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. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. ACCEPTED MANUSCRIPT 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 RI PT Izmir University of Economics, Sakarya Cad. No:156, 35330, Balcova/Izmir/TURKEY, [email protected] M AN U SC 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 TE D 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). AC C EP 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). ACCEPTED MANUSCRIPT 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): RI PT 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). M AN U SC 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). EP TE D 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. AC C 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. ACCEPTED MANUSCRIPT M AN U SC RI PT 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). TE D 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). AC C EP 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 ACCEPTED MANUSCRIPT RI PT 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). SC 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 M AN U 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. TE D 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). AC C EP 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. ACCEPTED MANUSCRIPT 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. RI PT • 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. SC 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. M AN U 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) TE D Step 2: Calculate individual measurement average and individual measurement range for each appraiser by the following formulas: n ∑Xi X app = i =1 EP n n i =1 n AC C 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) ACCEPTED MANUSCRIPT 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) RI PT UCLR = D4 x R app Equipment Variation: EV = rx R app (kx X ) − (EV 2 diff 2 nxr ) M AN U Appraiser Variation: AV = SC 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) EP % EV = 100 x EV TE D Step 8: Divide each value of variation by value of total variation to find the percentages: AC C 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. ACCEPTED MANUSCRIPT 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. RI PT 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). M AN U SC 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 AC C EP TE D 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) ACCEPTED MANUSCRIPT 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 M AN U SC 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 RI PT 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. TE D In the following section, the results of the gage control application will be presented 5. RESULTS EP 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. AC C 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 ACCEPTED MANUSCRIPT 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 RI PT Trail 3 X-bar R Appraiser C Trial 1 Trail 2 Trail 3 X-bar R Part Xdbar SC The difference between the maximum and minimum of individual measurement average was found: X diff = 88.92 − 88.24 = 0.68 ( ) M AN U 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 TE D LCLR = D3 x R app = 0 x1.04 = 0 AC C EP 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 ACCEPTED MANUSCRIPT 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 RI PT 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 M AN U Table 5: Dependent on Number of Parts (j) Part Number 1 2 3 4 5 j= 3,65 2,7 2,3 2,08 SC 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 TE D 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 AC C EP % 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. ACCEPTED MANUSCRIPT 6. CONCLUSION & DISCUSSION RI PT 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. SC 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. M AN U 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. TE D REFERENCES Assarlind, M., Gremyr, I., and Bäckman, K., 2012. Multi-faceted views on a lean Six Sigma application. International Journal of Quality & Reliability Management, 29(1), 21-30. EP Banuelas, R., Antony, J., and Brace, M., 2005. An Application of Six Sigma to Reduce Waste. Quality and Reliability Engineering International, 21, 553-570. AC C Bergmiller, G.G., and McCright, P.R., 2009. Parallel models for lean and Green operations. In: Proceedings of the 2009 Industrial Engineering Research Conference. Retrieved from http://zworc.com/site/publications_assets/ParallelModels.pdf Besterfield, D.H., 2009, Quality Control. 8th Edition, Pearson. Chan, C.C.S., Yu, K.M., and Yung, K.L., 2010. Green manufacturing using integrated decision tools. The IEEE International Conference on Industrial Engineering and Engineering Management, Venetia, Italy, 7-10 December. Chauhan, G., and Singh, T.P., 2012. Measuring parameters of lean manufacturing realization. Measuring Business Excellence, 16(3), 57-71. Dalalah, D., and Diabat, A., 2015. Repeatability and reproducibility in med labs: a procedure to measurement system analysis. IET Science Measurement & Technology, 9(7), 826-835. Devane, T., 2004. Integrating Lean Six Sigma and High-Performance Organizations: Leading the Charge toward Dramatic, Rapid, and Sustainable Improvement. John Wiley & Sons, New York, NY. ACCEPTED MANUSCRIPT Dües, C.M., Tan, K.H., and Lim, M., 2013. Green as the new lean: how to use lean practices to catalyst the greening of your supply chain. Journal of Cleaner Production, 40, 93-100. Florida, R., 1996. Lean and green: the move to environmentally conscious manufacturing. California Management Review, 39(1), 80-105. Furlan, A., Vinelli, A., and Dal Pont, G., 2011. Complementarity and lean manufacturing bundles: An empirical analysis. International Journal of Operations and Production Management, 31(8), 835850. RI PT Galeazzo, A., Furlan, A., and Vinelli, A., 2014. Lean and green in action: interdependencies and performance of pollution prevention projects. Journal of Cleaner Production, 85, 191-200. Garza-Reyes, J.A., Flint, A., Kumar, V., Antony, J., and Soriano-Meier, H., 2014. A DMAIRC approach to lead time reduction in an aerospace engine assembly process. Journal of Manufacturing Technology Management, 25(1), 27-48. SC Garza-Reyes J.A., 2015a. Lean and green e a systematic review of the state of the art literature. Journal of Cleaner Production, 102, 18-29. M AN U Garza-Reyes J.A., 2015b. Green lean and the need for Six Sigma. International Journal of Lean Six Sigma, 6(3), 226 – 248. Gremyr, I., Siva, V., Raharjo, H., and Goh, T.N., 2014. Adapting the Robust Design Methodology to support sustainable product development. Journal of Cleaner Production, 79, 231-238. Hajipour, V., Kazemi, A., and Mousavi, S.M., 2011. A Fuzzy Expert System to Increase Accuracy and Precision in Measurement System Analysis. Measurement, 46(8), 2770-2780. TE D Hilton, R.J., and Sohal, A., 2012. A conceptual model for the successful deployment of lean Six Sigma. International Journal of Quality & Reliability Management, 29(1), 54-70. Husby, P.C., and Swartwood, D., 2009. Fix Your Supply Chain: How to Create a Sustainable Lean Improvement Roadmap, Productivity Press, CRC Press, Taylor & Francis, New York, NY. EP Jurado, P.J.M., and Fuentes, J.M., 2014. Lean Management, Supply Chain Management and Sustainability: A Literature Review. Journal of Cleaner Production, 85, 134-150. AC C King, A.A., and Lenox, M.J., 2001. Lean and green? An empirical examination of the relationship between lean production and environmental performance. Production and Operations Management, 10(3), 244-256. Kuo, C.C., and Huang, P.J., 2012. Repeatability and Reproducibility Study of Thin Film Optical Measurement System. Optik - International Journal for Light and Electron Optics, 124(18), 34893493. Lee, J.H., Garza-Reyes, J.A., Kumar, V., Rocha-Lona, L. and Mishra, N., 2013. A comparative study of the implementation status of lean Six Sigma in South Korea and the UK. In Azevedo, A. (ed), Advances in Sustainable and Competitive Manufacturing Systems, Lecture Notes in Mechanical Engineering, Springer, 1, 1489-1502. Lewis, M.A., 2000. Lean production and sustainable competitive advantage. International Journal of Operations and Production Management, 20(8), 959–978. Ministry of Environment and Forestry, 2009. The Regulation of “Control over the Air Pollution Caused by Industry. Retrieved from http://www.resmigazete.gov.tr/eskiler/2009/07/2009070320..htm ACCEPTED MANUSCRIPT Montgomery, D.C., 2001. Introduction to Statistical Quality Control, 4th ed., John Willey and Sons, New York, NY. Ng, R., Low, J.S.C., and Song, B., 2015. Integrating and implementing Lean and Green practices based on proposition of Carbon-Value Efficiency metric. Journal of Cleaner Production, 95, 242255. RI PT Nunes, B. and Bennett, D., 2010. Green operations initiatives in the automotive industry: an environmental reports analysis and benchmarking study. Benchmarking: An International Journal, 17(3), 396-420. Ohno, T., 1988. Toyota Production System: beyond Large Scale Production. Productivity Press, New York, NY. Pyzdek, T., and Keller, P.A., 2003. Quality Engineering Handbook. 2nd Edition, CRC Press. SC Raisinghani, M.S., Ette, H., Pierce, R., Cannon, G., and Daripaly, P., 2005. Six Sigma: concepts, tools, and applications. Industrial Management & Data Systems, 105(4), 491-505. M AN U Rothenberg, S., Pil, F.K., and Maxwell, J., 2001. Lean, green, and the quest for superior environmental performance. Production and Operations Management, 10(3), 228-243. Salah, S., Rahim, A., and Carretero, J.A., 2010. The integration of Six Sigma and lean management. International Journal of Lean Six Sigma, 1(3), 249-274. Sawhney, R., Teparakul, P., Bagchi, A., and Li, X., 2007. En-lean: a framework to align lean and green manufacturing in the metal cutting supply chain. International Journal of Enterprise Network Management, 1(3), 238-260. TE D Snee, R.D., and Hoerl, R.W., 2003. Leading Six Sigma: A Step-by-Step guide Based on Experience with GE and Other Six Sigma Companies, FT Prentice Hall, NJ. Soltero, C., and Waldrip, G., 2002. Using kaizen to reduce waste and prevent pollution. Environmental Quality Management, 11(3), 23-38. EP Tsai, P., 1989. Variable Gauge Repeatability and Reproducibility Study Using the Analysis of Variance Method. Quality Engineering, 1(1), 107-115. AC C Womack, J.P., Jones, D.T., and Ross, D., 1991. The Machine that Changed the World: the Story of Lean Production. Harper Collins, New York, NY. Womack, J.P., and Jones, D.T., 2003. Lean Thinking: Banish Waste and Create Wealth in Your Corporation. Free Press. ACCEPTED MANUSCRIPT APPENDIX AC C EP TE D M AN U SC RI PT Appendix A: Factors for Computing Central Lines and 3σ Control Limits for X , s , and R Charts ACCEPTED MANUSCRIPT Highlights In this paper, • The integration of green lean approach is discussed. • The limitations of green lean approach are identified. RI PT • Six Sigma approach is integrated in order to overcome the limitations of green lean approach, and assess the performance of the green lean approach. AC C EP TE D M AN U SC • 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.