# A Review of the Basic Concepts

## Introduction

In the present century, quality engineering techniques have turned into applicable and effective tools for attaining advanced design and manufacturing technology as well as mass-production processes. The rationale behind these techniques from the very beginning was to help mass-production lines. It was only later that such methods were developed into useful instruments for other activities in the organization (both for pre-production activities such as product design, and for subsequent activities like after-sales services. Appropriate techniques are developed depending on the type of organization. The evolution path for quality engineering techniques (QET) (passing from low to high quality) has occurred synchronously with development in manufacturing lines. Various techniques have been developed and applied at each stage of the formation of a product (depending on the organization). However, it is important to note that most of the techniques are based on systematic processes, i.e., fewer inputs for converting qualitative outputs into quantitative ones. The result is that these techniques can establish a safe platform for decision making. That is to say, although these techniques can be applied individually, the logical nature is that when they are applied one after another, they act as reinforcements and exhibit double effects. The importance of the functional role of statistical techniques as a main core of QET for robust analysis of the data related to the indices of the strategic issues of quality management systems cannot be easily overlooked. The World Organization for Standardization, through one of its subcommittees, has shed light on identifying statistical techniques. This informative manual appears in two editions, in 1999 and 2003, where it is officially designated as ISO10017 which applies to all standards in the IS09000 family, especially to those in ISO9001. This standard is a very useful tool in the identification of statistical techniques in the deployment, maintenance, improvement, and development of quality management systems. Statistical techniques as a mathematical tool in quality engineering play a crucial role in measuring, describing, analyzing, interpreting, and modeling system changes even with limited data. Statistical analyses in data can help us understand the extent and causes of changes. Hence, statistical techniques can prove beneficial in exploiting available data to help with decision making and to continuously improve the quality of products and processes, eventually improving customer satisfaction which is the most important goal of the organization. These techniques can be applied to an extensive range of activities such as market research, design, development, production, verification, and servicing.

## The History of Research in QET

Statistical control charts were employed by Shewhart for the first time in 1920. However, the relevant sciences flourished in the years between the two world wars. Later years witnessed the use of statistical quality control techniques in manufacturing military equipment. In the years after the w'ar, i.e., in the 1950s and 1960s, the experiments, designs, and analysis techniques were also used. This time not only the military industries, but also the major automobile companies and their part makers turned to these sciences. Major companies like General Electric, General Motors, and Motorola provided a new' era for testing these statistical techniques and even resorted to other engineering techniques that did not require strong statistical bases (non-statistical techniques) (Rezaei, 2001). In the field of statistical and non-statistical techniques, a great number of studies have been carried out whose implementation records can be seen in numerous references. It seems that a perfect application grounds for designing and implementing of QET can be realized in the defense sector where widespread and consistent processes are observable at three levels of processes: namely, main, leadership, and support procedures. One particular study discusses the advantage of applying statistical and non-statistical techniques related to quality engineering in the form of an integrated model for creating productivity and sustainability in the main domains of industrial engineering associated with manufacturing factories. (Karimi Gavareshki et al., 2018). The popular statistical techniques based on ISO10017 and non-statistical techniques are presented in Figure 1.1.

Figure 1.2 presents another categorization for QET.

### The History of Research in Statistical Techniques

The family of statistical techniques that can help an organization achieve its objectives is as follows: (IS010017:2003)

A. Descriptive statistics

B. Design and analysis of experiments

C. Statistical hypothesis tests

D. Process capability analysis

E. Regression analysis

F. Reliability analysis

G. Sampling

H. Simulation

I. Statistical process control charts

J. Statistical tolerances

K. Time series analysis