70 L.J. Ladani et al.
Reference to this paper should be made as follows: Ladani, L.J., Das, D.,
Cartwright, J.L., Yenkner, R. and Razmi, J. (2006) ‘Implementation of Six
Sigma quality system in Celestica with practical examples’, Int. J. Six Sigma
and Competitive Advantage, Vol. 2, No. 1, pp.69–88.
Biographical notes: Leila Jannesari Ladani is a PhD candidate at the
University of Maryland’s Calce Electronic Product and System Center.
She holds a MS Degree in Mechanical Engineering with specialisation in
Fluid Mechanics from Isfahan University of Technology and a MS Degree in
Mechanical Engineering with specialisation in solid mechanics from the
University of Maryland. She is a Six Sigma Black Belt from the Celestica
Diganta Das (PhD, Mechanical Engineering, University of Maryland, College
Park; BTech, Manufacturing Science and Engineering, Indian Institute of
Technology) is a Researcher in the Calce Center. He has published in the areas
of electronic part uprating, operational environments of electronic parts,
organised international conferences and workshops, and worked in
international standards developments. He is the technical editor for the IEEE
Standards Society working group in reliability society. He is a Six Sigma black
belt from the Celestica Corporation. He serves on the Editorial Board of the
Journal Microelectronics Reliability. He is a member of IEEE and IMAPS.
Jerry L. Cartwright, PE, is a Master Black Belt with Celestica and holds a
BS Degree in Mechanical Engineering from the University of Kentucky. He is
a Registered Professional Engineer, an ASQ Certified Quality Engineer and
Certified Reliability Engineer. He has 20 years experience in quality and
reliability engineering and management. He is responsible for the development
and selection of Six Sigma projects, mentoring Six Sigma Black and Green
Belts across Celestica’s Americas Operations region and driving Six Sigma
initiatives to produce breakthrough results. He also facilitates the Six Sigma
Council for the Corporation.
Robert Yenkner holds a BS Degree in Business Administration from
Susquehanna University, and is a member of the Association for
Manufacturing. He previously held the positions of Principal Consultant with
the consulting firm PricewaterhouseCoopers; Manager of Business
Performance with Spectrum Management Group; Supply Chain Manager,
Manager of Business Development and Operational Audit Supervisor with the
Black & Decker Corporation; Plant Manager in a diamond tool machining
company; and Production Planning Supervisor for the Wiremold Company.
He has over 20 years of hands-on international experience in a variety of
industries including electronics, aerospace, automotive, consumer goods and
Jafar Razmi is a graduate student at the University of Maryland. He has
12 years’ work experience in establishment and documentation of Quality
Management Systems in a chemical company. He has held auditing positions in
Quality Management Systems.
Implementation of Six Sigma quality system in Celestica 71
Six Sigma draws its name from properties of Gaussian or normal distribution
(White, 1992). A normal distribution is defined by two key factors: the value of the
standard deviation and the value of the mean of the distribution. Standard deviation, σ, is
a measure of the spread of a distribution. The mean of a distribution, µ, is the measure of
central tendency (http://www.itl.nist.gov/div898/handbook/index.htm). These concepts
are represented in Figure 1.
Figure 1 Illustration of mean and standard deviation
Over time, the process band-width will increase in size due to process centring errors,
which degrade capability and increase the likelihood of defects. Consequently, a potential
loss of yield can occur that would increase costs. The average time-to-time centring error
for a typical process is 1.5σ (Davis, 2005; McCarthy and Stauffer, 2001). This amount of
shift and drift is inevitable and has to be accounted for during the design cycle of the
process, product or service. Process shift is demonstrated in Figure 2. Owing to change in
process variation during time, the total process variation is subdivided into short-term and
Figure 2 Six Sigma shift to the right and left
The objective of Six Sigma quality is to reduce process output variation so that on a
long-term basis, which is the customer’s aggregate experience with our process over
time, will result in no more than 3.4 defects per million opportunities (DPMO). For a
process with only one specification limit (Upper or Lower), this results in six process
72 L.J. Ladani et al.
standard deviations between the mean of the process and the customer’s specification
limit (hence, Six Sigma). For a process with two specification limits (Upper and Lower),
this translates to slightly more than six process standard deviations between the mean and
each specification limit such that the total defect rate corresponds to equivalent of six
process standard deviations.
Six Sigma provides a set of tools to be used when a focused technical breakthrough
approach is required to resolve complicated technical issues such as design and
manufacturing or business process issues. Six Sigma tools are based on data and
statistical analysis to assure that aggressive improvement targets are achieved through
elimination of variation within the process and recentring that may cause defects.
Six Sigma is also applied to improve targeted business processes including SCM (Supply
Chain Management), Information Technology (IT) and Accounts Payable and Billing.
It improves the capability of measurement systems. Six Sigma is generally implemented
through projects. The results of Six Sigma projects improve customer satisfaction and to
real cost savings to the bottom line.
Six Sigma was initiated in Celestica from April 2001 through the Celestica lean
manufacturing initiative to adopt an industry recognised process and add shareholder
value through the improvements Six Sigma could bring. In January 2002, it started
with the corporate recognition and awareness, and then they began, training Green Belts
(GBs), Black Belts (BBs) and Champions. The initial effort in 2002 included resources
for training services, material, instructors and travelling. Six Sigma is now implemented
in all the Celestica sites in various stages of maturity.
2 Electronic manufacturing services (EMS) industry and Six Sigma role
in EMS industry
Electronic manufacturing services industry is a part of electronic manufacturing, which
provides services for other electronic companies. It includes; contract circuit or wiring
board fabrication, assembly, system builds and packaging. The EMS definition comes
from contract manufacturing, which is an agreement where a manufacturer is contracted
by an original electronic manufacturer (OEM) to perform specific manufacturing
activities. This provides an option for the OEM to acquire high technology products at an
affordable cost (Sandborn, 2003).
Before the 1980’s, the EMS industry was utilised to reduce labour costs and provide
additional manufacturing capabilities. At that time, EMS customers provided all board
designs, components and testing. Today, the EMS industry has expanded its services to
not only provide consignment, but also complete turnkey services such as product design,
material management, final assembly, and in some cases, after sales services. Figure 3
shows schematically how EMS industry has evolved during the last two decades.
Figure 4 shows annual income of EMS industry in different sites of the globe for the
two years; 1997 and 2001. North America had the highest increase in income for the year
Implementation of Six Sigma quality system in Celestica 73
Figure 3 Evolution of EMS industry
Source: Sandborn (2003)
Figure 4 EMS industry growth
Contract manufacturers need to deliver on time, continue cost reductions and improve
yields and quality to be an advantage to OEMs. Consequently, it is essential for a contract
manufacturer to have a methodology to keep costs low and yields high to compete with
other contract manufacturers. Six Sigma deployment is part of the solution to these
74 L.J. Ladani et al.
3 Levels of Six Sigma infrastructure, deployment stages and training
Six Sigma implementation requires the creation of an infrastructure to assure that
performance improvement activities have the necessary resources. The human resource
levels typically used in Six Sigma organisation are: Leadership, Champions, Master
Black Belts, Black Belts and Green Belts. Table 1 shows qualification, responsibilities,
training and the number of employees trained for each level as it is implemented in
Celestica. There is a minimum of one Black Belt for each site and three Master Black
Belts so far in company. Black Belts are selected by the site general manager and Master
Black Belts are appointed. The first group of Black Belts was trained by an outside
Table 1 Qualification, responsibilities, training and the number of employee for different human
resource levels as implemented in Celestica
Champion Master Black Belt Black Belt Green Belt
Qualifications Senior executives and Technical degree Technical degree or Technical
managers, such as a vice orientation and support
Master Black Belt
presidents or directors of might be a chief background
Black Belt might be
engineer or head of an engineer or Their current
marketing billing administrator positions are
Familiar with basic and with five years or associated with
Mastery of basic and
advanced statistical tools advanced statistical more of experience the problems
needed to be
tools Mastery of basic
Responsibilities Create new Apply quality tools Apply the Implement Six
understandable vision of and methods to breakthrough Sigma on their
Six Sigma achieve process strategy for a area of work
Support and leadership improvement specific project Lead projects in
to employees to create Transfer leader and Find needed their area of
new ideas, find a champion commands changes and work
problem, make decisions to the team respond to them
and implement changes
Train Green Belts and Managing schedules
brought by Six Sigma
Assist Champions in
implement changes in
Implementation of Six Sigma quality system in Celestica 75
Table 1 Qualification, responsibilities, training and the number of employee for different human
resource levels as implemented in Celestica (continued)
Champion Master Black Belt Black Belt Green Belt
Training Two days of champion Two one-week Four one-week Three three-day
training per year training sessions sessions with three sessions with
weeks between four weeks
Black Belt training is
sessions to apply between
strategy to assigned sessions to
projects apply strategy
Project review in
second and third projects
sessions Project review
in second and
Number of One Site Champion One Master Black Belt Optimum, Two Minimum of
employees (General Manager) and per 30 Black Belts Black Belts per site four Green
one Champion per or one Black Belt Belts per Black
Master Black Belts do
business group or for every Belt
not have to be on- site.
manufacturing site. They can represent a $1,000,000 dollars
of savings required. be Green Belt
division or region
Source: Pande et al. (2000), Catherwood (2000) and Hoerl (2001)
Literature shows that the long-term goal of many companies that plans to implement
Six Sigma successfully is to train all the employees in such a way that they learn the
methodologies needed to improve everything they do. One of these groups is the
Green Belts. Green Belts have two important tasks: first, help to deploy techniques of
Six Sigma successfully, second, lead projects in their area of work. They must go through
a three-day course in the areas of Define and Measure and four weeks later there will be a
three-day course on Analyse and Improve, and four weeks after that a four-day course for
Control followed by an examination. The Green Belt projects are due four weeks after the
Control training and must include the use of Six Sigma tools in order to complete their
Successful Implementation of Six Sigma is strongly dependent on the Black Belts in
the company. Black Belts identify and execute significant projects that will decrease
errors and defects in the process; therefore, they have to be trained very well. Their
training includes three sessions and takes about 4 months. The aim of each session is
learning the phases of DMAIC (Dandekar, 2003; Goh, 2002; Beard and Welch, 2002;
Murugappan and Keeni, 2000) (Define, Measure, Analyse, Improve and Control). For
each session, they spend one week of classroom training and then three weeks
implementing their knowledge in the projects. Each Black Belt is expected to minimum
return equal to 15X it is total consumption. For example, if one Black Belt costs 100K$
(including benefits) that Black Belt should deliver 1.5M$ in improvement per year.
Currently Celestica has 114 Black Belts and 593 Green Belts. Table 2 shows the Green
Belt and Black Belt’s course of study. Champion and Master Black Belts would be
trained based on the companies’ implementation plan.
76 L.J. Ladani et al.
Table 2 Green Belt and Black Belt course of study in Celestica
Implementation of Six Sigma initially requires changing the culture and the way
employees think about themselves and the company. There are different practical and
technical ways to manage and control processes. Table 3 shows stages of Six Sigma
Table 3 Six Sigma initial deployment stages in Celestica
Week 1 Orientation and planning
Weeks 1–6 Champion review and training
Week 7 First group of Black Belts begin first five days class and begin the Define and
Measure phase of Six Sigma
Weeks 8–10 Black Belts implement the knowledge that they have learned in the Define and
Measure phase training to their projects
Week 11 The first group of Black Belts comes back to the second five days of training
to review the Measure phase and learn the Analysis and Improve phase
Weeks 12–14 The Black Belts apply what they have learned in Analysis and Improve phase
to their projects
Week 15 The first group of Black Belts returns to the third five-day training course to
review the Analysis and Improve phase and learn the Control phase
Weeks 16–18 Black Belts apply their knowledge of the Control phase to their projects
Week 16 Black Belts take 16 hours of exams
Weeks 18–24 Black Belts complete and submit their projects for review and their Black Belt
Source: Pande et al. (2000)
Implementation of Six Sigma quality system in Celestica 77
The basic concepts of Six Sigma are the same in all companies. The process of
implementation varies depending on the goals that are required to be achieved.
Two important factors in determining the level of quality in service companies are
customer satisfaction and cycle-time reduction. The strategy for Six Sigma deployment in
Celestica is to select a site per region. The criteria for selection are:
• good measurement system
• supportive site leadership
• resident full-time Black Belts
• four potential Green Belts
• customer pressure
• good change oriented culture.
At the next step available, Black Belts and Green Belts resources and improvement
projects at selected sites are identified with the cooperation of Black Belts and
world class management (WCM) leaders. Number of formally trained Black Belts is
increased and criteria are developed to qualify existing skills as Black Belts. WCM sigma
council reviews the project monthly or quarterly. Figure 5 shows flowchart of Six Sigma
deployment in Celestica.
Figure 5 Celestica Six Sigma deployment
In Celestica, projects are selected using Critical to Quality (CTQ) tree, which translates
broad customer requirements into specific critical to quality requirements. High-volume
repetitive projects that are in touch or affect the customers are to be selected.
Improvement in the cost of quality and manufacturing process improvement including,
efficiency, cycle time and number of defects are factors that are considered in selecting
projects. Minimum project requirements are:
78 L.J. Ladani et al.
• fix issues
• improve cost by 250 K annually
• improve process by 70%
• must be completed in less than 6 months.
Tools such as Pareto analysis and cause and effect diagram are used in the selection
process. Common Six Sigma tools and techniques that are used in Celestica are
• project definition
• process mapping
• cause and effect diagram
• failure modes and effect analysis (FMEA)
• variable and attribute measurement system analysis
• graphical data analysis
• confidence intervals
• hypothesis testing
• analysis of variance (ANOVA)
• design of experiment
• attribute and variable control charts
• correlation and regression analysis
• proportions testing
• advance SPC
• root cause analysis techniques
• response surface methods
• project plan
• project scoping tools.
By implementing the methods and tools described in this section, Celestica continues
to save more. Celestica was able to save significantly during the year 2002. After that,
each year savings have increased significantly. Celestica is leading EMS companies in
Six Sigma and it is expected to continue to be leader.
Implementation of Six Sigma quality system in Celestica 79
4 Comparison of Six Sigma implementation in Celestica with other
Table 4 shows a comparison of Six Sigma model in Celestica with GE and Motorola.
All three companies are using DMAIC approach and the same Six Sigma tools, but the
implementation and deployment of these tools and techniques differ in each company.
Celestica is evolving from a company dependent on a small number of Six Sigma experts
to a company where Six Sigma techniques and principles are practiced by the majority of
the employee population.
Table 4 Comparison of Six Sigma model in Celestica with Six Sigma model in Motorola
Celestica Six Sigma Motorola Six Sigma
model GE Six Sigma model model
Approach Total culture approach Cross corporate – Total culture approach
Applied to all applied to all functions Applied to all processes
processes – Opera/trans Technical and
Company Quality Delivery Cost Yield Quality Yield
metric based Cycle time Cost Quality Capacity Cycle time Cost
Project Customer Highest annual impact Customer problems
selection improvements Typical results -Avg Technical level
Technical level $100K cost Red’ns problems
problems and cost COQ based selection
Project duration 4 weeks–4 months 3 weeks–12 Months Always improving, no
GB 1–3 months to Dedicated core team time limit
completion BB 3 months/9 months Problem oriented
BB 2–4 months to completion Duration varies
Involvement Everyone involved, all Trained expert Everyone involved,
departments, supported facilitation all departments,
by Trained MBBs, Champions, Black supported by Trained
BBs, GBs and Belts, Master Black MBBs, BBs, GBs
champions Belt, Green Belts Trained people required
Training for all Projects limited by for projects everywhere
employees (Orientation resource availability
and basic 6 Six Sigma
Tools (‘Yellow Belts’)
Review Review part of the Reviewed by Improvement review
monthly operations champions part of the MOR, part
review (financials and Special review of every review
deployment metrics) structure Everyone involved
Everyone involved (suppliers, customers,
(suppliers, customers, partners)
80 L.J. Ladani et al.
Table 4 Comparison of Six Sigma model in Celestica with Six Sigma model in Motorola
and GE (continued)
Celestica Six Sigma Motorola Six Sigma
model GE Six Sigma model model
Review All processes Cost is the key All processes
improving everywhere, measure – cost saving improving everywhere,
all of the time is the key goal all of the time
Customer focus is the Six Sigma processes is
key goal the key goal
External Cultural change Project support Cultural change
consultant program program
program GB/BB material
In-house MBBs required for MBB required per MBB required for
consultant every region and major region per two/three every site
coaching manufacturing sites sites
Documenting Six Sigma Processes Six Sigma Process Six Sigma Process
(DMAIC and (DMAIC) (DMAIC)
Project tools Minitab, plus Minitab, improvement Minitab, plus CI tools
continuous training for GBs training for every
improvement tools department
training for all
Report Part of every agenda of Reports to site, Part of every agenda of
every progress review regional or corporate every progress review
meeting management teams meeting
Progress reported Progress reported
monthly for all sites monthly for all sites
Quality and cost are the common metrics in GE, Motorola and Celestica, but Celestica
includes delivery and cycle time in its metrics. GE, however, adds yield and capacity to
these metrics. In the approach, Celestica and Motorola follow the same line of attack,
which is a total culture approach and applying Six Sigma to all processes, as opposed to
GE, which follows cross-corporate approach and only applies Six Sigma to technical and
transactional processes. Celestica and Motorola also have the same attitude toward
selection of projects. Projects are selected in Celestica based on their level of impact on
customer improvements. Cost reduction and technical impact influence the selection of
the projects as well. Celestica and Motorola also have the same position towards
personnel involvement. Total culture approach implies involvement and training for
everyone in the company. Based on this point of view, a new level of infrastructure has
been introduced in Celestica, called Yellow belts. Yellow belts are oriented with the
Six Sigma and basic tools of Six Sigma. In GE, however, training is based on the
available resources and is limited to traditional Six Sigma levels; Green Belts, Black
Belts, Master Black Belts and Champions.
Project duration differs completely in these three companies. Duration of a project is
about 4 weeks to 4 months in Celestica. Motorola has no time limits for the projects and
duration of each project would vary based on the significance of the problem.
Implementation of Six Sigma quality system in Celestica 81
Similar to Celestica, GE has a time limit for each project, but time could be as long as
12 months until the project ends.
Projects are reviewed in comparatively same manner in both Celestica and Motorola.
In both companies, everyone is being involved in the review. In GE, however, projects
are reviewed by champions and special review structure. The key goal is different in all
companies; Celestica stresses on customer satisfaction, GE is looking for cost saving and
Motorola focuses on process improvement.
Celestica requires MBBs in every regions and major manufacturing sites. GE requires
two to three Master Black Belts per region and Motorola requires Master Black Belt for
each site. All the differences and similarities of these companies have been categorised at
5 Six Sigma implementation example in Celestica
Implementation of Six Sigma is often done through projects. Next example shows the
process of implementing Six Sigma on a Green Belt Project.
5.1 Defect reduction in surface mount soldering by controlling humidity
An analysis of SMT solder per card (DPU-Defect per Unit) vs. space humidity
shows the consequence (DPU increase) of operating outside the process engineering
specification limits. There were four GB involved in this project:
• goal/objective: improve SMT quality by determining optimal humidity
• financial benefits: rework reduction due to humidity caused defects
• non-financial benefits
• DPU improvements
• Cycle time reduction.
Problem statement. Rework reduction by determining the optimal humidity for the
minimum defects level (Defect per Unit-DPU (DPU = Total defects/Total cards)). The
project will include defect rates for days with different humidity levels.
A fishbone diagram was provided for manufacturing process defects. As it is observed in
Figure 6, Cause and Effect diagram indicates that humidity is one of the reasons for
Therefore, engineers group plan a measurement step to clarify the severity of the
effect. Process engineering identified five days – November 29–December 3 – where the
relative humidity (RH) levels were below the lower shutdown limit of 30% RH. For three
cards, Card A, B, and C, the DPU was calculated for this time-period below specification,
and for two other time-periods – November 15–28 and December 4–17. In these two
time-periods, RH was within specification. Humidity data were collected for these
82 L.J. Ladani et al.
time-periods. Table 5 shows time-period and relative humidity in each period.
Table 6 shows statistic of defects during these periods.
Figure 6 Cause and effect diagram for solder defects
Table 5 Time periods analysed
Before – RH in spec November 15–November 28
During – RH < 30% November 29–December 3
After – RH in spec December 4–December 17
Table 6 Cards defect information
November 15–28 November 29–December 3 December 3–December 17
Num DPU Num DPU Num DPU
Defects cards (million) Defects cards (million) Defects cards (million)
Card A 58 536 108,209.0 17 248 68,548.4 20 328 60,975.6
Card B 238 1152 206,597.2 64 348 183,908.0 0 0 0.0
Card C 17 93 182,795.7 102 312 326,923.1 303 2124 142,655.4
Totals 313 1781 175,744.0 183 908 201,541.9 323 2452 131,729.2
The normality of humidity was checked for the time-period within specification
(November 15–28 and December 4–17). The analysis shows that the data may
be normal and the mean of this data is 35.7%RH. The standard deviation is
3.93 and the 95% confidence interval for the mean is (34.05, 37.3). In addition, the
normality of the humidity was checked for the time-period not within specification
(November 29–December 3). The analysis shows that this data also may be normal.
The mean of this data is 4.0%RH, the standard deviation is 1.22 and the 95% confidence
Implementation of Six Sigma quality system in Celestica 83
interval for the mean is (22.48, 25.52). Further analysis of the humidity shows that there
is a significant difference in the mean humidity between the data within the specification
and not within the specification time-period.
A Hypothesis test of DPU (Defect per Unit) was conducted using the Null Hypothesis
for the proportions being equal (p1 = p2). The Alternate Hypothesis for the proportion
defective from the days with the RH below specification was greater than the
proportion defective from the days that were in specification (p1 > p2). This analysis
shows there is a significant difference (95% CL) in the proportion defective for
Cards A/B/C grouped together. Hypothesis testing result is shown in Table 7. Difference
in result means that null hypothesis is rejected and alternate hypothesis accepted.
Table 7 Hypothesis testing for proportions
Null hypothesis H0: P1 = P2
Alternative hypothesis H1: P1 > P2
One tailed test
Significance level 0.05
n1 = Sample size of the first
n2 = Sample size of the second
d1 = number of defects for the first
d2 = number of defects for the second
p1 = proportion of first (d1/n1) – calculated
p2 = proportion of second (d2/n2) – calculated
Chance (cannot reject are =) Hypothesis is accepted at the significance level
Difference Hypothesis is rejected at the significance level
Card p1 p2 n1 n2 d1 d2 p S z Result
A/B/C cards 0.150 0.202 4233 908 636 183 0.16 0.0134 3.83 Difference
Regression Analysis of Humidity vs. DPU (defect per unit) conducted for Cards A, B and
C indicates that the DPU decreases as the RH increases. However, a R2 value of
16.5% means we cannot conclude that there is a correlation between DPU and Humidity.
Please refer to Figure 7.
84 L.J. Ladani et al.
• Higher quality humidistats were installed for the northeast and southeast quadrants of
the production area to provide a more representative RH of the SMT lines.
• A humidistat was installed at SMT line 8 to indicate the humidity at the front of the
line that is most central to the production floor.
• A periodic maintenance schedule was established for calibration of the humidity
control system using an Extech Hygro-Thermometer Pen.
• The location and direction of the humidifier band nozzles were modified.
• Two strip charts were installed.
• Specifications in the CPS document requirements.
• Preventative maintenance.
Figure 7 Regression analysis for Cards A, B and C
5.2 Elimination of LED soldering defects by adjusting stencil thickness
During 2001, Customer A’s incoming inspection recorded an unacceptable level of
A, B, and C card assemblies, containing LED soldering defects. As the quality and
process engineering teams looked into this matter, it was discovered that the card
contained 0.012” diameter vias in the LED raw card pads – a Celestica design guide
violation. The vias were consuming the solder and starving the solder joint, resulting in
soldering defects. There were four GB and one BB involved in this project.
Implementation of Six Sigma quality system in Celestica 85
• Reduce in-house solder and assembly defects on Cards A, B, and C to a level
that more closely matched current sector DPMO
rates (solder = ~30 DPMO, assembly = ~250 DPMO).
• Eliminate LED soldering defects on Cards A, B, and C shipped to the customer.
This includes improving incoming quality levels and system line first pass
yield (FPY) at the customer.
• Financial benefits: savings
• Non-financial benefits
• DPMO improvements
• the elimination of LED defects on this card set affected customer A system line
first pass yield and significantly increased customer satisfaction.
Problem statement. During 2001, Customer A was rejecting an unacceptable level of
Cards A, B, and C assemblies for LED soldering defects. The incoming LED solder
defect level was 0.25%. More significantly, LED defects were affecting customer A
system line first pass yield (FPY). These cards were put on customer A ‘Tier 1’ card list,
which designated them as having significant impact on system line FPY.
New stencil was designed and introduced in the system. Queries were written to pull
defect data that could be attributable to insufficient solder for Cards A, B, and C for the
time-period 2 months prior to the new stencil introduction and 2 months after the new
stencil introduction. Soldering and placement defects claimed against part number
44H7340 that could be caused by insufficient solder were summed for comparison
Data from customer A’s incoming inspection were sorted for LED defects that could
be attributable to insufficient solder. Data from before and after the stencil introduction
into Celestica manufacturing were divided for comparison.
To validate that the stencil change made a statistical improvement in LED defects while
not increasing soldering problems on the other parts on the boards, a hypothesis test of
proportions was run using a 95% confidence level. The results of this testing is shown in
Table 8. The null hypothesis is that the stencil changes made no difference, while the
alternative hypothesis is that the stencil changes did make a difference (improvement).
Table 8 indicates that both Cards B, and C showed improvement both in-house and at the
vendor, while Card A showed that defect levels stayed about the same. At less
discrimination, 90% confidence level, Card A does show improvement. N1, N2, D1, D2, P1
and P2 shows sample size of the first, sample size of the second, number of defects of the
first, number of defects of the second, proportion of the first and proportion of the
86 L.J. Ladani et al.
Table 8 Hypothesis testing of proportions at 95% confidence (alpha = 0.05)
P1 P2 N1 N2 D1 D2 p Result
Card B 0.021 0.005 4011 3922 84 19 0.01 Difference
Card A 0.007 0.003 1383 1222 10 4 0.01 Chance
Card C 0.022 0.002 935 1023 21 2 0.01 Difference
Sums 0.018 0.004 6329 6167 115 25 0.01 Difference
Card B consequential 0.057 0.035 4011 3922 230 136 0.05 Difference
Card A consequential 0.177 0.162 1383 1222 245 198 0.17 Chance
Card C consequential 0.278 0.107 935 1023 260 109 0.19 Difference
Card B customer A audit 0.004 0.000 2862 971 12 0 0.00 Difference
Card A customer A audit 0.000 0.000 3346 1401 1 0 0.00 Chance
Card C customer A audit 0.011 0.000 274 757 3 0 0.00 Difference
Stencil thickness increases may result in the increase on solder defects on the other parts
contained on the board. These are considered the consequential metrics. Both Card B and
C showed a difference between the original and new stencil, but the change was actually
an improvement. The Card A showed no statistical defect differences between the two
Using MiniTAB, calculations were made to determine what percentage of cards was
defective before and after the stencil change and the expected lower and upper bounds of
defective cards at a 95% confidence interval. The resulting data are shown in Table 9.
Table 9 Defect bounding at 95% confidence interval
Percent cards Percent cards
defective before defective after Lower control Upper control
stencil change stencil change interval (%) interval (%)
Card B 2.09 0.48 0.29 0.76
Card A 0.72 0.33 0.09 0.84
Card C 2.25 0.20 0.02 0.70
Table 10 contains the changes that were made to each of the stencils. The thickness of
each stencil was increased. The finest pitched component on Card B was 0.025", while
Card A and Card C both had 0.020" pitch components. Due to space constraints around
the fine pitch components, the stencils could not be stepped down in these regions. To aid
in paste release, all fine pitch apertures were laser cut. In addition, to ensure full filling of
the fine pitch apertures on Card A and Card B, print speeds were lowered from 20 mm/s
to 16 mm/s. Stencil changes contained in Table 9 were reviewed and approved by
customer A prior to implementation.
Implementation of Six Sigma quality system in Celestica 87
Table 10 Stencil modification
Original LED New LED Original New thickness
aperture (mils) aperture (mils) thickness (mils) (mils)
Card B top 60 × 55 66 × 57 6 7
Card B bottom 60 × 55 60 × 55 6 8
Card A 60 × 55 66 × 57 6 7
Card C 60 × 55 68 × 57 6 7
To ensure that any LED defects that were created in spite of the stencil changes were
caught, visual inspection of LEDs was reemphasised to the functional test operators.
Even though there is still a low level LED defect level, these defects are caught and
repaired prior to the product shipping to the customer.
• No via in 1206 or smaller pad added to the Celestica-Rochester design guide
• Ask customer A to send stencil Gerber data with specially sized apertures where this
design guide specification had to be violated. This will act as notification of the
violation. The stencil may then be designed such that the violation is masked.
6 Summary and observation
Six Sigma is an effective process that can be used to mitigate the defect rate, increase the
margin of the company and increase customer satisfaction. The degree of the success in
each company depends on the company metrics. Implementation of Six Sigma in
Celestica has been a success in aspects of customer satisfaction and savings. Several
steps are involved in the process to make it an effective deployment. Even though the
Six Sigma method is robust and comprehensive, there is still room for improvement in
deploying it. Champions Master Black Belts have a key role in implementing Six Sigma
by managing and connecting the projects.
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