**4.1 Application of fuzzy logic to determine the internal logistics index of a company in the industrial pole of Manaus. Case study**

#### *4.1.1 Internal logistics index company case study*

The toolbox of fuzzy logic implemented in MATLAB with the four groups of the diffuse model is shown in **Figure 7**. In **Figure 8** the values of Internal Logistics Index reached according to the input variables are shown. For example, if each group (from A to D) have an average value (5 points), then the Internal Logistics Index reaches a value of 37.4 points. The MATLAB allows vary input values, and consequently modifying the value of Internal Logistics Index.

Another way of demonstrate the results between two groups and the Internal Logistic Index can be analyzed from **Figure 8**, where there are represented the A and B groups with the value of 5 points for each group. The maximal value of the Internal Logistic Index in this case will be of 37.50% as it is shown in **Figure 9**.

It was established a comparative analysis between results of both models: Excel Tab versus Fuzzy Logic. The obtained results of the Internal Logistic Index by using the Excel tab was of 79.17% when assessing the 13 component parts. These

**Figure 7.**

**Figure 6.**

Logistics

*Source: Authors.*

*General internal logistic index of a company.*

**Table 9.**

**140**

*Training and retraining of the ANN. Source: Authors (from MATLAB).*

*Operations Management - Emerging Trend in the Digital Era*

Component elements of the Internal

**Property Performance**

PMC- planning and material control

PP - planning and production control

General Internal Logistic Index 79.17

Receipt 96.00 6.8 6.53

Handling and movement 88.00 5.1 4.49 Picking/packing 90.00 6.8 6.12 Storage 86.00 8.5 7.31 Stocks management 86.00 8.5 7.31 Supplying 46.00 8.5 3.91

WIP- working in process 88.00 8.5 7.48 Order processing 88.00 8.5 7.48 Internal transports 90.00 6.8 6.12 Customer support 88.00 8.5 7.48 I. T. information technology 84.00 6.8 5.71

**Percent Weight Points**

94.00 8.5 4.62

92.00 8.5 4.62

*Fuzzy model for evaluating Internal Logistic Index. Source: Authors (from MATLAB).*

**Figure 8.** *Internal Logistics Index according to the input variables. Source: Authors (from MATLAB).*

component parts of the same company were grouped how was cited before in four groups: A, B, C and D, supported by 24 rules developed and applied in the Fuzzy Logic toolbox from MATLAB. Each input variable can reach a value between 0 and 10. If each input variable reach the average value of 5 points, the Internal Logistic

Index will be of 37.50%. Following the same procedure and way of thinking the top possible value of Internal Logistic Index will be of 75%, versus 79.17% obtained by Excel Tab method, demonstrating similarity between the both tools and a precision

*Obtained values of the internal logistics indexes and errors of these values in the 10 companies studied.*

**Company 1 2 3 4 5 6 7 8 9 10** ILI 70.65 75.00 67.37 72.13 78.15 60.03 70.05 65.0 78.0 64.04 Error in % 2.65 0.006 2.62 3.13 0.15 0.03 0.059 0.004 0.009 0.95

*Conceptualization, Definition and Assessment of Internal Logistics through Different…*

**4.2 Application of neural networks to determine the rate of internal logistics of**

The ANN enabled in MATLAB with data values of the 13 Internal Logistic Properties from the 10 companies was processed. The values of the indexes of

In this chapter three approaches and their expressions to assess the internal logistics of a company are established. The first method was based on dividing the internal logistics in 13 properties, having each property 10 indicators that were evaluated between 1 and 5 points. This leads to the maximum value of Internal Logistics Index (ILI) for each company can reach up to 100 points according to the

The second approach was based on the fuzzy logic and the third one was based

When assessing the Internal Logistics Index using the Excel tab developed or by the method of Artificial Neural Networks, very similar values consistent with the reality of the companies studied were obtained, demonstrating the validity of both

The methodological approach developed for the definition of the three models contains all the steps and procedures, allowing replication of the research, which is

When assessing this parameter, using the Excel tab or developed by the method of fuzzy logic, similar values were obtained in line with the reality of the company

The approach through the Fuzzy logic allows assess the rate of internal logistics for any position of the input variables, which can obtain a value between 0 and 10, depending on the appreciation of the user of this procedure. In the case of the

as important as the application of the developed models at the companies.

company studied, specialists gave more emphasis to groups A and C.

analyzed, indicating the rationality of both methods.

Validation errors of the neural network are shown in **Figure 10**.

on the order of 95% of the results.

*Source: Authors (from MATLAB).*

*DOI: http://dx.doi.org/10.5772/intechopen.94718*

**Table 10.**

weight stablished for each indicator.

**5. Conclusions**

on neural networks.

methods.

**143**

**an industrial company. Case studies**

**4.3 Internal logistic indexes of the studied companies**

Internal Logistics as well as their possible are given in **Table 10**.

#### **Figure 9.**

*Surface of values of the internal logistic index according to the input variables. Source: Authors (from MATLAB).*

**Figure 10.** *Validation of the artificial neural network errors. Source: Authors (from MATLAB).*

*Conceptualization, Definition and Assessment of Internal Logistics through Different… DOI: http://dx.doi.org/10.5772/intechopen.94718*


**Table 10.**

component parts of the same company were grouped how was cited before in four groups: A, B, C and D, supported by 24 rules developed and applied in the Fuzzy Logic toolbox from MATLAB. Each input variable can reach a value between 0 and 10. If each input variable reach the average value of 5 points, the Internal Logistic

*Operations Management - Emerging Trend in the Digital Era*

*Surface of values of the internal logistic index according to the input variables. Source: Authors (from*

*Validation of the artificial neural network errors. Source: Authors (from MATLAB).*

**Figure 9.**

*MATLAB).*

**Figure 10.**

**142**

*Obtained values of the internal logistics indexes and errors of these values in the 10 companies studied.*

Index will be of 37.50%. Following the same procedure and way of thinking the top possible value of Internal Logistic Index will be of 75%, versus 79.17% obtained by Excel Tab method, demonstrating similarity between the both tools and a precision on the order of 95% of the results.
