Six Sigma & DMAIC Applications

Engineering College
Industrial Engineering Global Program
Final project: Six Sigma-DMAIC applications
Benito Pablo Barajas Loaiza
Francisco Xavier Bastidas Moreno
Mexicali, B.C. May 19th of 2021
In this summary we will present four case studies each one applied on a different sector
of the industry, the main objective of this project is to identify how de DMAIC lean tool is
applied and analyzed in real life projects.
Up next we will introduce the DMAIC methodology as stated by Anderson & Kovach on a
study directed to minimizing welding defects on pipelines.
“The Lean Six Sigma methodology follows a five-phase approach, known as
DMAIC (define, measure, analyze, improve, and control).
The purpose of the Define phase is to identify the project goals and understand
the potential value that the improvement project will generate for the organization. This
phase includes obtaining approval of the project charter and developing a high-level map
of the current process.
During the Measure phase, the measurement system is verified, and data are
collected to establish a baseline measurement for the current process.
In the Analyze phase, information about the problem and underlying process are
analyzed to identify potential causes of the problem.
The objective of the Improve phase is to generate solutions for the vital few root
causes found in the previous phase and implement these solutions to create an improved
process. In addition, the performance of the improved process is measured and
compared against the baseline established in the Measure phase to determine the degree
of improvement achieved through the project.
The goal of the Control phase is to hold the gains made in the improved process.
This includes developing a control/process management plan that documents, monitors,
and controls the improved process.” (Anderson & Kovach 2014).
“A six sigma case for process optimization in water treatment plant”
Background: water treatment consists of separating impurities from raw water, in
order to do this, several chemicals are added to water in order to create bigger
compounds that are easier to take out. In this case, several actions are being made in
order to reduce the cost of the process; as part of this: new materials are added to
water and they are being analyzed to see the relationship between the NTU and the
Process to optimize:
The main goal of water treatment is to remove the suspended solids, in order to
achieve this raw water must go through several process. Starting with the pre-treatment
process, which includes: coagulation, flash mixing, flocculation and clarification.
Coagulation: in this process molecules of aluminum or iron-based
coagulants are added to raw water in order to destabilize the particle, therefore it
can be mixed.
Flash mixing: in order to assure the combination of aluminum
coagulants with raw water, mechanical flash mixing process must be done. This
step creates the environment that will facilitate efficiently water treatment.
Flocculation: this is a low step because it combines each particle with
an aluminum molecule, this new compound is called flocs.
Clarification: in this process the flocs are settled at the bottom of the
tank and clear water is suspended in the upper part.
Figure 1 & 2
(Chemical process and in-production process)
DMAIC Six Sigma Application:
The scope of the projects aims to optimize the cost process of raw water treatment.
The company look towards improved coagulation and enhanced softening process
through experimental analysis. Furthermore, turbidity was taken as a variable to
determine responses of coagulants.
In order to determine the focus area, a SIPOC was made which allows the
company to have a clearer view of the process and how to improve it. Once the issue was
located, a pareto chart was developed, with this tool the company can look towards how
the process is done and which things add or does not add value.
Figure 3 & 4
The pareto chart, which is made regarding data of the process part of the SIPOC,
states that the results are controlled 80% due to the chemicals that are being used in the
process, therefore the company must look to more commons chemicals treatments (liquid
alum and lime, as stated in the case study).
A run chart is analyzed taking 270 samples of Nephelometric Turbidity Units
(NTU) from all the year. In the chart it can be determine that the average NTU is 17,
being the highest value 43. Also, a control chart is set up regarding 40 ppm, as well as a
process capability chart. (Figure 5)
In the control chart it is notable that the process is not under control, as seen in
the Run chart the process tends to have higher values as time goes. (Figure 6)
To conclude, a process capability chart is designed in order to determine the
process variability. Since the values for process capability and performance are much
beyond the benchmark of 1.33, it can be assumed that the process archives six sigma
levels therefore it has low variation.
Figure 7 & 8
As demonstrated with the pareto chart, in order to reduce the costs, there must
be a change (optimization) in the dozing rates. The improve phase started with test
protocols for jar test, these jars will be the pilots of the study since each jar will have
certain amount of chemicals (alum flakes and lime). The studio shows that for 40ppm
alum dosing has optimum chemical consumption with a turbidity of 5 NTU. (Figure 9)
With the results of the jar test new protocols are being recorded and a training
program is being develop in order to demonstrate how the new dozing work and why it
is better.
With Six Sigma methodologies and DMAIC a new process that optimize water
treatment as well as reducing the cost for this process. In order to determine which part
of the treatment had to be optimized, a SIPOC and Pareto chart demonstrate that the
chemical dozing was the most expensive part of the process, therefore new compounds
were added to the water in order to assure lower costs and a process capability that
fulfill six sigma yields.
“Six Sigma-based to optimize the diffusion process of crystalline
silicon solar cell manufacturing”
Silicon-based photovoltaics have become one of the most necessary items in the
manufacturing process of solar cells. Among the process, there are p-type and n-type
doped silicon material, nowadays most of the cells used phosphor (p-type) and its
creation begin with boron-doped silicon wafers. In this case Six sigma-DMAIC tools are
use to analyze the process of phosphorus doping, aiming to optimize the process in order
to reduce manufacturing time.
Problem Statement:
Problem Statement: This study deals the Six Sigma-DMAIC approach on
throughput improvement by optimizing the POCl3 tube diffusion process. Six
Sigma investigates the functional form, Y = f (x), where Y is the output or result
(dependent variable) of the process and x’s are the causes (independent variables)
which affect the output. In the present case study, Y is the throughput of the
diffusion process.
(Diffusion refers to the process of creating p-type silicon wafers by introducing
phosphorous into it) (Figure 10)
In order to know the root of the diffusion issue, a pareto chart was created in which
4 causes were analyzed giving the following results: (Figure 11)
Diffusion process time: the time in which te wafers move into the
paddle and then into the quartz tube.
Rework and reprocess taking a material that it is out of the
specifications limits into another process in order to meet the specifications.
Bubbler change over time: this refers to POCL3 which is the liquid
used for n-type dopant for silicon wafers. When a silicon wafer is being dope with
this compound, a toxic chemical is being produce in the quartz tube therefore
several safety protocols are taken in action in order to replace the tube.
The pareto chart states that the major issue is regarding the processing time,
therefore a p diagram was develop taking in account that processing time is the biggest
problem in the diffusion process. (Figure 12)
In order to set the sigma rating, 100 runs and processing time in minutes were
recorded. These were the values: (Figure 13)
Since p value is less than .05 it can be assumed that the data does not follow
normal distribution. Since these data does not fit any known distribution, the process
capability was analyzed taking in consideration the process time.
By analyzing the ppm, we can assume that the process, right know, have a sixsigma value of 0. (Figure 14)
Analyze: (Figure 15 & 16)
In order to determine what affects the CTQ of the process, an Ishikawa diagram
was made in which all the possible causes are enlisted. To add more value to each of the
roots, the company created a cause validation plan in which all the roots are explained,
regarding their solution or how are they calculated.
Based on the cause validation plan, few causes were validated using regression
analysis. In the cases where regression analysis was used, all of the causes were
independent variables (potential causes), which are:
Using those variables, multiple linear regression analysis were made in order to
identify the root cause. Best subset regression was used to study the effect of each of
these variables on throughput time, these are the output of the regression analysis:
(Figure 17)
The team decide to conduct a DoE using all the variables from the “measure” part
of the root-cause diagram, in which each factor was experimented using 3 levels of
experimentation: (Figure 18)
Once the factors and the levels of experimentation where determined, a Signal to
Noise analysis was being made in order to know the minimum process time, these were
the results: (Figure 19 & 20)
With the new process optimum levels, new data was given after calculation. The
observed ppm is 0 and the sigma level just approached 6, also the process time was
improved from 74 to 62 minutes, lastly, the standard deviation move from 1.744 to .959.
(Figure 21)
Since the process was reduce several minutes, new control limits were set, within
this new specifications it is important to state that all the inspections were under control.
(Figure 22)
By applying six sigma DMAIC methodology the diffusion process has been
increased. Furthermore, the sigma level of the process has increase and, due to
optimization, the time has been reduce several minutes.
Reducing Welding Defects in Turnaround Projects:
A Lean Six Sigma Case Study
In this case study lean manufacturing techniques were used to improve metrics
that the customer is requiring, a DMAIC process was followed in addition of other external
tools ad hoc the instrument to create pivotal data gathering and useful information. The
company in the study is called “JV industrial companies (JVIC)” located in Houston, Texas
it prides itself as an industry-leading turnaround, construction, and fabrication services
organization headquartered near Houston, Texas. This company provides complete
construction solutions for industrial clients across the United States.
The company's core values include superior safety, quality, service, integrity,
personal responsibility, and personal accountability. In search of keeping these values
and the continuous improvement in quality and customer satisfaction the company looked
for help from a third-party company, the research specifically addresses the need to
reduce welding defects in turnaround projects. Using an action research approach to
address an increase in the number of weld repairs that occurred during turnaround
projects completed in 2011.
Little documented research exists on the use of this methodology in the turnaround
industry; hence, this research attempts to fill this gap in the literature by providing a case
study that demonstrates how Lean Six Sigma can be applied in service-based
environments such as turnaround projects.
Welding is commonly used in industry to join two base metals either on the
manufacturing of parts or in the repairment of them, in this case butt welds for pipelines
will be studied. By nature, welding creates stress concentration on the weld area that are
reinforced by filler material, welding is a complex process that requires constant and
precise inspections that become more demanding depending on the level of damage that
a failure on it could affect its surroundings.
Given the severity of a failure on the junction, welding operators must be qualified
technicians who understand the process not only by practicing and empirical knowledge
but also from theoretical knowledge, operators in industry are commonly certified b the
American Welding Society (AWS) which certifies them as skilled leveled workers and
keep track of their welds through a welders log which can't exceed a time lapse of 6
months without a welding operation or their certification would expire. Unfortunately, the
high cost of training welders in the proper way affects companies in the long run creating
constant welding issues.
There are many causes of defects on a welding process, external and internal
variables may affect the final part, pores are common ones and have a tolerance interval
as they are simply oxygen molecules trapped inside or outside the weld, so they are
categorized as defects, in the other hand when cracks are detected the part becomes a
“A turnaround strategy is backing out or retreating from the decision wrongly made
earlier and transforming from a loss-making company to a profit-making company”.
(Business Jargons 2021) The company implemented this type of strategy to reduce the
average weld repair rate; the total number of rejected butt welds divided by the total
number of butt welds inspected by X-ray.
The company implemented six sigma tools primarily DMAIC analysis to get to the
customers specified 2% repair rate, if the rate is higher than that the company will assume
repair costs, currently their monthly average is 3.3% generating high costs which were
compromising the company’s future.
JVIC conducted a Lean six sigma project through participatory action research
which required employees to be involved and to know what were the objectives of the
The first step to tackle this problem was a project charter which described the dutys
of each participant with the overall objective of reducing the weld repair rate for turnaround
projects. The problem statement was the following:
“Problem Statement: JVIC's butt weld repair rate for the La Porte division has
averaged 3.66% over the last 9 months (January 2011–September 2011), resulting
in increased repair costs.”
Mission Statement: Reduce the average butt weld repair rate for the La Porte
division to 2.75% in the next 6 months (by April 2012), resulting in an estimated
savings of $75,000- $100,000 per year.”
The team implemented six sigma tools like mapping techniques and flowcharts of
the process to prevent leaks and missing errors that inspections didn’t took into account,
the result of this was a well-executed traveler with all the process mapped and rerouted
if a defect was found. (Figure 23)
Information about the current process was gathered to quantify the weld repair
rate. Again, the team used mapping techniques to identify the QC inspections that are
made during the welding process, they realized that the inspections recorded only binary
information which only helped the process to re route it and not to create a record of the
defects on the process. To improve this a defect log was created where the defect was
investigated and recorded as soon as the inspection showed defects, these defects will
also be registered on the welders log of the operator for improvement purposes.
the focus of the project, data from detailed project weld logs for a 9-month period
ranging from January through September 2011 were reviewed. This information was
summarized by the project team using histograms and Pareto charts (Figure 25). With
these tool they concluded that the part of the company that they will focus on was La
Porte division given that it was the facility with the most welds performed and the second
place on inspections required for parts.
The team identified potential causes for hugh burr welds repair rates through
brainstorming and the 5 why´s analysis. They made a graphic Ishikawa diagram in which
the presented all the potential causes of a failed weld (Figure 26), There are many
possible welding defects such as welding angles, overlap, lack of fusion just to name a
few, the team found that the main ones were on the welding technique and the proper set
up of welding shields,
For this project four solution were implemented
Inspect windshield and train welders to use the windshield properly and
mitigate wind impact on the weld
E) Welder´s University; properly train welders to keep the related to modern topics
and let them practice on real parts.
F) Standard welding training.
H) Vision test: It requires welders to have 20/30 vision confirmed by eye test, if this
failed, they had optical insurance and loans to get the proper visual help.
The results of the project were the following: “by implementing windshield
standards, training welders through Welder University, and instituting eyesight tests for
welders company-wide, the weld repair rate decreased by more than 25%, which
translated into a savings of $90,000 for this company.”
To control the constant register of defects the team implemented a software for
inspections in which you don’t only report the defect, but you have to input defect
investigation and also the welder´s id to pass to the next step of the rerouting assuring
that every defect is precisely recorded.
Also, the welder´s university has a minimum passing grade of 6, grades are
monitored, and welders should constantly prove their knowledge on it, this allows the
corporate to maintain control of the welders and if by any cause the grades come down
take immediate action and start an investigation.
Utilizing six sigma to improve the processing time: a simulation study at an
emergency department
Emergency rooms play a crucial role on the patient’s treatment cycle as in there
the patient should be transferred to the appropriate clinic or room, given this situation and
that some patients might have severe complications time is key for nurses at the receiving
desk to have the optimal route to follow and to get the person with the correct physician
as soon as possible.
This case study takes places at “Dr. Georges-L-Dumont Hospital” in Moncton
Canada, this clinic strives to apply a project in which the waiting time (WT) and length of
visit (LOS) are reduced and fitted depending on the patient’s complication status.
For this project an Arena software is used to gather data as WT and LOS,
simulations are critical for these kind of departments as the demand is always variable
and the number of critical cases are not easily predictable. “The Society for Academic
Emergency Medicine Simulation Task Force” was created to share key findings from
emergency rooms such as successful practices and complications that these rooms find,
one of the objectives of the task force was to create a research agenda for the use of
simulation in emergency medical education and its applications.
The hospital implemented a DMAIC analysis in which they obtained the necessary
data and implemented a “triage model” the sorting of injured or sick people according to
their need for emergency medical attention (Torrey 2021).
DMAIC Analysis
For the define phase the current variables were analyzed, they defined key x’s an
y’s such as WT and LOS which were presented in the background these variables helped
defined the Critical to quality (CTQ) factors that were involved on an emergency room,
they developed a charter that was approved by the hospital’s management.
As previously stated, WT and LOS were identified as the relevant variables these
had to be measured in order to develop certain simulations to improve the process, the
results were the following.
The measured mean WT was 33.21 min, with a standard deviation of 15.77 min
The measured mean LOS was 84.49 min, and the standard deviation was 26.66 min.
Additional data as human resources, customer satisfaction and CTQ factors were
analyzed to be considered.
In this phase the results from the measured data were converted into information
which was followed by the identification of critical bottlenecks and root cause problem
solving. 81% of the respondents consider the current WT quite long.
In this analysis the third-party company noticed that they were using a “First in first
out” (FIFO) technique in which there were only two types of patients cold cases and noncold ones (cold ones could wait longer), which did not present many room for
improvement and often resulted on a diagnosis error. To add to this problem there was
only one nurse registering cold and non-cold cases and this was done by vital sign
detection and a quick form which described the state of the patient leaving the person’s
first diagnose to the criteria of the nurse and his/her interpretation of the situation.
After analyzing the information, the main results were: “Reducing WT received the
highest grade, followed by the LOS. The triage process received a grade of 99 as shown
in Figure 2, which indicates that it is the most critical to achieving patients’ VOC.”
Giving this analysis a triage system was implemented with the following criteria
Color tag
Wait time (min)
Within 15 min
Within 30 min
Within 90 min
Within 120 min
This new model added 3 more considerations to speed up the process on each particular
case of the patient.
Implementing the flowchart process from FIGURE # threw positive results in the
simulation, “The results for 96 replications, after the warm up period, show that under the
triaged simulation model the WT has reduced by 61%, where it reaches on the average
12.93 min compared to 33.21 min. Furthermore, LOS has reduced by 34%, where it
reached 55.5 min compared to 84.49 min” (REFERENVIA)
Constant model investigation and high demand simulations are constantly made
to hold the gains from this project, as conclusion they took a six-sigma level approach in
which they guided by the results of the triage simulation model revealed that the WT
sigma level is improved from 0.66 to 5.18, and the LOS sigma level is improved from 0.58
to 3.09 the company is striving to maintain this sigma level by qualifying their nurses on
accurate first diagnoses and constantly evaluating the questions on the receiving form.
By doing this project we have put in practice all our theorical and practical
knowledge due to the several tools and concepts that each article manages. Reading
these scholar papers can give us a preview of what will be doing in the future, right now
we have developed statistical analysis to short problems, but we are aiming to have the
capability to take actions in the industry based on the result of those calculations.
After analyzing these case studies, I have a broader vision about the impact that
six sigma and lean manufacturing could have on any company that intends to improve,
tangible results from a 9-month long project saved 90,000 dollars for a company which
motivates me to find useful but also creative solutions for industry problems.
I enjoyed reading the case studies and learning about how the industry uses
industrial engineering terminology and tools to analyze data in order to make decisions
and thanks to the control phase of the DMAIC retain all those gains that were made in the
process of the project because quick fixes that only improve the process and does not
give longevity to it will eventually disappear, and all the efforts will be wasted
Table of Images
Figure 1 & 2
Figure 3 & 4
Figure 5
Figure 6
Figure 7 & 8
Figure 9
Figure 10
Figure 11
Figure 12
Figure 13
Figure 14
Figure 15 & 16
Figure 17
Figure 18
Figure 19 & 20
Figure 21
Figure 22
Figure 23.
Figure 24
Figure 25
Figure 26
Figure 26
Anderson, N., & Kovach, J. (2014). Reducing Welding Defects in Turnaround Projects: A
Mandahawi, N., Shurrab, M., Al-Shihabi, S., Abdallah, A. A., & Alfarah, Y. M. (2017).
Utilizing six sigma to improve the processing time: a simulation study at an emergency
department. Journal of Industrial & Production Engineering, 34(7), 495–503.
Business Jargons (2021) Turnaround Strategy
Torrey, T (2021) What medical Triage is in a Hospital
What Medical Triage Is in a Hospital (verywellhealth.com)
Krovvidi, V. B., Pinninti, V. R. R., & Dwivedula, R. (2019). A Six Sigma Case for Process
Optimization in Water Treatment Plant. IUP Journal of Operations Management, 18(3),
Prasad, A. G., Saravanan, S., Gijo, E. V., Dasari, S. M., Tatachar, R., & Suratkar, P.
(2016). Six Sigma-based approach to optimise the diffusion process of crystalline silicon
solar cell manufacturing. International Journal of Sustainable Energy, 35(2), 190–204.