**5. Conclusion**

Nowadays, the measurement of accessibility and availability of IT services for IT administrators and also consideration of their associate costs are essential factors for future-oriented decisions from the capability of the system based on user expectation to accurate investment in the field. Accurate investment in IT is complicated for some IT administrators based on the number of current services in their organization. Identification of new artificial intelligence methods will help them to know the level of effectiveness of IT services in their organization in each period. In the present study, to prove and verify proposed prediction framework, MATLAB fuzzy logic toolbox is used.

This tool provides us with ANFIS as a selected learning technique to present and develop the model in two phases: first phase is quantifying the level of IT services' availability (six variables), and second phase is prediction of their maintenance cost of system failure by finding of six variables' measurement of services. To prevent any complexity for data collection resulting from large number of services, automation system placed in system maintenance department of study organization was selected as a user-involved and widely used system to find out indicators, affective reasons, and implementation of the model. And also this department helps to estimate lost maintenance costs and find its effective indicators. Regarding the result of this study, ANFIS can predict well with lowest fault and near to real data. It is kind of effective, new, practical technique than others with precise prediction.

**111**

specific periods.

**Author details**

Leila Moradi1

Qazvin, Iran

**Table 3.**

*Output of ANFIS training.*

*ANFIS to Quantify Maintenance Cost of IT Services in Telecommunication Company*

As for future work, (1) the model can be developed as a basic suggestive model to measure other kinds of cost-related service quantification; (2) it is practical for the understudy organization to develop a management module in monitoring systems by combination of both models' targets discussed in the article. It provides managers with views to check constantly the status of the system and the reasons of increased cost to be applied in future decisions; (3) more advanced method can be involved in the experiment as regression; and (4) extensive experiment on more

**Table 3** shows the outputs of the model with respect to forecasting of cost and quantifying IT services studied in automation services in the organization in

variables and attributes can contribute to more realization.

\* and Reza Ehteshamrasi<sup>2</sup>

\*Address all correspondence to: Moradi.lyla@gmail.com

provided the original work is properly cited.

1 University of Science and Research (Islamic Azad), Tehran, Iran

2 Department of Industrial Management, Qazvin Branch, Islamic Azad University,

© 2019 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium,

*DOI: http://dx.doi.org/10.5772/intechopen.82827*

*ANFIS to Quantify Maintenance Cost of IT Services in Telecommunication Company DOI: http://dx.doi.org/10.5772/intechopen.82827*


#### **Table 3.** *Output of ANFIS training.*

*Maintenance Management*

*Output table of the model in four times of testing.*

**4.4 Summary**

**Table 2.**

activity.

**5. Conclusion**

fuzzy logic toolbox is used.

at intervals, and the decrease and drop in speed, have a great effect on increasing the cost of maintenance in the organization studied leading to more work hours and replacing faulty components and increasing the time of support contracts to

The availability of services was determined by the quantitative characteristics of measurable indicators in the organization as inputs to the model. These characteristics were selected after the analysis of the results of the questionnaire with the senior organization's experts and data recorded in the monitoring system of the maintenance unit. The error obtained from actual data comparison with the predicted model data was confirmed by managers and experts in the organization. The variables of measurement indicators in system maintenance cost include human resources, equipment training, and processes and failure rates, in which behavior of each indicator in an organization varies depending on its type of

Nowadays, the measurement of accessibility and availability of IT services for IT administrators and also consideration of their associate costs are essential factors for future-oriented decisions from the capability of the system based on user expectation to accurate investment in the field. Accurate investment in IT is complicated for some IT administrators based on the number of current services in their organization. Identification of new artificial intelligence methods will help them to know the level of effectiveness of IT services in their organization in each period. In the present study, to prove and verify proposed prediction framework, MATLAB

This tool provides us with ANFIS as a selected learning technique to present and develop the model in two phases: first phase is quantifying the level of IT services' availability (six variables), and second phase is prediction of their maintenance cost of system failure by finding of six variables' measurement of services. To prevent any complexity for data collection resulting from large number of services, automation system placed in system maintenance department of study organization was selected as a user-involved and widely used system to find out indicators, affective reasons, and implementation of the model. And also this department helps to estimate lost maintenance costs and find its effective indicators. Regarding the result of this study, ANFIS can predict well with lowest fault and near to real data. It is kind

of effective, new, practical technique than others with precise prediction.

minimize the criticality in the service data shown in (**Table 2**).

**110**

As for future work, (1) the model can be developed as a basic suggestive model to measure other kinds of cost-related service quantification; (2) it is practical for the understudy organization to develop a management module in monitoring systems by combination of both models' targets discussed in the article. It provides managers with views to check constantly the status of the system and the reasons of increased cost to be applied in future decisions; (3) more advanced method can be involved in the experiment as regression; and (4) extensive experiment on more variables and attributes can contribute to more realization.

**Table 3** shows the outputs of the model with respect to forecasting of cost and quantifying IT services studied in automation services in the organization in specific periods.
