Six Sigma Methodology for the Environment Sustainable Development

Abstract

The Six Sigma (6-σ) methodology, as it has evolved over the last two decades, provides a proven framework for problem solving and organizational leadership and enables leaders and practitioners to employ new ways of understanding and solving their sustainability problems. While business leaders now understand the importance of environmental sustainability to both profitability and customer satisfaction, few are able to translate good intentions into concrete, measurable improvement programs. Increasingly, these leaders are looking to their corps (Six Sigma experts) of Six Sigma “Master Black belts,” “Black belts,” and “Green belts” to lead and implement innovative programs that simultaneously reduce carbon emissions and provide large cost savings. In my experience, and that of many others, Six Sigma processes show a proven approach for businesses and organizations to improve their performance and that sustainability programs are in need of this operational approach and discipline. Six Sigma rigors will help a business leader to design a sustainable program for both short- and long-term value creations. The aim of this chapter is to show the importance of applying Six Sigma methodologies to multidisciplinary sustainability-related projects and how to implement it.

1 Introduction

In 2000, the carbon disclosure project (Carbon disclosure project) was launched as a centrally organized effort to get companies to be transparent about carbon emissions, and by the end of 2009, almost 2,500 companies were participating. In 2010, the U.S. Securities and Exchange Commission issued guidance (2010) to public companies saying that they should explain the impacts of climate change and climate regulation on their financial disclosure forms. Whether the initial triggers are intrinsic or extrinsic, there are a multitude of triggers that compel a company dialog to consider launching a formal environmental sustainability program.

The aim of this chapter is to show the power of Six Sigma to solve the current global challenge of environmental sustainability. One of the most complex problems that organizations face today is achieving success through strategies that are compatible with and supportive of environmental sustainability. The goal is to show how typical Six Sigma define, measure, analyze, improve, and control (DMAIC) structures, such as program governance, transfer functions, measurement systems, risk assessment, and process design, lending themselves to environmental sustainability. In this chapter, a case study of sustainability problems, such as excess oxygen reduction, is analyzed using Six Sigma tools.

2 What Is Six Sigma?

The use of Total Quality Management (TQM) as an overall quality program is still prevalent in modern industry, but many companies are extending this kind of initiative to incorporate strategic and financial issues (Puksic and Goricanec 2005). After the TQM hype of the early 1980s, Six Sigma, building on well-proven elements of TQM, can be seen as the current stage of the evolution (Harry 2000): although some conceptual differences exist between TQM activities and Six Sigma systems, the shift from the firsts to a Six Sigma program is a key to successfully implement a quality management system (Wessel and Burcher 2004).

Six Sigma methodology was originally developed by Motorola in 1987 and it targeted a difficult goal of 3.4 parts per million (ppm) defects (Barney 2002). At that time, Motorola was facing the threat of Japanese competition in the electronics industry and needed to carry out drastic improvements in their quality levels (Harry and Schroeder 2002). In 1994, Six Sigma was introduced as a business initiative to “produce high-level results, improve work processes, and expand all employees’ skills and change the culture” (ASQ 2002). This introduction was followed by the well-revealed implementation of Six Sigma at General Electric beginning in 1995 (Slater 1999). Sigma is the Greek letter that is a statistical unit of measurement used to define the standard deviation of a population. Therefore, Six Sigma refers to six standard deviations. Likewise, Three Sigma refers to three standard deviations. In probability and statistics, the standard deviation is the most commonly used measure of statistical dispersion; i.e., it measures the degree to which values in a data set are spread. The standard deviation is defined as the square root of the variance, i.e., the root mean square (rms) deviation from the average. It is defined in this way to give us a measure of dispersion. Assuming that defects occur according to a standard normal distribution, this corresponds to approximately 2 quality failures per million parts manufactured. In practical application of the Six Sigma methodology, however, the rate is taken to be 3.4 per million.

Initially, many believed that such high process reliability was impossible, and Three Sigma (67,000 defects per million opportunities, or DPMO) was considered acceptable. However, market leaders have measurably reached Six Sigma in numerous processes.

According to the Six Sigma methodology a 6-σ process yields fewer defects than a 3-, 4-, or 5-σ process. It is a name given to indicate how much of the data falls within the customers’ requirements. The higher the process sigma, the more of the process outputs, products, and services meet customers’ requirements – or the fewer the defects. Table 1 and Fig. 1 provide further resolution of the riddle involving the relationship between σ value and process performance. The associated assumed process distributions in Table 1 are used to construct Fig. 1.

Table 1 The relationship between σ, process performance, and process capability

Fig. 1

Three, Four, Five, and Six Sigma processes for our laboratory example

The challenge of the Six Sigma methodology is to utilize a set of quality and management tools, through a systematic process, to improve key operational and business processes so they achieve 6-σ performances for key process indicators/metrics. Table 2 provides examples of 6-σ performances for selected processes.

Table 2 Examples of 6-σ performances

According to Mikel Harry and Richard Schroeder, each sigma improvement in a business process (e.g., moving from a 5-σ to 6-σ) translates into about “10% net income improvement, a 20% margin improvement, and a 10–30% capital reduction” (Harry and Schroeder 2000). This is supported with several success stories such as:

• By 1998, AlliedSignal saved $1.5 Billion from implementing its 6-σ program in 1994.

• By 1998, GE realized from initiating 6-σ programs in 1996 the following gains:

  • Revenues rose 11%.

  • Earnings rose 13%.

  • Working capital turns rose to 9.2% from 7.2% in 1997.

2.1 DMAIC Cycle

Six Sigma methodology is basically including five steps. They are definition, measure, analysis, improve, and control (DMAIC). The systematic improvement methodology has been successfully approved in solution of forging defects, achieved lower costs, and met customer requirements.

The DMAIC problem-solving methodology and the associated tools and training to support the methodology have evolved over the past 20 years to become a set of powerful, robust, and widely adopted practices. The methodology was specifically developed to help teams get root-cause problem solving more efficiently. The DMAIC (McCarty et al. 2011) problem-solving methodology (Fig. 2) was developed to help teams answer five key questions with regard to any problem:

Fig. 2

DMAIC processes

2.1.1 Define

The purpose of the define phase is to identify and/or validate the project opportunity, develop the process that will drive the green initiative, define critical stakeholder requirements, and prepare team members to act as an effective project team. This focused session has the effect of pulling the team together around a common understanding of the green problem that they are trying to solve and the goals and objectives that they share. Key activities of the define phase (Table 3) include the following:

Table 3 Define phase

• Validate/identify the green improvement opportunity.

• Validate/develop the team charter.

• Identify and map processes.

• Identify quick wins, and refine the work process.

• Gather expectations of various stakeholders and convert those expectations into critical project requirements.

• Develop team guidelines and ground rules.

This activity helps to get the team excited about the potential for the project and motivated team members to set an aggressive work plan and agree on team norms. With its define workshop completed, the team was ready to move into the measure phase.

2.1.2 Measure

In the measure phase, teams determine what they should measure and what techniques and tools they can use to conduct the measurement and data collection, and then they review methods for ensuring that their measurement process is valid and accurate. Once the measurement plan is in place, the measure phase continues as the measurement and data collection take place. Data collection continues until the team finds that it has a statistically valid sample size from which to conduct valid data analysis. Typical activities during the measure phase (Table 4) include the following:

Table 4 The measure phase

• Determine process performance.

• Identify input, process, and output indicators.

• Develop operational definitions and a measurement plan.

• Plot and analyze data.

• Determine if special causes exist.

• Collect other baseline performance data.

2.1.3 Analyze

The purpose of the analyze phase is to provide teams with the techniques and tools they need to stratify and analyze the data collected during the measure phase in order to identify a specific problem (root cause) and create an easily understood problem statement. When teams reach a point in which they want to analyze available data, they are confronted with two potential failure modes. These failure modes are either a lack of relevant data or too much data and an inability to determine how to analyze those data in ways that will lead to relevant conclusions aligned with the problem the team is trying to solve. Teams typically follow a process of first creating a problem statement or hypothesis of what the problem is (e.g., “Lighting is the number one source of energy loss in this data center”). Then teams use data-stratification techniques, comparative analysis, and regression analysis to either prove or disprove the hypothesis. Teams will run through a number of hypothesis statements and the associated analysis until they can statistically prove that they have identified the sources of variation that are the most valid root causes of the problem. The list of activities and techniques employed by teams in the analyze phase (Table 5) typically could include the following:

Table 5 The analyze phase

• Development of the problem statement.

• Stratification of the data.

• Comparative analysis of multiple data sets.

• Performing sources-of-variation studies.

• Analysis of failure modes and effects.

• Regression analysis to determine the strongest correlations with the problem statement.

• Identification of root causes.

• Design of root-cause verification analysis.

• Validation of root causes.

• Design of experimental studies to statistically prove the root cause.

2.1.4 Improve

The purpose of the improve phase is to enable teams to identify, evaluate, and select the right improvement solutions and then to develop a change-management approach to assist the organization in adapting to the changes introduced through solution implementation. The typical sequence of activities during the improve phase (Table 6) is as follows:

Table 6 The improve phase

• Generate solution ideas.

• Determine solution impacts and benefits.

• Evaluate and select solutions.

• Develop the process map and high-level plan.

• Develop financial analysis and the business case.

• Develop and present the solution storyboard.

• Develop the change-management plan.

• Communicate the solution to all stakeholders.

2.1.5 Control

The purpose of the control phase is to help teams understand the importance of planning and executing against the plan and to determine the approach to be taken to ensure achievement of the targeted results. The control phase also helps teams to understand how to disseminate lessons learned, to identify replication and standardization opportunities processes, and to develop related plans. Most important, the control phase forces teams to think through strategies so that identified benefits and financial impacts actually will be realized when the solution is fully implemented and institutionalized. It also will ensure that the solution will deliver results over a long period of time.

Typical activities that occur during the control phase (Table 7) are as follows:

Table 7 The control phase

• Develop the pilot plan.

• Conduct and monitor the pilot.

• Verify reduction in root causes resulting from the solution.

• Identify whether additional solutions are necessary to achieve goal.

• Identify and develop replication and standardization opportunities.

• Integrate and manage solutions into the daily work processes.

• Integrate lessons learned.

• Identify the team’s next steps and plans for remaining opportunities.

In summary, the DMAIC problem-solving methodology, as well as the associated tools and training to support the methodology, is a powerful, robust, and widely adopted set of practices designed to improve the success rate of problem-solving teams. The methodology was developed specifically to help teams get to root-cause problem solving more efficiently and with greater consistency and repeatability across teams. This overview was developed to help the reader gain an appreciation for how the methodology can be applied in the green project team arena and encourage team members to learn the methodology and supporting tools.

While the DMAIC methodology provides teams with the process and tools required, that methodology is not sufficient to ensure that the solutions developed will achieve any level of organizational acceptance and adoption. Throughout a sustainability initiative, the leadership team must implement solid change-management strategies to ensure that the team remains committed, the overall organization understands and supports the sustainability objectives, and the organization therefore is ready to support adoption of the green project team’s solutions.

3 Case Study I: Reduce Excess Oxygen in Plant X

In this section the application of the DMAIC cycle to reduce the excess oxygen in plant X (Fig. 3) is explained.

Fig. 3

Plant X, six boilers

3.1 Define Phase

In this phase the problems of excess oxygen of six boilers in plant X is examined. It is observed that there are some essential problems of the current system: the percentage of excess oxygen which leads to high cost and indirect pollution. The system structure is believed to be convenient for Six Sigma approach and DMAIC cycle. Additionally in this phase, we must define the defect, opportunity, expected annual savings, the objective, and the project plan:

• Defect: Any day for any boiler (B1, B2, B3, and B4) average excess O₂  >  4% and B5 and B6 O₂  >  4.5%

• Opportunity: Average reading of 66% of excess O₂ reading  >4.0% for the 4 boilers and 4.5% for the remaining 2

• Objective: Reducing 70% of existing defect, i.e., reduce excess O₂  % for (B1, B2, B3, and B4) ≤ 4.0% and B5 and B6 O₂ ≤ 4.5%

• Annual savings: 148.300 $/year

Project plan (Fig. 4):

Fig. 4

Project plan

3.2 Measure Phase

For measure phase, one has to measure the right process and in the right time. It is so important for latter phases of the project. So the oxygen excess percentage in the boilers has been analyzed and relevant times are measured.

The current measure of the oxygen average excess for last 3 years (2008–2011) is given in the following chart (Fig. 5), and the current 6-Sigma calculation is given in Fig. 6:

Fig. 5

Oxygen average excess

Fig. 6

Current 6-Sigma calculation

DPMO: In process improvement efforts, a defect per million opportunities or DPMO is a measure of process performance.

3.3 Analyze Phase

After it is decided that correct and enough data is collected, the analyze phase has begun. During the analysis of the data, it is determined that there are five main root causes (RC) that affect directly the oxygen excess problem:

• 1. Control parameter not connected to APC (Air Pollution Control) system and manual most of the time (67%) (Fig. 7).

  Fig. 7

  

  APC manual most time

• 2. No close follow-up and supervision: Based on the survey results: 50% of surveyed operators confirmed lack of adequate follow-up.

• 3. O₂ analyzer reading not matching with lab analysis:

  • Operator leaves O₂ in excess.

  • Operator does not take action to reduce O₂.

  • Operator does not refer to analyzer.

  • Operator does not trust analyzer reading.

  • Lab analysis does not match analyzer reading.

• 4. Operators not aware of excess O₂ operating limits: Based on survey results: 40% of surveyed operators answered correctly.

• 5. B2 working below the low air pressure alarm: Low combustion air pressure alarm was set at 100 mm-W.G. Most of the time, operations were done while the alarm was on.

3.4 Improve Phase

In improve phase, relevant solutions are investigated. While searching for solutions, their applicability is also taken into account. Additionally, its cost should be low (Figs. 8, 9, 10, and 11).

Fig. 8

1st and 2nd root-cause solutions

Fig. 9

3rd root-cause solution

Fig. 10

4th root-cause solution

Fig. 11

5th root-cause solution

Improvement result (Fig. 12) and Six Sigma before and after (Fig. 13):

Fig. 12

Result of the improvement

Fig. 13

Six Sigma calculation before and after

3.5 Control Phase

The control phase is applied where the changes are indeed valid in the reduction of oxygen excess. Therefore, the O₂ excess percentage is being examined continually. In this phase, we should propose a control plan (Fig. 14).

Fig. 14

Control plan

4 Conclusion

Consumers, regulators, and shareholders are all clamoring for sustainability. With the public’s growing environmental awareness, consumers are actively seeking “greener” options. Regulators and legislators are changing the landscape for environmental reporting, compliance, and transparency. Shareholders and investors have made environmental and social performance a top consideration. At the same time, many environmentalists claim that cutting greenhouse gases, reducing waste, increasing recycling, and broadly shrinking a company’s “impact footprint” will reduce costs.

The sustainability imperative is growing, but along with it comes the recognition that improving sustainability is more difficult than some companies hoped – and many environmentalists would admit. However, by broadening Lean Six Sigma to include sustainability goals, companies can leverage a powerful and well-established performance improvement methodology to jump-start new sustainability programs or substantially boost existing ones. In this way, companies may well be able to marry together the critical goals of being good corporate citizens while improving their bottom line.

In this chapter, we study the applicability of Six Sigma concept to sustainable project. The studied case study shows a remarkable improvement to sustainability.