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Results of Implementing the ModelTable of Contents:
IntroductionThis chapter presents the results of implementing the model. The most important results attained through executing the proposed model in industrial organizations are as follows:
ZMR Control Charts for Describing the Impacts of Different Units on Productivity and SustainabilityThe Process Capability Indices (PCI) can be defined for a selected QET—for example, in this book, the ZMR control charts are considered to be a strong analytical and graphical tool—where the clustered bar chart for each unit of the selected industry and the relevant details are displayed in Figures 4.1a to 4.1g. It should be noted that these operational tables describe the integrated objectives in the form of whole operations that each section must accomplish. In other words, these applied tables contain more details where the subprocesses and related indices are introduced. One of the most important and innovative aspects incorporated in the current research, for the first time, is that the relevant data (in this book, the mean value) of all subunits are controlled by ZMR control charts (statistical process control) for the main data (in this book, the target value). It should be remembered that under these conditions, the benefits of relevant indices are underscored. Hence, the first chart in all of Figures 4.1a to 4.1g includes the ZMR control chart and the second chart in all Figures 4.1a to 4.1g includes relevant C_{pmk }for these indices. In each figure, the unit condition, the number of subobjectives, the related subprocesses, and other descriptions are explained at the top of each section. What we intend to show is that the proposed model functions and covers the productivity and sustainability concepts leading up to added values in organizational processes through related calculations. It is to be remembered that the FIGURE 4.1 В Process capability indices in Quality Assurance Unit. TABLE 4.1 Role of Impact of Each Unit on Productivity and Sustainability
relevant calculations for C_{pm} and C_{pmk} are defined based on the following formulas (Karimi Gavareshki et al„ 2019):
where USL is the upper specification limit and LSL is the lower specification limit. o^{2} is the variance, p is the mean value, and T is the target value. It should be pointed out that in all calculations, the standard deviation is о = 10 by default, the tolerance range in the field of indices is %100 ± %20, the target value (7) in all processes is not equal to the mean value (jj), and the midpoint of technical specification limits is named (M). Generally speaking, the process capability indices are compared; in fact, a couple of indices (C_{p}, C_{pk}) are compared with their respective (C_{pm}, C_{pmk}). The impacts of different units on productivity and sustainability are presented in Table 4.1 (Karimi Gavareshki et al., 2019). Total Score Calculation of Productivity and Sustainability before and after Implementing QETThe total scores for productivity and sustainability—before implementing the selected QET—are usually figured out by means of simple numerical methods found in any books on elementary statistics. However, since there are applications related to different statistical and nonstatistical techniques in QET, in order to find out the total score of productivity and sustainability after implementing the selected QET, we preferred to use Analytic Hierarchy Process (AHP) analysis. In this book, we found the latter score in 6 values (between 1 and 9). The relevant calculations were conducted considering the general conditions of the selected industry. These calculations, together with more details, are provided in Table 4.2. TABLE 4.2 AHP Analysis for Statistical and NonStatistical Techniques in Productivity and Sustainability AHP analysis for statistical and nonstatistical techniques in productivity and sustainability
It can be seen that, the total score for productivity and sustainability after implementing the selected QET equals 6.00. The following relevant formulas can be used in this regard (Karimi Gavareshki et al., 2018): where A, is the average of the statistical techniques and A_{y} is the average of the non statistical techniques. /, is the main index of the statistical techniques and /_{2} is the main index of the nonstatistical techniques.
/_{lk} is the subindex of the statistical techniques and 1_{2}, is the subindex of the non statistical techniques.
7_{lk} is the multiple factors of the statistical techniques and T_{2}, is the multiple factors of the nonstatistical techniques. But, 7, is the total multiple factors of the statistical techniques and 7_{2} is the total multiple factors of the nonstatistical techniques.
In order to calculate the total score of productivity and sustainability (7), the following formula can be used: 
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