Green Process Management using Six Sigma Concepts

Green Process Management using Six Sigma Concepts

Seifedine Kadry (American University of the Middle East, Kuwait)
DOI: 10.4018/978-1-4666-3658-3.ch003
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The Six Sigma () 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. Six sigma is a powerful execution engine and 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.
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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 & 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 & 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 & 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. This introduction was followed by the well-revealed implementation of six sigma at General Electric beginning in 1995 (Slater, et al., 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 process yields fewer defects than a 3, 4, or 5 processes. 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 Figure 1 provide further resolution of the riddle involving the relationship between value and process performance and capability. The associated assumed process distributions in Table 1 are used to construct Figure 1.

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