Basic Metal Industries Research Articles

Structural Equation Modeling of Relationships Among Lean Operational, Financial Performance, and Customer Satisfaction

Objectives: The challenges of implementing a lean business system can be solved by Structural Equation Modeling. In this research, the link between lean operational, financial performance, and customer satisfaction has been investigated in the Akaki Basic Metal Industry in Ethiopia. Methods: A lean operational performance that met the needs of the industry was identified by conducting a structural survey questionnaire to collect feedback data from experts. The model was developed including 28 variables from the experts in the industry. The collected responses were tested for reliability and validity using Cronbach's alpha, composite reliability, convergent, and discriminant validity. Findings: From the use of Analysis of Moments and Structures (AMOS-SEM), the results confirmed that C.R. > 1.96, p < 0.001, and p < 0.05 on quality and delivery. This shows a significant positive direct effect on customer satisfaction by 25.8% on quality, 24.9% on delivery, and 10.3% on cost. Novelty: Even though LM has become a fundamental aspect of industrial manufacturing processes, lack of understanding (knowledge) and practicing how to implement the positive impact on lean operational, financial performance, and customer satisfaction are still not sufficiently exploited particularly in developing countries. This research adds values to the practical use of the concept by examining the relationship among lean operational, financial performance, and customer satisfaction within basic metal manufacturing industry in Ethiopia. In addition to this, the findings have demonstrated improved organizational performance through structural equation modeling. It also helps leaders to focus more on training employees on quality issues and developing an awareness of quality achievement as a core value in the industry. Keywords: Confirmatory factor analysis, Exploratory factor analysis, Lean operational performance, Structural equation modeling, Customer satisfaction

Innovative activity in the basic metals industry: comparative analysis with EU countries

Methods. In the process of studying innovation activities in the basic metals industry of Ukraine and European countries, methods of analysis and synthesis, empirical methods, induction and deduction techniques, graphical, logical and tabular methods were used. The methodological basis of the study was the methods of comparative analysis and observation, which made it possible to assess the current state of innovative activity in the basic metals industry of Ukraine. The graphical method made it possible to visualize the results of the study. Results. The use of comparative analysis made it possible to assess the current state of innovation activity in the basic metals industry of Ukraine in comparison with European countries. A comparative analysis, based on key indicators of innovation activities of industrial enterprises, was carried out. These indicators include the number of innovation activities of industrial enterprises; enterprise innovation expenditure of industrial enterprises; the number of introduced technological processes of industrial enterprises; the number of introduced innovative products (goods, services) of industrial enterprises; and total turnover from innovation activities of industrial enterprises. The analysis was carried out using public statistical data from the State Statistics Service of Ukraine and the statistical office of the European Union. It is revealed that innovation activities in metallurgical industry of Ukraine in terms of key indicators is significantly lower than in European countries. The main problems of functioning of the metallurgical industry of Ukraine are identified as part of the global industry. Novelty. There are proposed the main directions for improving innovation policy in the manufacture of basic metals to increase its competitiveness in the world market. Practical value consists in developing proposals to improve innovation policy in the basic metals industry. The results of the study can be applied for further research of innovation activities in metallurgical industry of Ukraine.

Basic Industry Strategy and Orientation Strategy

This research is motivated by so much competition in a market orientation that is increasingly emerging, but competition sometimes pays little attention to product innovation in the eyes of consumers. The purpose of this study is to determine market orientation that occurs in the world of marketing, Entrepreneurial Orientation, Market Power as an independent variable, Marketing Performance as the dependent variable and Product Innovation as a mediating/intervening variable. The method used is the literacy method. The result of the research is that the basic industrialization strategy is an industrial strategy that focuses on the production of products and services for export, not for domestic sales and circulation. Consists of basic chemical industry and basic metal and machine industry. While the orientation industrialization strategy is the company's strategic direction in creating the right behavior so as to achieve superior performance. Market orientation as a process and activity related to creating and satisfying customers by continuously assessing customer needs and wants. Application of market orientation will bring increased performance for the company. Companies implementing market orientation have advantages in terms of customer knowledge and these advantages can be used as a source to create products that suit the wants and needs of customers.

Tracing the CO2 emissions embodied in Chinese mainland's exports with multinational enterprises: From source to sink

The production technologies and carbon intensity of multinational enterprises (MNEs) is significantly different from those of domestic enterprises (DEs). However, there are few studies have paid attention to the enterprise ownerships in tracing the carbon emissions embodied in export (CEEE). This paper constructs a new decomposition framework of total export that considering the MNEs. Based on the OECD AMNE input-output table that distinguishing DEs and MNEs from 2005 to 2016 and the related carbon emissions data, it traces the carbon emissions embodied in Chinese mainland's exports based on the value-added trade. It separates Chinese mainland's domestic carbon emissions export (DCEE) that distinguishing enterprise ownerships, analyzes its changes, and further analyzes the differences of the DCEE between DEs and MNEs from the perspective of industrial distribution and export destinations. The results show that: (1) In addition to 2016, every year more than 10.0% of Chinese mainland's carbon emissions are used to meet the demand of foreign countries or regions, and the MNEs located in Chinese mainland has produced about 6.8%–11.1% of the DCEE. (2) About two-thirds of Chinese mainland's DCEE is produced by Chinese mainland's DEs through intermediate products export, and it is mainly concentrated in the basic metals industry, other non-metallic mineral products industry and chemicals and pharmaceutical products industry. (3) In terms of the DCEE through intermediate products export, the industrial distributions of the DEs and the MNEs are similar, and they both mainly concentrate in the three industries mentioned earlier; While for the final products export, it has much difference that labor-intensive processing trade is mainly produced by the DEs. (4) In terms of the main export destination regions of the DCEE, the DEs and MNEs are very similar both for the intermediate products export and the final products export. And ROW and USA are the biggest export destinations, followed by JPN, KOR, IND, GBR, DEU, etc. The research results of this article have important research significance for promoting the achievement of China's dual carbon goals.

Comparison of main metal industry sub-business lines from occupational health and safety perspective using CIRITIC and EDAS methods

The basic metal industry is one of the most economically important business lines in the manufacturing industry. According to the Social Security Institution (SSI) 2020 data, the basic metal industry in Türkiye is represented by 16 sub-businesses lines. In this study, it is aimed to evaluate the risk levels of the sub-business lines of the basic metal industry class, which is included in the SSI economic activity classification. Occupational Health and Safety data included in the 2020 SSI statistics were used to determine risk levels. The number of employees who have an occupational disease, the number of deaths because of work accidents, the period of temporary incapacity for work (inpatient), the period of temporary incapacity for work (outpatient), and the number of employees who have had a work accident are the criteria selected from these data. In the evaluation made according to these criteria, Multi-Criteria Decision-Making methods were used. Criteria Importance Through Intercriteria Correlation (CRITIC) and Evaluation Based on Distance from Average Solution (EDAS) methods were used to determine and classify the importance levels of the criteria determined for 16 sectors. As a result of the analysis, it has been determined that the riskiest sector among the main metal industry sub-business lines is the “Manufacture of basic iron and steel products and ferrous alloys” and the most important criterion is the number of insured persons with occupational diseases.

Electricity supply chain hybrid long‐term demand forecasting approach based on deep learning—a case study of basic metals industry

AbstractDemand forecasting is a key parameter to achieve optimal supply chain management at different levels. The basic metals industry is one of the most energy‐intensive industries in the electricity supply chain. The impossibility of large‐scale energy storage, reservation constraints, and its high costs, limitations on electricity transmission lines capacity, real‐time response to high priority strategic demands, and variety of energy rates at different hours and seasons are issues that challenge the electricity supply chain. A hybrid approach is presented in this paper to improve the accuracy of long‐term demand forecasting in the electricity supply chain. The proposed approach uses wavelet decomposition and long short‐term memory (LSTM) neural networks to produce new predictors for demand forecasting in case of defective data. The data used in this study consists of the recorded hourly demand of Espidan Iron Stone (EIS) company and data of Mobarakeh Steel (MS) company in Isfahan Province. The understudy time series includes a lot of interruptions due to non‐production of the factory or power outages and spikes which cause uncertainty that makes the forecasting more difficult in comparison with continuous time series. Multiple machine learning models including decision trees (DTs), boosted and bagged trees (BGTs), support vector regression (SVR) models, extreme learning machines (ELMs), and a LSTM neural network model are employed to evaluate the effectiveness of the proposed approach in this paper. A comparison of the results obtained using the hybrid proposed approach and training machine learning (ML) methods shows a notable reduction in forecasting error.

Industrial settlement in Diyala District, Baquba and Baladrooz as a model

The research aims to identify the reality of industrial settlement, Diyala Governorate, as a judicial model in Baqubah and Baladruz The research problem is to answer the following question: What is the current reality of industrial settlement in the study area? The study area is characterized by the presence of industrial diversity and different sizes, including large, medium and small, as the industrial activity varies between food industries, basic equipment industry (electrical industries), coke industry, refined petroleum products, and large-sized non-metallic mineral products industry with a total of (72) factories, as well as medium-sized industries. Which included the food industries that were concentrated within the district of Baquba only, and the construction industries that settled in the study area with a total of (17) factories, as well as small-sized industries, which formed the largest industrial settlement in the study areas (Baquba and Baladruz) and the largest number in terms of factories that reached (1067) factories, which varied between food, basic metal, construction and wood industries, which were distributed within the study area. As for other manufacturing industries and textile industries, they were concentrated in Baquba district in particular. The most important conclusions reached by the research were the following

Long-Term Electricity Demand Forecasting in the Steel Complex Micro-Grid Electricity Supply Chain—A Coupled Approach

Demand forecasting produces valuable information for optimal supply chain management. The basic metals industry is the most energy-intensive industries in the electricity supply chain. There are some differences between this chain and other supply chains including the impossibility of large-scale energy storage, reservation constraints, high costs, limitations on electricity transmission lines capacity, real-time response to high-priority strategic demand, and a variety of energy rates at different hours and seasons. A coupled demand forecasting approach is presented in this paper to forecast the demand time series of the metal industries microgrid with minimum available input data (only demand time series). The proposed method consists of wavelet decomposition in the first step. The training subsets and the validation subsets are used in the training and fine-tuning of the LSTM model using the ELATLBO method. The ESC dataset used in this study for electrical demand forecasting includes 24-h daily over 40 months from 21 March 2017, to 21 June 2020. The obtained results have been compared with the results of Support Vector Machine (SVM), Decision Tree, Boosted Tree, and Random Forest forecasting models optimized using the Bayesian Optimization (BO) method. The results show that performance of the proposed method is well in demand forecasting of the metal industries.

TÜRK ANA METAL SANAYİ ŞİRKETLERİNİN COVID-19 PANDEMİSİ DÖNEMİNDEKİ FİNANSAL PERFORMANSLARININ ENTROPİ – MARCOS BÜTÜNLEŞİK YAKLAŞIMI İLE DEĞERLENDİRİLMESİ

Dünya Sağlık Örgütü’nün (WHO) Mart 2020’de pandemi olarak ilan ettiği COVID-19 salgınının yol açtığı küresel üretim daralması, birçok ülkenin ekonomisini durma noktasına getirmiştir. Türk imalat sektörünün lokomotifi konumunda olan ana metal sanayisi de pandemiden en çok etkilenen sektörlerin başında gelmektedir. Sektördeki şirketlerin finansal performansları, yöneticilerin ve sermaye piyasası yatırımcılarının karar verme süreçlerini ciddi bir şekilde etkilemektedir. Bu çalışmada Borsa İstanbul Metal Ana Endeksinde (XMANA) yer alan 20 şirketin pandemi dönemi finansal performansı analiz edilmiştir. Pandemi dönemi (2020/06 – 2021/06) analiz sonuçları bir önceki bir yıllık dönem ile karşılaştırılmış ve önemli değişimlere rastlanmıştır. Performans ölçümü amacı ile bilanço ve gelir tablolarından derlenmiş; likidite, kârlılık, maliyet, değer, büyüme, sermaye yapısı ve faaliyet oranlarından oluşan 15 farklı kriter belirlenmiştir. Çalışmada çok kriterli karar verme tekniklerinden Entropi Yöntemi kriterlerin önem ağırlıklarının hesaplanmasında; 2020 yılında geliştirilmiş yeni bir yöntem olan MARCOS Yöntemi ise şirketlerin belirlenen kriterlere göre sıralanmasında kullanılmıştır. Çalışma sonuçlarına göre, pandemi dönemi finansal performansı en yüksek firma olan ÇEMTAŞ Çelik Makina Sanayi ve Ticaret A.Ş. (CEMTS)’yi; sırasıyla Kardemir Karabük Demir Çelik Sanayi ve Ticaret A.Ş. (KRDMD) ve AYES Çelik Hasır ve Çit Sanayi A.Ş. (AYES) takip etmiştir.

Significance and Role of Industrial Inputs in Productivity of Large-scale Manufacturing in Karachi: A Correlation Analysis

In an industrial process, productivity is a basic concern. A combination of production input, namely employment, labour cost, energy and raw material cost, result in value addition expressed as value- added. The effective role of each input in the manufacturing process can be assessed through the influence of variables involved. Pearson's correlation coefficients show various degrees of effects through interrelationships of variables and bring out the outstanding or relatively more important role of one or more variables (inputs) in specific combinations in the manufacturing process. The paper in hand analyses the interrelationship of structuring variables in the major groups of manufacturing in Karachi using Pearson's correlation measurement techniques. The role of the variables has been assessed through sets of ten correlations in each of the six major industrial categories. The results of the analyses show the leading or the dominant contribution of the variables towards value addition and productivity. In the case of the textile industry, strong correlations are indicated between Value Added (VA) and 'Other Cost' (OC) and Average Daily Employment (ADE) and Employment Cost (EC) and also VA and ADE. For chemical and chemical products strong correlation exist between VA and OC, OC and IC (industrial cost) and ADE and EC. In respect of the wearing apparel category, a strong correlation occur between OC and IC, VA and OC, ADE and EC. Basic metal industry shows strong correlation in all ten sets of relationships, thus every variable exercising an equal influence. The food and beverage group has only two strong correlations that are VA and OC, IC, and OC. In the case of motor vehicles and trailers, strong relationships are indicated by all sets of correlation ship.

Artificial intelligence framework for MSME sectors with focus on design and manufacturing industries

Artificial Intelligence (AI) is gaining high popularity in multiple domains targeting different industries. Though initial adoption of AI was thought of as an enabler for automation in industries, but later it presented vast possibilities in innovation and design. Also, with advent of Industry 4.0 standards, AI emerged to play a big role in its adoption across different sectors of industries. Micro, Small and medium enterprises (MSMEs) with limited resources look upon AI as a tool for accelerating their growth. In India, the MSME sector although varies with different States, but dominantly includes textiles, machinery and parts, mining and quarrying, basic metal industries, electrical machinery and apparatus, transport equipment and parts, paper products and printing, food products, chemical and chemical products, leather, wood, rubber, plastic and other non-metallic mineral products, beverages and tobacco products. This paper proposes architectural framework and also provides probable applications of Internet of Things (IoT) and AI for design and manufacturing MSMEs of India.

Türk İmalat Sanayinde Rekabet ve Verimlilik İlişkisi: Firma Düzeyinde Bir Analiz

This study aims to analyze the effect of competition on total factor productivity (TFP) for selected 77 companies operating in the manufacturing industry for the 2011-2019 period. The competitiveness of firms is calculated using the Lerner index. Total factor productivity is estimated based on the cost function using the stochastic frontier analysis (SFA) method. As a result of the estimation, the total factor productivity growth rate decreases rapidly after 2015 and becomes negative in 2018 and 2019. Although the average Lerner index, which shows the competitive power of the companies, is 35.06%, there is no significant change in the level of competition. Among the sectors, while the Basic Metal Industry is the lowest sector in terms of TFP growth rate; the sector with the highest TFP growth is the Chemicals, Petroleum Rubber and Plastic Products sector. On the other hand, while the most competitive sector is the Paper and Paper Products, Printing and Publishing, the sector with the least competition is the Non-Metallic Mineral Products Sub-sector. The findings of the study shows that the relationship between competition and total factor productivity is not linear. The estimated competition (1-Lerner index) threshold value for firms is 0.322. When competition is below the threshold, the effect of increased competition on total factor productivity is insignificant. When competition is above the threshold, the effect of increased competition on total factor productivity is positive and statistically significant.

FINANCIAL PERFORMANCE EVALUATION OF TURKISH BASIC METAL INDUSTRY: COMBINING AHP AND TOPSIS

Financial performance evaluation is crucial for competitive business world. Steel and iron industry is both high-tech heavy machine and labor-intensive industry, which makes it operating in a highly competitive environment. Therefore, the aim of this study is to evaluate the financial performance of Turkish steel and iron sector. For this aim, financial ratios of 17 basic metal industry firms listed in Borsa Istanbul in 2017 are calculated. Liquidity, efficiency, profitability and capital structure ratios are considered as main criteria of financial performance. By using Analytical Hierarchic Process (AHP), these criteria and related sub-criteria of each of them are assessed and weighted. These weights are then used in Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) for ranking. Obtained ranking is compared with ranking based on companies’ closing prices and results reveal that for 15 companies (88.235%) two rankings are very close. Therefore, it is concluded that combining AHP and TOPSIS should be used in financial performance evaluation.

The Regional Performance Impacts in the Supply Chain integration: Evidence from East Africa Basic Metal Industry

The objective of this study was to investigate the relationship between regional firm performance and supply chain integration practices. In addition to literature survey, the primary data collection instrument used was a questionnaire which was administrated to a total sample of 200 industry experts, administrative officers, functional department heads and directors, the industry managing directors and senior staff from 21 manufacturing firms in Ethiopian were incorporated. The data were studied using the AHP, fuzzy TOPSIS, the mind map, and descriptive analysis. Further, the challenges, practices, performance, the practices of East African basic metal industry supply chain integration influences the dynamics contribution of the regional economy was fragmental. According to this study, due to weak, fragmental and non-integrated supply chain systems, the regional economic dynamic contribution of the sector is weak and poor in quality, productivity, flexibility, and global competitiveness were investigated. Moreover, these investigations and analysis of this paper have both practical and scholarly implications. The result is provided to contribute to the future supply chain integration model development and make an action to supply chain integrations for process improvements. Finally, the implications for the design on the dynamic capability of supply chain strategies are showing, and the future research agenda was presented

The Roles of Cloud-Based Supply Chain Integration on Firm Performances and Competitiveness

Cloud computing systems have emerged as a useful technology that contributes to this optimization SCI process by providing infrastructure, platform, and software solutions for the whole supply chain via the internet. But in developing regions like Ethiopia, there's a weak traditional, fragmental, non-integrated, weak, and underdeveloped supply chain process. There’s still space for research on real industrial applications where Ethiopian firms strive for cloud-based integration and implement new models and integration strategies practices in their supply chains. Thus, this study is designed to investigate the impacts of cloud computing technology on the supply chain integration process to improve the performance and comparative advantage of manufacturing industries in general, and the Ethiopian basic metal industry in particular. Using primary and literature survey data, the study has been carried-out through SPSS correlation and regression analysis methods. The study shows that the adoption and utilization of cloud-based services in supply chain integration leads to improvements in both the performance and competitiveness of the firm at regional and global levels. In particular, the cloud-based supply chain integration strategies provide enhanced resource utilization, reduce the constraints on information flow in the firm, increase product and service visibility, and create flexibility within systems within supply chain partners, and manufacturing industries as a whole. According to these investigations, cloud computing has a great impact on supply chain integrations so as to improve the performance and comparative advantage of the firm. Thus, effective and efficient utilization of cloud-based supply chain integrations has a higher positive impact on firm performance and competitiveness. Furthermore, this study demonstrates that data privacy, information risk, security issues, cloud architecture, process customization, and trust among collaborators must be prioritized for long-term supply chain systems and joint ventures in cloud-based supply chain integration.

Enhancing Basic Metal Industry Global Competitiveness Through Total Quality Management, Supply Chain Management & Just- In -Time

The selection and implantation of sufficient and appropriate continuous improvement strategy are the key success factors for improving firm performance and enhancement of competitive advantage on manufacturing industries. As a result special role are given to Continuous improvement programs such as Supply Chain Management (SCM), Six-Sigma, Total Quality Management (TQM), Kaizen, Just-in-Time (JIT) and Total Productive Maintenance (TPM) in a production system, which attain sustainable business environment and competitive advantage. Thus, the aim of the study is to study the challenges and trends of global competitiveness of basic metal industries, investigates the way of integrating continuous improvement tools and then developed an integrated continuous improvement model from TQM, JIT, and SCM to improve the global competitiveness of steel and metal industries. From numerous contentious improvement programs, we have to emphasize integrated SCM, JIT and TQM implementation on basic metal industries to improve competitive performance. Mainly the study was conducted through field observation, questioner, company reports used as primary data and literature review of research articles, books, manuals, magazines, and electronic-sources, which used as secondary data. The survey data is analyzed using descriptive analysis, SPCT (fishbone diagram).The result found that poor supply chain system, workforce, raw material uncertainty, energy fluctuation, outdated technology, manufacturing systems, financial and logistics problems were identified as the obstacles and influence the Ethiopian basic metal industry performance and global competitiveness. Thus, to tackle the problems an integrated continuous improving model was developed to improve the performance and global competitiveness of basic metal industries.

Real Exchange Rate Shocks and Export-Oriented Businesses in Iran: An Empirical Analysis Using NARDL Model

In this paper, the asymmetric and nonlinear effects of the real exchange rate shocks on different export-oriented businesses, i.e. Petrochemical, Basic Metal, and Mining industries stock indexes, in Tehran Stock Exchange is examined. From the policymakers’ perspective, this idea is theoretically interpreted as a of hypothesis that refers to the intervention of central banks in foreign exchange markets to restrict currency appreciation rather than depreciation to defend export competitiveness. To this aim, in addition to the main variables, the monthly time series data of the control variables, i.e. inflation, OPEC oil price, and international sanctions, from 2012:01 to 2020:01 are used. Our findings based on the NARDL approach illustrate that not only have exchange rate shocks significant effects on different stock indexes, but these relationships are asymmetric and nonlinear. Moreover, the results have confirmed the fear of depreciations hypothesis in the export-oriented industries, that means the central bank of Iran tends to pursue the “leaning-against-the-depreciation-wind” policy rather than “leaning-against-the-appreciation-wind” one.

Conventional procedurevis-à-visbootstrap-based corrections of efficiency analyses

PurposeThis paper aims to evaluate the performance of basic metals industry in India and analyze its determinants, using data envelopment analysis (DEA) method. It also intends to compare the results through conventional two-stage and bootstrap-based inferences.Design/methodology/approachConsidering technical efficiency as a measure of performance, this paper specifically investigates whether the participation of a firm in the global market affects its performance. The conventional two-stage procedure is used to test the export intensity and firm performance nexus. The bootstrap-based algorithms (by Simar and Wilson, 2007) are used to correct the bias and serial correlation issues involved in the conventional approach.FindingsThe result shows a negative relation between export intensity and firm performance while following the conventional procedure. Even after accounting for serial correlation, the relation remains more or less similar to that of conventional analysis. However, a strong negative relation between export intensity and firm performance is not observed in a more reliable inference obtained after correcting for possible bias as well as serial correlation.Research limitations/implicationsThis paper is based on cross-sectional analysis, and a more reliable result can be obtained by considering a larger sample and longer period.Originality/valueThis paper shows how the conventional two-stage procedure may result in misleading inferences due to bias in the estimation of efficiency scores and the serial correlation during the second stage inferential analysis. This paper also empirically exemplifies how the double bootstrap DEA procedure can overcome these limitations of the conventional two-stage approach.

Identifying path of Global Financial Crisis Contagion Direction on Industries of Iran Stock Market

Simultaneous understanding of volatilities and changes in financial markets is very important to optimize the portfolio and risk management methods. The 2008 financial crisis led into devaluation of most assets, increased volatilities and endangered several institutional investors' survival. When the stock market' correlation is highly enhanced, risk and return management with the classic portfolio theory becomes severely challenging. In this study, to manage systematic and non-systematic risks by investors and policymakers in case of similar financial crises, the Effect of global financial crisis contagion is examined through the path of S&P500 global index, and DFM regional index of different industries of Iran Stock Market is examined using DFGM contagion test and stochastic Ornstein Uhlenbech process. The results show that Dubai Stock Market has an important role in crisis expansion into different sectors of Iran Stock Markets so that the fundamental contagion effects are channelled via this direction. Also, according to the results, the starting point of the global financial crisis contagion was the basic metals industry, and the contagin happened in metal ores and petroleum products sectors with different rates. Finally, the global financial crisis is spread into different industries of Iran Stock Market via financial links and not trough commercial ones. Identifying the direction of contagion of financial crisis provides an opportunity for investors to apply hedging and asset allocation strategies optimally.

TOPSIS Yönteminde Maliyet ve Karlılık Oranlarının Kullanılmasıyla Finansal Performansın Ölçümü: Ana Metal Sanayi Sektöründen Bir Şirket Örneği S

TOPSIS method which is one of the multi-criteria decision-making methods, is one of the most widely used methods in measuring the financial performance of enterprises. In the TOPSIS method, the most cost-effective, the highest-benefit option is the optimal option. For accurate selection of financial ratios to be used in the TOPSIS method, the features of the enterprise whose performance is to be measured should be considered and such a selection process has a positive effect on the success of the analysis. The use of the cost and profitability ratios, obtained from financial statements, in the financial performance analysis carried out with the TOPSIS method in production enterprises is quite appropriate to the essence of the method. The aim of this study is to test the success of cost and profitability ratios in the financial performance analysis which will be carried out with the TOPSIS method in production enterprises. Accordingly, the success of a company listed in the Istanbul Stock Exchange Basic Metal Industry Index was calculated by years and the accuracy of the obtained results and the economic reasons underneath were examined. According to the cost and profitability ratios between 2008 and 2017, 2008 was the most successful year and 2015 was the least successful year. Although the findings of the analysis are confirmed by the results of the fundamental analysis, national and international developments in the sector support the results of the analysis. As a result, the use of cost and profitability ratios with the TOPSIS method in the study revealed accurate results.