Identification and Determination of the Desired QET in Leadership Processes
These processes divided into relevant subprocesses based on the suggested QET are presented in Figure 3.14.
FIGURE 3.14 Desired QET in leadership processes.
Note that in our total process map, the productivity and sustainability processes are not independent entities. This interdependence is presented in Table 3.11.
Numerical Application of QET in the Determined Scope for Leadership Processes
• Processes Assessment: General Status (Statistical Hypothesis Tests)
The related indices of the selected industry in DIO are divided into three categories: the support, the main, and the leadership indices. To ascertain a confidence level of 95%, the performances of these processes indices are assessed as being independent or not. To that end, first, the performance table for the related industry with practical frequencies in different departments is set according to Table 3.12. Then, the performance table for the same industry with theoretical frequencies in the same departments is set according to Table 3.13.
The distribution function of x^{2} (КSquare) is obtained using the following formula:
TABLE 3.11
Productivity and Sustainability in Processes
Processes 
Subprocesses 
Productivity management 
Suppliers’ assessment. Production productivity. Production control. Establishment of Information Management System (IMS). Industrial psychology. Work health. Work hygiene. Work safety. General trainings. Skills optimization. Knowledge optimization. 
Sustainability management 
Production sustainability. Product designing. Environmental hygiene (water + electricity + fuel) & (noise + air + pollutants). Mean time to failure (MTTF). Mean time to repair (MTTR). Availability (AV). Failure rate [R(t)]. Total productive maintenance (TPM). 
TABLE 3.12
Performance of Indices in Selected Industry in DIO (Practical Frequency)
N 
Department name 
Oto 25% 
25 to 50% 

75 to 100% 
4 
1 
Top management 
0 
0 
0 
1 
1 
2 
Quality assurance 
1 
2 
5 
10 
17 
3 
Material and product planning 
1 
1 
4 
22 
28 
4 
Internal and external business 
1 
2 
4 
16 
23 
5 
Research and development 
0 
1 
2 
5 
8 
6 
Financial and economics 
0 
0 
2 
4 
6 
7 
Human resources 
1 
0 
4 
13 
18 
8 
warehouses management 
0 
1 
4 
0 
5 
9 
Safety and health 
1 
3 
5 
15 
24 
10 
Manufacturing groups 
2 
2 
8 
10 
22 
N, 
6 
12 
38 
96 
152 
In this formula, Fe_{:j} is the theoretical frequency in the (t) row and (j) column and Fo_{t) }is the actual frequency in the (/) row and (j) column. It should be noted that df is the degree of freedom; and the related formula df= (m  l)(n  1) is obtained when m is the number of rows in the table and n is the number of columns in the table. However, a is also the error or uncertainty. Moreover, we have:
In this formula, Fe_{{j} is the theoretical frequency in the (i) row and (j) column. N, is the frequency in the (i) row and Nj is the frequency in the (j) column. N is the total number of frequencies.
TABLE 3.13
Performance of Indices in Selected Industry in DIO (Theoretical Frequency)
N 
Department name 
Oto 25% 


75 to 100% 
N, 
1 
Top management 
0 
0 
0 
1 
1 
2 
Quality assurance 
1 
1 
4 
11 
17 
3 
Material and product planning 
1 
2 
7 
18 
28 
4 
Internal and external business 
1 
2 
6 
14 
23 
5 
Research and development 
0 
1 
2 
5 
8 
6 
Financial and economics 
0 
0 
2 
4 
6 
7 
Human resources 
1 
1 
5 
11 
18 
8 
Warehouses management 
0 
0 
1 
3 
4 
9 
Health and safety 
1 
2 
6 
15 
24 
10 
Manufacturing groups 
1 
2 
5 
14 
22 
N, 
6 
11 
40 
96 
The test of the statistical independence x^{2} is as follows:
So, we have:
The calculations of the hypothesis test related to the ^distribution function are as follows:
• Processes Assessment: Special Status (Design of Experiments)
The related indices of the selected industry in DIO are assessed in a classification, based on sixmonth periods as treatment data and subsidiary units as sources of disturbance data. The results of twoway variance analysis of processes performance related to the selected industry in DIO are shown in Table 3.14.
The related formulas and calculations for the numerical application are as follows:
In this formula, is the sum of treatment data, Y_{io} is the sum total related to
(0 row, (a) is the number of treatment states or rows, and (b) is the number of disorder states or columns. So, we have:
Moreover, we have:
In this formula, MS_{treatmem} is the average sum related to the treatment data. Therefore, we have:
TABLE 3.14
TwoWay Variance Analysis of Processes Performance in Selected Industry in DIO
Department name 
Quality assurance 
Material and product planning 
Internal and external business 
Financial and economics 
Human resources 
Health and safety 
Warehouses management 
Y_{io} 
Spring & Summer 
62.71 
87.45 
96.75 
165 
58.43 
69.07 
76.5 
615.91 
Autumn & Winter 
106.2S 
94.64 
119.75 
81.75 
42.31 
96.71 
101 
642.44 
^{Y}0i 
168.99 
182.09 
216.50 
246.75 
100.74 
164.78 
177.50 
1258.35 
In this formula, SS_{block} is the sum of disorder data, Y_{0}j is the sum total related to (j) column, (a) is the number of treatment states or rows, and (b) is the number of disorder states or columns. So, we have:
Moreover, we have:
In this formula, MS_{block} is the average sum related to disorder data. Therefore, we have:
In this formula, SS_{error} is the sum of error data, Y_{;j} is the sum total related to (i) row and (J) column, (a) is the number of treatment states or rows, and (b) is the number of disorder states or columns.
So, we have:
Moreover, we have:
In this formula, MS_{error} is the average sum related to the error data. Therefore, we have:
In these formulas, F_{treatmenl} is the statistical distribution function for treatment and Рыск* *^{s} the statistical distribution function for disturbance. So, we have:
Now, if an interpretative framework is considered for the latter issue, we take a look at the following pattern:
Analysis:
The time period does not make any significant difference
The kind of department does not make any significant difference