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Identification and Determination of the Desired QET in Leadership Processes

These processes divided into relevant sub-processes 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 x² (К-Square) is obtained using the following formula:

TABLE 3.11

Productivity and Sustainability in Processes

Processes

Sub-processes

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%	50 to 75%	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%	25 to 50%	50 to 75%	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 x² 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 six-month periods as treatment data and subsidiary units as sources of disturbance data. The results of two-way 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

Two-Way 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
^Y0i	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, Y₀j 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

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