1. SIX
and
International Journal of
Competitive
Advantage
SIGMA

2. Int. J. Six Sigma and Competitive Advantage, Vol. 7, Nos. 3, June 2012
(Accepted for Publication- Oct. 24, 2011)
____________________________________________________________________________
A Six Sigma Approach for R&D: Measuring Dissolved Oxygen
_____________________________________________________________________________
Michael J. Johnson
Department of Mechanical Engineering
Case Western Reserve University
Cleveland, Ohio
E-mail: mjjohnson2236@gmail.com
Monique T. Claverie
Microptix Technologies, LLC
Wilton, Maine
E-mail: mclaverie@microptix.com
Joseph E. Johnson*
Microptix Technologies, LLC
Wilton, Maine
E-mail: jjohnson@microptix.com; xjoej@juno.com
*Corresponding author
Abstract: A Six Sigma approach was applied to an R&D application with the goal of
understanding and reducing measurement variability. Typically, Six Sigma is applied to a
manufacturing process. Many aspects of processes and R&D applications are similar, e.g., input
variables, measurements, and output values. In the current study, variables were identified, a
Gauge R&R test and Pareto analysis performed, and correction actions implemented to improve
measurement variations. The Gauge R&R study included an analysis of variances (ANOVA)
using the Fisher test. The system under study was a new method to quantitate dissolved oxygen
(DO) by colorimetry. Specifically, measurements were performed using a newly developed i-
LAB® spectrometer. Various statistical and mathematical tools were employed to improve
measurement variation, resulting in the standard deviation decreasing from 0.66 to 0.23.
Additionally, the Six Sigma approach may be applied to other relevant R&D systems.
Keywords: Six Sigma. R&D, Research and Development, analysis of variances (ANOVA),
Gauge R&R, statistical analysis, quality improvement, dissolved oxygen (DO), colorimetry,
spectrophotometer, quality, statistics
Reference to this paper should be made as follows: Johnson, Michael J., Claverie, Monique, and
Johnson, Joseph E. (2011) ‘A Six Sigma Approach for R&D: Measuring Dissolved Oxygen’,
Int. J. Six Sigma and Competitive Advantage, Vol. 7, No. 3, pp. X-XX, June 2012

3. p. 3 of 23
Biographical notes: Michael J. Johnson is a Mechanical Engineering student at Case Western
Reserve University in Cleveland, Ohio. He was an Engineering Intern at Microptix
Technologies, LLC in Wilton, Maine for part of 2011. His areas of research at Microptix
included engineering, application development, computer programming and statistical analysis.
Monique T. Claverie is an Application Engineer at Microptix Technologies. She received her BS
degree in Chemical Engineering from the University of Maine at Orono. Her areas of research
include application development, quality control, and new product development. She has several
white papers and a patent application.
Joseph E. Johnson is the Vice-President of Technology and Operations at Microptix
Technologies. He received his PhD from Clarkson University in Potsdam, New York and a BS
degree in Chemistry from Syracuse University in Syracuse, New York. His areas of interest
include R&D, operations, Six Sigma, and new product development. He has twenty patents and
published eight papers in peer-reviewed journals.
______________________________________________________________________________
1 Introduction
Quality and process improvements have evolved greatly over the last few decades. Motorola and
General Electric are two companies that initially implemented methodologies to reduce product
defects, and coined the term, “Six Sigma” (Harry and Schroeder, 2000; Pande and Holpp, 2002).
At that time, the Six Sigma concept was used to count or quantitate defects. However, prior to
that, Edward W. Deming was teaching statistics and applying quality control in the automotive
and other fields (Aguayo, 1990). Deming and his disciples were determining quality by
correlating manufacturing tolerances (sigmas) to input variables. Quality, as defined by
Montgomery, is “the fitness of use” with properties being “inversely proportional to variability”
(Montgomery, 2009). Today, it is commonplace in numerous manufacturing processes to have a
quality program based on Six Sigma concepts. The advent of enhanced measurement tools
coupled with data capture, analysis, and modeling by use of computers has resulted in many
automated quality control systems. The new Six Sigma programs are in stark contrast to those of
a decade ago when complex, bureaucratic, and expensive programs were implemented by only
large companies (Dusharme, 2001). Within the last ten years there has also been an emergence to
apply Six Sigma and quality concepts to the service industries (Tennant, 2001; Uzzi, 2004;
Guarraia, Carey, Corbett and Neuhaus, 2008). The measurable results are improved service
quality, enhanced efficiency, and less wasted time. Thus, productivity and profitability have also
improved for service industries, (e.g., insurance, banking, food service and hospitals). A common
overlap between the manufacturing and service industries is that both have defined measurables
that can be analyzed and related to input variables. In other words, the DMAIC (Design,
Measure, Analyze, Improve, and Control) procedure may be applied to industries that center on
operations and metrics. An operation may be defined as jobs or tasks consisting of multiple

4. p. 4 of 23
elements or subtasks. Additionally, the Six Sigma goals are the same- improve quality, and
understand input and output variables and the related tolerances.
An R&D (Research & Development) process for NPD (New Product Development) also has
defined measurables which can be directly related to input variables. Additionally, the goals for a
new product are to understand variability in measurement or function, as well as to optimize
quality. Thus, it is conceivable to apply a Six Sigma methodology to an R&D process. However,
differences between an R&D process and a manufacturing operation exist. The main difference
is that the manufacturing operation has a known optimization point, e.g., in making a one inch
nail, the desired length and centering point of the nail length is one inch. For an R&D process,
the optimization point may be unknown. To circumvent this notion, multiple “points” can be
measured and then the mean may be chosen as the optimization point. Additionally, in a
manufacturing process variables may not include multiple machines. For NPD, multiple
instruments need to be considered. The conventional DMAIC Procedure (Figure 1) has been
modified to account for these and other differences, with the “R&D” DMAIC Procedure in Figure
2.
Figure 1 Conventional DMAIC Procedure

5. p. 5 of 23
Figure 2 Modified “R&D” DMAIC Procedure
Note that Figures 1 and 2 are similar in many respects, but differ in their respective procedures.
The DMAIC outline is followed for new product development, as it is for “conventional”
operations and services. However, the measurement step varies from the operational procedure
and that for the R&D step. In the operation method the capability of the system to be measured
and current status is determined and a target value is known. In an R&D procedure the capability
to measure is already known, as is the status, so they are unnecessary. A conventional operation
is focused on obtaining a known target value. Using the example above, the center point is 1.00
inches for an operation to produce a one inch nail. For an R&D measurement, the focus is on
obtaining the least amount of error for an unknown value. As an example, an average Absorption
value for many spectrometer units may be calculated at 0.525. The 0.525 value, unknown before,
now becomes the center point. Another difference between an operation and an R&D procedure
is the variables. The variables used in an operation are CTQ, or those that are critical to quality.
CTQ variables are used to adjust to a known value, say 1.00 inch for the nail in the previous
example. The CTQ variables may be adjusted resulting in an increase or decrease in nail length.
The variables in the R&D procedure are those that only contribute to the measurement error.
R&D variables are adjusted to only decrease the measurement error around the average. In other
words, adjustment results in a decrease of error, not a change in the center point. Even with these
differences, the general scope of an R&D procedure is similar to that of an operational procedure.
The goal for an operation is to stay within defined limits or control levels around a center point.
An R&D procedure does not initially have defined limits, but creates a center point and then
drives out error to reduce the limits by adjusting variables.

6. p. 6 of 23
2 Case study
The goal of the case study was to optimize measurement values (reduce variability) of
colorimetric measurements using a spectrometer (an i-LAB® instrument). Other goals are to
understand, control, and optimize input variables. Calibrated dissolved oxygen (DO) colored
standards were used in the study (Cat. No. K-7512, CHEMetrics, Calverton, VA). The colored
standards have a blue dye that absorbs light at 580 nm. The standards varied in the amount of
dye, so that an increase in dye concentration results in an increase in absorption. In other words,
the more dye present, then the darker blue the standard. The dye solutions were in sealed
ampoules that were well within their expiration date, and their color did not change over time.
The standards are normally used as a visual comparison reference to determine dissolved oxygen
content. This study used instrumentation to quantitate PPM value by measuring absorption.
While the sealed standards contain blue liquid in varying depths of color, actual test vials contain
a yellow-green reduced form of indigo carmine dye in an acidic solution that undergoes a
colorimetric oxidation reaction to turn blue in the presence of oxygen (Gilbert, Behymer, and
Castañeda, 1982). Typical applications for actual dissolved oxygen measurements include water
(drinking, sewage, industrial waste and bio-habitats, e.g., lakes, oceans, and rivers), power plants
(cooling water and boilers), and fermentation (Biofuels, beer, and liquors). The amount of
oxygen has a large effect on a wide-range of measurements, e.g., determining the growth of flora
and fauna in water, corrosion rate of metal pipes and cooling towers, and fermentation in ethanol
and non-aerobic processes. Thus, it is imperative that the dissolved oxygen measurements be as
accurate and reproducible as possible. Errors in accuracy and reproducibility can be due to
measurement variance as well as sampling procedure. Thus, a major goal is to reduce
measurement variance as much as possible.
The spectrometers used were Visible i-LAB® spectrophotometers, (Model S560, Microptix
Technologies, Wilton, ME). Spectrometers are used to quantify the wavelength and intensity of
light waves. Visible spectrometers are used to determine the amount of color at one (or more)
specific wavelength. The principle involved is to shine white light, with known intensity, through
a material and to determine the Absorbance value. Light transmits through a colored liquid and
the amount of absorbed light is determined. The amount of absorbed light (Absorbance) is
proportional to the amount of color. In this way, the color of the DO standards may be measured.
Definition Stage
The first step was to determine what needed to be measured and the acceptable tolerance level.
The depth of color of a colored liquid in an ampoule was measured and reported as Absorbance.
The acceptable tolerance or variation in measurement is strongly application dependent, but
generally the smaller the better. The tolerance or variability of the dissolved oxygen standards
was unknown, needed to be determined, and then improved upon.
The second step was to determine the variables that affect the measurement values. For this step a
plan was conceived with an ANOVA (analysis of variation) gauge R&R (reliability and
reproducibility) test in mind. In general, an ANOVA gauge study determines the contribution of

7. p. 7 of 23
variables to errors in measurements and compares them to errors of the total system. Several
factors may affect a system, including:
• Measuring instruments- variability from one instrument to the other (Unit to Unit Variation)
as well as variability within one instrument (Within A Unit Variability);
variations may be caused by differences in optics, light sources and sensors for the current
study
• Operators- variability in operator ability to clean and place samples, measure and use the
instrument
• Environment- variability that may affect the process, instrument and operator measurements;
this may include extraneous light and changing temperature
• Test Method- variability in protocol and operation (how the product or service is measured)
• Specifications- comparison of a measurement with a reference standard
• Sample- what is being measured and how does it change with time or placement;
the current experiments use standards with a defined holder that did not change over time
In the current study, all factors were considered with the focus on reducing measurement error.
The specifications and samples were merged together, with known calibration standards being
measured. In that manner, the reference and the samples were the same, thus eliminating these
variables. Attempts were made to minimize any environmental differences and any variations
they would emerge in operator differences (if a cap wasn’t placed correctly so extraneously light
was introduced) or within unit differences (if temperature differences caused light intensity or
detection differences). Test methods remained consistent throughout the study, eliminating this
variable as well. So the variables that were examined were the within unit variability, unit to unit
variability, and operator measurement.
With these variables in mind, the primary focus for the gauge R&R study was on determining: 1)
consistency of readings for a specific sample with an individual spectrometer; 2) variability of
readings between spectrometers, given the same sample and operator; and 3) the effect of
operator on measurement readings and measurement variances. Normally, point 1) equates to
repeatability, or variation in measurements taken by a single person or instrument on the same
item and under the same conditions. Also point 3) equates to reproducibility, or variation in
readings contributed by different operators measuring the same sample. It is very important to
note that this study also includes point 2), which is unit to unit variability. This point expands a
conventional gauge R&R study, but is absolutely necessary for R&D and new product
development.
Using ANOVA, the contribution of the variables to measurement error was determined. After
that, a Pareto analysis was completed to find the largest source of error. Pareto analysis is a

8. p. 8 of 23
statistical technique for decision making. It is used for selecting a limited number of task(s) to
do, which would cause the most significant changes to an overall effect. In this case, focus
would be on understanding the largest contributor(s) to measurement error. The next step was to
determine how to adjust each specific variable to reduce error. Typically the Pareto principle is
based on the concept that a relatively small (~ 20%) amount of critical variables contribute to a
large (~80%) degree of errors. To extend the Pareto principle, one needs to limit or remove these
critical causes to achieve significant quality improvement. In the current study, the Pareto
analysis was used to identify the critical contributors that cause errors in measurement. Next,
improvements will focus on decreasing the specific variables. And then a new Pareto assessment
will be made verifying the contributors were the areas to focus on, and statistically significant
improvements were obtained. The improvements should be monitored to ensure the variable
changes work.
As mentioned previously, the main controllable variables were determined to be the individual
unit, multiple units, and operator. A plan was made involving the main controllable variables and
how they could be quantitated. For every standard, three measurements would be performed.
There were nine blue standards of varying PPM levels of dissolved oxygen to be measured. Five
units would be used in the study. Two highly-trained operators would perform the measurements.
Each standard would be measured on each unit, by each operator, in triplicate. So, in total 270
measurements would be made based on three variables.
Measure Stage
The next step was performing the actual measurements. The experimental set up was as follows:
The five instruments used were calibrated i-LAB® visible spectrometers each having their own
ampoule adapter. The instrument specifications include the ability to measure 400 nm to 700 nm
wavelengths of light, with a resolution of ~0.7 nm. The adapters for the spectrometer were
designed to accommodate glass ampoules. The adapter snapped onto the i-LAB unit such that
light was focused through the ampoule, to the back side of the adapter, and then reflected through
the sample again and to a detector. The light path was the same for every unit.
The ampoules standards were from CHEMetrics (Calverton, VA) catalog number C-7512 (Color
Comparator), and were composed of uniform glass with dimensions of 4 inch long by ~1/4 inch
outer diameter. The glass ampoule standards contained blue-colored aqueous liquids with
differing dye concentrations. The degree of color correlated to the amount of dissolved oxygen
(DO) for their ampoules kit K-7512 samples. The sample ampoules are meant to measure DO
from 1-12 ppm (parts per million) using an indigo carmine reaction. Only the sealed colored
standard ampoules were used in the study.
Two trained operators performed the measurements using the same standard testing procedure
and software. The operators measured the standards on the same day, with the same instruments
to minimize environmental variations. The measuring procedure consisted of initially calibrating

9. p. 9 of 23
the spectrometer with an ampoule filled with clear, distilled water and also with an ampoule filled
with an opaque, black ink. The clear or black ampoule was cleaned, then placed into the adapter,
aligned in the holder, and a 3 3/4 inch black plastic cap was placed on the adapter and over the
ampoule to ensure the blockage of all ambient light. The calibration procedure was performed to
ensure that the spectrometer LEDs and sensor contained the correct optical specifications. After
calibration, the operator measured the ampoule filled with clear, distilled water to obtain the
background. Next, the standard colored ampoules samples were cleaned, placed, and measured.
The background or “blank” was subsequently accounted for during this sample measurement.
Even though the spectrometer inherently measures light wave intensities from 400 to 700 nm at 1
nm increments, in this study only the Absorbance at 580 nm was used in accordance with the
recommended procedure (Gilbert, Behymer, and Castañeda, 1982).
Five visible i-LAB® visible spectrometers (unit numbers 719, 791, 805, 818, and 823) were used
in the study. Three absorbance measurements at 580 nm were taken for each standard by each
operator on each unit. Two trained operators performed the tests. In this manner, the 5x3x2
study yielded 30 reference points for each part of the study. So, together with the nine blue
dissolved oxygen standards, the amount of measurements was two 270 points.
Analysis Stage
The data was analyzed in multiple ways. After the measurements were done in triplicate by each
operator on five spectrometers, the average Absorbance (ABS) values were calculated and are
shown in Table 1. It is apparent that ABS values increase as the standards increase in PPM of
dissolved oxygen. Variation amongst measurements with differing units, variation within units
and operators are also apparent, and will be explored.

10. p. 10 of 23
Table 1: Absorbance Averages for each Unit and Operator as a Function of PPM Standards, with
Operator Standard Deviation (Sigma)
Unit Number
PPM V791 V818 V805 V719 V823
Operator1
(sigma=0.031)
1 0.128 0.163 0.164 0.138 0.107
2 0.259 0.29 0.305 0.281 0.225
3 0.374 0.431 0.408 0.389 0.347
4 0.498 0.551 0.539 0.503 0.47
5 0.603 0.656 0.652 0.633 0.588
6 0.695 0.771 0.736 0.72 0.68
8 0.874 0.934 0.899 0.9 0.84
10 1.056 1.122 1.1 1.072 1.044
12 1.236 1.298 1.28 1.261 1.225
Operator2
(sigma=0.038)
1 0.103 0.179 0.151 0.116 0.079
2 0.227 0.294 0.303 0.259 0.206
3 0.343 0.418 0.38 0.37 0.326
4 0.46 0.533 0.521 0.489 0.449
5 0.571 0.631 0.635 0.605 0.562
6 0.672 0.728 0.74 0.713 0.664
8 0.833 0.892 0.892 0.874 0.818
10 1.033 1.116 1.089 1.086 1.013
12 1.211 1.297 1.273 1.282 1.204
The average ABS values vs. the PPM standards for each operator were also plotted in Figs. 3 and
4, respectively. The graphs show linearity between Absorbance and the standard PPM values.
The connected lines also show that each unit has variances, and that each unit performed
differently for each operator. Error bars are at 2 standard deviations (2 sigma levels).

11. p. 11 of 23
Figure 3: ABS vs. Colored PPM Standards Measured by Operator 1 (Error Bars at 2 Sigma)
Figure 4: ABS vs. Colored PPM Standards Measured by Operator 2 (Error Bars at 2 Sigma)
The first step to understanding variations in the Analyze Stage measurements is to identify
variables. The second step is to quantify their contribution to variations. A broad way to see
variances is to “normalize” the measurements with respect to the average for each PPM standard.
Further classification by variables can also ease in seeing their contribution and magnitude.
0
0.2
0.4
0.6
0.8
1
1.2
1.4
0 2 4 6 8 10 12
Absorbance(580nm)
DO Standards (ppm)
V791
V818
V805
V719
V823
0
0.2
0.4
0.6
0.8
1
1.2
1.4
0 2 4 6 8 10 12
Absorbance(580nm)
DO Standards (ppm)
V791
V818
V805
V719
V823

12. p. 12 of 23
So, in detail, for each colored PPM standard, the overall average absorbance values were
determined. The difference (delta) between the measured absorbance value for each point and the
average absorbance value was then calculated and plotted in Figure 5. Figure 5 highlights that
there is variation in all three areas; unit to unit, between operators, and within unit. Two standard
deviations for between units was 2σ=0.039, within unit variations had 2σ=0.018, and between
operators was 2σ=0.009. Thus, the variation contributions in percentage were: between unit 59%;
within units 27%, and between operators 14%. The variables were further segmented and
analyzed statistically for the degree of error and its significance.

13. p. 13 of 23
Figure 5: Differences Between Absorbance and Mean Absorbance for Standards
Unit to Unit Variability
In order to determine if unit to unit variations were statistically significant, an analysis of
variation (ANOVA) was done using the f-type distribution. The f-test is used to determine
whether the expected values of a variable differ significantly from each other. In the case of
comparing multiple units, the ANOVA analysis indicates if the instruments get similar results of
if they differ substantially. Inherent in the test is a Fisher (or F) number that is calculated and is
defined as the between group sum of squares divided by the within group sum of squares,
Equation 1.
Equation 1: Fisher Number Calculation Equation
∑ (< 𝑌 > 𝑖
−< 𝑌 >) 2
𝑛𝑖
/(i-1)
F= _______________________________
∑ (< 𝑌 > 𝑖𝑗
−< 𝑌 > 𝑖𝑗
) 2
𝑛𝑖𝑗
/a(i-1)
From the Fisher number, a probability (p) factor is calculated. The probability is that of
observing a result as extreme or unlikely than a result using a null hypothesis that the sample
measurements are not alike. The Fisher method is often used to determine the statistical
likelihood, actually unlikelihood, for different measurements coming from the same variable
(Fisher, 1925; Miller, Jr., 1998; Nolan and Speed, 2000).

14. p. 14 of 23
In Equation 1, Y is the measurement average for a specific unit or measurement; i is the number
of data values per group; j is the within group number; and a is the number of groups. After
calculating the F number, a probability factor (p) was determined. The probability factor, which
ranges from 0 to 1 (0-100%), indicates the likelihood of the measurements from differing groups
being the same number. An α value or significance level is compared with the p value to
determine if the groups (differing units) were significantly the same. For an p value of >0.05,
there is a high statistical likelihood that the groups would yield the same measurement number.
Table 2 shows the results of the Fisher calculations for each of the PPM levels tested. For 6 of the
9 samples p < α = 0.05 and for 3 of the 9 samples p < α = 0.01. This shows that the variation
between units is statistically significant (reproducibility is compromised). Thus the five units
differ substantially in yielding expected results.
Table 2: Results from Unit To Unit AOVA Testing Using Fisher Analysis
Since this variable showed a statistically significant variation with Fisher Analysis as well as
contributed most (59%) in the 2 sigma standard deviation analysis, it had potential for greatest
improvement. Drastic reduction in variation was achieved after much brainstorming and testing.
These alterations are outlined in the improvement section.
Within A Unit Variability
The Fisher analysis was also performed for the Within A Unit Variable, with results that were
unlike that of the Unit to Unit results. Table 3 shows the probability, p value, was significantly
greater than 0.05 for all standards. In fact the p value was >0.90 for each calculation. These
values indicate there is a very high probability that each unit will produce values that are alike.
Table 3: Results from Within A Unit ANOVA Testing Using Fisher Analysis
Even though the Fisher Analysis did not show the Within A Unit Variation to be statistically
significant, avenues for improvement were still explored and eventually achieved. These are
again outlined in the improvement section.
1PPM 2PPM 3PPM 4PPM 5PPM 6PPM 8PPM 10PPM 12PPM
Fisher# 3.43 6.10 5.17 2.36 2.37 4.83 2.21 4.15 3.43
p 0.023 0.001 0.004 0.081 0.080 0.005 0.097 0.010 0.023
1PPM 2PPM 3PPM 4PPM 5PPM 6PPM 8PPM 10PPM 12PPM
Fisher# 0.10 0.05 0.13 0.14 0.29 0.09 0.16 0.03 0.02
p 0.991 0.998 0.984 0.981 0.914 0.993 0.975 0.999 1.000

15. p. 15 of 23
Operator Variability
Like the Within a Unit variability results, the analysis of variation (ANOVA) with the Fisher tests
showed that significant variation did not appear between operators (Table 4). The ANOVA
study showed that the p number for the Operator variables was >0.59, significantly larger
than 0.05. This probability indicates that the measurements made by one operator are
the same as those made by the other. So, the variation in measurements between
operators is not statistically significant.
Table 4: Results from Operator Variability ANOVA Testing Using Fisher Analysis
The error in measurements attributed to operator differences can be shown schematically.
In Figure 6, the center green bar shows the average difference between operators for all
measurements with respect to the PPM numbers. The red and blue bars show the average
delta and the standard deviations for operator 1 and operator 2, respectively. These bars
show +/- one standard deviation, which illustrates that there is a good deal of
measurement overlap. From this analysis and a Fischer analysis, it was determined that
operator error did not play a significant role, relative to the other two variables, in
contributing to the overall error. For this reason, improvement strategies were not
explored for this variable.
1PPM 2PPM 3PPM 4PPM 5PPM 6PPM 8PPM 10PPM 12PPM
Fisher# 0.11 0.08 0.24 0.18 0.30 0.13 0.30 0.05 0.02
p 0.743 0.779 0.628 0.675 0.588 0.721 0.588 0.825 0.889

16. p. 16 of 23
Figure 6: Measurement Errors for Operator 1 and Operator 2
Summary of Variables
A Pareto chart shows the relative contribution to error as evidenced by plotting two standard
deviations for each variable (Figure 7). The graph shows the majority of error in measurements
can be attributed to differences between unit to unit variations. Again, 59% of error results from
unit to unit variation, and combined with within unit variation both account for 86% of the error.
These results are not surprising and were shown before in Figure 5.
Ave- Std Dev
-0.31
Operator 2 Ave
0.06
Ave + Std Dev
0.44
Ave - Std Dev
-0.07
Operator 1 Ave
0.24
Ave + Std Dev
0.55
0.06
0.24
-0.40
-0.30
-0.20
-0.10
0.00
0.10
0.20
0.30
0.40
0.50
0.60
AVERAGEDELTA(PPM)
Measurement Error

17. p. 17 of 23
Figure 7: Pareto Analysis of the Main Variables for the DO Standard Study
Improvement Stage
Each variable was analyzed for ways to improve measurements. In other words, determine what
can be done to decrease variation. Typically, improvements for one variable should not affect
others. The ways may not be, and usually are not the same for each variable. However,
any improvement in one area should also be assessed in other areas, especially if
variables are dependent on each other. The three variables used in the study were
independent of each other.
With that in mind, each improvement was analyzed for the specific variable.
Unit to Unit Variability
It became apparent during analysis that Unit to Unit Variation was the largest contributor to
variation. It was determined that individual calibration of each unit could reduce the unit to unit
variation. To accomplish this, the data from each unit was looked at individually. The absorption
data of the standard ampoules was graphed to create Absorption vs. PPM graphs, complete with
regression lines for each unit. The average slope and intercept of the regression lines was then
used as the model. An automatic correction factor was programmed into the software of each
unit to normalize the data in accordance with the individualized regression line. In a sense, each
unit was “re-calibrated” in alignment with the standard ampoules. Each unit had slightly different
slopes and intercept lines for the ABS vs. PPM standards due to differences in the light sources
0%
20%
40%
60%
80%
100%
0
0.005
0.01
0.015
0.02
0.025
0.03
0.035
0.04
0.045
Unit to Unit Within Units Operator
PercentContributiontoError
2SigmainMeasurementError

18. p. 18 of 23
(i.e., the LEDs had some variation), the sensors, or the optical path. The newly improved
software program corrected for any physical variances. New ANOVA tests showed no
statistically significant variations; meaning the units gave statistically similar readings. Further
testing of the standard ampoules resulted in much more uniform measurements between units,
decreasing the overall standard deviation significantly. Figure 8 shows a graph like Figure 5, but
with the new programming improvements for unit to unit variation.
Figure 8: Differences between Absorbance and Mean Absorbance for Standards
Within Unit Variability
Despite this variation contributing less than the Unit to Unit Variation, avenues to improve upon
it were still explored. Focus on improvement centered on the software correlation used to
calculate a PPM measurement from the Absorption data. Initial software used a linear regression
line to correlate the absorption to PPM level, as seen in Equation 2.
Equation 2: Linear Correlation Equation
PPM = (9.8912*ABS) – 0.7003
Data generated with this equation resulted in an average delta standard deviation of 0.35PPM,
with a 0.995 coefficient of determination (R2
) number for a linear regression. The high
correlation number shows that there is a strong relationship between the color of the PPM
Operator 1 Operator 2
+1 ϭ
-1 ϭ
+2 ϭ
-2 ϭ
Unit
V791
V818
V805
V719
V823
-0.08
-0.06
-0.04
-0.02
0
0.02
0.04
0.06
0.08
0 10 20 30 40 50 60 70 80 90 100

19. p. 19 of 23
standards and the Absorbance values, especially the average value. However, improvements can
be made by using a quadratic curve fit program. Equation 3 illustrates a quadratic regression line
for one of the units. Figure 9 shows an even stronger correlation generated by using a quadratic
regression line rather than a linear regression line.
Equation 3: Quadratic Correlation Equation
PPM = (1.894*ABS2
) + (7.186*ABS)
Data generated with the quadratic regression line resulted in an average standard deviation of
0.22 PPM, thereby improving precision and going from an R2
of 0.995 to 0.9991.

20. p. 20 of 23
Figure 9: PPM vs. Absorption Data with both Linear vs. Quadratic Fitting
Operator Variability
No improvements were performed for this variable as it was determined in the analysis
stage that average delta standard deviations for each individual operator had significant
overlap (as seen in Figure 6) and operator differences were not a large contributor to
variation. Further improvements may be made by having a more uniform procedure or
having operators watch each other perform measurements to determine any differences in
sample placement or reading.
Summary of Variables After Improvements
The next step in the improvement process is to implement means of improvement and determine
their effect. With that in mind, figure 10 shows the standard deviation before and after
improvements for between units and within unit variables. The standard deviation for between
units went from 0.039 to 0.003, while that within unit decreased from 0.018 to 0.011. A Pareto
chart (Figure 11) shows the new relative contribution to error for each variable. Note that the 2
sigma value decreased from 0.066 to 0.23 in total. The largest variable after the improvements is
now within unit variation. Both charts show a large decrease in measurement error. Re-
measuring the units showed the same significant improvements (as was previously seen the
differences between Figures 5 and 8).
R² = 0.9991
R² = 0.995
0
2
4
6
8
10
12
14
0 0.2 0.4 0.6 0.8 1 1.2 1.4
PPM
Absorption

21. p. 21 of 23
Figure 10: Impact of Improvements on 2 Standard Deviations
Figure 11: Pareto Analysis of the Main Variables after Improvements for the DO Standard Study
Before
Improvement
(Red)
After
Improvement
(Blue)
0
0.01
0.02
0.03
0.04
Unit to Unit WithinUnits
2SigmaMeasurementError
0%
20%
40%
60%
80%
100%
0
0.002
0.004
0.006
0.008
0.01
0.012
Unit to Unit Within Units Operator
PercentContributiontoError
2SigmainMeasurementError

22. p. 22 of 23
Control Stage
The next step is to maintain changes. Since the changes are primarily in software, they
are not expected to change. The units will be periodically checked with the standards just
to make sure that there are no added variances. It has become apparent that the methods
used to achieve improvement in this study can be applied to also decrease variability in
several other applications using the i-LAB® instrument. Learning from this study will be
used to improve i-LAB® measurement variability for many applications. The last step is
to publish and share the information.
3 Concluding Remarks
The study revealed that a Six Sigma methodology can be successfully applied to new
product development (NPD). The modified DMAIC procedure incorporating a Gauge
R&R study, a quantitative analysis of variations using Fisher testing, and a Pareto
diagram resulted in an exceptional system understanding. Through the Gauge R&R
study and ensuing analysis of variance (ANOVA) the magnitude of variance between the
three main variables was determined. From the metrics, two variables were specifically
improved. In regards to improvements, the measurement error at a 2ϭ level decreased by
factor more than three, resulting in PPM variability going from 0.7 to 0.2. Although at 1
PPM that means a 20% error, another dissolved oxygen test kit, having greater sensitivity
and ranging from 0 to 1 PPM, may be used. Thus the error ranges from a more viable
10% to 1.7% from 2 PPM to 12 PPM, respectively. A future focus will be to further
reduce reading error from both the within unit variable and by different operators. In
summary, the objective of implementing a Six Sigma approach to new product
development is realistic. The approach is useful to understand, quantify and improve
variables for NPD.

23. p. 23 of 23
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