Abstract

Soft Computing Fusion with Applications www.scfa.reapress.com Soft. Comput. Fusion. Appl. Vol. 1 , No. 1 ( 2024 ) 1 ² 9 . Paper Type: Original Article Multi-Criteria Decision-Making Model for Rank Strategy to Overcome Barriers to Integrating the AI and Cloud Systems in the IT Industry

Abstract

This study proposed a decision - making framework for identifying key barriers when implementing Artificial Intelligence (AI) and cloud systems in the Information Technology (IT) industry. Then, it proposed a set of strategies to overcome these barriers. We proposed a Multi - - Making (MCDM) methodology for various criteria. Multiple barriers, such as cost, technology, environment, and digitization, should be analyzed. The Evaluation - based on Distance from Average Solution (EDAS) method is an M CDM method logy used to rank the alternatives. The criteria weights are computed by the average method. This study used ten barriers and ten strategies.

The results show that technological barriers have the highest importance in this study. The sensitivity analysis is conducted to show the stability rank of alternatives. There are eleven cases in which criteria weights are proposed. Then, the EDAS method is used to rank the other options. The results show the stability of the rank under different cases.

Keywords:

Artificial intelligence, Cloud systems, Information technology industry, Barriers, Multi - making, Strategy .

1 Introduction Most businesses depend entirely on Cloud Computing (CC) and Artificial Intelligence (AI) technologies to

safeguard and manage resources. Since every company owner wants to automate their activities, the need for

CC and AI technologies has increased dramatically. Advanced technology-equipped organizations provide great flexibility and sca lability while being relatively easy to administer. With time, people started interacting with different technologies increasingly, becoming dependent on new developments. T he CC industry is

Corresponding Author : aahafeez.scis@o6u.edu.eg https://doi.org/10.22105/scfa.v1i1.17 Licensee System Analytics . This article is an open - access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0). Multi - - « growing daily to achieve automated procedures that aid in improving operations, saving expenses, and

accelerating corporate development [1] , [2] .

According to current predictions, the worldwide public CC industry is expected to rise at a Compound Annual

Growth Rate (CAGR). According to Shift research, eighty-one percent of businesses are using cloud infrastructure, which is expected to increase in t he coming years. This circumstance makes it evident that to compete in today's market, most organizations are becoming digital and introducing cutting-edge CC technology. Information Technology (IT) is thus essential for efficiently arranging corporate str uctures and developing future market strategies [3] , [4] .

With the invention of novel innovations and the rising demand for digital services, the IT industry has seen tremendous growth in recent years. The industry is expected to expand as technology becomes more pervasive

in our everyday lives. Therefore, it is essential that the business closely monitors the factors impacting the [5] , [6] . Multi--Making (MCDM) techniques are simple but dependable instruments for deciding intricate and multifaceted real-world issues [7] , [8] . Using MCDM techniques, decision-makers may compare

and contrast several options based on endless criteria, then choose the option that receives the most incredible

score while considering the requirements. Generally speaking, the development of MCDM tech niques aims to tackle two critical difficulties in decision-making [9] , [10] : 1) determining the significance of the choice

criteria, and 2) ranking or prioritizing a set of options about the requirements. A significant development in

management science and related subjects has been the development of MCDM techniques [11]-[14] . The main contribution of this study: I.

Identify the critical barriers of AI and cloud systems in implementation with IT industry systems.

II. Identify the critical strategies for overcoming these barriers. III. We developed an MCDM methodology named the Evaluation-based on Distance from Average Solution (EDAS) method to rank the alternatives. 2 Evaluation-Based on Distance from Average Solution Method

The EDAS method is an MCDM methodology used to run the alternatives from various options. This study used this method to rank the key strategy to overcome the barriers to implementing AI and cloud systems in

[15]-[18] . Fig. 1 shows the steps of the EDAS MCDM method. Phase 1. Build the decision matrix .

The decision matrix is built between a set of criteria in the key barriers and a set of strategy to overcome the

barriers to implementing AI and cloud systems in the IT industry.

? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? (1)

?? ? ? ?

Phase 2. Compute the average solution . The average solution is computed as:

? ? ?

? ? ( 2 ) Phase 3. Compute the positive and negative distance .

The positive and negative distances from every alternative are computed for each beneficial and non

-beneficial criterion as:

Abdelhafeez and Shawky Aziz | Soft. Comput. Fusion. Appl. 1 ( 1 ) ( 2024 ) 1 - 9

? ? ? ?

? ? ( 3 )

? ?

? ? ? ?

? ? ( 4 )

? ?

? ? ? ?

? ? ( 5 )

? ?

? ? ? ?

? ? ( 6 )

? ?

Phase 4. Compute the criteria weights . Phase 5. Compute the weighted

and

?? .

The weighted positive and negative distance are computed as:

? ?

? ( 7 )

? ? ? ?

? ?

? ( 8 )

? ? ?

Phase 6. Compute the weighted normalized

and

? .

? ? ( 9 ) ?

? ? ( 10 ) ?

Phase 7. Compute the appraisal score . ? ? ? ? ? ? ( 11 )

? ? ?

Fig. 1. The steps of the evaluation - based on distance from average solution method. Multi - - «

3 Application

This section introduces the results of the EDAS method for identifying key strategies of the key barrier in implementing AI and cloud systems in the IT industry. Ten key barriers are used in this study, as shown in

Fig. 2 .

Fig. 2. The key barrier to implementing artificial intelligence information technology

Phase 1. Build the decision matrix .

The decision matrix is built between a set of criteria in the key barriers and a set of strategy to overcome the barriers to implementing AI and cloud systems in the IT industry. The decision matrix is built by using

Eq. (1) . Phase 2. Compute the average solution . The average solution is computed by using Eq. (2) . Phase 3. Compute the positive and negative distance by using Eqs. (3)-(6) . The cost criterion is negative .

The positive and negative distances from every alternative are computed for each beneficial and non

-beneficial criterion, as shown in Tables 1 and 2 .

Abdelhafeez and Shawky Aziz | Soft. Comput. Fusion. Appl. 1 ( 1 ) ( 2024 ) 1 - 9 Table 1. The positive distance. KBC 1 KBC 2 KBC 3 KBC 4 KBC 5 KBC 6 KBC 7 KBC 8 KBC 9 KBC 10 KBA 1 0 0.117647 0.21875 0 0 0 0 0 0.2 0.21875 KBA 2 0.137931 0 0 0.027778 0.365079 0.152542 0 0.433962 0.6 0.21875 KBA 3 0 0.264706 0.0625 0.305556 0.365079 0.661017 0.387755 0 0 0 KBA 4 0 0.411765 0.21875 0.166667 0 0 0.183673 0 0.2 0 KBA 5 0 0 0.375 0 0 0 0 0 0.6 0 KBA 6 0.310345 0 0 0.027778 0.206349 0 0 0.056604 0.4 0 KBA 7 0 0 0 0.027778 0 0 0.183673 0.622642 0 0 KBA 8 0.137931 0.264706 0 0 0.047619 0.152542 0.183673 0 0 0.375 KBA 9 0.655172 0.117647 0 0 0 0.152542 0.183673 0.433962 0 0.21875 KBA 10 0.310345 0 0.21875 0.166667 0.52381 0.661017 0 0.245283 0 0.0625 Table 2. The negative distance.

KBC 1 KBC 2 KBC 3 KBC 4 KBC 5 KBC 6 KBC 7 KBC 8 KBC 9 KBC 10 KBA 1 0.37931 0 0 0.111111 0.111111 0.525424 0.632653 0.320755 0 0 KBA 2 0 0.323529 0.25 0 0 0 0.22449 0 0 0 KBA 3 0.034483 0 0 0 0 0 0 0.132075 0 0.09375 KBA 4 0.551724 0 0 0 0.428571 0.355932 0 0.698113 0 0.25 KBA 5 0.37931 0.029412 0 0.111111 0.428571 0.355932 0.22449 0.509434 0 0.40625 KBA 6 0 0.323529 0.09375 0 0 0.016949 0.020408 0 0 0.25 KBA 7 0.206897 0.176471 0.25 0 0.269841 0.525424 0 0 0.2 0.09375 KBA 8 0 0 0.25 0.25 0 0 0 0.132075 0.8 0 KBA 9 0 0 0.25 0.25 0.269841 0 0 0 0.6 0 KBA 10 0 0.323529 0 0 0 0 0.020408 0 0.4 0

Phase 4. Compute the criteria weights as shown in Fig. 3 . The technological barriers have the highest weight.

Fig. 3. The key barrier weights.

Phase 5. Compute the weighted

and

?? .

The weighted positive and negative distances are computed by using Eq. (7) and Eq. (8) as shown in Tables 3 and 4 .

Multi - - « Table 3. Weighted positive distance. KBC 1 KBC 2 KBC 3 KBC 4 KBC 5 KBC 6 KBC 7 KBC 8 KBC 9 KBC 10 KBA 1 0 0.013601 0.024025 0 0 0 0 0 0.012717 0.021496 KBA 2 0.01754 0 0 0.003693 0.031654 0.015871 0 0.040135 0.03815 0.021496 KBA 3 0 0.030602 0.006864 0.040623 0.031654 0.068776 0.026896 0 0 0 KBA 4 0 0.047603 0.024025 0.022158 0 0 0.01274 0 0.012717 0 KBA 5 0 0 0.041185 0 0 0 0 0 0.03815 0 KBA 6 0.039466 0 0 0.003693 0.017892 0 0 0.005235 0.025434 0 KBA 7 0 0 0 0.003693 0 0 0.01274 0.057585 0 0 KBA 8 0.01754 0.030602 0 0 0.004129 0.015871 0.01274 0 0 0.03685 KBA 9 0.083317 0.013601 0 0 0 0.015871 0.01274 0.040135 0 0.021496 KBA 10 0.039466 0 0.024025 0.022158 0.045417 0.068776 0 0.022685 0 0.006142 Table 4. Weighted negative distance.

KBC 1 KBC 2 KBC 3 KBC 4 KBC 5 KBC 6 KBC 7 KBC 8 KBC 9 KBC 10 KBA 1 0.048236 0 0 0.014772 0.009634 0.054668 0.043883 0.029665 0 0 KBA 2 0 0.037402 0.027457 0 0 0 0.015572 0 0 0 KBA 3 0.004385 0 0 0 0 0 0 0.012215 0 0.009212 KBA 4 0.070161 0 0 0 0.037159 0.037033 0 0.064565 0 0.024566 KBA 5 0.048236 0.0034 0 0.014772 0.037159 0.037033 0.015572 0.047115 0 0.039921 KBA 6 0 0.037402 0.010296 0 0 0.001763 0.001416 0 0 0.024566 KBA 7 0.026311 0.020401 0.027457 0 0.023397 0.054668 0 0 0.012717 0.009212 KBA 8 0 0 0.027457 0.033237 0 0 0 0.012215 0.050867 0 KBA 9 0 0 0.027457 0.033237 0.023397 0 0 0 0.03815 0 KBA 10 0 0.037402 0 0 0 0 0.001416 0 0.025434 0

Phase 6. Compute the weighted normalized

and

by using Eq. (9) and Eq. (10) . Phase 7. Compute the appraisal score using Eq. (11) , as shown in Fig. 4 . Alternative 4 is the best, and alternative 6 is the worst.

Fig. 4. The appraisal score.

4 Sensitivity Analysis

This study changes the criteria weights under different cases to show the rank of alternatives under different cases. We proposed 11 cases in criteria weights. In the first case, we put all criteria in equal weight. In the

second case, we put one criterion with 0.12 weight; the other criteria are equal, as shown in Fig. 5 . Then, we

Abdelhafeez and Shawky Aziz | Soft. Comput. Fusion. Appl. 1 ( 1 ) ( 2024 ) 1 - 9

used the EDAS method to rank the alternatives under different cases. Fig. 6 shows the appraisal score under different cases. The results show the rank is stable, as shown in Fig. 7 .

Fig. 5. The 11 cases in criteria weights. Fig. 6. The appraisal score under different cases. Fig. 7. The rank of alternatives under different cases. Multi - - «

5 Conclusion This study proposes a decision-making model for identifying key barriers when implementing cloud systems

and AI in the IT industry. We proposed an MCDM method logy to rank the alternatives. The EDAS method

is an MCDM method used to rank the options. Ten ba rriers and ten strategies are used in this study. The

mean method is used to complete the criteria weights. The technological barrier has the highest weight

³ the

EDAS method starts with the decision matrix between criteria and alternatives. Then, the positi

ve and

negative criteria are computed. Then, the weighted normalized positive and negative criteria are calculated by multiplying the criteria weights by the positive and negative criteria. The cost criterion is negative, and all

other criteria are positiv

e. The EDAS shows that alternative 4 is the best and alternative 6 is the worst. The sensitivity analysis is conducted to show the stability of the results. There are 11 cases proposed in this study.

The first case has equal weights; in the other case, we put one criterion with 0.12 weight, and the equal criteria

are equal. The results show the rank of alternatives under different cases is stable.

Funding No financial support was provided by any external entity for this study. Conflicts of Interest

The authors confirm that there are no competing financial or personal interests that could influence the

outcomes or interpretations of this research. References

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Figures Figure 1