View of Technology selection in the steel industry based on multi-criteria decision-making models: Fuzzy TOPSIS and AHP

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Technium

41/2023

2023 A new decade for social changes

Social Sciences

Technium.

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Technology selection in the steel industry based on multi- criteria decision-making models: Fuzzy TOPSIS

¹

and AHP

²

Ehsan Moradi

PhD student, University of Klagenfurt, Institute of Innovation and Entrepreneurship, Klagenfurt, Austria

[email protected]

Abstract. Since the most significant and most essential effective factor in today’s competitive markets are quality improvement and reduction of the final price of the manufactured products, the present study has aimed to recognize the most important criterion that affect selection of technology and to determine the weight of each of these criteria in order to prioritize the existing alternatives. In order to determine the weight of criteria in this study, the paired comparison survey has been used which has been distributed among 8 of the skilled experts who were members of the technology management team in Khuzestan Steel Company. In order to analyze the answer given by these experts, the AHP decision-making model and EXPERT CHOICE Software have been used. According to the obtained results, we have concluded that among the indexes of technical evaluation of the technology, economic evaluation of the technology, evaluation of legal and strategic limitations of the technology, evaluation of the market of the technology, evaluation of a company’s financial limits and abilities regarding technology selection, the criterion “economic evaluation of technology” is the first priority and the criterion

“evaluation of the market of the technology” is the last one. The Fuzzy TOPSIS method has been used in order to rank the technologies. The second survey used in the research have been distributed among 30 skilled experts. The innovation in this research is the fact that it recognizes and prioritizes the technology selection criteria using the AHP³ technique and uses Fuzzy TOPSIS for selecting the technology. Therefore, the present study proposes a framework to Iranian companies for evaluating different technologies and selecting the suitable one.

Keywords. Fuzzy Theory, Fuzzy TOPSIS, Technology Selection, Paired Comparison, Analytical Hierarchy

Introduction

One of the most obvious features of today’s world is how fast the world surrounding us changes. This speed is still dramatically increasing. Until a century ago, the collection of human knowledge doubled every thousand years. It was in the early years of the third century that we saw that the speed of growth of human knowledge doubles every twelve years.

1 The Technique for Order of Preference by Similarity to Ideal Condition (TOPSIS)

2 Analytical Hierarchy Process

Technium Social Sciences Journal Vol. 41, 251-263, March, 2023 ISSN: 2668-7798 www.techniumscience.com

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With the facilitation of global communications, the speed enhancement trend is growing; in that, there is news of a new product and a new evolution every few hours in every day. Each of these changes might not be too significant, but the multitude of these changes depicts this progression. The new world is a world of technology and technology leads to continuous promotion of processes, improvement of quality and a decrease in the final price of the manufactured products. It is the most basic effective factor in competitiveness which means that it is the most important principle of sustainability and success in today’s complex markets.

Currently, technology has influenced almost all social and economic areas, including agriculture, industry and services and all of their subsectors such as banking, biotechnology, agriculture, aircraft manufacturing, information, information revolution and transportation and it has somehow become in all of these areas.

Nowadays, technology plays a key role as far as competitiveness of firms is concerned;

thus, it must be managed based on a strategic view. The first step in managing technology is recognizing it in an economic firm. The second step, which identifying technologies, is quite crucial as well. The third step is the evaluation and then selection of the suitable technology out of the ones that were identified in the last step. Unfortunately, since nowadays technologies used in the firms of our country are not selected by experts, sometimes due to the mistakes made by authorities, the selected technologies don’t have the expected return.

The review of the research findings indicates that two fundamental issues are effective in not paying attention to discussing sustainable development purposes by various businesses.

1. Difficulty of evaluation and determination of the effect of business performance on economy, society and environment as the 3 dimensions of the concept of sustainable

development;

2. The shortage of awareness of the sustainable entrepreneurship paradigm as an operational model involving the three purposes of sustainable development.(Moradi et al., 2021)

In the present study, paired comparison has been used in order to determine the weight of the criteria that are of importance regarding the selection of the suitable technology. Then, the Fuzzy TOPSIS method has been used in order to select the best furnace out of the identified technologies.

The plant pilot furnace (case study), which is currently available in the company, has some problems and disadvantages which have been mentioned below. These problems can be solved if the company selected the proper furnace

1- The level of automation of the current plant pilot furnace is lower than that of Khuzestan Steel Company. In this furnace, the baking is not measured by pellet stove and it is done manually by the personnel of the plant pilot unit. Whereas, by using the new technology, the process of measurement of the indexes that affect the pellet stove can be determined and the obtained results would be sent to the MIS system.

2- If the new technology (iron making process simulation furnace) is selected properly, it will be able to reduce the level of energy consumption approximately by 20%.

3- In the current system, the human force injects the sufficient amount of oxygen using the trial and error method; while in advanced systems, the desirable amount of oxygen will be automatically injected and the new technology will inject a certain amount of oxygen to the system so that the baking by the pellet stove would be done perfectly. Then, all of these obtained results will be given to the pelletizing units of the company so that the process would be completed based on these results.

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Research method

The present study is a descriptive – survey and quantitative research. In terms of objective, this is an applied research. In this study, by reviewing library studies, becoming familiar with the technology management system of Khuzestan, reviewing the opinions and views of skilled experts and consultants of the technology management team of the company, the survey was designed and the required information was collected.

In the present study, in order to determine the weights of indexes, the paired comparison method has been used and in order to prioritize the technologies, the fuzzy TOPSIS method has been used. Each of these methods will be discussed in relevant sections of the study.

The survey related to paired comparison (survey 1) was distributed among 8 of the experts for the purpose of determining the weight of these criteria using the AHP method. On the other hand, the survey related to prioritization of the alternatives (survey 2) was distributed among 30 of the experts who were completely familiar with the considered technology (case study) using the fuzzy TOPSIS. After the surveys were collected, the data obtained from the first survey was analyzed using the EXPERT CHOICE Software. Then, the data obtained from the second survey was analyzed using the EXCEL Software so that different stages of the fuzzy TOPSIS method would be completed. The hierarchal structure follows:

Figure 1 – hierarchal structure

The criteria have been determined and selected by reviewing library studies, meetings for reviewing and reengineering technology management in Khuzestan Steel Company, archive Technium Social Sciences Journal

Vol. 41, 251-263, March, 2023 ISSN: 2668-7798 www.techniumscience.com

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of the instructions of the department of planning and development and the consulting team of Khuzestan Steel Company.

Results and discussion:

Analytical Hierarchal Process (AHP)

In this section, the data obtained from the collected survey will be reviewed. Firstly, the inconsistency rate of the paired comparisons will be reviewed and then, after reviewing all of the surveys related to paired comparisons, the final ranking of the alternatives will be cited.

Analytical hierarchal process (AHP) is a decision-making method which enables the person (or the group) that makes the decision to form the issue and make comparisons based on the shaped structure in order to prioritize the alternatives regarding the decision-making process. AHP requires paired comparisons and the decision makers starts their work by drawing a hierarchal structure for their decision. This hierarchy specifies various alternatives and factors that must be taken into consideration. Then, paired comparisons are made which ultimately lead to determination and evaluation of the factors. In this method, the alternative with the highest weight is selected as the best alternative. One of the most important advantage of this method is the fact that it is used when there are qualitative criteria. Another advantage is that it forms a hierarchy for structuring the issue. Criteria are classified from top to bottom of the tree so that complex problems would be systematically reviewed by AHP. Currently, AHP is mostly used for making decisions about socioeconomic systems including resource allocation, performance evaluation, sequencing work, etc. This is a decision-making method which enables the decision maker to form the issue and make comparisons based on the shaped structure in order to prioritize the alternatives regarding the decision-making process. This technique was first proposed by Thomas Saaty in 1980. Another advantage of this method is that it is used when there are qualitative criteria.

Analytical hierarchal process is one of the most well-known multiple criteria decision making techniques which was created by Thomas L. Saaty Araghi Al-Asl in 1970s (Izadbakhsh et al. 2009). AHP reflects human thinking and natural behavior. This technique reviews complex issues based on the interactions between them, simplifies them and solves them (Zebardast, 2001).

Analytical hierarchal process can be used when there are multiple criteria and alternatives. The aforementioned criteria can either be quantitative or qualitative. This decision making method is based on paired comparisons. The decision maker starts the analysis by decomposing their decision problem into a hierarchy. The purpose of decision making is on level zero. On level one, the indexes (criteria) and on the second level, the alternatives that must be prioritized are mentioned. Depending on the type of problem, the number of the level of primary and secondary criteria might vary (Momeni, 2008).

We follow the algorithm below in order to go through with AHP:

a) Normalizing the matrix of paired comparisons

b) Obtaining the mean of each row of the normalized matrix of the paired comparisons (which is referred to as relative weights)

c) Multiplying the relative weights of indexes by the arithmetic mean of the alternatives

d) Ranking the alternatives

After this stage, the inconsistency rate will be calculated. In order to calculate this index, the following items will be calculated:

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First step) calculating the weighted sum vector (WSV): matrix of paired comparisons (D) is multiplied by the relative weights vector. The obtained vector is called “weighted sum vector”.

(1) WSV = D × W

Second step) calculating the consistency vector (CV): the elements of the weighted sum vector will be divided into the relative weights vector. The obtained vector is referred to as “consistency vector”.

Third step) calculating the maximum eigenvalues of paired comparisons matrix (maxλ): in order to calculate the maximum eigenvalues of paired comparisons matrix, the mean of the elements of the consistency vector will be calculated.

Fourth step) calculating the inconsistency index (II): the inconsistency index is calculated as follows:

(2) II =^λmax−n

n−1

Fifth step) calculating the inconsistency rate (IR): this index is calculated as follows:

(3) IR = ^II

IRI

Here, IRI is the random inconsistency rate.

Analysis of the surveys

In the AHP method, the levels of the criteria are compared two by two. The Expert Choice Software is used to analyze the paired comparison survey and to determine the inconsistency rate. If inconsistency rate was lower than 0.1, the paired comparisons would be acceptable. In the paired comparison tables, if the criteria in the rows are preferred to the criteria in the columns, the numbers will be written in black and if the case is reversed, the numbers will be written in red. Here, the ranking of the criteria based on each of the surveys won’t be cited. We will only mention the ultimate ranking of the alternatives (based on all criteria).

4-5 Ultimate ranking of the alternatives (based on all criteria)

The ranking of the criteria based on each survey has already been reviewed. As follows, the paired comparison matrixes will be combined using the Expert Choice Software and finally, the ultimate ranking of the alternatives, which has been calculated based on all surveys, will be presented.

Table 1 – integrated paired comparison matrix (using the geometrical mean method) Technical

evaluation of the technology

Consistency with objectives

and strategies

Financial limits and

abilities

Economic evaluation of

the technology

Evaluation of the market

of the technology Technical

evaluation of the technology

▒ 1.46165 2.25643 1.70674 1.93434

Consistency with objectives

▒ 1.01683 2.3943 2.0

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and strategies Financial limits and abilities

▒ 2.24619 1.96689

the technology

▒ 3.03337

Evaluation of the market of

the technology

Inconsistency rate: 0.02

▒

According to the table above, inconsistency rate of the paired comparison matrix has been calculated to be 0.02. Given that the inconsistency rate is lower than 0.1, the integrated comparisons can be used for ranking the criteria. According to the information presented in the table above, the ranking of the criteria is as follows:

In the graph below, the ultimate ranking of the criteria has been illustrated. As the information presented in the graph suggest, the criterion “economic evaluation of the technology” is at the first rank and the criterion “evaluation of the market of the technology” is at the last rank.

Graph 1 – ultimate ranking of the criteria (integration of all surveys) Ranking using Fuzzy TOPSIS method

The TOPSIS technique or the Technique for Order of Preference by Similarity to Ideal Condition, was originally introduced by Hwang and Yoon in 1981 and it is a multi-criteria decision analysis method. This technique can be used for ranking and comparing different alternatives, selecting the best alternative and determining the distance between the alternatives.

One of the advantages of using this method is the fact that the used criteria or indexes to be compared can have different measurement units and be negative or positive. In other words, a combination of positive and negative indexes can be used in this technique.

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Based on this method, the best alternative or condition is the alternative or condition that is closest to the ideal alternative and the one that is farthest from the ideal is the worst alternative or condition. The ideal alternative is one with highest profit and lowest cost. To summarize, the ideal condition is the sum of the maximum eigenvalues of all of the criteria;

however, the non-ideal condition is the sum of the minimum eigenvalues of all of the criteria.

In cases where the available data is not absolute, it is usually difficult to make decisions and it is hard to collect accurate and true data. It seems that using the fuzzy logic in the multi- criteria decision making techniques can facilitate the decision making process. When fuzzy logic is used in the TOPSIS method will turn into the fuzzy TOPSIS method which is different from the TOPSIS method. It is obvious that the basic logic of the fuzzy decision making techniques is to combine the impact of the fact that data is not absolute and human’s way of thinking on the decision that is being made.

The process of the fuzzy TOPSIS has the following steps:

Step 1) creating an evaluation matrix for ranking alternatives consisting of m alternatives and n criteria;

Step 2) normalizing the decision matrix;

Step 3) calculating the weighted normalized matrix;

Step 4) determining the positive ideal alternative and the negative ideal alternative;

Step 5) calculating the distance between each alternative to the positive and negative ideal condition;

Step 6) calculating the coefficient of similarity of each of the alternatives Step 7) ranking the alternatives based on the similarity coefficient

Calculating the fuzzy numbers

Before expressing the steps to fuzzy TOPSIS method, first the mathematical operations will be briefly explained. If M̃ = (m₁, m₂, m₃) and Ñ = (n₁, n₂, n₃) are two triangular fuzzy number and k is a real number, the following equations would apply to them:

(4) M̃ ⊕ Ñ = (m₁+ n₁, m₂+ n₂, m₃+ n₃) M̃ ⊕ Ñ = (m₁× n₁, m₂× n₂, m₃× n₃)

M̃

Ñ = (^m¹

n1 ,^m²

n2 ,^m³

n3) k/Ñ = (^k

n₃, ^k

n₂, ^k

n₁)

The five-level Likert scale with verbal phrases (very low, low, average, high and very high) is usually used in the fuzzy TOPSIS surveys. The information obtained from these items consist the fuzzy TOPSIS decision matrix data. In order to do the analyses, in the Likert method, a quantitative value is ascribed to each of the verbal phrases which are qualitative values based on an arbitrary scale which has been presented in table below.

Table 2 – verbal phrases and their fuzzy equivalents Verbal phrases Triangular fuzzy

numbers Very low (0, 0.1, 0.3)

Low (0.1, 0.3, 0.5)

Average (0.3, 0.5, 0.7) High (0.5, 0.7, 0.9) Very high (0.7, 0.9, 1)

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Solving the fuzzy TOPSIS algorithm

Step 1) creating an evaluation matrix for ranking alternatives consisting of m alternatives and n criteria.

In the table below, the decision matrix has been illustrated. According to the information presented in the following table, the alternatives are relatively of importance in comparison to each criterion with triangular fuzzy numbers. Given that a few experts have been asked to give their opinions, each verbal phrase turns into triangular fuzzy numbers. In the following section, the mean of opinions given by experts for each criterion will be calculated and finally, the decision matrix that is the outcome of an agreement between all of these experts will be presented in a table that follows. The weight of each of these criteria has already been calculated using the AHP method which can be seen above the decision matrix.

Table 3 – fuzzy decision matrix data Weight of

criteria

0.351 0.161 0.152 0.098 0.239

Criteria Alternatives

the technology

Consistency with objectives and strategies

abilities

the technology

Technical evaluation of

the technology Technology 1 (0.23, 0.43,

0.63)

(0.33, 0.53, 0.7)

(0.24, 0.43, 0.63)

(0.25, 0.44, 0.64) Technology 2 (0.51, 0.71,

0.88)

(0.56, 0.76, 0.89)

(0.3, 0.49, 0.69)

(0.4, 0.6, 0.8) (0.45, 0.65, 0.83) Technology 3 (0.16, 0.34,

0.54)

(0.37, 0.54, 0.71)

(0.19, 0.37, 0.57)

(0.2, 0.38, 0.58)

(0.2, 0.38, 0.58) Step 2) normalizing the decision matrix;

If we show each cell of the decision matrix as triangular fuzzy numbers (aij, bij, cij), the normalization must be done in this step in order to eliminate the impact of the scale of each criterion. With the help of the equations below, the normalized decision matrix (R)̃would be obtained:

(5) R̃ = [r̃_ij]_max

R̃_ij(^a^ij

c_j^∗,^b^ij

c_j^∗,^c^ij

c_j^∗) , j ∈ B;

r̃_ij(a⁻_j c_ij,a_j⁻

b_ij,a⁻_j

a_ij) , j ∈ C;

c_j^∗ = ^max_ic_ij if j ∈ B;

a⁻_j = ^min_ia_ij if j ∈ C;

As it can be seen in the equations above, B and C show a series of positive and negative criteria, respectively.

In the following table, the normalized decision matrix has been presented.

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Table 4 – normalized fuzzy decision matrix data Weight of

criteria

0.351 0.161 0.152 0.098 0.239

Criteria Alternatives

Economic evaluation of the technology

Consistency with objectives and

strategies

abilities

the technology

the technology Technology 1 (0.26, 0.48,

0.71)

(0.37, 0.59, 0.78)

(0.35, 0.63, 0.92)

(0.3, 0.54, 0.79)

(0.3, 0.53, 0.77) Technology 2 (0.58, 0.81, 1) (0.63, 0.85, 1) (0.43, 0.71, 1) (0.5, 0.75, 1) (0.54, 0.78, 1) Technology 3 (0.18, 0.38,

0.61)

(0.41, 0.6, 0.79)

(0.27, 0.53, 0.82)

(0.25, 0.48, 0.73)

(0.24, 0.45, 0.69)

Step 3) calculating the weighted normalized matrix.

By multiplying the normalized decision matrix (r̃_ij) by fuzzy weights of the criteria (W̃), the weighted normalized matrix will be obtained (Ṽ).

(6) ṽ_ij = r̃_ij⊗ w̃_j

Ṽ = [ṽ_ij]_m×n i = 1,2, … , m , j = 1,2, . . , n

Table 5 – creation of the weighted normalized fuzzy decision matrix Criteria

Alternatives

the technology

Consistency with objectives and strategies

abilities

the technology

the technology Technology 1 (0.09, 0.17,

0.25)

(0.06, 0.09, 0.13)

(0.05, 0.1, 0.14)

(0.03, 0.05, 0.08)

(0.07, 0.13, 0.18) Technology 2 (0.2, 0.28,

0.35)

(0.1, 0.14, 0.16)

(0.07, 0.11, 0.15)

(0.05, 0.07, 0.1)

(0.13, 0.19, 0.24) Technology 3 (0.06, 0.13,

0.21)

(0.07, 0.1, 0.13)

(0.04, 0.08, 0.12)

(0.02, 0.05, 0.07)

(0.06, 0.11, 0.17) Step 4) determining the positive ideal condition (Ṽ_j^∗) and the negative ideal condition (Ṽ_j⁻);

(7) Ṽ_j^∗ = {

i=1,…,mmax ṽ_ij ; j ∈ B

i=1,…,mmin ṽ_ij ; j ∈ C

ṽ_j⁻ = {

i=1,…,mmin ṽ_ij ; j ∈ B

i=1,…,mmax ṽ_ij ; j ∈ C (8) Ã^∗ = (ṽ₁^∗, ṽ₂^∗ , … , ṽ_n^∗ ),

Ã⁻ = (ṽ₁⁻, ṽ₂⁻ , … , ṽ_n⁻)

In the equations above, B and C show the series of positive and negative criteria, respectively.

In the following table, the positive ideal condition and the negative ideal condition for each criterion have been presented.

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Table 6 – positive and negative ideal conditions

Positive ideal condition (𝑉̃_𝑗^∗) Negative ideal condition (𝑉̃_𝑗⁻)

Technical evaluation of the technology

(0.24, 0.24, 0.24) (0.06, 0.06, 0.06) Evaluation of the market of

the technology

(0.1, 0.1, 0.1) (0.02, 0.02, 0.02) Financial limits and abilities

of the firm

(0.15, 0.15, 0.15) (0.04, 0.04, 0.04) Consistency with objectives

and strategies

(0.16, 0.16, 0.16) (0.06, 0.06, 0.06) Economic evaluation of the

technology

(0.35, 0.35, 0.35) (0.06, 0.06, 0.06)

Step 5) calculating the distance between each alternative to the positive (d_i^∗) and negative ideal condition (d_i⁻).

If A^∗ was the positive ideal condition and A⁻ was the negative ideal condition in the fuzzy method, the distance from each alternative to A^∗ would be a positive distance and the distance from each alternative to A⁻ would be negative distance. Both of these distances are calculated through the following equations:

(9) D_i^∗ = ∑ⁿ_j=1d(ṽ_ij, ṽ_j^∗) , i =

1, … , m

(10) d_i⁻ = ∑ⁿ_j=1d(ṽ_ij, ṽ_j⁻) , i = 1, … , m The distance between two triangular fuzzy numbers M̃ = (m₁, m₂, m₃) and Ñ = (n₁, n₂, n₃) is determined through the following equation:

(11) d(M̃ , Ñ) = √¹

3(m₁− n₁)²+ (m₂− n₂)²+ (m₃− n₃)²

In the table below, the distance between each alternative and the positive ideal condition has been calculated.

Table 7 – the distance from each alternative to the positive ideal condition (d_i^∗)

Technology 1

0.5006134 Technology

2

3

0.5592203

In the table below, the distance between each alternative and the negative ideal condition has been calculated.

Table 8 – the distance from each alternative to the negative ideal condition (d_i⁻)

Technology 1

0.3517035

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Technology 2

3

0.2940242

Step 6) calculating the coefficient of similarity ( ) of each of the alternatives.

After calculating the positive distance ( ) and the negative distance ( ) for each alternative , the similarity coefficient for each alternative ( ) is calculated through the following equation:

(12) 𝐶𝑐_𝑖 = ^𝑑^𝑖

−

𝑑_𝑖⁻+𝑑_𝑖⁺

Table 9 – calculation of the similarity coefficient for each alternative (d_i⁻) (d_i^∗) ( ) Rank Technology 1 0.35170.35 0.5006134 0.4126 2 Technology 2 0.5673983 0.2874054 0.6638 1 Technology 3 0.2940242 0.5592203 0.3446 3

In the table above, the similarity coefficient has been calculated for each alternative. The alternative for a higher similarity coefficient is the first priority and the same goes for ranking the rest of the alternatives.

Step 7) ranking the alternatives based on the similarity coefficient

In the following section, the ranking of the alternatives has been illustrated. As it can be seen in the graph, the second technology is at the first rank, the first technology is at the second rank and the third technology is at the third rank.

Figure 1 – ranking technologies based on the similarity coefficient Conclusion

In the present study, after reviewing the documents and information available in the firm and holding numerous meetings with experts, the required indexes in five dimensions were specified. The number of obtained indexes had to become acceptable so that the organization would be able to measure and trace them. Measuring these indexes must have been quite easy for the firm. In order to more efficient and useful indexes, out of the determined indexes, those

CCi

di⁺ d_i⁻

Ai CC_i

CCi

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that were of more importance must have been specified and the experts must have given these indexes specific scores. This evaluation was done and the indexes that were of more significance were identified. It can be said that the most important step to be taken throughout this entire research was the selection of these indexes because omitting an important index leads to the elimination of a valuable metric from the evaluation chain which might then lead to obtainment of results that are false or less accurate.

Given the aforementioned points, the results obtained from data analysis in the Expert Choice Software, the following indexes are ranked as follows: (1) “economic evaluation of the technology”, (2) technical evaluation of the technology, (3) consistency with objectives and strategies, (4) financial limits and abilities of the firm and (5) “evaluation of the market of the technology”. The results obtained from using the fuzzy TOPSIS method regarding the ranking of the criteria has been presented in table 10. According to the information presented in table 10, the pilot plant furnace technology of the Danieli Company in Italy is at the first rank, the pilot plant furnace technology of Germany’s Auto Tech Company is at the second rank and the pilot plant furnace technology of Metal ArcelorMittal Company was at the third rank. Other studies have concluded that Iranian firms face considerable limits in the field of technology management. One of the most important limits that firms face is the tendency towards traditional management, maintenance of the current condition and unwillingness to take risk.

Changing the attitude of the managers of the firm towards the positive outcomes of accurate usage of technology management was identified to be the first and most important measure by taking which these limits could be eliminated.

Table 10 – results of ranking

Rank Ironmaking process simulation furnace technologies

1 pilot plant furnace technology of Italy’s Danieli Company

2 pilot plant furnace technology of Germany’s Auto Tech Company

3 pilot plant furnace technology of Metal ArcelorMittal Company

At the end, in the respect of broadening the scope of the application of this technique, it is recommended to the future researches to consider the results and indexes specified in this research as a basis for similar firms so that way to improving and developing this technique in this particular sector in firms would be paved.

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