are also interested in determining the reliability of that commitment. Global competition puts so much pressure on management that they must make quality their top priority for the company. Managers can no longer satisfy employees, customers, or investors with just encouraging words: they must show solid outcomes, such as how much sales have gone up, how many new customers have been attracted, and the retention rate of old customers. All of this will fall in line only if management has made quality an important ongoing process.
Furthermore, management must understand that the customer defines quality, so management must make commitments to customers about the increased quality and value of the company’s products. In addition, management should understand that it is the employees who build the quality into products. Thus, management must make commitments to employees and give them the resources and tools they need. Management must also obtain the same kind of commitments from suppliers. Any sloppiness on the part of suppliers can ruin all the plans for ongoing process improvement or quality improvement. In the modern economic age, only companies and managements that make such commitments and follow through on them can assure themselves a bright future, job guarantees, and better compensation for their employees.
Management and quality are a two‐way street. Any company with good management delivers better quality, and having better quality means there is good management.
1.4.3 Risks Associated with Making Bad Decisions
It is important to note that whenever decisions are made based on samples, you risk making bad decisions. Bad decisions in practice lead to difficulties and problems for producers as well as consumers. These bad decisions in statistical terms are referred to as type I and type II errors as well as alpha (α) and beta (β) risks, respectively. It is important to know the following key points about these risks:
Sooner or later, a bad decision will be made.
The risks associated with making bad decisions are quantified in probabilistic terms.
α and β risks added together do not equal 1.
Even though α and β go in the opposite direction (that is, if α increases, β decreases), there is no direct relationship between α and β.
The values of α and β can be kept as low as you want by increasing the sample size.
Figure 1.3 Detecting practical and statistical differences.
Definition 1.2
Producer risk is the risk of failing to pass a product or service delivery transaction on to a customer when, in fact, the product or service delivery transaction meets customer quality expectations. The probability of making a producer risk error is quantified in terms of α.
Definition 1.3
Consumer risk is the risk of passing a product or service delivery transaction on to a customer under the assumption that the product or service delivery transaction meets customer quality expectations when, in fact, the product or service delivery is defective or unsatisfactory. The probability of making a consumer risk error is quantified in terms of β.
In Figure 1.3, our comparison points change from the shaded region under the distribution tails to the center of the distribution. A practical decision then requires that we consider how far off the intended target the observed process behavior is as compared with the statistical difference identified in Figure 1.3. Note that differentiating between a practical and statistical difference is a business or financial decision. When making a practical versus a statistical decision, we may well be able to detect a statistical difference; however, it may not be cost‐effective or financially worth making the process improvement being considered.
1.5 Conclusion
In this chapter, we have given a general overview of quality improvement and its management. For more details on these topics, we refer you to the works of Philip B. Cosby, W. Edwards Deming, Joseph M. Juran, and Armand V. Feigenbaum (see the Bibliography). Also, the Six Sigma methodology, which we introduce in the next chapter, is a step forward to help achieve and manage quality improvement, since understanding the idea of Six Sigma means customer requirements must be met. In the remainder of this book, we discuss statistical techniques and SPC tools that are essential to implementing the Six Sigma methodology for improving process performance.
2 Basic Concepts of the Six Sigma Methodology
2.1 Introduction
Far from being just another quality fad, Six Sigma has continued to grow in popularity and influence since its creation at Motorola in 1986. Six Sigma techniques have been adopted by a wide range of manufacturing firms and have also translated successfully into other sectors, including retail, hospitality, financial services, high tech, transportation, government, and healthcare. According to the American Society for Quality, as of 2009, 82 of the largest 100 companies in the US had deployed Six Sigma. Fifty‐three percent of Fortune 500 companies have adopted Six Sigma practices, saving an estimated $427 billion over the past 20 years [1]. In a broad sense, it can be said that if a company has customers and a process, it can benefit from implementing Six Sigma.
2.2 What Is Six Sigma?
In statistics, the lowercase Greek letter sigma (σ) represents the population standard deviation, which is a measure of variation or spread. In the quality field, we aim to reduce the process standard deviation to achieve more consistent outcomes. Whether we are measuring the dimension of a metal flange, inner diameter of a pipe, burst strength of a package, monthly labor costs for a division, or repair time of a subassembly, a repeatable process is the desired state.
If we were limited to a single‐sentence elevator speech to describe Six Sigma, a reasonable definition might be “Six Sigma is a quality approach that strives to reduce variation and decrease defects, which results in increased customer satisfaction and improved bottom‐line results.” If we dig a little deeper, however, it becomes clear that there are at least three different definitions of the term Six Sigma. Six Sigma can be correctly classified as a management philosophy; it is also defined as a systematic approach to problem‐solving. Last, Six Sigma is a term used for a statistical standard of quality. Let’s explore each of these definitions in more detail.
2.2.1 Six Sigma as a Management Philosophy
Six Sigma is a management philosophy that emphasizes reducing variation, driving down defect rates, and improving customer satisfaction. Specifically, the tenets of the philosophy include:
Enterprise‐wide deployment of Six Sigma to improve processes
Implementation driven from the top down
Process improvements achieved through projects completed by teams
