Results of Implementing the Model

Introduction

This chapter presents the results of implementing the model. The most important results attained through executing the proposed model in industrial organizations are as follows:

• • Augmenting of productivity and sustainability
• • Creation of added values in organizational processes
• • Identification of risks and opportunities in organizational processes
• • Knowledge of the method to assess the components of industrial economics via statistical and non-statistical means of the quality engineering techniques (QET) model.

Z-MR Control Charts for Describing the Impacts of Different Units on Productivity and Sustainability

The Process Capability Indices (PCI) can be defined for a selected QET—for example, in this book, the Z-MR 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 sub-processes 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 Z-MR 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 Z-MR 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 sub-objectives, the related sub-processes, 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. o² 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 QET

The 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 non-statistical 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 Non-Statistical Techniques in Productivity and Sustainability

AHP analysis for statistical and non-statistical techniques in productivity and sustainability

Standard statistical tools based on IS010017	Average	1, (Index)	Sub-index	T,
Descriptive statistics	5.58	0.83	Im 0.08	T,„ 0.46
Design of experiments (DOE)	5.92		1,12 0.09	T, 12 0.52
Statistical process control (SPC)	7.25		I,n 0.11	T, и 0.78
Statistical hypothesis test	6.58		1,14 0.10	T„4 0.64
Process capability analysis	5.42		I, l5 0.08	T, ,5 0.43
Statistical tolerances	4.75		I„6 0.07	T„6 0.33
Time series analysis	6.67		I„7 0.10	TM7 0.66
Regression analysis	6.25		I„S 0.09	T„s 0.58
Reliability analysis	7.25		I,,» 0.11	T,„ 0.78
Simulation	5.15		1,20 0.08	Тис 0.39
Sampling	6.82		I,21 0.10	T,2| 0.69
Total average	6.15		Sum 1.00	Sum 6.26
Non-statistical tools	Average	l2(lndex)	Sub-index	T2
Quality function deployment (QFD)	5.00	0.17	I2, 0.35	T21 1.74
Value engineering (VE)	4.75		122 0.33	T22 1.57
Value stream mapping (VSM)	4.58		I,, 0.32	T,3 1.46
Workflow analysis (WFA)	4.12		I24 0.29	T26 118
Cost of quality (COQ)	3.98		I2S 0.28	T,7 1.11
Failure mode effects analysis (FMEA)	6.15		I26 0.43	T,8 2.64
Designing failure mode effects analysis (DFMEA)	5.75		I27 0.40	T2, 2.31
Production failure mode effects analysis (PFMEA)	4.65		Ij* 0.32	T30 1.51
Total average	4.87	1.00	Sum 1.00	Sum 4.78
Sum
T= Total score of productivity and sustainability (between 1 and 9)				6.00

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 non-statistical techniques.

/_lk is the sub-index of the statistical techniques and 1₂, is the sub-index of the non- statistical techniques.

7_lk is the multiple factors of the statistical techniques and T₂, is the multiple factors of the non-statistical techniques. But, 7, is the total multiple factors of the statistical techniques and 7₂ is the total multiple factors of the non-statistical techniques.

In order to calculate the total score of productivity and sustainability (7), the following formula can be used: