sagnak2016

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Accepted Manuscript
Integration of green lean approach with six sigma: an application for flue gas
emissions
Muhittin Sagnak, Yigit Kazancoglu
PII:
S0959-6526(16)30276-1
DOI:
10.1016/j.jclepro.2016.04.016
Reference:
JCLP 7040
To appear in:
Journal of Cleaner Production
Received Date: 10 December 2015
Revised Date:
29 February 2016
Accepted Date: 4 April 2016
Please cite this article as: Sagnak M, Kazancoglu Y, Integration of green lean approach with six
sigma: an application for flue gas emissions, Journal of Cleaner Production (2016), doi: 10.1016/
j.jclepro.2016.04.016.
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INTEGRATION OF GREEN LEAN APPROACH WITH SIX SIGMA: AN APPLICATION
FOR FLUE GAS EMISSIONS
Muhittin Sagnak
Corresponding Author: Izmir University of Economics, Sakarya Cad. No:156, 35330, Balcova/Izmir/TURKEY, [email protected]
Yigit Kazancoglu
ABSTRACT
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Izmir University of Economics, Sakarya Cad. No:156, 35330, Balcova/Izmir/TURKEY, [email protected]
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Environmental considerations have led organizations to take an important role in designing
environmentally-friendly, recyclable products to complement improvements in the environmental
standards of services. In this context, the application of lean practices may result in pollution
reduction. In this paper, firstly, the integration of green lean approach is discussed, and then the
limitations of green lean approach are identified. Finally, we integrate the Six Sigma approach in
order to overcome these limitations, and assess the performance of the green lean approach.
Measurement System Analysis and Gage Control are used as methodology to measure the
variations of the process in order to decrease unfavorable ecological impacts of companies’
products or services, while enhancing environmental efficiency.
Key Words: Green, lean, green lean approach, Six Sigma, Measurement System Analysis, Gage
Control
1. INTRODUCTION
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In order to be competitive in the global market, ensuring products/services with highest quality and
lowest cost has great importance. In various fields, many ideas and approaches were generated
during the last years of World War II. Correspondingly, in recent years, the lean manufacturing
approach has attracted much interest from many different fields (Lewis, 2000).
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Lean practices can be defined as a set of techniques that intend to remove various types of waste
throughout the value chain. Techniques can be grouped together as clusters such as total quality
management (TQM), just in time (JIT), and total preventive maintenance (TPM) (Furlan et al.,
2011), all of which put into practice the lean philosophy of removal of waste and continuous
improvement. Lean philosophy can also be applied to the supply chain by way of cooperation with
stakeholders, aiming to improve the efficiency of the whole production process (Galeazzo et al.,
2014).
As Womack et al. (1991) stated, lean production originates from the Toyota Production System
(TPS). The research in the automotive industry, as applied by Toyota, highlighted seven significant
types of waste to be eliminated in production processes. With reference to Ohno (1988), who was
labeled as the father of TPS, the seven wastes are identified as overproduction, excessive inventory,
transportation, unnecessary motion, defects, waiting and delay, and overprocessing. In addition,
Womack and Jones (2003) introduced an 8th waste, underutilized human talent.
The application of lean practices has the potential to reduce pollution, remove the obstacles to the
application of pollution reduction measurement, and emphasize the value of pollution reduction.
Therefore, lean may complement green. Also, the adoption of lean production may decrease the
marginal cost of reducing pollution, either by reducing the cost of applying environmental
development, or bringing greater awareness of the value of pollution reduction. Hence,
environmental management systems (EMS) show the similar characteristic features to lean
philosophy. Like lean philosophy, EMSs deal with the removal of wastes and the application of
continuous improvement (King and Lenox, 2001).
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Environmental considerations lead organizations to take an important role in designing
environmentally-friendly, recyclable products, in addition to providing cleaner services. Therefore,
the green philosophy has emerged as an operational approach for companies to decrease the
unfavorable ecological impacts of products or services, while enhancing environmental efficiency.
Likewise, lean is an operational approach aiming the reduction of waste in every area of
organizational activity; therefore, it seems natural to integrate lean and green (Garza-Reyes, 2015a).
The studies within the operations management area were in agreement over the complementarity of
lean and green concepts under the following principles (Jurado and Fuentes, 2014):
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1) Waste Reduction Principle: The basic principle of lean production is to improve added value by
decreasing and/or removing the non-value-added activities throughout the value chain. Similarly,
reducing and/or removing waste is a fundamental issue for environmental sustainability by way of
reducing and preventing environmental pollution (Florida, 1996; King and Lenox, 2001).
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2) Process-Centered Focus: One of the fundamentals of lean production is managing quality over
the whole production process. The lean focus is emphasized not only in order to solve a particular
problem, but also to prevent its reoccurrence. The same is valid for the green approach, which
underlines the preventive action, rather than focusing on the end of the process (King and Lenox,
2001; Sawhney et al., 2007).
3) High Levels of Participation: Another important lean production principle is the participation of
people in management, which is also valid for environmental focus. Human resource management
activities allow organizations to create a culture of continuous improvement, which enables
environmental management principles to be applied (Rothenberg et al., 2001; Soltero and Waldrip,
2002).
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Moreover, Dües et al. (2013) examined two philosophies of lean and green to produce a detailed
account of the differences and similarities. “Waste and waste reduction techniques”, “people and
organization”, “lead time reduction”, “supply chain relationship”, “Key Performance Indicator
(KPI): service level”, and “tools and practices” were found to be common attributes. They described
a lean environment as being a catalyst for the application of green approaches. In addition, it has
been proposed that the integration of lean and green will lead to better performances and results for
companies (Ng et al., 2015). Specifically, Bergmiller and Mccright (2009) provided empirical
evidence that companies which integrate lean and green philosophies have yielded greater benefits
than those focusing solely on lean production.
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The remainder of the paper is structured as follows. First, in Section 2, the limitations of green lean
approach are examined. Then, in Section 3, six sigma methodology, measurement system analysis
and gage control methodologies are presented. Section 4 describes the application. The data
preparations and findings of the analysis are outlined in Section 5. Section 6 is the conclusion and
discussion of future research directions.
2. THE LIMITATIONS OF GREEN LEAN APPROACH
Although the integration of lean and green seems natural and logical, there is uncertainty over
whether integration alone is sufficient to achieve simultaneous operational competitiveness and
environmentally sustainable results (Garza-Reyes, 2015b).
Generally, the studies in the literature determined the relationships between lean and green concepts
through underlining the similarities and differences between the two paradigms, investigating the
possible benefits of their combination in different industries, and identifying their impacts on
organizations and supply chains’ performances (Garza-Reyes, 2015b). However, the integrated
green lean approach may be subject to potential limitations, according to Garza-Reyes (2015b), who
proposed integration with Six Sigma to overcome those limitations.
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The integrated green lean approach is subject to the same limitations as the two approaches
practiced separately. In the case of lean, Salah et al. (2010) described it is a toolbox comprised of
tools to identify the potential for waste reduction. From this viewpoint, one of the main limitations
is that it fails to scrutinize and target the reduction of variations in processes (Devane, 2004; Lee et
al., 2013). The green approach is also subject to such variations in areas such as storage space,
energy consumption, and inventory waste. Montgomery (2001) and Snee and Hoerl (2003) claimed
that variation reduction is necessary to improve operations; presumably, this claim is also valid for
the green approach. Moreover, the examination of variation is important because it declares,
notifies, and assists the decision-making process (Devane, 2004), allowing the improvement of the
performance of green operations. Another limitation of lean is a lack of lean tools related to the use
of quality and mathematical tools. Statistical data to monitor the process and determine the related
remaining problems may not be collected until waste has been removed (Devane, 2004; Lee et al.,
2013). Because of this, Assarlind et al. (2012) proposed that lean organizations fail to make
effective use of data in the decision-making process, and therefore, that organizations should
employ methodologies to encourage a more scientific approach. Within this context, the lack of a
data-driven approach to improve the process results in a less precise lean process (Hilton and Sohal,
2012). Lean is an operational approach focused on satisfying customers’ needs and wants (Chauhan
and Singh, 2012); however, it does not offer a systematic and structured approach either to
monitoring processes, or solving the related problems. This systematic drawback may undermine
confidence in lean’s ability to scrutinize the root causes (Husby and Swartwood, 2009). Hence, the
operational and green problems may not be removed from their root cause (Garza-Reyes, 2015b).
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On the other hand, in the case of green, Chan et al. (2010) claimed that the implementation of
decision support systems and expert systems tools has only limited effectiveness in solving
problems. Unlike lean, green philosophy cannot be identified simply as a toolbox; rather, it is a
concept composed of series of practices and methods (Garza-Reyes, 2015b). Because of this, the
integrated green lean approach will be subject not only to the limitations of lean, but also the
limitations of green. From the perspective of environmental management systems and green
approach, it is possible to indicate that the limitations of green are related to strategic aspects, such
as the difficulties of making strategic decisions related to investment priorities, or how to apply
green initiatives in ways which also satisfy the corporate goals of profitability (Nunes and Bennett,
2010).
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Since the limitations of both lean and green are inherited by the integrated green lean approach, it is
important to integrate additional tools that are able to contribute to the reduction and removal of
these limitations. Six Sigma is associated with such tools (Garza-Reyes, 2015b). Within this
context, Six Sigma tool allows us 1) to assess the performance of the green lean process, and 2) to
overcome the limitations of green lean process. Therefore, in this paper, Six Sigma methodology is
applied in order to eliminate the limitations and assess the performance of the green lean process.
As such, in Taguchi’s loss function, environmental pollution is, in fact, a loss for a society (Gremyr
et al., 2014), and therefore, there must be zero tolerance for the measurement errors or variations in
environmental pollution, due to the consequences for human health. A Six Sigma tool,
Measurement System Analysis and Gage Control methodology, is integrated with the green lean
approach in order to identify the variability of measurement process, by analyzing resource use
across the entire process. The major contribution of this paper is the emphasis of the need for the
application of Six Sigma methodology to the green lean approach, and the proposal of Measurement
System Analysis and Gage Control technique to satisfy the need for measurement.
3. METHODOLOGY
3.1. Six Sigma
The manufacturing arena meets with Six Sigma in the early 1980s when Motorola suffered a serious
loss of productivity due to the costs of non-quality products. When their defective part rate reached
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2600 per million, their support systems were perceived as unreliable. Bob Galvin, chairman of
Motorola at that time, stated that, much effort was required to address their problems. Bill Smith, a
Motorola engineer, determined that the quality level in association with Six Sigma measurement
gave better results (Raisinghani et al., 2005). Technically, Six Sigma measurement provides a
failure rate of 3.4 parts per million opportunities in which sigma is a term used to reflect the
variations (Banuelas et al., 2005). Since then, the popularity of Six Sigma has grown due to its
effects on the financial and operational performance of organizations, and the improvement of
customer satisfaction through reduction or removal of defective products or services (Garza-Reyes,
2015b). This approach emphasizes the critical quality characteristics of products or processes. By
careful analysis of those characteristics, Six Sigma is able to determine and remove defects, and
variability (Garza-Reyes et al., 2014).
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One of Six Sigma’s distinctive approaches to problem-solving and improvement is measurement
system analysis. It is important for organizations to measure, monitor and evaluate its
environmental performance in a continuous manner. However, to ensure successful data analysis at
all stages of the measurement, monitoring, and evaluation processes, effective data gathering
processes are essential.
3.2 Measurement System Analysis
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From green lean integration point of view, measuring the variations of the process is key to
decreasing the unfavorable ecological impacts of products or services, while enhancing
environmental efficiency. Within this context, there is a need for a tool that is able to validate and
verify the data gathering process, in this case, the Measurement System Analysis (MSA) and Gage
Control.
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Measurement System Analysis (MSA) is a set of principles for assessing the measurement system.
As a part of quality management system, it is an analytic technique for the evaluation of
measurement system (Dalalah and Diabat, 2015). The method concentrates on the analysis of
resource use across the entire measurement process. The main objective of MSA is to specify the
impacts of different factors in the variability of measurement process; therefore, statistics are used
to emphasize the repeatability and reproducibility of the measurement (Dalalah and Diabat, 2015).
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An organization should measure, monitor and evaluate its environmental performance in a
continuous manner. To ensure successful data analysis in all stages of measuring, monitoring, and
evaluation processes, it is essential to implement an effective data gathering process. A tool with the
capacity to validate and verify the data gathering process was needed to achieve this; and it was
decided to employ Measurement System Analysis (MSA) and Gage Control.
3.3. Gage Control
In quality management systems, measurements are important in enabling effective analyses, and,
thus, realistic decisions (Pyzdek and Keller, 2003). Measurement data show whether the production
processes are functioning as planned; if values are found to be outside of statistical control limits,
various corrective actions can be taken. On the other hand, if the process is within control limits, the
process should be allowed to continue under the current configuration. Since the total variation is
made up of process variation and measurement variation, firstly, the measurement variance must be
identified and separated from process variance (Montgomery, 2001).
Measurement system variance can be classified under two headings:
• Accuracy: The difference between the value measured and the true value of the related item.
• Precision: The variation observed between repeated measurements of the same item.
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Gage control was developed to identify the degree of reliability of the measurement data. The
variance between analyzers and appraisers is calculated in order to identify the total variance. This
process performed using the measurement data, known as gage control, is used to assess the
statistical characteristics of repeatability, reproducibility and part variation (Tsai, 1989).
• Repeatability (Equipment Variation): The variance between measurements of the same part
taken by the same appraiser. If variance is low, repeatability is satisfactory.
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• Reproducibility (Appraiser Variation): Average variance between measurements of the same
part taken by different appraisers. If the difference is low, reproducibility is satisfactory.
• Part Variation: Total variance between measurements taken from parts sampled within a
fixed period by a single appraiser. If the difference is low, part variation is satisfactory.
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The data gathering and analysis for the measurement system is given below, with the relevant
formulas (Besterfield, 2009; Hajipour et al., 2011; Kuo and Huang, 2012).
Let n is number of parts, and k is number of appraisers where X is the average of the measurements,
and R is the range.
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Step 1: Calculate part average and part range for each part and each appraiser by the following
formulas:
r
X =
∑ Xi
i =1
r
R = X max − X min
(1)
(2)
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Step 2: Calculate individual measurement average and individual measurement range for each
appraiser by the following formulas:
n
∑Xi
X app =
i =1
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n
n
i =1
n
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R app =
∑ Ri
(3)
(4)
Step 3: Calculate overall part average and overall part range for each part by the following
formulas:
k
X
p
=
∑Xi
i =1
k
Rp = X
p , max
−X
p , min
(5)
(6)
Step 4: Calculate average of range for appraisers:
k
R app =
∑ R app ,i
i =1
k
(7)
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Step 5: Calculate difference between maximum and minimum of individual measurement averages:
X diff = X app ,max − X app ,min
(8)
Step 6: Calculate upper and lower control limits (The values of control chart constants, D3 and D4,
can be found in Appendix A):
(9)
LCL R = D3 x R app
(10)
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UCLR = D4 x R app
Equipment Variation: EV = rx R app
(kx X ) − (EV
2
diff
2
nxr
)
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Appraiser Variation: AV =
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Step 7: Calculate value of repeatability (equipment variation), value of reproducibility (appraiser
variation), value of measurement system variation, value of part variation, and value of total
variation.
(11)
(12)
Measurement System Variation: R & R = EV 2 + AV 2
(13)
Part Variation: PV = jxR p
(14)
Total Variation: TV = R & R 2 + PV 2
(15)
( TV )
% AV = 100 x (AV
)
TV
% R & R = 100 x(R & R )
TV
)
% PV = 100 x(PV
TV
(16)
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% EV = 100 x EV
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Step 8: Divide each value of variation by value of total variation to find the percentages:
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If %R&R < 10%, then the gage system is satisfactory, if %R&R < 30%, gage system may still be
acceptable based on the importance of the application, cost of gage, and cost of repairs; if not, it is
considered unsatisfactory.
When the gage system is unsatisfactory, if %EV > %AV, then the determinant problem is
concerned with measuring the equipment, and can be solved by calibration. If %EV < %AV, the
determinant problem is concerned with appraisers, and can be solved by training.
4. APPLICATION
The main aim of this paper is to assess the performance of a green lean approach, and eliminate its
limitations. The application is conducted for a particular instance of flue gas emission analysis. The
accurate measurement of gasses emitted into the atmosphere by various enterprises is important in
decreasing pollution and increasing fuel consumption efficiency. As the most critical element
affecting human health and environmental degradation is the presence of carbon monoxide, this
study focuses on the measurement of CO (carbon monoxide) emissions in the flue gas from a
natural gas powered boiler.
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The data consists of the flue gas emission results obtained from randomly selected engineers from
the Izmir Chamber of Mechanical Engineers, Technical Services Department. Each appraiser
performed 3 tests upon 10 different parts, amounting to a total of 30 measurements.
As the methodology of the study, firstly data were gathered from at least 10 randomly selected
parts, which were enumerated. A minimum of 2, maximum of 3 appraisers were randomly selected,
and coded as A, B, and C. Then, the random gaging process was conducted either 2 or 3 times by
the each of the three appraisers. Finally, gage control methodology was applied to the recorded data.
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The regulations entitled “Control over the Air Pollution Caused by Industry” came into force in
2009, aimed at minimizing smoke, dust, gas and other emissions resulting from industrial activity.
These regulations defined the “legal emissions limit” for industrial facilities. The limits for flue gas
emissions for facilities using natural gas are given in Table 1 and Table 2 (Ministry of Environment
and Forestry, 2009).
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Table 1: Flue Gas Emission Limits for Facilities with Thermal Power under 100 MW
Carbon
Nitrous Oxide
Dust
Type of Fuel
Sulphur
mg/Nm
Dioxide
Monoxide
mg/Nm3
mg/Nm3
mg/Nm3
Natural Gas,
100
100
800
10
LPG, Still Gas
Coking Plant
200
100
100
Gas
Biogas
800
100
100
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Table 2: Flue Gas Emission Limits for Facilities with Thermal Power equal to or higher
than 100 MW
Type of Fuel
Sulphur
Carbon
Nitrous Oxide
Dust
Dioxide
Monoxide
mg/Nm3
mg/Nm
mg/Nm3
mg/Nm3
Natural Gas,
60
100
50
10
LPG, Still Gas
Coking Plant
60
100
50
10
Gas
Biogas
800
100
50
10
Emission measurements include waste gases emitted into the atmosphere via the flue. For each type
of pollutant, there exist distinct measurement techniques which conform to national and
international standards; emission measurements for carbon monoxide, carbon dioxide and oxygen
are governed by the TS ISO 12039 standard, while, those for nitrous oxide, by the EPA CTM 22
standard.
Two techniques are used to measure flue gas emissions; direct measurement, and flue part analysis.
The former is used to identify gases produced by incineration, the latter to identify dust levels.
Before starting the experiment, the part sampling location was identified and examined. Part sample
locations must comply with the relevant standard (TS ISO 9096), and must be conducted on a
regularly-shaped section of the flue or channel, with no obstructions to gas flow or direction.
Three types of analyzer device can be used for measurements, each based on three different
measurement principles; the electrochemical cell method, UV (ultra-violet), and IR (infra-red)
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measurements. In this study, CO (carbon monoxide) gas emissions were measured by a device
based on the electromechanical cell method.
6
7
8
9
10
76.1
76.41
74.43
85.52
86.55
85.11
88.73
88.1
87.9
94.19
94.38
95.53
96.28
96.04
96.77
75.39
75.11
75.15
85.48
85.5
85.41
87.08
86.66
87.32
94.23
92.98
93.63
96.1
96.04
95.9
76.08
74.73
75.24
85.68
85.4
85.07
86.72
87.59
86.73
94.06
94.35
93.67
96.17
95.86
96.19
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Table 3: CO (Carbon Monoxide) Measurements
Part Number
1
2
3
4
5
Appraiser A
Trial 1
81.34 88.85 86.54 94.68 97.63
Trail 2
79.7
88.72 87.49 96.19 98.33
Trail 3
80.77 87.93 85.24 95.14 96.9
Appraiser B
Trial 1
80.03 88.42 86.69 94.59 96.25
Trail 2
81.06 88.12 86.65 95.78 96.44
Trail 3
81.12
89.1
86.7 96.46 97.55
Appraiser C
Trial 1
79.75 87.46 86.75 94.98 97.21
Trail 2
79.99 87.23 84.92 94.59 96.55
Trail 3
81.06 87.28 85.33 94.92 95.54
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For the burnt gas measurements, at least three separate trials were performed. The values displayed
on the screen were recorded after becoming stable. The number of trials may vary according to the
type of measurement requested by the facility. Because measurement reliability and validity can be
affected by human and mechanical factors, it was decided that 3 appraisers each perform 3 separate
trials upon each of the 10 parts in the CO (carbon monoxide) tests. The measurements were taken in
mg/m3. The data can be seen in Table 3.
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In the following section, the results of the gage control application will be presented
5. RESULTS
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The data given in Table 4 were analyzed using gage control methodology. The calculations were
based on the sequence and formulas. Table 4 shows the part averages, and part ranges; individual
measurement averages, and individual measurement ranges; overall part averages, and overall part
ranges.
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Table 4: Values of part averages, and part ranges; individual measurement averages, and individual
measurement ranges; overall part averages, and overall part ranges
Appraiser
A
Trial 1
Trail 2
Trail 3
X-bar
R
Appraiser
B
Trial 1
Trail 2
Part Number
4
5
Apprais Apprais
er
er
1
2
3
6
7
8
9
10
81.34
79.7
80.77
80.60
1.64
88.85
88.72
87.93
88.50
0.92
86.54
87.49
85.24
86.42
2.25
94.68 97.63 76.1 85.52 88.73
96.19 98.33 76.41 86.55 88.1
95.14 96.9 74.43 85.11 87.9
95.33 97.62 75.64 85.72 88.24
1.51 1.43 1.98 1.44 0.83
94.19
94.38
95.53
94.70
1.34
96.28
96.04
96.77
96.36
0.73
80.03
81.06
88.42
88.12
86.69
86.65
94.59 96.25 75.39 85.48 87.08 94.23 96.1
95.78 96.44 75.11 85.5 86.66 92.98 96.04
X
R
88.92
1.41
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81.12
80.73
1.09
89.1
88.54
0.98
86.7
86.68
0.05
96.46 97.55 75.15 85.41 87.32 93.63 95.9
95.61 96.74 75.21 85.46 87.02 93.61 96.01
1.87
1.3
0.28 0.09 0.66 1.25
0.2
79.75
79.99
81.06
80.26
1.31
87.46
87.23
87.28
87.32
0.23
86.75
84.92
85.33
85.66
1.83
94.98
94.59
94.92
94.83
0.39
86.25
Part
95.25 96.93 75.40 85.52 87.42 94.11 96.15 Range=
80.53
88.12
97.21
96.55
95.54
96.43
1.67
76.08 85.68 86.72 94.06 96.17
74.73 85.4 87.59 94.35 95.86
75.24 85.07 86.73 93.67 96.19
75.35 85.38 87.01 94.02 96.07
1.35 0.61 0.87 0.68 0.33
88.57
0.78
88.24
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Trail 3
X-bar
R
Appraiser
C
Trial 1
Trail 2
Trail 3
X-bar
R
Part Xdbar
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The difference between the maximum and minimum of individual measurement average was found:
X diff = 88.92 − 88.24 = 0.68
(
)
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Upper (UCLR) and lower (LCLR) control limits were calculated for ranges. If any individual
measurement range value was found to be outside the control, it should be discarded, and
calculations repeated. The values of control chart constants, D3 and D4, in the formulas were
obtained from a table in the Appendix A.
R app = R a + R b + R c / 3 = (1.41 + 0.78 + 0.93) / 3 = 1.04
UCLR = D4 x R app = 2.574 x1.04 = 2.68
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LCLR = D3 x R app = 0 x1.04 = 0
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Figure 1 reveals that the values range between UCLR and LCLR. This means the range values are
within limits. The part range of Appraiser A appears to be above average, while the values recorded
by Appraisers B and C seem to be below. This difference can be explained by human factors, is
caused by differences in measurement techniques used, and the experience level of the appraisers.
Figure 1: Appraiser Range Graph
Repeatability (Equipment Variation): As mentioned before, repeatability specifies the variability
between the measurement devices used to measure the same part taken by the same appraiser. In the
formula, r = 4.56 for 2 trials and 3.05 for 3 trials.
EV = rx R app = 3.05 x1.04 = 3.17
0.93
21.53
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Reproducibility (Appraiser Variation): It concerns the variability resulting from different appraisers.
In the formula, k = 3.65 for 2 appraisers and 2.70 for 3 appraisers.
n = number of parts
r = number of trials
AV =
(kx X ) − (EV
2
diff
2
)=
nxr
(2.70 x0.68) − 3.17
2
10 x3
= 1.74
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Repeatability and Reproducibility:
R & R = EV 2 + AV 2 = 3.17 2 + 1.74 2 = 3.62
PV = jxR p = 1.62 x 21.53 = 34.88
Total Variation:
6
1,93
7
1,82
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Table 5: Dependent on Number of Parts (j)
Part Number
1
2
3
4
5
j=
3,65
2,7
2,3
2,08
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Part Variation: The part variation is essentially a measure of the variation of the process. Value of j
in the formula, is chosen from Table 5.
8
1,74
9
1,67
10
1,62
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TV = R & R 2 + PV 2 = 3.62 2 + 34.88 2 = 35.07
The percentage of variations in respect to total variations is found as follows:
( TV ) = 100 X (3.17 35.07) = 9.04%
) = 100 x(1.74 35.07) = 4.96%
% AV = 100 x (AV
TV
) = 10.32%
% R & R = 100 x (R & R ) = 100 x (3.62
TV
35.07
) = 100 x(34.88 35.07) = 99.46%
% PV = 100 x(PV
TV
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% EV = 100 x EV
According to the calculations performed, the %R&R value for this study was 10.32%. If %R&R <
10%, then the gage system is satisfactory, if %R&R < 30%, gage system may still be acceptable
based on the importance of the application, therefore, it can be said that this figure confirms the
validity of the gage control procedure.
In addition, the EV proportion was found to be higher than the AV proportion (%EV = 9.04%,
%AV = 4.96%), indicating that the flue gas analyzers used need to be calibrated. Furthermore, the
analysis shows that the largest value was 98.33, and the average of all measurements was 88.57 (Xdbar = 88.57). This reveals that while the establishment in this study was operating within the legal
limits (<100 mg/m3), and the values are only just within the limit, maintenance on the boiler is
recommended.
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6. CONCLUSION & DISCUSSION
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Environmental considerations have led organizations to take an important role in designing
environmentally-friendly, recyclable products to complement improvements in the environmental
standards of services. In this paper, the integration of green lean approach was recommended to
provide those standards because lean is an operational approach aiming to reduce waste, and green
is an operational approach to decrease the negative ecological impacts of the companies. Within this
context, although it seems logical to integrate green and lean philosophies, this approach has some
limitations. Since the limitations of both lean and green are inherited by the integrated green lean
approach, additional tools, originating from Six Sigma approach, were integrated in order to
overcome these limitations, and assess the performance of the green lean approach. Accordingly,
this paper emphasized the need for six sigma methodology applied to the green lean approach, and
Measurement System Analysis and Gage Control techniques were proposed as measurement
systems.
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Environmental pollution is, in fact, a loss for a society, and therefore, there must be zero tolerance
for the measurement errors or variations in environmental pollution, due to the consequences for
human health. Therefore, it is essential to integrate Measurement System Analysis and Gage
Control methodology to green lean approach.
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In the case of private enterprises with the potential to harm human health and the environment, it is
important that the relevant legislation is amended to ensure that adequate checks and analyses are
performed by independent official institutions, rather than the companies themselves.
Regarding the improvement of environmental and operational performance, future studies can
explore the use of other Six Sigma methodologies, such as Design of Experiments (DOE), DMAIC,
process control charts, or process capability index.
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APPENDIX
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Appendix A: Factors for Computing Central Lines and 3σ Control Limits for X , s , and R
Charts
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Highlights
In this paper,
• The integration of green lean approach is discussed.
• The limitations of green lean approach are identified.
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• Six Sigma approach is integrated in order to overcome the limitations of green lean
approach, and assess the performance of the green lean approach.
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• Measurement System Analysis and Gage Control are used as methodology to measure
the variations of the process to decrease the unfavorable ecological impacts of the
companies’ products or services while enhancing the environmental efficiency.
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