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    COMPARISON OF OECD COUNTRIES WITH CLUSTERING ALGORITHMS ACCORDING TO THE ECONOMIC FREEDOM INDEX
    (Rtu Press, 2018) Bayram, Seliha Secil; Ersoz, Filiz
    The economic freedom scores of all United Nations member countries are published annually by the Heritage Foundation under the Economic Freedom Index. The concept of freedom is an important ideal for humanity. In terms of countries, economic freedom can be regarded as a sign of sustainable growth and prosperity. Countries included in the index are scored and ranked by twelve independent variables that determine the economic freedom score. In this study, Clustering models have been used to find relationships between variables that determine the economic freedom score of Organization for Economic Co-operation and Development (OECD) member countries according to the 2018 Economic Freedom Index. OECD member countries have been identified with similar and non-similar countries according to selected indicators. As a result of the study, the best cluster selection was made by comparing the different clustering algorithms, and the similarities and differences between the OECD countries in the literature are presented.
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    Defective products management in a furniture production company: A data mining approach
    (Wiley, 2022) Ersoz, Taner; Guven, Ilker; Ersoz, Filiz
    Quality is one of the main focuses of the manufacturing companies. Therefore, this issue takes attention of many researchers from both academic and professional environment. In industries like furniture where company types are most likely workshop or small-medium enterprise and production method is traditional, effective methods to prevent faulty production must be considered. Traditional or statistical methods are good to track defective products and keep the within desired levels, however not as good to prevent them from occurring. These methods come with an acceptance to some level of defective production. In this study, it is aimed to evaluate the in the furniture production sector and to reveal the source of the defective production and the factors that cause the defect in terms of which department. In addition, finding the most appropriate methods to accurately analyze the data coming from the company is another research topic of this study. In the research, artificial neural networks and decision tree models were established and inferences were made from the data sets. The model established in the application revealed the causes of the problems experienced in the production process of the company, thus root cause of defective products can be found and prevented. By using data mining techniques, this study developed an effective approach to improve the quality of the production process and to predict and prevent errors before they occur. According to results of the study classification and regression tree algorithm outperformed other methods by yielding 90.12% correct prediction rate. 87.5% of the defects caused by cover and seat cushion problems account for defects in the textile department.
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    Digital Transformation: Digital Maturity Model for Turkish Businesses
    (Gazi Univ, 2023) Merdin, Deniz; Ersoz, Filiz; Taskin, Harun
    Changing market expectations and the increasing prevalence of the new technological trend in the world force businesses for digital transformation. However, the late realization of transformation opportunities may have devastating effects on businesses. As the first step of digital transformation, it is necessary to determine the status and deficiencies of businesses. Therefore, businesses need to make a comprehensive assessment with the digital maturity model. This study was conducted to provide businesses with an idea about the relevant digital transformation processes, to direct them toward the processes, and to support these activities when they are initiated. In the study, seven scales were developed, and the dimensions of the digital maturity model were formed. The dimensions of model were determined as strategy, customers, employees, process management, technology and data management, organizational culture, and innovation. This study aimed to examine the reliability and validity of the dimensions of the digital maturity model developed. In this context, the developed scales were applied to businesses in Turkey, and explanatory factor analysis (EFA) and validity analysis were performed. The scales were updated according to the analysis results. Moreover, the analysis results of the study were also used to specify the criteria of the model. The findings indicated that the developed scales were usable. It was purposed to provide researchers and businesses with significant opportunities since the model had a wide area of application and included environmental elements.
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    Effects of memory and genetic operators on Artificial Bee Colony algorithm for Single Container Loading problem
    (Elsevier, 2021) Bayraktar, Tugrul; Ersoz, Filiz; Kubat, Cemalettin
    The Artificial Bee Colony (ABC) algorithm is widely used to achieve optimum solution in a short time in integer-based optimization problems. However, the complexity of integer-based problems such as Knapsack Problems (KP) requires robust algorithms to avoid excessive solution search time. ABC algorithm that provides both the exploitation and the exploration approach is used as an alternative approach for various KP problems in the literature. However, it is rarely used for the Single Container Loading problem (SCLP) which is an important part of the transportation systems. In this study, the exploitation and exploration aspects of the ABC algorithm are improved by using memory mechanisms and genetic operators to develop three different hybrid ABC algorithms. The developed algorithms and the basic ABC algorithm are applied to a SCLP dataset from the literature to observe the effects of the memory mechanism and the genetic operators separately. Besides, a joint hybrid ABC algorithm using both reinforcement approaches is proposed to solve the SCLP. The results show that the joint hybrid ABC algorithm has emerged as a promising approach to solving SCLP with an average performance, and the genetic operators are more effective than the memory mechanism to develop a hybrid ABC algorithm. (C) 2021 Elsevier B.V. All rights reserved.
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    Health Information Management and Patient Profile Estimation Using Data Mining Classification Techniques
    (Sage Publications India Pvt Ltd, 2023) Simsek, Duygu Karabulut; Ersoz, Filiz
    Nowadays, the rapid advancement of technological developments consists of complex and large databases. Software programmes are needed because it can be very difficult to process the complex data. The aim of the study is to reveal the patient profile by data mining of the hospital information system and to help make strategic decisions according to patient profile analysis in the establishment of a new hospital or additional hospital departments in the future. The application of data mining in the health sector provides the sector with a faster, reliable and different perspective on decision-making by reaching the desired data. For this purpose, data mining decision tree algorithms were applied, and patient profile estimations were found by using the data of patients receiving service from a private health institution with hospitals in different settlements. By categorising the characteristics of the individuals receiving service and the branches from which they receive service, it was revealed with which variables the patient profile is related. It is thought that determining patient profile will help to follow a more accurate path in the stage of meeting their demands.
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    Intolerance of Uncertainty and Coping Mechanisms in Nonclinical Young Subjects
    (Turkish Neuropsychiatry Assoc-Turk Noropsikiyatri Dernegi, 2015) Doruk, Ali; Dugenci, Muharrem; Ersoz, Filiz; Oznur, Taner
    Introduction: We aimed to explore the relationship between intolerance of uncertainty (IU) and coping mechanisms in a nonclinical sample with the same age and educational level. Methods: The Coping Orientations to Problems Experienced (COPE) scale was used to evaluate the coping mechanisms. The IU scale was used to evaluate IU situations. Results: We found that the negative impact of uncertainty on the action in female students was greater than males. While female students used more planning, instrumental support, reinterpretation, religion, emotional support, venting, and mental disengagement coping styles, male students used more humor, denial, and alcohol/drug abuse coping styles. Subjects with psychological problems had higher IU scores and used some more coping mechanisms (restraint, acceptance, behavioral disengagement, and alcohol/drug abuse) than the others. Conclusion: Our results suggest that healthy subjects use different coping styles and respond differently to uncertainty in both genders.
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    Measuring carbon performance for sustainable green supply chain practices: a developing country scenario
    (Springer, 2020) Ali, Sadia Samar; Kaur, Rajbir; Ersoz, Filiz; Altaf, Bothinah; Basu, Arati; Weber, Gerhard-Wilhelm
    Carbon emissions due to economic activities are recognized to be global problem. Governments of all countries need to evolve environmental policies and practices for large-scale collective actions to regulate green house gas emission. Fuel quality standards for vehicles, stricter codes for construction, emission limits for industrial units and power plants are some of measures advocated to speed up emission control. This study investigates how far different sectors of a developing economy are able to manage green supply chain with respect to 4 aspects of environmental practices viz. Green procurement, green logistics, green products and process designs and regulatory framework. Globally, corporate social responsibility (CSR) assumes significance in recent years not only with respect to societal issues but also for environmental protection. Research suggests that CSR department creates culture for implementation of CSR activities. We investigate whether CSR departments in the sample organizations have made any difference in achieving emission control objectives. Data are from manufacturing organizations in a congested industrial region of India. We apply non-parametric Kruskal-Wallis and Mann-Whitney tests; then regression analysis is carried out to ascertain predictability of carbon reduction performance with respect to 4 environmental constructs. Results highlight positive roles of inclusion of green enablers-green procurement, green logistics, green product and process design as contributory factors for improvement in carbon performance and reveal that green logistics in the given scenario need major improvement in carbon performance. Our model also considers the impact of size of the organization on carbon performance in terms of workforce.
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    Predictive analytics in human resources using machine learning and data mining
    (2023) Ersoz, Taner; Ersoz, Filiz; Bedir, Emre
    Human resource management information systems (HRIS) are rapidly evolving as a result of today's technologies and global technological developments. With the digitalization of businesses, it is widely used in predictive applications in human resources (HR) and HRIS. HR and HRIS, better managing human resources data and making more accurate and reliable decisions are of critical importance for businesses. In this field, data mining and machine learning approaches are used to reveal meaningful relationships and trends between data in management decisions through predictive analysis. Both approaches are very important in the field of HR and are very effective for businesses to transform data sets into useful information. It helps businesses understand trends that can lead to more accurate and reliable business decisions by using analytical capabilities. Within the scope of this study, research was conducted on the use of the HRIS system with white-collar employees of a company in the automotive sector in Bursa. The cost, time saving and strategic impact of the human resources information system on the company and information technology infrastructure, its differences and relationships according to the department worked, age, gender and education level were investigated through statistics and data mining. Knime and SPSS Statistics programs, which are machine learning tools, were used in the research. HRIS results were evaluated and suggestions were made for future planning.
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    Process Improvement in Furniture Manufacturing: A Case Study
    (Ieee, 2018) Ersoz, Filiz; Ersoz, Taner; Peker, Hamza
    Today, with the increasing competition, enterprises are working to increase the product quality in order to meet the demands of the customers and increase the market share. The Six Sigma philosophy that emerged in the 1970s is a study to reduce the costs of poor quality in the production and service process. In this study, Six Sigma philosophy was applied in order to reduce the cycle time of the diamond sofa product produced by a furniture enterprise. Firstly, the production line was examined in detail by SIPOC analysis method and the cycle times of the processes were collected by chronometry method. Then, statistical methods such as project identification document, Pareto diagram, Fishbone diagram were used. The control of the change in cycle time in the production of diamond sofa was calculated by ARENA 9.0 simulation program.
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    Project and cost-based evaluation of solar energy performance in three different geographical regions of Turkey: Investment analysis application
    (Elsevier - Division Reed Elsevier India Pvt Ltd, 2019) Ozcan, Onur; Ersoz, Filiz
    In this study, the implementation of photovoltaic system for the evaluation of the solar energy potential of Turkey is presented in comparative. In the application phase, PV system sizing is performed for a designated area. This area consists of 5 sub-production areas and occupies 180,330 m(2) . The total nominal installed power of the PV system is 8865 MW. Production and financial performance of the system, provided that the equipment used in the system is the same as the operated personnel; Turkey's socioeconomic development has been compared for the cities of Istanbul, Izmir and Ankara. The regions' annual solar radiation data and their respective optimal solar panel angles; The Pvsyst program was accessed using the relevant literature and GEPA data and annual production calculations were made for the specified cities of the system. The results of the production simulations, the area used (parcel) and the equipment were used to perform financial calculations. In the financial analysis part of the study, all costs incurred during the lifetime of the project were calculated. The return on investment and project Payback times were calculated for the 3 alternatives created. For the three cities, comparisons have been made for annual production and financial performance, and the results of the investments have been investigated. (C) 2019 Karabuk University. Publishing services by Elsevier B.V.
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    A quantitative analysis of low carbon performance in industrial sectors of developing world
    (Elsevier Sci Ltd, 2021) Ali, Sadia Samar; Ersoz, Filiz; Kaur, Rajbir; Altaf, Bothinah; Weber, Gerhard-Wilhelm
    The world is moving towards carbon restricted scenario where excellence and growth in business is contingent upon good governance and excellent low carbon emission related policies, strategies, and long-term values to stay ahead in competition. Existing research insists that multiple external pressures push organizations to navigate towards sustainable internal practices leading to low carbon performance. Rooted in the theoretical concepts of institutional and contingency theory, this study explores the impact of uncertainties of external business environment on complexities of business internal environment. Hypothesis developed is tested by taking data from 134 manufacturing organizations based on their size and sector, and sustainability practices. The data is analyzed using correlation and multiple linear regression analysis (MLR). Chi-square, Cramer's V and MLR is used for regression analysis and correlation whereas ANN is applied for validating the robustness of the MLR model. Comparison of results of MLR and ANN provide similar outcomes. Results rule out the impact of size of organizations on carbon performance. 'Environmental regulations activities' and 'political and legal regulations' along with efficient implementation of Corporate Social Responsibility (CSR)/Sustainability activities backed by top management are the main contributors of low carbon performance. The study identified the 'interest of foreign investors' in the region as one of areas requiring further exploration for improved carbon performance. Mode of technology came as major roadblock for low carbon performance and requires top management to ponder over for improved results. Also, found wanting for improvement are-modes of transport, employee motivation and external stakeholders. (C) 2020 Elsevier Ltd. All rights reserved.
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    Stock Detection of Iron and Steel Products with Image Processing and SDSS Decision Support System
    (Hashemite Univ, 2024) Akinci, Ismail Burak; Ersoz, Filiz; Boran, Semra
    The iron and steel industry is one of the leading sectors contributing to the economic power of countries. It is incorporated into numerous fields, including automotive, construction, manufacturing, agriculture, defense, and healthcare. Today, the increasing supply and demand balance in iron and steel products makes the stock management and control of the products crucial. The large quantity and variety of products in the iron and steel industry make stock management and control difficult. Creating stock for the business is a very costly process and is one of the important elements of the business. Successful stock management can make the business financially advantageous. This study aims to estimate the stock costs of different products and quantities according to different dates found in the business by using data mining. For this purpose, data mining classifier models are used, and estimated costs are found. By establishing a stock tracking system throughout the supply chain, the business should register all inventory movements of the products. It should work in an integrated way with stock management costing. Thus, unforeseen decreases and financial losses in products can be detected. The iron and steel sector, a key player in enhancing competition among nations and shaping the economic landscape, caters to the needs of various industries through its commitment to sustainable steel production. The industry has encountered challenges due to the swift advancements in recent years, impacting the costs associated with iron and steel products and posing issues for the sector. Tackling these challenges is essential for fostering national development and enhancing the competitiveness of businesses. This research delves into the elements influencing the expenses within the iron and steel industry, emphasizing the importance of minimizing factors leading to elevated costs. An automation system was developed using image processing techniques, and iron and steel products were analyzed. In the automation system, k -means and twostep, one of the clustering analysis methods, was applied.A decision support system defined as SDSS (Steel Decision Support System) and BIPS (Burak's Image Processing System)has been developed to determine the inventory costs of iron and steel products, adopting a data mining approach and including clustering methods to reveal similar inventory costs. Measurements with the SDSS-BIPS automation system were 94.4% successful. Withcost levels were determined by matching the products in the iron and steel business database with their costs. (c) 2024 Jordan Journal of Mechanical and Industrial Engineering. All rights reserved