**3.2 Assessment of the weight of the component parts of internal logistics by companies**

To evaluate the weight of each component part of the Internal Logistics were sent a survey to 93 companies to analyze them and to attribute a weight of importance in a Likert scale of 1–5 where 1 was minor and five very important according to the particularity and priority that represents the component parts for the aforementioned companies. In **Table 4** there are offered the results of three of the companies investigated.

It was found that depending on the company and its respective sector, the priorities and the degree of importance may be subject to change and therefore affect the performance of internal logistics index.

The maximum score that each company can get is 65 points, which is the result of the multiplication of the 13 items by the maximum value of each item according to the Likert scale. It is noted for example that the company 1 attributed a very low note for the items: Storage, WIP and internal transport, while companies 2 and 3 attributed notes 5, 5 and 4 respectively for these same items, therefore, it follows which depending on the sector and type of production, whether continuous or discrete, the degree of importance may change. An arithmetic mean of the 3 companies was also developed in this tabulation and it was appreciated that from the maximum possible score of 65 points, company 1 scored 35 points, followed by 61 points by the company 2 and finally the company 3 with 59 points, and the arithmetic mean was 51.67 points.

#### **3.3 Evaluation of the internal logistics index by companies using excel solver**

Based on the literature investigated was developed the structure of diagnostic model of the component parts of the internal logistics, its filling, testing and subsequent validation. They were developed 10 questions to assess each property and was conducted a survey in different companies. These questions were developed based on the literature review, the survey results according to the criteria of specialists of logistics management, and consulting and business managers. It was developed an Excel tab to evaluate the performance of each of the component parts of the internal logistics as well as the Internal Logistics Index of a company.


#### **Table 3.** *Firms demographics: Industry and size.*

For demonstrating that a process has been improved, it is necessary to measure the process competence before and after improvements are implemented. This permits to measure the process improvement (e.g., defect reduction or productivity growth) and translate the effects into a projected financial result – something that corporate leaders can understand and appreciate. Determining sample dimension is a vital topic because samples that are too large may waste time, resources and money, while samples that are too small may lead to inaccurate results.

*Operations Management - Emerging Trend in the Digital Era*

In the case of the industrial pole of Manaus, it is composed for 565 companies, and using the formulation expressed in [92], the number of companies to be considered for a good statistical representation has to be more than 60 companies.

Analyzing the sectors of the Industrial Pole of Manaus, it was possible to identify

the components to assess the internal logistics. They were redesigned through interactions with business professionals from different companies in order to obtain the greatest possible standardization of component parts of internal logistics. **Figure 2** shows these parts. From this picture can be observed that there are component parts that have to do with the physical flow and other with the

Each component part of the figure above was evaluated by 10 properties or pertinent questions reflecting the respective training component behavior for performance, supported by Likert scale of 1 to 5, with 1 indicating little or no adhesion and 5 full adhesions between the question versus practice where each part can reach a maximum of 50 points is that the resulting properties of 10 x 5 points, and a total

The questionnaire applied in enterprises, medium and large, the following segments: electronics, appliances, components and two wheels. In March 2015, 539

of 130 questions as a result of the 13 component parts of 10 questions each.

information flow.

**Figure 2.**

**132**

*Component parts of the internal logistics. Source: Authors (2020).*


#### **Table 4.**

*Answers from the companies on the degree of importance of the elements of internal logistics.*

The Excel Tab developed to calculate the Internal Logistic Index was based on the following equations:

$$ILI = \sum\_{i=1}^{13} \left[ \left( \frac{Z\_i}{100} \right) . W\_i \right] \tag{1}$$

**3.4 Assessment of internal logistics through fuzzy logic**

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

adapt easily to changing parameters of imprecision [94].

fuzzy inference of Mamdani type were [97, 98]:

shown therein. A letter from A to B. defines each group.

*Fuzzy system for asses the internal logistics. Source: Authors (2020).*

Assessing the Internal Logistic Index of a Company is a very complex task due in some case to the lack of information and in other cases to the excess of information for decision-making. This leads to difficulty in defining, measuring and monitoring of objectives and targets to set rates compliance associated with measuring the performance of the Internal Logistics [93]. In response to these challenges of business management there have been emerged theories, approaches and methodologies (flexibility, resilience, etc.) using tools such as fuzzy logic for reliable solutions that

In addition to the treatment of imprecise environments, another emerging challenge is to achieve that the measurement of organizational performance transcends the traditional financial approach and to be conducted throughout with suitable means to new generations of applications in the management of internal logistics.

A fuzzy inference method allows deriving conclusions (a fuzzy value) from a set of if-then rules and a set of input values to the system, by applying composition ratios. The two inference methods commonly used are the Mamdani introduced by Mamdani and Assilian [95] and the TSK (Takagi-Sugeno-Kang) proposed by Takagi and Sugeno [96]. The main difference between these methods is the consequent type of the fuzzy rule. The systems Mamdani type use fuzzy sets as consistent rule and TSK used linear functions of the input variables with discrete data outputs. In this research the type Mamdani inference system (**Figure 3**) with outputs continuous values is used.

To facilitate the modeling of the problem in fuzzy logic, it was used the Fuzzy Logic Toolbox ™ of MATLAB software. The steps for formulating the model of

Performance measurement of the internal logistics can be based on the selection and definition of indicators used to evaluate the efficiency and effectiveness of its operations. Indicators should have a holistic approach and facilitate the implementation of initiatives for improvement. Indicators selected for the proposed fuzzy model to measure the performance of the Internal Logistics of the company studied are described in **Table 5**. The components were grouped into larger groups as

*3.4.1 The fuzzy logic and internal logistics*

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

*3.4.2 Method of fuzzy inference*

*3.4.3 Selection of indicators*

**Figure 3.**

**135**

where ILI = General index of the performance of the Internal Logistics; *Wi* = Weight attributed to each component part *i*; *i* = each of the properties analyzed; *Zi* = value reached in % for the property i based on the sum of all values given to each parameter of the corresponding property of the Likert scale from 1 to 5 and divided by the maximum possible value to reach in% i.e.:

$$Z\_i = \sum\_{j=1}^{10} \left(\frac{P\_j L\_j}{50}\right).100\tag{2}$$

where *P <sup>j</sup>* = Each of the parameters that assess the *Zi* property (always it going to assume the value 1 in the above expression); *Lj* = Value assigned to the parameter *P <sup>j</sup>* at the Likert scale from 1 a 5.
