes are not set in stone and are readjusted
every quarter depending on the forecast for the next quarter. Daigle [2] talks in more
detail about the advantages and disadvantages of this system vis a vis other methods
and their suitability to Applied Materials. This 2-bin Kanban system has also been
shown in Figure 3-10. In Figure 3-10 the expected inventory level at all times shall
be 1 single bin.
Anand, Daigle and Ismail have focused on KC parts in their work since these part
shortages are the highest fraction when compared with the total number of KC parts.
Figure 3-11 shows that KC parts shorted on the TRIDENT tools are 39% of the total
KC parts on the tool.
3.5.5 Current Bin Sizing Method for KC Parts
The process of bin sizing for the KC parts starts at the beginning of each quarter
starts with the forecast of the quarter. The bin size for any part is calculated using
the following:
1. Lead Time (T): Time between the placement of an order and delivery of part.
This time is 5 days for most of the KC parts.
2. Weekly Safety Factor (WSF): This is based on the 2-week desired level of supply.
3. Daily Demand Average (𝜇𝑑𝑎𝑦): Average of the daily demands.
4. Daily Demand Standard Deviation (𝜎𝑑𝑎𝑦): Standard Deviation of the daily de-
mands.
47
Figure 3-10: 2-Bin Kanban System
48
Figure 3-11: Shorts of various procurement types
The bin size formula used at Applied Materials currently is shown in Equation
3.4.
Bin Size = (T×WSF× 𝜇𝑑𝑎𝑦) +1
2𝜎𝑑𝑎𝑦 (3.4)
One of the critical issues with the Equation 3.4 is the fact that the standard devi-
ation for a day has been taken into account. Rather the correct formula should have
standard deviation of the number of days for which it is intended to hold inventory.
Therefor considering a 10 day demand period in a two week time, a factor of√
10
shall be multiplied to the 𝜎𝑑𝑎𝑦 term in the Equation 3.4. Correcting the above issue
in the Applied Materials formula the new formula is shown in Equation 3.5.
Bin Size = (T×WSF× 𝜇𝑑𝑎𝑦) +
√10
2𝜎𝑑𝑎𝑦 (3.5)
The 12𝜎 term in the Equations 3.4 and 3.5 are supposed to take any variation in
the two week demand. This discussion on sizing of bins at Applied Materials is also
mentioned in the works of Daigle [2] and Ismail [3].
49
3.5.6 Gold Squares
Sub-assemblies made in the SMKT area fall either in the build-to-order category or
the build-to-stock category. Gold Squares are fixed number of certain assemblies that
always need to be present on the designated shelves. These gold square items are
build-to-stock and take into account various demand sources like tool assembly de-
mand, sales demand and emergency demands. The gold square number for each part
type on it is determined by the weekly average demand and the standard deviation
for the entire week. Gold Squares form a subset of the KC part types. The number
of squares for any part type is calculated according to the Equation 3.6 at Applied
Materials currently.
# of Items on Gold Squares = 𝜇𝑤𝑒𝑒𝑘𝑙𝑦 + 𝜎𝑤𝑒𝑒𝑘𝑙𝑦 (3.6)
Considering the demand to be normally distributed, which will be shown later
that in reality is not the case, it would be a 84% service level for gold square parts.
Anand, Daigle and Ismail further worked on the demand characterization since it did
not look like demand follows a normal distribution.
Proposed Method of Calculating Bin Sizes
The MIT team of Anand, Daigle and Ismail propose a continue review policy stock
sizing which is shown in the Equation 3.7 [5].
Stock Size = (LT× 𝜇daily demand) + (𝑧 × 𝜎daily demand ×√LT) (3.7)
In Equation 3.7:
∙ LT: Lead time in days
∙ 𝑧: 𝑧 score covering a desired range of demand
However the above formula is based on the assumptions that the demand over a
week is normally distributed. A detailed explanation on the characterization of daily
and the weekly demands has been shown in Appendix A.
50
Current Service LevelsService Level
95% 97% 99%
Inventory Cost $ 5,050,730 $ 6,967,099 $ 7,544,401 $ 8,494,405
Shortage Cost $ 1,567,377 $ 535,249 $ 321,149 $ 107,050
Total Cost $ 6,618,107 $ 7,502,348 $ 7,865,551 $ 8,601,455
% Increase - 13% 19% 30%
Table 3.1: KC Parts bin sizing cost analysis at different service levels
Service level
Current Service Levels 95% 97% 99%
Shortage Occurrences 8,375 2,860 1,716 572
Percentage reduction from current 66% 80% 93%
Table 3.2: KC Parts predicted shortage analysis
3.5.7 Inventory Levels Restructuring: Results and Potential
Benefits
The work by Ismail [3] in modeling the demand follows with the proposed restructur-
ing of the inventory levels and comparing different service levels vis a vis the economic
impact. At the same time forecast is created for the expected annual shorts for dif-
ferent service levels. Table 3.1 shows the current total cost versus the total costs that
would be incurred if all the KC parts are kept at uniform service levels of 95%, 97%
and 99%. Clearly the total costs would increase as compared to the present value but
it shall help mitigate quality issues arising because of shortages. Here the shortage
cost reflects the cost associated with the rework that has to be carried out when a part
arrives late. This rework time was extracted from time cards, which the employees
fill out to give a description of the time spent on a particular day.
Table 3.2 shows the percentage by which the shortages shall be reduced on fol-
lowing different service level strategies for all KC parts.
Tables 3.3 and 3.4 show the cost increase and the shorts prediction for Gold Square
parts at different service levels.
The above Tables 3.1, 3.2, 3.3 and 3.4 are based on the assumption of normality
of demand. These tables can be used by the Applied Materials team to decide on
a strategy to target a service level that makes economic sense for the company. A
51
SMKT Lead Time Current Total CostsCost at Service level
95% 97% 99%
6 $ 714,719 $ 1,021,152 $ 1,050,834 $ 1,136,071
5 $ 698,749 $ 858,836 $ 976,111 $ 1,044,244
4 $ 677,393 $ 804,160 $ 818,964 $ 941,964
3 $ 651,088 $ 691,630 $ 709,706 $ 781,045
2 $ 624,033 $ 626,717 $ 638,278 $ 654,769
1 $ 607,767 $ 532,698 $ 468,162 $ 460,631
Table 3.3: Gold Square sizing cost analysis
SMKT Lead Time Current Total ShortsShorts at Service level
95% 97% 99%
6 578 152 91 30
5 493 183 110 37
4 379 228 137 46
3 238 304 183 61
2 94 456 274 91
1 7 913 548 183
Table 3.4: Gold Square sizing predicted shortage analysis
detailed discussion on the characterization of daily and weekly demand has been
shown in Appendix A after which it was decided to assume a normal distribution for
the weekly demands.
SMKT Backlogs Handling
Backlog reduction at SMKT is another important area to focus on since they lead
to sub-assembly shortages while building the modules. The MIT team developed
demand model for characterizing demand has been used to calculate effect of reduc-
ing SMKT total lead time from 6 to 2 days progressively and the expected annual
shortages. Also upfront investment has been projected to reduce these backlogs. The
MIT team proposes to place a special emphasis on the SMKT backlogs since it is a
big source for tool shortages and highly responsible for building around the procedure
issues.
The new demand model and inventory levels shall go a long way in reducing piece
part shortages which would ultimately positively impact quality.
However, the shortages of sub-assemblies made at SMKT is an area of serious
52
concern. As discussed earlier, it is statistically responsible for causing quality issues
on the modules built on the shop floor. The new inventory strategy as detailed by
Sean shall go a long way in reducing quality issues only if Gold Squares at SMKT are
maintained at desired levels. It is currently a bottleneck and just inventory increase
shall do no good and rather end up increasing the backlogs. The backlog at the SMKT
is a capacity issue and workforce restructuring as well as assembly prioritization needs
to be looked into as well. For these purposes a Value Stream Map (VSM) has been
developed for the current state as well as the ideal state in Daigle’s [2] work. Assembly
prioritization has also been detailed in Daigle’s [2] work which shall help decongest
the SMKT area.
As shown in Table, 3.4 on moving to a total lead time of 2 days Gold Square
shortages shall be around the number 94 per year. This is far less than the current
projected number of around 300-400 per year on the basis of the data gathered by
the team over a duration of 1.5 months during the period June 15th 2016 - July 31st
2016.
3.6 Re-Bucketing
The way the Applied Materials FPY program handles the QNs, limits the team in
further reducing the number of QNs in the buckets. In the current system, there are
four buckets one of which is assigned to each QN. This approach was very useful to
the team when they started the program back in 2011. It was mainly because there
were a lot of "low hanging fruits" to be plucked. But for the past few years, this
approach only helps keep a close tab on all QNs and no significant progress is being
achieved anymore.
A different novel approach has been proposed by the team of Anand, Daigle and
Ismail to further attack the quality problems arising on the shop floor.
53
The MIT Approach
The team came up with this approach after studying the QNs for the period January
- June 2016. A thorough analysis on all the QNs and problem reports for the period
revealed that there are common failure modes for various quality issues which pervade
across multiple buckets in the current scheme of things. Earlier, these quality issues
with similar causality were landing up in different buckets. In the new approach
suggested by the MIT team, all QNs with a particular failure mode can be treated as
belonging to a category and will need similar solutions. Various common failure modes
were identified on which projects shall be taken to mitigate quality issues. Another
issue that the team came up with was that too many QNs were being dismissed by
saying that they are caused because of "Attention to Detail".
The team went on to suggest categories which would help determine specific pre-
ventive techniques for all QNs in that particular category. Tables 3.5 and 3.6 show
the categories that the team has come up with vis-a-vis the existing four categories.
Ismail’s [3] thesis talks at length about the MIT approach and a project that
the team proposed to reduce the failure opportunities to half on the NCS computer
harness bundle connections. Moreover, the work talks about various potential projects
that can be taken to address various failure modes on the basis of the analysis and
re-bucketing done on the QNs for the period January 2016 - June 2016.
In the current methodology at Applied Materials, the buckets are made in a way
that it makes it easy to divide work among manufacturing or quality engineers. The
MIT team approach categorizes QNs in a more logical way so that similar failure
modes fall in the same bucket and many issues are resolved rather than categorized as
an "attention to detail" problem and the root cause never found. The new approach
shall go a long way in making further advances in reducing the QNs per module
numbers and thereby improving the FPY metric.
54
Current Approach Buckets The MIT Team Approach Categories
1. Parts 1. Loose Connections
∙ Swage Fitting
∙ Fastener
∙ Water
∙ Cable/Communications/Electrical
∙ Mechanical
2. Harnessing 2. Swapped Connections
∙ Signal/Electrical
– Lightlink (Optical Fibers)
– Non-light links
∙ Mechanical
– Air
– Vacuum
– Water
3. Connections 3. Debris
∙ Connection debris
∙ Vacuum Surface debris
∙ Cleanliness
4. Vacuum 4. Damaged
∙ O-Rings
∙ Graphite
∙ Surface Finish
∙ Fastener
∙ Ion-Gauge Filament
∙ Electrical/Signal
∙ Over-Tightening
∙ Dropped part
Table 3.5: Re-Bucketing of QNs for the period Jan.-June 2016: Current Method vs.the MIT Method (continued on next page).
55
Current Approach Buckets The MIT Team Approach Categories
- 5. Others
∙ Procedure
∙ Wrong Setting
∙ Lines too short
∙ Circuit Failure
∙ Wrong part installed
Table 3.6: Re-Bucketing of QNs for the period Jan.-June 2016: Current Method vs.the MIT Method (continued).
3.7 FPY Metric Benchmarking
It is of special interest to the Applied Materials management to develop a methodol-
ogy and find the maximum possible theoretical FPY value or the minimum possible
QNs per module count. This is of importance since it will help the company going
forward and setting annual goals for quality of its assembly operations. Since the
FPY numbers have stagnated for the past couple of years, as mentioned in the earlier
chapters, the team faces a tough task ahead to decide on the target FPY metric in
the future.
The team looked into various methods to calculate this theoretical FPY value
which shall be achieved without making any significant capital investments. One
of the most promising ways is to compare the Applied Materials functioning with a
similar assembly operation and look for the number of failures to the number of failure
opportunities ratio. This exercise requires two essential data sets. Firstly, the failure
opportunities for each sub assembly and each module needs to be captured which
shall be a time taking but a straight forward process. Secondly, a benchmark level of
quality shall be needed from a similar assembly operation. For example if we know
that a similar assembly operation at some company has achieved and operates at a
3.4 defective parts per million opportunities (DPMO) value we can easily calculate
the target defects per module value for each module once we know the opportunities
for failures for all these modules.
56
The two main reasons for which the MIT team did not try to figure out the target
FPY metric are: Firstly, it would have been a time taking process to figure the number
of failure opportunities for each module and would not justify the time frame of this
project. Secondly, proprietary data detailing the level of quality in terms of DPMOs
are not available in the public domain for most of the companies, which is needed to
obtain benchmark values. At the same time, this exercise helps us set targets and
does not tell anything about how to achieve to achieve it. So the MIT team rather
chose to work on how to improve the FPY metric.
3.8 Conclusions
The FPY team needs to take into considerations both the FPY and the QNs/module
metric to gauge their quality performance. Also it was not very clear, from the limited
data available, as to how much of an affect experience has on quality. Shortages have
been shown to be a main reason for quality issues. The main takeaway is the impact of
shortages on quality. Service levels versus cost analysis has been done which shall help
Applied Materials in adjusting their inventory strategy to mitigate shortage issues.
The re-bucketing approach to look into QNs is a completely new method of looking
at the quality issues. Finally a method was explained which can be used to find the
benchmark values for the FPY metric.
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Chapter 4
Results
4.1 Data Collection: Improvements and Suggestions
"We get what we measure" - W. Edwards Deming
The right type of data is very important to understand any phenomena accurately
and make progress along the way. Only in the presence of the right data, causality
can be attributed correctly and conclusively to various observations. Throughout the
course of this study, the team of Anand, Daigle and had access to data from various
sources and most of the analysis, findings and conclusions in this work derive from that
data. However, many a times there were areas where the availability of a better data
set would have been more helpful to carry out certain analyses and make necessary
improvements. In the following sections all such data collection improvements and
suggestions shall be discussed and how they shall serve the organizational goals.
The approach of the team where various hypotheses were laid out and then then
tested is built around data obtained from the ERP package (SAP) at Applied Ma-
terials and other sources which includes tailor made software packages like Rapid
Response, Agile and data collected by manufacturing or quality engineers in excel
sheets etc. During this process, the team was unable to prove or disprove certain
hypotheses because of lack of suitable data. This is the motivation for this section
where various deficiencies in the current data acquisition system shall be highlighted
and improvements suggested to boost the data to bolster the continuous improvement
59
quality journey.
4.1.1 Critical Shortages Data
The work done by Anand, Daigle and Ismail attributes shortages of parts as one of
the key reasons for the quality issues arising during the assembly of the modules on
the shop floor. This thesis refers to many shortages as critical shortages; shorts that
critically impact the quality since employees have to get around the part to build
them and in the process work out of procedure and potentially create quality issues
and adversely impact FPY. In this regard, effort was made to associate the QNs with
the number of shortages on a module. One of the issues during this analyses was
the unavailability of data that tells whether a QN happened because of a shortage.
Also not all shortages are critical to quality. So the concept of critical shorts was put
forward by the team.
Assemblers while filling in QNs for any quality issue write the issue they faced as
a text and put it into one of the buckets after which the concerned bucket manager
takes it through. This process has been explained in detail in the Chapter 2.
These QNs when looked at by the team for the period January - June 2016 never
attribute its occurrence to a shortage issue. Even if the root cause of a QN is a
shortage, the reason mentioned in the QN text is the most superficial one; the one
without 5 Why analysis done. This made it difficult for the team to relate QNs to
shortages data.
Therefore the manufacturing engineers or the quality engineers who analyze the
QN should ensure that the shortage reason if found should be included clearly in the
QN. The FPY meeting should also ensure that QNs arising out of shortages be given
special attention. This can further be used to create a metric for the procurement
as well as the SMKT team to incentivize their performance to minimize shortages.
Also a direct provision in the SAP QN logging page or the under development iOMS
quality page should be provided where a QN can be directly attributed to a shortage
issue. iOMS is new system that will integrate the work done in the ERP software
atmosphere as well as outside it. This will also help manufacturing engineers zero out
60
critical shortages and pay special attention to them in the future.
A better capture of the critical shorts would be a great initial step as it is still not
conclusively known as to which parts/assemblies be considered critical. Such a data
set would be very helpful in quantifying the expenses made towards issues because of
critical shorts and at the same time pay more attention to these shorts.
It has also been acknowledged by the management that such a data set would be
valuable for adjusting inventory levels and taking steps to reduce QNs.
Relating Shortages, QNs and Corresponding Rework
For all the work that any assembler does during the course of the day he/she files
a time card which are logbooks which tell in detail about the breakup of the times
spent on various activities throughout the shift.
This work hour is segregated into various groups. The rework associated with
shorts are keyed into these time cards. However, it has been noted during the work
by Anand, Daigle and Ismail that reworks associated with material unavailability do
not have a short associated with it in the time card or the ERP on multiple occasions.
At the same time, occurrences keep on cropping where a QN happened because of a
part short which is also discussed also at FPY meetings.
Going forward, it would be very useful to capture every QN associated with a
shortage issue. In the current SAP system or/and the upcoming iOMS system this
provision must be provided as it will help improve the sanctity of the QN data col-
lected and foster the journey of continuous quality improvement.
4.1.2 SMKT Gold Square Shortages Data
The supermarket (SMKT) makes sub-assemblies which are either used in the modules
built on the shop floor at Applied Materials or shipped directly to a customer on
order. Certain sub-assemblies built in the SMKT are categorized as Gold Squares.
They are made on a pull basis. Around 50 sub-assemblies built by SMKT fall under
this category. A pre-determined certain numbers of sub-assemblies for each of the
61
items on Gold Squares are desired to be always present. The number of items on
Gold Squares takes into account demand from the three streams, namely tool build
orders (internally assembled tools), global sales orders and emergency orders . The
number of items required for gold square items takes into account the forecast of the
tools which shall be built in the next quarter which is based on the past consumption
history and the market performance. Accordingly each quarter the number of items
on the gold squares are altered.
During the time of the project the Gold Squares system is very poorly followed
and the calculated number of parts for most of the items items is hardly maintained.
This lends to very frequent shortages of Gold Square parts when needed for tool
builds on the shop floor. This is potentially an area of concern as it leads to quality
issues on the build which has been described in detail in this work. At the same time,
there is no data available to tell the number of gold square shorts for a certain period
of time which would have helped in understanding better as to how these shortages
affect the tool build and quality.
This data collection has been started by the team and shall be handed over to
the Applied Materials team to take it forward. Just over a period of around one
and a half months (June 15th 2016 - July 31st 2016) around 50 SMKT Gold Square
shortages were observed. In the short term the Applied Materials team can track
the Gold Square shortages through a shared excel sheet. Another important step
should be to include this in the upcoming iOMS system so that Gold Squares are
given the attention they deserve. Another important reason for the lack of attention
meted out to gold squares is the lack of responsibility. Just like a bucket leader
handles the quality issues of his/her bucket, responsibility should be allocated for
Gold Square shortages. Also, Gold Square inventory and its shortages should be
included in SMKTs performance metric. This thorough attention on Gold Squares
will help capture and analyze frequent shortages, re-size bins, take focused steps for
certain sub-assemblies etc. This shall go a long way in reducing the number of quality
issues as well as material unavailability rework which will help garner gains in FPY.
62
4.1.3 Flagging Procedural Changes
Whenever a quality issue is encountered, a QN is noted against it in the ERP system.
This issue, upon analysis, many a times leads to some changes in the SOP to mitigate
any chances of recurrence of the same quality issue. However, it is of utmost impor-
tance that changes in SOP are reflected as soon as possible in the system through
which the assemblers are accessing the document.
During the course of this project it has happened a few times that a SOP change
was made while addressing a quality issue. Further when this assembly step was
carried out the next time, the workmanship issue happened again. The main reason
for this was attributed to time taken to analyze the issue, make changes in the SOP
and then reflect these changes in the system. Also, in the current system even if a
new modification has been made in the SOP there is a very high probability of the
employees missing out on the new change by virtue of being so used to the old SOP
so as to miss it. This makes it imperative that a positive check be introduced at
the change stage in the SOP such that the workman has to acknowledge the newly
changed SOP part before progressing further on the build. This ensures that all
assemblers working on it should not miss any new updates to the SOP. This shall be
a fail-safe approach as compared to the current verbal communication approach to
sensitize people on the shop floor about the QN encountered and the SOP change.
This can alternately be done by flagging or coloring the changed part to draw
attention. Moreover this acknowledgement or flagging or coloring should be discon-
tinued after a certain period of time which shall be enough to imbibe the new change
in all the assemblers. This is important to keep the directed special attention to
new changes and not make it something that becomes more of a habit than actually
paying attention to.
Applied Materials is coming up with a completely new iOMS system which shall be
handling all the SOPs. The new system seamlessly combines all the multiple systems
at present at Applied and provides one platform for all tasks. This is different than
the current system where the assembler works in a different system to access SOPs but
63
has to enter into the ERP software to log quality issues. It is hereby suggested that
this suggestion be implemented in the new system so that such errors are eliminated.
4.1.4 ERP (SAP) QN Updates
All QNs are entered in to the ERP(SAP) system at Applied Materials. Anand,
Daigle and Ismail worked extensively to re-bucket the QNs on the basis of their
failure modalities which has been talked at length in the work of Ismail [3]. During
this work, the QNs for the period January 2016 - June 2016 were analyzed and trends
were developed after looking into the details provided for each QN in the ERP system.
One of the major areas for improvement noticed in this exercise is the quality of
the QN. Many of the QNs are unclear because of poor language or minimal details
and it is difficult to decipher any pattern after looking at the text accompanying the
QN. QNs detailing the problem as well the solution well shall be helpful in doing
quality analyses to all; Internal engineers as well as contractors like the MIT team
working on quality projects.
There are two ways proposed to take care of this. Firstly, the training of the
employees needs to be updated to ensure that they log meaningful and detailed QNs,
which contains helpful language as well as keywords. The other is regarding the role
of the FPY team and bucket leaders. These manufacturing or quality engineers look
into each of these QNs and so therefore be responsible for updating the QNs so that
they reflect the details of the problem and the steps taken to mitigate it. This data
set shall help incorporate new failure modes for QNs as described in detail in the
earlier chapter and in the work done by Ismail [3].
4.1.5 Capturing the MIT Rebucketing Approach
The MIT team of Anand, Daigle and Ismail has suggested a paradigm shift in the
bucketing approach followed at Applied Materials which has been talked about in
Chapter 3 as well as in detail in Ismail’s [3] thesis. This approach however demands
that these new buckets based on failure modalities are maintained in the online realm
64
so that all people involved in the quality journey have access to view and modify
it. The easiest as well as the quickest thing to go forward as discussed with the
management shall be to have a common excel sheet to start with. The team has
handed over the sheet that it followed to analyze the QNs for the period January
2016 - June 2016. Going forward changes can be made in the ERP system as well as
in the upcoming iOMS system to capture these new categories and also have provision
to modify them easily going forward since they shall be very fluid, depending on their
occurrence frequency with time, as detailed in Ismail’s [3] work. The main benefit
of doing so shall be to come up with actionable information rather than the current
bucketing approach which only leads to group QNs to divide the work among the
bucket leaders. These updates shall help implement the new QN approach and help
move further in the quality journey.
4.2 QN Feedback
Whenever a quality issue in encountered on the shop floor a QN is entered in the
ERP system against it. In the current QN logging system, the person logging a QN
is generally different than the person or the team resolving the QN. Also, the person
who was working on the part, when the quality issue that led to the QN happened
may not necessarily be the same as the person logging the QN. Further, a team works
to find and eliminate the root cause of the QN rather than a single person.
As a natural human tendency, any person writing a QN or the one who encountered
the QN, expects to hear back as to what was done to resolve the issue. This has also
been felt by the team in their interactions with the assemblers. In the current scheme
of things, the loop is not closed properly and people on the floor not always know
what was done with the QN they logged. Also, if the QN leads to a design change or
any other engineering modification it should be communicated to all the concerned
parties through a proper channel besides the current method of verbally disseminating
the information on the shop floor among the assemblers. A system, which may be an
automated email or a personal communication, needs to be established so that the
65
person logging the QN gets a feedback on what was done with it. Such a step shall
motivate employees to write informative QNs and thereby develop a feeling of being
involved in the quality journey. This kind of exercise can be included in time based
training exercises for assemblers where all QNs of respective area can be discussed.
All of these shall incentivize people to log quality QNs and in the process improve
quality.
4.3 Conclusions
All the above suggested improvements shall act as enablers to improve quality and
help improve the FPY metric. Some of the above can be implemented immediately
whereas others will take some time and will make economical sense to implement
them only in the upcoming iOMS system which shall be active by the end of the
calendar year 2016.
To summarize, all these measures are of critical importance going forward and
serve as a guide book of improvements from the work done by Anand, Daigle and
Ismail. These suggestions once implemented shall be very beneficial to the quality
journey at Applied Materials as well as internal and external teams, in analyzing
quality issues and formulating policies in the right direction.
66
Chapter 5
Conclusions, Recommendations and
Future Work
5.1 Conclusions
Quality is a journey and it takes continuous improvements to progress on this path.
The quality journey at Applied Materials through the FPY program has been a
successful one since it has continuously brought forward different types of quality
issues and helped take appropriate steps to tackle them. This work looked into the
reasons responsible for the current plateauing of the FPY metric as well as analyzed
the probable reasons responsible for the same.
The underpinnings of the FPY metric were highlighted and it was put forward
as to how this metric can be deceiving if looked at in isolation. The importance of
looking at both the FPY as well as the QNs per module metric was brought into
notice. Also the mathematical model developed in Chapter 3 develops a relationship
between the two metrics. Moreover, this helped bring forward a target for QNs per
module metric for poor performing modules to improve their FPY metric.
The hypothesis tree helped test various potential reasons for the stagnated FPY
metric. Among the ones tested, complexity of module is determined to be one of the
major reasons for the poor quality performance on these modules. Workers experience
was also tested if it makes an effect on the quality. The results on this test were not
67
conclusive but it was clear from observations that the recurrent change of contractor
workforce makes a dent on quality. Also, an important point to be put forward is
that it is not the type of workforce, contractor or permanent, but the experience that
positively or negatively effects quality of the assembly and consequently the FPY
metric.
Critical parts shortage was one the key findings of the team and it is one of the
major reasons responsible for FPY stagnation. Every shortage on the shop floor
leads to around 2 hours of rework. This is a direct setback to the assembly. Besides
this, the assembly is now more susceptible to any quality issue since it gets build
out of procedure, is exposed for a longer period of time etc. The two bin Kanban
system for procurement of parts has also been critiqued and its disadvantages brought
forward. Another key point highlighted is the fallacy of the normality assumption
in the calculations for demands. The daily demand is not normally distributed but
rather exponentially distributed as detailed in this work.
Another key highlight of this work is the proposed Re-bucketing approach which
shall prove beneficial over the current approach followed at Applied Materials. The
proposed method focuses on grouping QNs by failure modalities which is a signif-
icant improvement over the current approach and discourages categorizing QNs as
"attention to detail".
Finally during the course of the project various data insufficiencies were found
which hindered the team in making sound conclusions and attributing causalities in
a quantitative way. Various suggestions in this regard are made which shall be very
helpful to the organization in reaching its quality goals.
5.2 Recommendations
One of the first and foremost recommendations is to place an equal, if not more,
emphasis on the QNs per module metric. A holistic picture of quality can only be
painted by using both the metrics namely, FPY and QNs/module.
Another recommendation from looking at the FPYs and QNs per module of various
68
modules is to direct special attention to the low performing modules like the UES
and the 90 Module rather than paying equal attention to all the modules. This
includes taking up six-sigma projects directed to these specific modules to bring up
their quality.
Moving onto recommendations arising out the hypotheses tests by the MIT team,
the first one is the placing special emphasis on the complex modules. Again these
are the modules which have higher than 1.0 value of QNs/module. Complexity is
a real cause for more number of quality issues and so complex modules need more
attention. The next important recommendation is in the area of worker experience.
On the most complex of modules like the UES and the 90 Module care should be
taken to have as few inexperienced people as possible. There certainly is an issue with
the constant inflow and outflow of contractor workforce. However, it should be done
in such a way that there is minimum disturbance to the complex modules. Moreover
these changes should be staggered in such a way that the fraction of inexperienced
hours to the total hours on a module does not exceed around one fourth. Critical
shorts is another area where special attention needs to be put. This is the first time
at Applied Materials that an effort has been put forward to understand the effect
of shortages on quality of the build. The current service levels are not good enough
and need to be improved to reduce short occurrences and need to be improved upon.
The MIT team has proposed 99% service levels for all parts including Gold Square
parts to improve quality by reducing shortages and thereby avoiding building around
a part on a tool or building out of procedure. Shortages on Gold Squares is another
critical issue and needs to be paid special attention. One of the foremost things
needed to be done is to have an engineer responsible for maintaining Gold Squares.
Gold Squares maintenance or the SMKT performance in general, with reference to
shortages, should be monitored as if it were any other vendor for Applied Materials.
Also the incentive structure for Gold Squares should be set up to facilitate this.
Bucketing of QNs is another area where this work recommends radical changes.
The current approach does not categorize QNs by failure modes which is the main
addition from the new approach. The Applied Materials team can categorize buckets
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into the new buckets and more as suggested by the MIT team. This approach em-
phasizes on following 5 Whys technique from Six Sigma to attack the root cause of
all quality issues. It also discourages the quality team from blaming the employee for
lack of "attention to detail" but rather eliminate the root cause of the issue.
Finally, various data improvement recommendations are made which shall help
achieve significant gains in the quality journey. Enumerating the data improvement
suggestions in brief we have the following:
1. The procedural changes need to be flagged and acknowledging a new change be
incorporated into the system so that repeat QNs do not occur because of lack
of "attention to detail" to the change.
2. Any QN which has shortage as its root cause shall capture this in the QN detail
when logged in SAP. This needs to be paid attention since it is very easy to
miss reporting this, mainly because the QN will get noted under the guise of
some other cause whereas the root cause for it shall be a shortage issue.
3. SMKT Gold Square shortages should be accounted for and its record duly main-
tained.
4. Feedback for actions taken on any QN logged should be available to the people
logging the QN as well as all working in the concerned area.
5. All QNs in the SAP should be updated by the Manufacturing or Quality engi-
neers looking after them so that they detail out the issue as well as the coun-
termeasure taken afterwards to rectify it. This shall help in analyzing as well
as understanding QNs later on.
5.3 Future Work
Applied Materials has the new system iOMS coming up which shall supersede a
multitude of data acquisition methods present now. The development of this system
is in its initial phase. It is therefore imperative that the suggestions towards quality
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data collections be integrated into this new system. This change presents a perfect
opportunity to incorporate all the needed changes to bolster the quality journey.
Following from the MIT team re-bucketing approach, the FPY team needs to
incorporate the new buckets as well as create new ones as and when required. This
approach has also been suggested with various potential projects to hit certain failure
modes and projects in those directions needs to be taken forward by the FPY team.
This shall help eliminate one or more of the new buckets proposed by the team.
The interest of the management in coming up with a scientific method to set
targets for its FPY metric in understandable. In this regard, data collection of number
of failure opportunities can be pursued starting with the complicated modules. This
can then be used to set scientific targets for QNs per module metric for these modules
and continuous improvements can be made to reduce the defects on these modules.
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Appendix A
Discussion on Distributions of
Demand
This appendix explains all the work done in the area of characterizing the demand
at Applied Materials and how it is different from the present assumptions. The MIT
team of Anand, Daigle and Ismail found that the daily demand for KC parts at
Applied Materials is not at all normal. Rather it is exponentially distributed. Also,
the MIT team found out that the negative binomial distribution fits for a five day
demand period and this comes out to be pretty close to a normal demand curve. In
interests of the simplicity and ease of understanding for the Applied Materials group
and also ensuring that the work done by the team is of value to them, the MIT
team finally proposes a normal distribution of the weekly demand to go forward with.
All the discussion regarding the exponential daily and the negative binomial weekly
demand has been explained in this appendix.
A.1 Demand Characterization
The demand for any sub-assembly or a piece part comes from three different sources
as described earlier:
1. Applied Global Services (AGS) sales demand
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2. Shop Floor manufacturing demand
3. Emergency customer orders
The demand stream is not normally distributed as detailed by Daigle [2], which
has been the primary assumption of the current method of calculating bin sizes at
Applied Materials. Both the 2-bin Kanban system and the Gold Squares assume a
normal distribution of daily demand. The daily demand distribution turns out to be
close to a geometric distribution. In his work, Ismail [3] fits geometric distributions
to the demand for each part and found the fits to be very close to geometric. This
distribution fits strongly to the daily demands of both the Gold Square parts as well as
the KC parts. Ismail’s [3] work describes the process of plotting the demand forecast
for different part numbers of the KC and Gold Square parts and showing that they are
not at all normally distributed, as assumed in current Applied Material calculations.
A.1.1 Curve Fits
The demand distribution for all parts was done and curve fitting was done to see how
close they are to a geometric distribution for further calculations. Figure A-1 shows
a few example parts where demand was plotted and geometric curves fitted to them.
This process has been further detailed in Ismail’s [3] work.
This is one of the main derivatives from the work done by the MIT team where they
present that the daily demand is not normally but rather geometrically distributed.
The probability mass function and the cumulative distribution function of a geometric
distribution are shown in Figure A-2.
A.1.2 Weekly Demand: Negative Binomial Distribution
As it turns out, sampling from multiple geometric distributions is in fact the same as
sampling from a new negative binomial distribution.
Before going any further, the definition of service level used in this text needs to be
made clear. Service level in this context of inventory represents expected probability
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(a) Part 1 daily demand (b) Part 2 daily demand
(c) Part 3 daily demand
Figure A-1: Example daily demand distributions and curve fits - Three differentrepresntative TRIDENT KC parts. [3]
of not hitting a stock-out, and not losing sales or having the shortage of a part on the
assembly floor when needed. Anand, Daigle and Ismail in their work suggest service
levels vis-a-vis economic expenses for KC parts as well as Gold Square parts. This
has been detailed in Chapter 3.
Theoretically speaking, the weekly demands should follow negative binomial dis-
tribution, which is a cumulative of the geometric distributed daily demands. This
attempt has been shown in Figure A-4, where weekly demands for three representa-
tive parts have been plotted. They follow the negative binomial distribution which
they should have in theory if their daily demands follow geometric distribution.
The probability distribution function and the cumulative distribution function of
the negative binomial distribution function are shown in Figure A-3.
However, it was noticed that the daily demands have an unusually high zero de-
mand bar. A possible explanation for this is that the daily demands are not geomet-
rically distributed. Rather the daily demand is a combination of zero demand days
75
(a) Probability mass function
(b) Cumulative distribution function
Figure A-2: Probability mass function and Cumulative distribution function of ageometric distribution.
76
(a) Probability distribution function
(b) Cumulative distribution function
Figure A-3: Probability mass function and Cumulative distribution function of ageometric distribution.
77
(a) Part 1 weekly demand (b) Part 2 weekly demand
(c) Part 3 weekly demand
Figure A-4: Weekly demands for three representative parts showing negativebinomial distribution [2].
and a geometric distribution. This is hinted by the fact that the zero demand bars in
Figure A-1 are unusually high. As a result, as shown in Figure A-4 the representative
parts weekly demands do not strongly allude to a negative binomial distribution.
However, the calculations for inventory and shortage predictions show that the
weekly demands as negative binomial distributions are not very far from normal
distributions. So for the sake of simplicity and ease of inventory staff at Applied
Materials, a normal distribution has been recommended to them to go forward for
their inventory calculations.
78
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[1] A. S. Bhadauria. Production lead time reduction in a semiconductor capitalequipment manufacturing plant through optimized testing protocols. Master’sthesis, Massachusetts Institute of Technology, 2014.
[2] S. Daigle. Title to be determined, thesis in progress. Master’s thesis, Mas-sachusetts Institute of Technology, 2017.
[3] E. Ismail. Quality improvement at a semiconductor equipment manufacturing fa-cility through error re-categorization and proper inventory management. Master’sthesis, Massachusetts Institute of Technology, 2016.
[4] S. Jain. Assembly lead time reduction in a semiconductor capital equipmentplant through improved material kitting. Master’s thesis, Massachusetts Instituteof Technology, 2014.
[5] D. Simchi-Levi, P. Kaminsky, and E. Simchi-Levi. Designing and Managing theSupply Chain. McGraw-Hill, 3 edition, 2007.
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