Endüstri Mühendisliği Bölümü / Department of Industrial Engineering

Permanent URI for this collectionhttps://hdl.handle.net/11413/6819

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Triggering Corporate Sustainable Performance in Construction Sector through Green Training: Moderating Effect of Barrier in Construction Management
(Yıldız Teknik Üniversitesi Rektörlüğü, 2023) WARIS, IMRAN; ÜLKÜ, İLAYDA
Construction barrier plays a significant but negative role-play between green training and corporate sustainability performance due to limited resources. The research question in this study is to explore the relationship between green training and sustainable performance in the construction industry, while also considering the moderating role of construction barriers. This study gives extensive knowledge of green training and corporate sustainability performance. Data is obtained from 225 employees using a convenience sampling technique from the construction sector. The research employed SPSS/PROCESS and follows a cross-sectional research design. Study findings show green training is an antecedent of the sustainable performance of the construction sector. The result shows that Green training significantly and positive role-play in sustainable performance. Person-organization fit theory covers the whole phenomenon. That focuses on productivity, performance, and personal well-being. Under P-O fit theory results are showing the compatibility between a person and an organization where they are doing work. This study's results highlight the green training that transforms the em- employee’s mindset towards corporate sustainable performance. In the future, need longitudinal studies that will be more acceptable. This study provides insights to the managers, policymakers, and practitioners of sustainable environment and performance. The current study will help economies in the developing world, such as Pakistan.
The Impact of Perceived COVID-19 Threat and Death Anxiety on Buying Behavior Among Academics: A Comparison Between Türkiye and Northern European Countries
(Türk Kooperatifçilik Kurumu, 2023) Turna, Gülçin Bilgin; Pekmezci, Halil; IŞIK, OKAY
Academics, one of the occupational groups exposed to isolation in the COVID-19 epidemic, tried to adapt to the remote working regulations by not being able to maintain the social environment they were accustomed to in universities. This study aimed to examine the impact of perceived COVID-19 threat and death anxiety on revenge buying behavior by comparing academics who work in Türkiye and Northern Europe (the United Kingdom, the Netherlands, and Norway). “Revenge buying” is a term popularized in the marketing field during the global COVID-19 quarantines. It refers to the surge in shopping desire observed after the lifting of pandemic-related lockdown measures. Data was collected online by using three scales: the “Perceived COVID-19 Threat Scale”, “Templer’s Death Anxiety Scale” and “Revenge Buying Behavior Scale”. The sample consisted of total 327 academics: 163 from Türkiye (TR) and 164 from Northern Europe (NE). Structural Equation Modelling (SEM) was adopted for the analyses. The results showed that academics in NE were more prone to revenge buying behavior than TR. As the perceived threat of COVID-19 and death anxiety increased, it was determined that academics working in both TR and NE have tendencies for revenge buying. However, there was insufficient evidence to support the mediating role of death anxiety in the relationship between perceived COVID-19 threat and revenge buying behavior. In this interdisciplinary study, statistically significant differences identified based on the demographic characteristics of academics working in TR and NE have also been indicated.
Examining the Impacts of Military Expenditures on Economic Productivity: A System Dynamics Approach
(SAGE Publications Ltd., 2023) Damla Gönül-Sezer, Eylül; DEMİREL, DUYGUN FATİH
The relationship between military expenditures and economic productivity has taken the attention of many researchers and there exist an important number of studies approaching the topic through several techniques. However, there is no consensus among the scholars whether military expenditures trigger economic growth, productivity, and other macroeconomic indicators. Such arguments are mainly due to unclear results obtained from the existing studies, in which the complex relationships between military expenditures and macroeconomics are not fully incorporated. Considering the bidirectional and nonlinear relationships among macroeconomic indicators and complex feedback mechanisms, a system dynamics (SD) model for examining the impacts of military expenditures on economic productivity in Turkey is proposed. The proposed SD model aims to reflect the complex environment surrounding the military spending–economic productivity nexus and to analyze the feedback structures that lead to miscellaneous consequences with delays. A stock–flow model is developed to represent the complex nonlinear relationships and causalities between the variables. Data from SIPRI, the World Bank, and several local statistical sources covering the years 2009–2018 are utilized to simulate the existing case, warfare in neighbors, economic shrinkage scenarios, and the combination of the latter two. The results obtained from the scenarios suggest that short fixes such as importing military products instead of national investments give rise to chronic issues like continual dependence on foreign supply, hence, leading to decrease in overall economic growth. To the best of our knowledge, this is the first attempt to integrate SD methodology with military expenditure and economic productivity analysis.
An Integrated Assessment of Food Waste Model Through Intuitionistic Fuzzy Cognitive Maps
(Elsevier, 2023) EMİR, OĞUZ; Ekici, Şule Önsel
In recent years, the waste management field has received substantial attention from policymakers, organizations, academics, and researchers due to increased focus on sustainability and carbon footprint reduction as well as concerns around rapid depletion of natural resources, public health, and environmental impact. Studies on food waste management have become especially important given the dramatic growth of the world's population and global hunger and malnutrition crisis. Considering the fact that one-third of the world's food supply is wasted or lost annually while hundreds of millions of people are living with food insecurity, it is easy to understand the importance of research studying appropriate food waste management actions for sustainability. Since the subject of food waste involves complicated linkages, the determination of a suitable model is pivotal. Integrated assessment models (IAMs) have been commonly used to uncover hidden patterns and present insights to policymakers. Furthermore, these models are well-designed to integrate data, information, and multidisciplinary knowledge into a single framework. This project presents a Fuzzy Cognitive Map (FCM) extended with intuitionistic fuzzy sets using the documentary coding method. This intuitionistic FCM (iFCM) is used to analyze the primary factors, explore the interactions between food waste factors, and prioritize some policies to reduce food waste by incorporating hesitancy weight factors representing the lack of information. Then several what-if scenario analyses are generated to review the interrelationships between factors in the developed model and facilitate a decision-making process for researchers. Eventually, it is concluded that food waste reduction is achievable with the implementation of the right policies, and this also improves the other concepts such as the intention not to food waste, shopping routines, and planning routines.
Forecasting of Turkey's Total Electricity Consumption in Sectoral Bases Using Machine Learning Algorithms
(Institute of Electrical and Electronics Engineers Inc., 2022) HAJJAR, MHD KHAIR; ÜLKÜ, İLAYDA
Electrical energy is a milestone in the economic growth of each country. This study forecasts the sectoral and total electricity consumption in Turkey until the year 2050. This study, utilize two distinct time series forecasting methods namely Multilayer Perceptron (MLP) and Sequential Minimal Optimization (SMO) as a model to generate the forecasting formulas. The sectoral and total electricity consumption for Turkey from the year 1970 to 2020 was obtained from the Turkish Statistical Institute and fed to the models to forecast the upcoming years. The two models were evaluated and compared using determination coefficient R2 and mean absolute percentage error MAPE. It is found that MLP performed better in forecasting the commercial, governmental, illumination and other sectors and SMO performed better in forecasting the industrial and household sectors alongside the total electricity consumption. © 2022 IEEE.
Global Impact of the Pandemic on Education: A Study of Natural Language Processing
(Institute of Electrical and Electronics Engineers Inc., 2022) AYAZ, TEOMAN BERKAY; USLU, MUHAMMED SAFA; AĞCABAY, İBRAHİM; AHMED, FARUK; KORKMAZ, ÖMER FARUK; KÜREKSİZ, MESUT; ULUÇAM, EMRE; YILDIRIM, ELİF; KOCAÇINAR, BÜŞRA; AKBULUT, FATMA PATLAR
School closures due to the Covid-19 pandemic have changed education forever and we have witnessed the rise of online learning platforms. The education units of the countries made great efforts to adapt to this new order. The expanding, quick spread of the virus and careful steps have prompted the quest for reasonable choices for continuing education to guarantee students get appropriate education and are not impacted logically or mentally. Different methods were attempted to understand how students were affected by this big change. In addition to the significance of traditional surveys and consulting services, the utilization of social media analysis is used as a supportive approach. This paper analyzes the feedback of students on social media via tweets. Deep sentiment analysis is employed to identify embedded emotions such as negative, neutral, and positive. We also aimed to classify irrelevant tweets as the fourth category. Our experiments showed that the tweets are mostly biased toward negative emotions. © 2022 IEEE.
Prediction of University Students' Subjective Well-Being with Sleep and Physical Activity Data using Classification Algorithms
(Elsevier B.V., 2022) KILIÇ, AKİF CAN; Karakuş, Ahmet; Alptekin, Emre
Daily activities affect mental health. One of the most used scales is "subjective well-being (SWB)", which is a self-reported questionnaire. This study aimed to predict SWBs using step count, heart rate and sleep duration data from sensors instead of questionnaires. NetHealth data from the University of Notre Dame1 has been used. Attributes included average daily steps, average heart rate, heartbeat standard deviation, average sleep duration, and sleep duration deviation. Preprocessing, processing, classification, and evaluation followed. Naive Bayes, K-Nearest Neighbors (KNN), Support Vector Machine (SVM), and Ensemble classifiers were used. Performance metrics include accuracy, precision, recall, F1-Score, and ROC (Receiver Operating Characteristic) curves. Model accuracy was 62%. This indicates that machine learning could be beneficial in detecting SWB levels using sensor data. © 2022 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0) Peer-review under responsibility of the scientific committee of the 26th International Conference on Knowledge-Based and Intelligent Information & Engineering Systems (KES 2022).
A New Organizational Agility Assessment Approach Applied in the Logistics Industry
(Melih Topaloğlu, 2022) GERGİN, ZEYNEP; AK, MEHMET MİRAÇ; ÇOLAK, EREN; KAYALAR, MUSTAFA; YAVAŞOĞLU, CAN DENİZ
Purpose – Today, the variable and numerous demands from customers, changing market conditions and the depletion of resources have made agile working methods more necessary than in the past. In this study, it is aimed to increase the awareness of the concept of agility in companies and to accelerate the agile transformation processes. Design/Methodology/Approach – In this research study, a new measurement scale is developed to assess the current agility levels of the companies operating in logistics sector. The new instrument measures the agility levels of seven business processes for eight components of agility and an aggregated level for company agility score is identified. The detailed questionnaire, which is composed of 51 questions, is applied to 4 companies operating in the logistics sector, differing in size and structure, and their current agility level is evaluated. The weights for business processes and agility component are also defined sector based via Analytic Hierarchy Process (AHP) during the calculation of the final agility score. Due to the detailed structure of the tool, the results gave opportunity for a detailed analysis of the strengths and weaknesses of the business processes. Hence, custom recommendations could be made in order to improve the agility level of the company. Findings – It is seen that there are common elements like technology and innovation, that are highly effective on agility for every company studied, as well as there are prior elements to be focused that differ with the size and structure of the company. It is also concluded that companies need to be more agile as their operation size increases and they can meet these needs with managers who have an innovative perspective. Discussion – This study has developed its own methodology, its own research tool and recommendations, unlike previous studies in the field of agility. Aiming to bring a new link to the academic chain with these features, this project can shed light on more diverse studies in the field of agility.
Analyzing the Wastewater Treatment Facility Location/Network Design Problem via System Dynamics: Antalya, Turkey Case
(Academic Press Ltd. - Elsevier Science Ltd., 2022) DEMİREL, DUYGUN FATİH; Gönül-Sezer, Eylül Damla; Pehlivan, Seyda Alperen
Wastewater treatment facility location selection and network design issues have become attractive topics in the field of wastewater management due to increasing human population, resource scarcity, environmental concerns, and rise of necessity for sustainable solutions for future policy designs. Especially in areas where the demand for wastewater treatment increases dramatically over the years because of reasons such as high migration levels, rapid industrialization, and tourism activities, the problem turns out to be more critical and dynamic. The existing studies try to deal with the issue through mathematical modeling approaches based on optimization perspectives, which require significant computational effort. In this study, an alternative approach based on system dynamics (SD) method is proposed to examine the complex dynamic and nonlinear structure of waste-water treatment facility location selection and network design problems. The proposed SD simulation model is designed for a densely populated industrial and tourism spot, the city of Antalya, located on the Mediterranean coast of Turkey. The model is capable of determining where and when to build a new wastewater treatment facility as well as generating the generic wastewater network structure to be built for the five districts situated in the city center based on cost issues for 2015-2040 period. In addition, the impacts of demand level changes for wastewater treatment due to population variations are analyzed via several scenarios to help decision makers to develop sustainable and cost-efficient management policies. Although SD is a frequently utilized approach in the water/wastewater management arena, to the best of our knowledge, this study is the first attempt to examine the complex and dynamic nature of wastewater treatment facility location selection and network design problems through SD approach.
Forecasting Greenhouse Gas Emissions Based on Different Machine Learning Algorithms
(Springer International Publishing, 2022) ÜLKÜ, İLAYDA; Ülkü, Eyüp Emre
With the increase in greenhouse gas emissions, climate change is occurring in the atmosphere. Although the energy production for Turkey is increased at a high rate, the greenhouse gas emissions are still high currently. Problems that seem to be very complex can be predicted with different algorithms without difficulty. Due to fact that artificial intelligence is often included in the studies to evaluate the solution performance and make comparisons with the obtained solutions. In this study, machine learning algorithms are used to compare and predict greenhouse gas emissions. Carbon dioxide (CO2), nitrous oxide (N2O), methane (CH4), and fluorinated gases (F-gases) are considered direct greenhouse gases originating from the agriculture and waste sectors, energy, industrial processes, and product use, within the scope of greenhouse gas emission statistics. Compared to different machine learning methods, support vector machines can be considered an advantageous estimation method since they can generalize more details. On the other hand, the artificial neural network algorithm is one of the most commonly used machine learning algorithms in terms of classification, optimization, estimation, regression, and pattern tracking. From this point of view, this study aims to predict greenhouse gas emissions using artificial neural network algorithms and support vector machines by estimating CO2, CH4, N2O, and F-gases from greenhouse gases. The data set was obtained from the Turkish Statistical Institute and the years are included between 1990 and 2019. All analyzes were performed using MATLAB version 2019b software.
Implementation of MCDM Approaches for a Real-Life Location Selection Problem: A Case Study of Consumer Goods Sector
(Springer-Verlag Singapore Pte Ltd., 2022) EMİR, OĞUZ
Selecting a suitable warehouse location elevates the supply chain performance by reducing the lead times and increasing the response efficiency. Hence, location selection emerges as a strategic decision-making problem for competitive advantage. In this paper, a real decision-making problem of a multinational company operating in the consumer goods sector has been examined. The company's Turkey office is responsible for the operations in many regions such as Middle East, Africa, Central Asia, and Eastern Europe. A case study is handled by the logistics network planning team of the company to evaluate the new warehouse request coming from the regional sales team. For this decision problem, two different multi-criteria decision-making methods TOPSIS and VIKOR, are employed to evaluate four alternative scenarios. In addition, the AHP technique is also applied to determine criteria weights. The results of both methods revealed the same alternative as the best decision.
The Effect of Seed Value Choice in an Incomplete Fuzzy Preference Relations Guided by Social Influence
(Springer International Publishing, 2022) ÇİÇEKLİ, SEVRA; Gürbüz, Tuncay
Nowadays, there is an enormous increase in alternatives almost in every area with the development of technology. Thus, it has become harder for a single decision maker (DM) to completely evaluate each alternative for a problem on hand. In group decision making (GDM) problems, each DM has a different effect on the final decision because of their background. Also, it has been observed that DM's judgments are affected by those of other DMs in the group. This is defined as social influence (SI). Social Influence Network (SIN) is useful especially when there is incomplete preference information. In this paper, a proposed model has been used to demonstrate the effect of seed value choice in completion process of missing fuzzy information given by DMs.
An Analytical Approach to Machine Layout Design at a High-Pressure Die Casting Manufacturer
(Springer-Verlag Singapore Pte Ltd., 2022) EMİR, OĞUZ; AKTİN, AYŞE TÜLİN
Sahin Metal was founded in 1975 on a 7500 m(2) area in.Istanbul to supply high-pressure aluminum casting parts to a variety of industries, especially automotive manufacturers. 64 different products are produced with various routings through 14 workstations in the plant. Being a Tier 2 company, these products are then sent to Tier 1 firms and finally reach the leading vehicle manufacturers. Nowadays, increasing competition, changing customer demands, and quality targets bring the necessity of restructuring internal processes. In this regard, Sahin Metal plans to rearrange the existing machine layout to minimize the distance traveled between departments by taking into account the material flow. This study aims to determine an efficient machine layout design by implementing analytical approaches. The study is started by visiting the production facility and meeting with the company's engineers to determine the project roadmap. Following that, the data collection process is initiated, and ABC analysis is performed to define product classes. After this identification, two approaches are utilized simultaneously. The Hollier method is employed to find a logical machine arrangement. In addition, a mathematical model based on the Quadratic Assignment Problem (QAP) is developed to obtain the optimum machine layout. The developed integer nonlinear model is solved by CONOPT using GAMS software under various scenarios. Finally, these results are compared with the existing system, and a convenient layout design is proposed to the company.
Review of Fuzzy Multi-Criteria Decision Making Methods for Intelligent Supplier Selection
(Springer International Publishing, 2022) AKBURAK, DİLEK
Supplier selection is a focal process that affects the quality and cost performance of the company. Therefore, it is one of the well-known decision making problems that researchers and practitioners are most interested in. In order to choose the most strategic supplier, determining and prioritizing the selection criteria and choosing the appropriate method(s) directly affect the supply chain performance. In this study, the publications focused on multi-criteria decision making (MCDM) on the supplier selection problems in the uncertain environment are reviewed from 2017 to the present (Feb. 2022). Due to the effect of uncertainty and vagueness, most recent approaches developed for supplier selection, are constructed by integrating MCDM approach(es) with fuzzy set theory. The most widely applied and integrated fuzzy MCDM methods are stated as AHP, ANP, TOPSIS, and VIKOR. These studies are categorized into single or multiple/integrated MCDM approach(es) with different fuzzy sets in various application industries. This study contributes to the literature to examine the most frequently applied and recently developed fuzzy MCDM approaches for intelligent supplier selection by considering various assessment criteria under imprecise environments. Also, newly improved ideas can be proposed with the help of the analysis of studies about intelligent supplier selection up to the present.
Monthly Logistics Planning for a Trading Company
(Springer-Verlag Singapore Pte Ltd., 2022) ÜLKÜ, İLAYDA; KULLAB, HUTHAYFAH; AL MULQI, TALHA; ALMSADI, ALI; ABOUSALEH, RAED
In this study, the transportation problem is considered in order to minimize the total traveling distance between the nodes. The nodes represent the hospitals and the warehouse. The data is obtained by Salem Trading Company (STC) that operates in various medical sectors including Urology, Gynecology, Endoscopy, Surgery, and other operations. STC has two types of vehicles; the big vehicle and the small vehicle. The big vehicle is used for the machines and it contains up to 4 machines. Whereas, the small vehicle is used for delivering the masks and gloves and its capacity is up to 200 boxes. In order to serve all the market need, they currently have a traditional logistics plan to ship products from the main warehouse to the Hospitals located in Qatar. In this study, there are two stages proposed to solve the transportation problem. In the first stage, the assignment problem is used to determine the unused capacity of each vehicle used. Then, the output of the first stage is used as an input in the second stage in order to apply the Vehicle Routing Problem (VRP). With this second stage, the optimal routes are obtained with the minimum cost. Firstly, the required data will be obtained from the company, which include the number of hospitals, the demand of each hospital, the capacity of the vehicles, the distance between hospitals and the warehouse, loading and unloading time of the vehicle, and the volume of the products. This study focus on finding the low-cost weekly logistics plan for hospitals in Qatar. Two mathematical models are developed to solve the problem with GAMS software. As a result, by using the assignment and VRP model, the unused capacity is increased from 38 to 18 and reduce the cost from 2470 TL to 1170 TL as in Qatar the cost of the fuel is not high, also on the time restriction is focused.
Solving the Cutting Stock Problem for a Steel Industry
(Springer-Verlag Singapore Pte Ltd., 2022) ÜLKÜ, İLAYDA; TEKEOĞLU, UĞUR; ÖZLER, MÜGE; ERDAL, NİDA; TOLONAY, YAĞIZ
The problem of stock cutting takes an important place in the steel industry, as it is in many sectors. It is desired to ensure a minimum amount of waste in every manufacturing factory. Reaching the minimum level of waste reduces the costs of factories and companies and strengthens the possibility of making profits. In this way, it is ensured that our resources are used correctly in our developing world. In this study, the Cutting Stock Problem has been handled, in order to minimize the amount of waste within the scope of the complete the study and to solve this problem. In this study, first of all, the size of the steel to be used in line with the customer's request, the process of taking the steel plates from the stock and the molds of the parts to be cut are prepared by the production planning department, and these operations are carried out with the program used in the factory. Then the dies are sent to the workshop for cutting, where they are cut. Before creating the model, the data of the molds made for the previous cut are collected. In order to create the correct mathematical model, the data are collected by making simultaneous measurements at the cutting table. The cutting Stock Problem is used to formulate the molds prepared for cutting and the amount of waste loss is calculated. Determined parameters and constraints GAMS software is used to create a mathematical model using the GAMS MIP (Mixed Integer Programming) method and then analyze the created model. As a conclusion, we investigated 3 different scenarios. According to those experiments, the study with the proposed scenario can achieve a better production line especially about time and waste amount. Those developments can be important earnings for a real production line.
Yalın Sistem Tasarımı İçin Simülasyon Destekli Değer Akış Haritalama Uygulaması
(TMMOB Makina Mühendisleri Odası, 2021) EMİR, OĞUZ; GERGİN, ZEYNEP
İşletmeler, küresel rekabette hayatta kalabilmek için, üretim kaynaklarını daha iyi kullanarak verimliliklerini arttırmak zorundadır. Bu sebeple, katma değerli olmayan faaliyetlerinin ortadan kaldırılması ve süreçlerde sürekli iyileştirme yapılması yoluyla, değer yaratmalarına ve müşterilerinin gereksinimlerini karşılamalarına yardımcı olan yalın yönetim ilkelerini giderek daha fazla uygulamaktadırlar. Bu çalışma, hızlı tüketim ürünleri sektöründe faaliyet gösteren global ölçekteki bir üretim şirketinin, Türkiye tesisine ait paketleme hattında uygulanan bir yalın sistem tasarımı örneğidir. Firma 150’den fazla ülkeye şekerleme üretimi ve dağıtımı yapmaktadır ve söz konusu paketleme hattı bu ürünler için çalışmaktadır. Bu çalışmada üretim sisteminde bulunan paketleme hatlarının performansını arttırmak amaçlanmıştır. Bunun için hatlar yalın yönetimin 7 başlık altında grupladığı israf kalemleri – gereğinden fazla üretim, gereksiz işlemler, fazla stok, hatalı üretimler, taşımalar, gereksiz hareketler ve beklemeler – açısından incelenerek israfları azaltacak şekilde yeniden dizayn edilmiştir. Çalışma, süreç adımlarının mevcut durumdaki genel hat kullanım oranları ve operasyon verimliliklerinin belirlenmesi, Mevcut Durum Değer Akış Haritası (DAH) ile israf kalemlerinin görsel olarak tanımlanması ve istatistiksel süreç kontrol yöntemleri ile analizi ile başlar. Daha sonra, talep miktarı ve termin süresi parametrelerine bağlı olarak hat verimliliğini arttıracak iyileştirme önerileri belirlenir ve değerlendirilen önerilerin hesaplanan sonuçları gelecek durum DAH'ları ile sunulur. Bu çalışmada ayrıca, DAH metodunu desteklemek ve alternatif iyileştirme senaryolarını karşılaştırmak amacıyla Arena yazılımı kullanılarak sistemin simülasyon modeli geliştirilmiştir.
A Comparative Sectoral Analysis of Industry 4.0 Readiness Levels of Turkish SMEs
(Springer Science and Business Media Deutschland GmbH, 2021) EMİR, OĞUZ; GERGİN, ZEYNEP; YÜKSEKTEPE, FADİME ÜNEY; Dündar, Uğurcan; Gençyılmaz, Güneş M.; Çavdarlı, Ali Ihsan
The concept of Industry 4.0 brings new standards and has an essential impact on business models, work organizations, and processes. Nowadays, companies are enforced to perform such challenges as meeting personal customer requests, flexibility, responsiveness, customer-oriented solutions, and delivering continuous value to survive in the competitive market. The most innovative companies are resourceful to integrate new digital tools into their business models by following initial trends. For Small and Medium-Sized Enterprises (SMEs), adopting these new industrial challenges, and responding to them quickly is vital when it is considered that they have 99.8% of the enterprise share of Turkey. To do so, an intensive effort is needed to integrate Industry 4.0 applications within the enterprises. It has been suggested in the literature that manufacturing organizations should begin with understanding their current level of maturity by defining their strengths and weaknesses. Further, a significant effort is required with the collaboration of government, academia, and industry leaders to guide enterprises for improving their capabilities in an objective and standardized manner. Despite the diversity of approaches directed to Industry 4.0 concepts, it is not easy to find a study that measures how the manufacturing sector is adopting Industry 4.0 and compares manufacturing sectors with each other. To this end, broad research is initiated in March 2018 to identify the current situation of SMEs in Turkey. In this paper, it is intended to extend the study by providing a comparative sector analysis for manufacturing SMEs in Turkey in terms of Industry 4.0 transformation. Possible action plan suggestions are presented according to the results obtained at the end of the paper. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.
A Decision Support Tool for Classification of Turkish SMEs’ Industry 4.0 Score Levels
(Springer Science and Business Media Deutschland GmbH, 2021) Dündar, Uğurcan; YÜKSEKTEPE, FADİME ÜNEY; GERGİN, ZEYNEP; EMİR, OĞUZ; Gençyılmaz, Güneş M.; Çavdarlı, Ali İhsan
The concept of industry 4.0 aims to enhance the employment of digitalization with the help of burst in computer usage after the third major revolution in industry. Hence, to keep up with the new trends in industry, companies should transform their processes into digital platforms. In Turkey, 99.8% of the companies are SMEs, so in order to be in the global economic competition Turkey should enable their SMEs to make the process shifts to digitalization. In this manner, in 2018 a project is initiated by KOSGEB to evaluate the readiness of SMEs and help them for Industry 4.0. As the last step of this project, a decision support tool is created with classification algorithm behind. In this paper, chosen instruments are used to predict the Industry 4.0 score of SMEs that are calculated with previously published algorithm in the scope of the project. In terms of classification, three algorithms are compared with ROC metric. CatBoost algorithm which is specifically created for categorical classification is compared with Support Vector Machine algorithms (SVM and µ-SVM) that performed best in previous research. CatBoost outperformed other algorithms and is used as base classification method in decision support tool. This decision support tool will be used in decision making process of KOSGEB to which companies to help. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.
A Novel Genetic Algorithm-Based Improvement Model for Online Communities and Trust Networks
(IOS Press, 2021) Bekmezci, İlker; ERMİŞ, MURAT; Çimen, Egemen Berki
Social network analysis offers an understanding of our modern world, and it affords the ability to represent, analyze and even simulate complex structures. While an unweighted model can be used for online communities, trust or friendship networks should be analyzed with weighted models. To analyze social networks, it is essential to produce realistic social models. However, there are serious differences between social network models and real-life data in terms of their fundamental statistical parameters. In this paper, a genetic algorithm (GA)-based social network improvement method is proposed to produce social networks more similar to real-life data sets. First, it creates a social model based on existing studies in the literature, and then it improves the model with the proposed GA-based approach based on the similarity of the average degree, the k-nearest neighbor, the clustering coefficient, degree distribution and link overlap. This study can be used to model the structural and statistical properties of large-scale societies more realistically. The performance results show that our approach can reduce the dissimilarity between the created social networks and the real-life data sets in terms of their primary statistical properties. It has been shown that the proposed GA-based approach can be used effectively not only in unweighted networks but also in weighted networks.

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