Proceedings of the International Symposium for Production Research 2019

The objective of this study is to examine the influence of machining parameters on surface finish in turning of medical steel. A new approach in modeling surface roughness which uses design of experiments is described in this paper. The values of surface roughness predicted by different models are then compared. Used were adaptive-neuro-fuzzy-inference system (ANFIS). The results showed that the proposed system can significantly increase the accuracy of the product profile when compared to the conventional approaches. The results indicate that the design of experiments with central composition plan modeling technique can be effectively used for the prediction of the surface roughness for medical steel difficult to machining. Optimizations of surface roughness parameters was done by use of ant colony method.

In the defense industry, which is one of the determinants of the political and economic power of the countries, there is a constant need for change, innovation, and modernization in parallel with the technological developments in the world. Therefore, in the field of defense technologies, Military and non-governmental organizations are working to develop high-performance armor systems that will increase mobility by reducing weight and against developing weapon systems. In this paper will be discussed, how artificial intelligence (AI) can be used for the improvement of weapon efficiency.

Todays most important industrial concept for companies is to be able to incorporate new technologies into their planning and monitoring systems. These technologies include sensor systems, cloud technologies, automation, predictive/pre-emptive maintenance, 3D printing, smart warehouses, etc. In order to survive in today’s competitive business environment, this revolution is very important for Small and Medium Sized Enterprises (SMEs) in Turkey. Hence, in order to analyze the relationship between new technologies and Industry 4.0 score of Turkish SMEs, a data mining study is performed in this research. A survey is performed to gather information on the awareness, readiness and interests of SMEs in new technologies in addition to their Industry 4.0 scores. Aim of this study is to predict whether Industry 4.0 scores of SMEs is low or high by using their technology utilizations. As this is a typical data classification problem, many different data classification methods are applied to determine the best alternative by using WEKA software. Among them, the highest prediction accuracy is 69.11%, obtained by Support Vector Machines. Thus, a Turkish SME’s Industry 4.0 score level could be predicted by just investigation of its new technology usage. Therefore, Turkish government could use this approach to determine the current situation of a SME. Moreover, government could determine their supporting programs based the technology usage levels of the SMEs.

There are more data than ever to analyse and support higher education management decisions and trends nationally and internationally. National higher education systems should rely more on data-based decision making, so on data driven decision support systems (DSS). Using education data provided by Eurostat between 2013–2017, a DSS has been built. By using this DSS, enrolled students related gender data analysed in detail. The data dimensions of the research are enrolled students, gender, education level, fields of education. But, a data-driven DSS for all education levels and fields of European countries will be next research subject in order to provide education intelligence for all education policy makers, managers, teachers, academic staff students and families.

Nowadays, it is evident that electricity is an indispensable source of energy in the production sectors when industry 4.0 transformation and sustainability become important at the same time. Electricity consumption forecast has crucial importance for effective energy planning in many production sectors. It is important to predict the total consumption of energy consumption and to make a production plan according to it and therefore to make all the functions in the supply chain cost and optimization plans. In this study, Artificial Neural Networks (ANN) method is used for electricity demand estimation for production processes of cold chain product in the fast moving consumer goods sector (FMCG). The impact of the observed independent variables is analyzed on electricity consumption. Estimates in the model are made for the following periods based on the last three years’ electricity consumption of the one of the big fast moving goods company located in Turkey.

Humanity must be aware of the possibilities for sustainable energy technologies from their childhood onwards in order to enable a clean and prosperous future. Until now, youth receives only limited and mostly theoretical knowledge about new forms of energy usage, which leaves open doors to the usage of conventional energy sources. It is vital that youth develops a solid understanding of renewable energy’s power and its various possible applications, thereby taking into account. Currently, youth are getting increasingly used to learn through transactional forms of communication via their (online) media consumption behaviour and contact with peers through social media. Considering these new communication forms, this study examines if peer-learning methodology (PLM) is an efficient method to train children in increasing their practical knowledge of sustainable energy usage. Nowadays, PLM is used as an educational methodology based on an eclectic integration of multiple theoretical insights from different scientific disciplines, such as developmental psychology, education science and paediatrics, that might be an effective learning methodology. The main idea behind PLM is that in order to educate youth effectively, a communication must motivate the receiver to actively attend to messages and perceive and interpret their content that is provided by peers, include iterative and transactional solicitation of feedback, and activate elaboration of message arguments and counterarguments to encourage individuals to move through the process of learning. In the current study, we investigated how students learn from other peers that have made posters that reflect on new and sustainable forms of energy. In total 14 posters and four movies were shown during an exhibition in both Ankara (Turkey) and in Tilburg (the Netherlands). During the exhibition, 30 pairs of students in Ankara and 12 pairs of students in Tilburg were equipped with a GoPro and they should talk about the exhibition according to the think aloud method. This data will be recoded afterwards by trained researchers in order to establish participants responses.

This study concerns with a dedicated flexible manufacturing system with closed loop layout. The production system consists of different types of parts with different processing times moving on the closed loop conveyor. An analytical model is proposed to show the dynamics and interactions in the system. Since the model is nonlinear and ignores random machine failures, a detailed simulation model has been developed to be able to make a proper analysis of the system. The objective is to find the best configuration in order to maximize the throughput. A number of scenarios representing different configuration settings have been evaluated and compared with respect to the objective function. Existing optimization methods and tools, which are used along with simulation models, have been addressed and used to find the best solution. The results have been discussed and recommendations have been made for future.

The subject of the paper deals with the plant layout optimization of the existing production halls in a specific company producing components. The aim is to suggest a layout based on analysis of the product portfolio, realization of the capacity calculation and proposition of the optimal machine and device configuration. For this purpose, some optimizing calculations with utilization of simulation were realized. The result is a detailed layout including 3D visualization.

This paper is conducted to establish a better pathway for rare disease patients and doctors especially in Turkey. Through the project, contacts made with numerous rare disease patients, geneticists who expertise in this field, academicians, students of medicine department, and many more. After collection and evaluation of the raw data, the analysis is made to implement it for real life improvement scenarios. The analysis technique of this project was value stream mapping, which is broadly used to find the bottlenecks and slacks in the healthcare sector. Afterwards, final suggestions are explained on the last section. To conclude, the patient flow analysis for rare disease patients and especially the detailed flow for cystic fibrosis patients presented that a comprehensive strategy should be designed and plans should be made for rare diseases case in Turkey. This project was the first step for this mission, and also, the first project focusing on the rare diseases field from an engineering perspective.

Improving quality is a very important issue in hospitals. Measuring quality is directly proportional to measuring sustainability criteria. Customer satisfaction is seemingly correlated with the factors which affect sustainability evaluation. Besides, the evaluation sustainability and satisfaction consist of different kinds of uncertainties. In this case, Quality Function Deployment (QFD) is very useful tool to evaluate customer satisfaction integrated with the fact that the weights of QFD are calculated by the method of Analytic Hierarchy Process (AHP). In this study, we propose an integrated approach for the evaluation of sustainability in Turkish Hospitals by using Quality Function Deployment and Analytic Hierarchy Process in order to assess patient satisfaction by considering the most important criterium in hospitals. This study enlightens the relations between patient satisfaction in hospitals and sustainability assessments in the same place.

Measurements of form deviations of 3D elements can be conducted with the use of various strategies, differing in a number and distribution of sampling points located on an investigated surface. Low number of sampling points or measured sections can lead to the situation that some surface irregularities are not detected by a measuring system. An application of higher density of sampling points, in turn, results in significant lengthening of measurement time, which is undesirable. This is the reason why authors propose a novel, adaptive measurement strategy consisting of two stages: a preliminary measurement and additional measurements. During the preliminary measurement an investigated area is scanned along preselected trajectory. If measurement results show that there is significant change of sensor readings in a certain fragment of an investigated surface, then we conduct additional measurements. In this paper authors present methods that can be used to localize areas where significant change of signal may occur.

Application of the computed tomography (CT) within industry has been rising in recent years due to its non-destructive abilities and accuracy. Nevertheless, there are some challenges related to CT scanning, such as presence of artefacts. The aim of this research is to investigate to what extent the application of some advanced algorithms can influence the accuracy of the X-ray CT images. In this paper, after a brief overview of different existing methods used for reduction of different types of artefacts, preliminary research of a new approach for CT image enhancement is presented. It is based on a hybrid methodology using two different methods - Fuzzy Clustering and Region Growing - joined in order to exploit their advantages. Results show that the proposed methodology contributes to CT image enhancement, with borders of segmented objects on CT images more easily extracted.

Hole making process monitoring of irreplaceable parts is crucial in terms of waste production elimination. Defects on machined part do not directly affects its end user. Strict quality control regulations in all production levels ensure the required product quality. However, faulty parts increase production costs proportionally. These costs are included in the sale price for which end user purchases the product. In order to achieve a significant reduction of the waste production a new generation of sensors will be applied to obtain appropriate signals directly from the machine control unit. For the real time and effective signal processing, wavelet transformation and neural network approach will be used to identify changes of the cutting tool or machined surface. Such a proposed system has all necessary assumptions to reduce scrap production in manufacturing chain and thus reduce the price of final product. The paper describes a approach in real-time monitoring for drilling boltholes. In an experimental setup, process data origination from the CNC of a Sinumeric 840D, collected by an OPC-Server had been processed. Comparing OPC data logging, DAU data logging and profibus data logging with respect to data quality, sample rate and real-time behaviour, profibus data logging appears to be the favourable choice.

At least since the diesel gate scandal, the public has become aware of the possibilities of software engineers, and so it is not surprising that inspection personal does not trust the on-board diagnostic functions too much. At this point only a modern tail pipe test can bring the required result. The patented procedure is similar to that exhaust gas test, which has already been carried out for several decades on gasoline engines and three-way catalytic converter. In addition to the known quantities CO2, CO, HC and O2, the determined emission values additionally include NOx, NO2, NO, particle number and average particle size. A main development goal for this device was the application of state-of-the-art Internet-of-Things (IoT) technologies. The first step was the implementation of wireless communication between the measurement device itself and its control-computer. They are able to communicate via WiFi 5-Protocol. The next step was to create a network-application to upload the gained data and make it accessible for the user through a web-browser based GUI. The final step is linking all those measurement systems together by the use of a Message Queuing Telemetry Transport based data platform. In using this widely acclaimed IoT standard protocol, it is possible to control, collect the data from, and service a huge amount of measurement systems. The investigations showed predominantly good correlations between the emissions during the short test in comparison to RDE measurements. This instrument significantly upgrades the periodic inspection of motor vehicles on the road, as it reflects a real emission behavior at the tailpipe of the exhaust system and does not make use of car specific engine control data.

There are some factors to be considered while developing production planning policy of chocolate and chocolate-based products. One of these factors is that chocolate is a perishable food, therefore it has limited shelf life. Another factor is that; its demand is not always easy to forecast. Holidays, mothers’ day, teachers’ day, valentines’ day and new year are examples of the peak periods. For these special days, demand generally follows a seasonal demand pattern where seasonality may also contain trend for some specific product types. Moreover, there are two religious holidays (Ramadan Feast and Feast of Sacrifice) in Turkey whose dates shift each year. This phenomenon makes forecasts challenging. Underestimating demand causes loss of customer goodwill, lost customers and market share whereas overestimating demand causes excess inventory to keep in stock and risk of fat blooming. Accurate forecasting is critical since it provides a fundamental input for the production plan. The study is conducted in a chocolate company in Turkey. The company does not implement a systematic planning method for chocolate production, instead the planning is based on past experiences with respect to experts’ opinions. The objective of this study is to determine the optimal production and ending inventory levels for the period of 2018 so as to minimize total production and inventory holding cost subject to production, inventory and capacity related restrictions. A two-phase optimization method is adopted as a solution method. Firstly, a mathematical model is developed for the monthly aggregate production planning on product group basis. In order to solve the problem, monthly demand for product groups are forecasted based on the sales data of the previous two years. Secondly, a mathematical model for weekly disaggregate production planning is developed for each end item using the outputs of the aggregate planning as input. The disaggregate production plan gives the weekly planned production and inventory levels for end items for year 2018 with minimum deviation from the aggregate plan. Although the proposed solution model is implemented for year 2018, it can be used for the coming years by updating some parameters.

In this study, NACA 66-206 wing type was modelled and analysed to understand to mechanical behaviour under lift and weight forces. Structural and modal analyses were applied using Ansys Workbench Static Structural and Modal Analysis tools. Three different materials (aluminum 6061, carbon fiber and strong unidirectional epoxy glass) were selected for structure of the wings. The wings were created with three parts as airfoil, rib and spar. The affected forces were calculated and applied to the wings for three motion velocities as 10, 50 and 100 m/s. In the analyses; the lift forces were calculated by the buoyancy equation for the aircraft. The weight forces were also added to get more realistic results. The motion velocity and material effects to the structural strength of the wings were compared according to the results obtained from static and modal analyses. The maximum deformations were observed for the 100 m/s motion velocity with the values of 2.28 mm for aluminum, 0.16 mm for carbon fiber and 7.71 mm for epoxy glass. The similar effects of the motion velocity were found for the strains and equivalent stresses. It can be concluded from these results that it was better to manufacture the NACA 66-206 wings with the carbon fiber material as it had high natural frequency values and caused to lowest weight force.

Metal cutting operations are usually employed for the manufacturing of various components in the automotive and aerospace industries. Especially, face milling is particularly preferred for creating high quality surfaces for high-end components, due to its versatility and high efficiency. A major challenge of this process is the achievement of essential goals, such as low surface roughness, minimum deformation and workpiece damage, avoidance of unfavorable residual stresses and at the same time the achievement of high productivity, i.e. high material removal rate. For that reason, it is essential to determine the optimum process parameters in order to reach the necessary compromise between productivity and quality of the manufactured product. In the present study, the effect of feed and cutting speed on cutting forces and specific cutting forces during face milling with square shaped cutting insert is investigated and conclusions are drawn on the importance of each process parameter, as well as the recommended values of these parameters in order to achieve the desired goals.

The fourth industrial revolution proposes digitization and networking by providing more productive, intelligent, controllable, and transparent factory environment. Although organizations have increasing attention to such a paradigm, changes in production and automation technologies led organizations to consider various areas. The necessity of individual qualifications and skills will eventually require more qualified managers. Furthermore, in a high technological environment which requires expertise on new materials, machines and information need more skilled labour. The increased complexity of workspaces eventually resulted in a need for a high level of education for the employees. The presented study contributes to research by providing a framework to structure an education scale in the context of Industry 4.0. Preliminary work for an I 4.0 education scale through an extensive literature review and academic reviews were provided.

In digital transformation era, technology selection stands as a critical strategic technology management process for manufacturing companies in developing countries who struggle to adapt themselves to technological change despite their limited financial, technological and organizational resources and capabilities. In literature background, various scholars discussed that, regardless of scale, companies which had been able to apply lean production principles and techniques in their manufacturing processes, have the advantage of utilizing their process management (including measurement and control) systems as a leverage in effective needs and benefit analysis. However, determining both the various implications and costs together with the potential benefits of technology transfer requires a multidimensional analysis which includes technological, organizational, operational, economic, legal aspects in a common framework. Even if the company is competent in technology assessment, they may still face the challenge of combining multiple criteria that can enable contextual decision making for “appropriate” technology acquisition. In this framework, by utilizing a case study from a major automotive parts supplier company in Turkey, our study aims to explore the criteria and methods/techniques that can be used within a structured feasibility analysis (which may serve as a) decision making model for identifying appropriate technologies for effective digital transformation in accordance with the process improvement needs of manufacturing companies in developing country contexts. For this, after a detailed review of literature and similar practices in the selected industry, we defined the multiple dimensions of technology selection decision models. We also analysed current technology transfer needs of the selected company by process analysis techniques from lean production theory (Value Stream mapping, SPC) and also collected opinions of the practitioner experts. As the selected company is a supplier of a Japanese automotive manufacturer and has been applying lean production techniques for several decades, we could be able to use the information/data which the company provided. As well, to define the potential technologies that can respond to the defined needs, we applied technology auditing for available Industry 4.0 technologies with content analysis on literature and with interviews with the experts from the field. Together with the financial feasibility with NPV, ROI, Technology Acquisition Cost Analysis and Impact on labour productivity measures (where the horizon value is adapted for income/cost saving analysis), we designed a multi criteria decision making model which includes organizational, economic, technical and regulative factors and their sub-criteria, we proposed the feasibility analysis model. The proposed model is applied for the potential Industry 4.0 technologies which may respond to the process improvement needs of the studied company. To rank the feasibility results, we used TOPSIS multiple criteria decision-making method in the final phase.

The paper analyses the concept of a series of operations on data obtained from monitoring and supervising the machining processes in the aspect of the Industry 4.0 idea. The architecture of distracted data processing, i.e. Edge Computing, is compared with the architecture of centralized structure of data processing in the Cloud. An analysis is presented for a stand of physical phenomena monitoring in the cutting zone using vision systems. The analysis results aimed at defining the limitations of data processing systems with different architecture are presented.

Increased utilization of digital elements and computer assistance in industrial enterprises leads to the acquisition of data encompassing all company levels and areas. Subsequently, manufacturing companies face large amounts of collected data from different sources and in various formats. Today this data is beginning to be viewed as a resource, comparable to material or machines. Thus, the ability to effectively collect and utilize internal and external data, is becoming an increasingly important factor in determining their economic success.In this paper we develop a readiness model which can assess the Capability to Utilize Data in Industrial Enterprises (CUDIE). This model enables industrial decision makers to determine their organization’s current status, to define strategies to increase data usage and to identify individual fields for improvement. Our model is based on various frameworks for readiness and maturity model development, as well as an Industry 4.0 maturity model that has already been applied in industrial settings. To assess readiness in real industry environments we transformed the theoretical CUDIE-model into a practically applicable tool that combines self-assessment with an external audit-based assessment.Finally, in order to validate the model, and to collect feedback concerning its applicability and suitability, we tested our model on an industry use-case. There we could conclude that our method can be used to help companies to identify their current capability for data utilization, to derive areas of improvement and to define their future strategic steps.

This paper focuses on the introduction of digitization in the production process. When deploying Industry-Oriented 4.0 components in the plant, emphasis is placed on HMI and workplace visualization. Designing a suitable way to visualize data obtained from the production process can have a significant impact on the workplace response. Timely and properly conducted responses to potential changes in the production process have a positive impact on the resulting quality of the production process. Continuous development of the elements of virtual and augmented reality also increases their usability in the field of data visualization from the production process. These technologies make it possible to meet the high demands on the clarity of a great deal of information. This paper introduces the creation of a production cell into virtual and augmented reality. Particular emphasis is placed on the way of data visualization, including the environment, geometric accuracy of the CNC machine tool, and information from the safety parts. For efficient information handling, access to their display in virtual and augmented reality has been chosen.The first part of the publication introduces methods for creating a virtual model based on photogrammetry. In the second part of the publication, procedures are presented for collecting and visualizing information on the geometric accuracy of the machine. Finally, procedures related to the risk analysis and functional safety of CNC machine tools are presented. In conclusion, the advantages, disadvantages, and recommendations of the presented solution, the critical places and the difficulty with the realization of the virtual workplace are referred to.

It can be declared that the adaptation of companies to the new industrial revolution, so called Industry 4.0, is not a choice but an issue of survival. The structure of business processes are changing with the newly introduced, radical and highly value adding technological drivers, such as internet of things, cloud computing, autonomous robots, augmented reality, cyber security system, etc. In the globalized trade markets none of the companies can close their eyes to this new era and ignore the required change in their operations. Small and Medium Sized Enterprises (SMEs) have 99.8% enterprise share of Turkey, and consequently have obvious contribution not only to the local economy but also to the global competition strength of the country. In that sense, catching up the fourth industrial revolution has to be the prior agenda of the SMEs, and also of the government institutions and the universities from the perspective of giving the required support. This study is the continuum of the broad research that is initiated in March 2018. The purpose is to identify the current situation of SMEs in Turkey with respect to their various implementations for Industry 4.0 transformation. A specifically designed questionnaire is applied to SME’s with the support of KOSGEB (Small and Medium Sized Enterprises Development Organization), and in this stage of the research, the companies located in four major cities, namely İstanbul, Ankara, İzmir and Bursa, are analyzed and compared. The study aims to find out the level of awareness and readiness of SMEs in these cities and measure their Industry 4.0 scores. The final target is to support the Industry 4.0 implementations by defining the current situation and proposing suitable actions for adaptation based on varying results.

Just as in the previous industrial revolutions, the rules of the game were rewritten and a new paradigm that is known as “Industry 4.0” was born. However, it is seen that everyone does not understand the concept of Industry 4.0 in a similar manner. For instance, Germany, where the concept was first introduced, is one of the technologically advanced and oldest countries, while labor is abundant and cheap in China, which is the most populous country in the world. On the other hand, Turkey is a developing country that is technologically dependent on center countries and has an import-intense manufacturing structure. Due to the potential changes in productivity level and flexibility capability of companies that Industry 4.0 technologies can enable in manufacturing industries, strategies followed by companies from different countries and industrial contexts should be different and be customized to local conditions, but these differences were not considered in many recent studies in the related literature. As it is understood, there is a need for scientific studies based on real practices of Industry 4.0. In this context, this study aims to explore the current practices of two Turkish white goods manufacturing companies through a case study. The first company offers products and services around the world and the second company is the supplier of the first one.

With the emergence of new media and interactive media technologies, the mediatized transformative process began. Specifically within the science communication context, the way we perceive issues transformed drastically. With the mediatization of forms of understanding and literacy, the public perception towards many topics, including environmental sciences has gained recognition. This research paper will be focusing on the connection between Industry 4.0 and science communication. By making a valuable contribution to the communication process through constructing a bridge between the industry and the general public, science communication provides a smooth and effective transition of mutually beneficial relationships. This paper concentrates upon the notion of sustainability and its relevance to the industrial revolution, which is also known as Industry 4.0. The paper will be exploring the ways in which how humanity can stay within the safe operating limits with the existing technology and resources. By taking into consideration the notion of sustainability, the paper will attempt to discover possible ways that how people can construct their discourse on the big framework where everything is interconnected to one another. By meaning, Industry 4.0 indicates the connection between automation, computers, smart technologies and the cloud system in which the flow of information has increased in unprecedented levels. Since Industry 4.0 is already happening and has an immense impact on our lives, science communication plays a big part in generating the accessible and relatable message of the implications regarding the age of Industry 4.0. From the automobile industry to the electronic products that we use every day, the majority of the markets use renewable energy in order to lower the costs of production and increase their profits. A disruption in the sustainable development of any given society may cause irreparable damages which can result in colossal economic and political instability. Hence, science communication plays a vital role in terms of providing an understanding of possible pros and cons regarding the practices of the industrialists and the business world. Since it can be quite hard to comprehend the full scale of the consequences of technical occupations and practices, science communication will be the vanguard in terms of constructing a meaningful framework for the general society. This paper will explore what might happen in the lack of understanding of sustainability in Industry 4.0 through renewable energy consumption. Particularly, how a situation looks like when a country experiences a huge setback for its economy, massive loss of life and property in an unsettled global economy. What kind of a scenario would people witness with a disruption in one part of the world, in a world of interconnected production of supply chains that stretch across the world? Therefore, this paper will explore the possible implications of Industry 4.0 through renewable energy consumption on a grand scale.

The utilization of Digitalization and Automation (DA) is currently one of the determining factors of increasing prosperity, productivity, and efficiency. Although widely promoted and incentivized by policy- and decision makers, industrial enterprises seem reluctant to actively push DA-development in their organizations. From a scientific point of view, we find missing operationalization resulting in a lack of analysis and understanding of basic DA-elements in real production environments as the main barrier towards Industry 4.0- and DA-implementation. Thus, in this paper we introduce a novel approach to transfer abstract Industry 4.0-concepts into industrial environments through the utilization of basic concepts of industrial digitalization and automation. Based on this framework we develop a model to assess the degree of digitalization and automation of value creation factors and organizational factors (DAVO). Knowledge about the company’s DAVO-degree enables their decision makers to derive operational Industry 4.0 target-states, define strategies thus decide targeted investments into organizational and technological developments. We applied the developed and operationalized measurement-metric and model in an empirical study in the Austrian industry assessing the DAVO-degree of 200 industrial companies. Besides insights into their maturity status, we find clear evidence in our study for the need of operationalized Industry 4.0-concepts. Furthermore, the DAVO-approach seems to encourage practitioners to reflect their organization’s DA-status with more accuracy thus enables them to derive more targeted and sustainable strategic steps.

The Internet and the associated data highway have radically changed lifestyles and forms of production/services in a transcendent manner. Many authors characterize this qualitative change as a new industrial revolution. Exchange emails/information in electronic format and video conferences is already part of daily uses applied by vast sectors of the world population for the most varied purposes. Computers, tablets and cell phones are the access and interface nodes to this process. For several years now, Telepresence has allowed us to operate remote equipment more easily and monitor with online vision in order to control processes of different types that occur in almost any point of the global village from almost anywhere in the global village. INDUSTRY 4.0 has as its central objective the use of all these tools in an integrated manner and to reinforce a path of continuous improvement, increasing the efficiency and effectiveness of organizations. The present work reflects the experience in two new aspects, opportunities and difficulties that arise with the new paradigms of the organization of teams for creative and participative workers. On the one hand, the difference between implementing Telepresence and not just telecontrol is analyzed. On the other hand, we present a new small software modules complementary to those already developed/implemented in our technological research team, that underpin a “Virtual Engineering Office” with its similarities and differences to the “Virtual Classroom”.

Technological developments accelerating in the 21st century are making great changes in the automotive sector. Due to the reliance on technologies such as artificial intelligence, big data, robotics, cloud computing and internet of objects, self-driving cars have an essential place in Industry 4.0. However, it seems that these technologies will change the global market share of developing and less developed countries’ in the automotive industry. The rising trend of reshoring activities of developed countries are expected to affect the global automotive industry. In this paper, we aim to investigate the impact of Industry 4.0 and the digital transformation on the automotive industry. Also, we aim to put a brief description of the social impacts of reshoring and how developing and less developed countries will keep or increase their market share in the global automotive industry.

In Industrial Revolution 4.0 applications the production is increasingly digitized. Moreover, component-driven production is supported. Machines used in production systems exchange very big amounts of process data with one another. The most impressive part is the big amounts of data which can be analyzed for production improvements. Of course, die casting industry has faced these challenges very close. Because each component of an integrated manufacturing system of die casting process requires managing the big data. According to the Industry 4.0, it is possible to adapt various tools and concepts to the manufacturing system with the help of modern information technology and the machine flexibilization. If users can access the process data easily the material flow can be analyzed and optimization along the process can be done. Although there are some challenges in applications, practical potentials of the tools of Industrial Revolution 4.0 especially in die casting production systems offers many advantages especially in management of big data for process improvements.

This paper presents the results of a research on the usage of Industry 4.0 (I4.0) technologies among ISO 500 manufacturing firms in Turkey and the factors affecting the usage rate. For this purpose, secondary data has been collected and examined. News, reports and other relevant information obtained from the internet about the I4.0 technologies used by ISO 500 manufacturing firms were used in the research. The descriptive results indicate that the usage rates of the I4.0 technologies vary among the ISO 500 manufacturing firms. A difference in usage is also observed across the categories of firms. I4.0 technologies are mostly used in discrete manufacturing firms. According to the results of the regression analysis, the rate of usage is most affected by the type of manufacturing technology and the number of employees. Finally, the usage rates were compared with the ones of the study carried out by Boston Consulting Group (BCG) for TUSIAD (Turkish Industry and Business Association).

Industry 4.0 is often described as a wave of transformation that enforces the digitalization of processes to create integrated and autonomous systems. In this regard, the collection of process data is a necessity to analyze data with advanced techniques for various purposes. Statistical techniques in machine learning might provide solutions for fault detection and other tasks in manufacturing processes. In our study, a learning model is proposed for a fault prediction task with the use of Logistic Regression. The data used for the analysis involve measurements from sequential processes carried out in a large-scale wheel rim manufacturer. The pre-processing and analysis of process data was introduced along with a case study. Moreover, findings of the model were presented and the potential use of the model will be discussed.

Together with the rise of industry 4.0 and emergence of 5G possibility, swift technological developments take place in the IoT field. The increasing customer attention on smart home applications and systems containing IoT devices, necessitates diminishing the time to market and also maintain the required quality while sustaining the profit.Quality in IoT systems can only be ensured if all the involved systems and components (the devices and their sensors, gateways, apps and websites, backend systems and transmission protocols) are viewed and checked together.With intaQt® (INtelligent Test Automation & Quality-assurance Tool) it is possible to perform the tests of these different elements automatically under one context and collect the evidences in milliseconds basis. This increases the efficiency of IoT testing and decreasing the time to market dramatically.

In this study, customer service activities of the company, which is actively serving in the e-commerce sector in Turkey, provided through the call center were reviewed and a solution was proposed to improve the existing problems. In this respect, the literature research has been conducted on e-commerce sector and call centres, increasing the customer demands with the campaigns implemented by the companies serving in the e-commerce sector in certain periods, and thus the effect of the intensity experienced on customer services in call centres has been examined from different perspectives. Later on, with a company that provides international services, including Turkey it has reached to more detailed information and data relating to jointly carrying out a study subject. Surveys were carried out with the planning team and team leaders of the company, moreover, current problems were investigated and suggestions for solutions were evaluated. The data obtained, the previous year’s statistics of the company were examined, and the needs were determined. In order to develop solutions, different concepts have been created and evaluated along with one of the concepts created in line with these results has been selected. The design of the study was prepared according to the results obtained from these stages. Finally, risk analyses were made, the points that should be prevented by the company were reached, and ideas were presented for the future stages.

Accurate and applicable production plans are a must for manufacturing companies. Although companies tend to prepare ideal production plans, some exogenous factors can affect their validity. These risks, which occur unexpectedly, will have a negative influence on the plan. This study aims to determine the exogenous factors affecting the success of production planning of square and rectangular food containers manufactured by Öztiryakiler, and analyse their impacts on the plans. The risk factors are evaluated using Failure Mode and Effects Analysis, and their risk priority numbers are calculated. A mixed-integer linear programming model with the objective of total cost minimisation is developed to obtain the production plan of containers. Initially, an ideal data set is used as input; hence, this model’s output displays a risk-free plan. Similarly, for each of the risk factor scenarios, mathematical models are solved with risk-related data. GAMS software and CPLEX solver is utilised in the solution of all models. Finally, for each of the selected high risk alternative, the expected total costs are calculated. This is achieved by multiplying the normalized risk priority number obtained from the Failure Mode and Effects Analysis with the corresponding optimal total cost of the risky plan. This analysis highlights the most critical risks, and comparison with the risk-free plan helps in proposing system improvements.

With the Industry 4.0, a new trend has started for all sectors and enterprises have entered into a period of intense working and investment to turn into intelligent systems. Cold chain logistics also has the same trend, and companies have pushed the button to be involved in this process. Due to changing lifestyles and generally decreasing prices, the world market for perishable products such as processed foods and cooled products is growing. Cold chain transportation is an important issue in basic food and health product logistics to prevent the growth of harmful microorganisms and to ensure that the products remain intact. In this study, it is aimed to determine the right cold chain logistics firm for company EDB that is a small and medium-sized enterprise (SME) operating in the food industry. Firstly, the selection criteria are defined according to the literature survey and expert views. AHP approach has been used to calculate the weight of them. After the determination of weight of criteria, VIKOR approach has been used to identify the best cold chain logistics firm for company EDB. The results were obtained by using Expert Choice 11.0 and Excel calculator finally the best one has been determined.

In the present research is granted a mathematical model in order to obtain the field temperature for composite materials. This model has its origin in Fourier- Kirchhoff equation. This mathematical model is useful in analysis finite elements. Other areas of use of them are also presented such as their structure. In the present work is conferred the importance of finite elements using analysis in composite materials area, too.

Production of different kind of parts has increasing character. They are different according to materials, shapes, production technology and accuracy. Possibilities of their quality and accuracy control have to taken into consideration. In addition to optical (non – contact method) and contact (CMM) methods there are applied progressive technologies which bring new opportunities into the metrology processes. One of them is application of X - ray beam in measurement process. Computed tomography finds application in the field of different parts quality evaluation. This new method has many unexamined areas which are necessary to research. One of them is effect of voxel size on quality and accuracy of achieved model.

It is widely accepted that the advent of robotics and automation in previous industrial revolutions brought about paradigm shifts in the manufacturing industry worldwide. It is therefore conceivable that the fifth industrial revolution will bring about a similar shift in norms and make fundamental changes in our approach to industry and manufacturing. From the first industrial revolution to the fourth, science and technological innovations, training and capital, were the key elements in evolving how products are conceived, designed and manufactured. Looking forward to the fifth industrial revolution it is likely that there will be a paradigm change in how industry will evolve given the tools of industry 4.0; the internet of things, digitization, blockchain, advanced materials, additive manufacture, artificial intelligence and robotics, drones, energy technology, biotechnology, neurotechnology and virtual and augmented reality.

To enable advanced mobile phone recycling and circular economy strategies like reuse of mobile phone components, a modular collaborative disassembly machinery demonstrator has been developed. The demonstrator consists of flexible usable dismantling modules, a collaborative robot and a human workspace. The remaining challenge is to scale the demonstrator from the prototype level up to line variants in series applications. This is achieved through simulation of different scenarios where cycle time, workload, economic - as well as environmental aspects are determined. The objective of the study is to define a virtualise disassembly line with blocks and algorithms in order to perform a simulation of different line variants and disassembly scenarios. With this quick evaluation next to the consideration of technical aspects, economic and environmental implications are simulated.

In the Industry 4.0 world, both service and manufacturing companies should review their systems and processes, remove any application that causes waste, ensure lean flow and change business models if necessary, in order to fulfill the requirements of this trend. Introducing Industry 4.0 on a problematic system or process might harm it enough to cause the company disappear instead of benefiting it. For applications correctly decided to be built upon a correct system, data flow must be accurate and timely. And at this stage, data amount that increases with process mining and complexity of the big data will be solved and more information will be obtained about real production processes and data. In this study, a prototype is developed using the data of a previously studied manufacturing research. This prototype handles only one phase of the manufacturing process and extracts all the initial possible pathways of this phase through process mining.

The aim of the project was to increase the success rate of molds due to the measurement mistakes in AKPA Aluminum. Several problems of defects were identified by using 5S and six sigma methods and Pareto analysis, the quality function deployment (QFD ANALYSIS) with the help of Ishikawa Diagram. The project duration included the progress in four quarter periods using Tricolor that helped to collect all the data from the production line. Along with the data collection, they were analyzed for potential improvements. In addition, the results were provided on the tables with the grouping of the failure fields. Finally, the defects and reasons of the defective molds were analyzed and optimized using same methods.

Powder bed fusion technology is one of the additive manufacturing methods that build up near net-shape complex parts from powder form of the material. Direct Metal Laser Sintering (DMLS) and Selective Laser Sintering (SLS) are primary powder bed fusion technology methods for metal and polymer materials respectively. AlSi10Mg is one of the most widely used materials in DMLS applications while polyamide 2200 (PA) is frequently used in high quality functional parts production and various prototyping in SLS applications. By developing technology, AM has begun to provide solution for generating final products with complex geometries. Dimensional and structural inspection of the AM parts without any destruction has become important issue in parallel with the development of the manufacturing technology. Industrial X-Ray Computed Tomography (X-CT) is one of the promising methods that provide opportunity to nondestructive inspection of inner structure and porosity of AM manufactured part. In this study, nondestructive porosity measurement of DMLS manufactured AlSi10Mg test part and SLS manufactured PA 2200 functional part were applied by X-CT method. Density of manufactured parts was calculated by measured volume data. Moreover, density of AlSi10Mg part was defined by Archimedes method and the results were compared with each other. It can be concluded that X-CT was an effective technique to define porosity and pore distribution of the AM parts in comparison with Archimedes porosity measurements.

Human beings are not completely and always rational and are influenced by several cognitive biases and heuristics in how they evaluate and perceive the quality of a product or service offered to them; moreover, their expectations and perceptions of quality varies along customer journey and product lifecycle. Therefore, professionals dealing with product development, marketing and selling cannot deliver results if they do not understand these two perspectives and how to manage effectively customer perception of product quality. The usual perspective of quality management on quality perception on the one hand, and behavioral economics – which deals with how cognitive biases and heuristics affect economic decisions of people in real-life – on the other hand, have been both researched extensively. Yet there are just few research publications proposing a structured knowledge at the intersection of these two domains. This paper proposes a conceptual framework on how to use behavioral economics concepts and understanding in quality management, bringing together these two domains in a synthesized and exhaustive manner. The paper focuses on the influence of cognitive biases and heuristics on how quality of products or services is perceived, and how this influence occurs across customer journey, and consolidates other factors which may influence expected and perceived quality. It proposes a structured approach to manage those perceptions. It provides readers from the quality management and new product development research areas with an exhaustive list of the behavioral (cognitive) biases and heuristics that influence how people (customers) perceive the quality of products and services, and it provides readers from the behavioral economics research area a new, deeper, perspective on quality perception and its role in economic decision making. The paper does not propose itself at this stage to test and demonstrate the theoretical contributions it makes, but rather to make professionals from these domains aware of the possible implications – hypotheses remain to be tested in future research.

Total Quality Management is the new raising value of all industries. The more it is revealed that TQM is one of the key success factors for the companies, the more it is being absorbed by the industries. This study aims to analyze TQM approaches considering its history and development worldwide while observing manufacturing industry with machine learning applications in order to identify the defects in the process before completed which contributes continuous improvement to the system. Also, different descriptions of quality according to the customer satisfaction will be examined.

Production systems of today are developing an ever-increasing dependency on information systems, as a result of which intelligent systems are being transformed into smart systems. A production facility can be called a ‘smart factory’ provided that all the processes are being managed, which can be done only if sensor based an intelligent measurement data derived from production systems are analyzed and processed. To increase the quality of production and decrease energy waste, cloud computing, edge computing, energy management systems (EMS) and continuously updating simulation system must be applied to facilities. All these cutting-edge ICT technologies help the designers make more effective planning, control the Total Quality Management (TQM), and build more efficient production systems whose components interact with each other in real time. To accomplish this, previously we have set up a prototype complete which includes with sensor-based intelligent measurement nodes with a semi-edge computing and cloud-computing system to run on the real production process. And with this study we have strengthened the designed prototype with cloud-based simulation for: Modeling the energy consumption of the air conditioning system, which ensures the control of environmental conditions, Modeling the production quality that under the influence of environmental conditions. With this new model, the product quality can be standardized by controlling the environmental conditions of the production lines as well as avoiding unnecessary energy expenditure thus ensuring cost efficiency.

For improved machining efficiency, in addition to the increase in material removal performance, the operating conditions and the assurance of the roughness values specified by the manufacturers for the surface of the component must be considered. In this article the topography of surfaces created with the same cutting data is analyzed with different geometry inserts. It is shown how the 2D and 3D roughness parameters can be estimated in different cases. Then the theoretical (estimated) and actual values from cutting operations are inspected, and the differences between them are discussed. Finally, the different versions are ranked based on the smallest achievable roughness values, and it was found, that the round insert gives the lowest roughness values.

In the quality assurance of machine components finishing processes have important roles and the post-machining cold forming methods such as burnishing, rolling, shot peening and others have occupied a main place in industry. This work refers to the study of the ball-burnishing process which is a particular finishing treatment that provides good surface finish, high compressive residual stress and increased hardness in the surface layer. These characteristics are key for the fatigue life improvement of the component. In the present study, burnishing technique was used to investigate the effect of input parameters of the process (number of passes, feed rate, burnishing force) on selected surface layer features like micro-hardness and residual stress of the low alloyed aluminium components. For plan and execute the experiments we used the Taguchi type full factorial experimental design method by which empirical formulas can be created easily. The measurement of the surface micro-hardness was executed with Wilson Instruments Tukon 2100B measuring equipment, while the analyzation of the changing of tension conditions was performed by X-ray diffraction method. The measured results were evaluated by the comparison of a special correlation formula to determine the optimal combination level of the different parameters in the given interval.

Management of a supply chain is a complex process to model and optimize. Nowadays it is getting even more complex, due to environmental and economic problems we face. Supply chains require higher efficiency, energy and material recovery, and working with less cost. To fulfil these requirements, the concept of closed loop supply chain arose. There exist studies about closed loop supply chains in the literature and each of them proposes different methods to implement. In this study, it is aimed to put forward a literature review about closed loop supply chains, see which methods are used, and make a classification among them. Furthermore, the study will give a perspective about the most preferred methods and specified topic in closed loop supply chains in details.

Supply chain management, which require doing better with less cost and with maximum quality, is one of the most important subjects in the developing aviation world. The aviation industry has many district regulations, complex products and systems. In order to deal with that, aviation companies endeavour to follow recent technologies, to develop new innovation projects and to associate them in supply chain operations effectively. Moreover, airline companies purpose to focus on sustainable development of their systems. Sustainability on the system performance can be supported by adding values to the supply chain operations. Innovation projects contribute to value-adding activities that can support sustainable supply chain management. One of the breakthrough projects in the airline industry is Wireless In-Flight Entertainment (WIFE) system. The project is represented as the most innovative version of in-flight entertainment systems. It enables connectivity of passengers on the internet by their own portable electronic devices (PED). Additionally, the system provides reduction of size, weight and energy consumption. The system also offers passenger satisfaction, cost saving, additional revenue and functional efficiency for airlines. The aim of the study is to evaluate the WIFE system in respect of economic aspect during the management of the supply chain operations. Cost-benefit analysis is envisaged to be employed to compare two different systems in the same research efficiently. The research is planned to be implemented in Turkish Airlines Technic that is a maintenance, repair and overhaul (MRO) company.

Supply chain management becomes more important in the developing world in order to provide a material flow with an optimum cost and time generally. Many sectors focus on supply chain management optimization and qualifications to construct a better network of suppliers for their end consumers. Especially in healthcare sector, not only for pharmaceutical products but also for hospital materials, supply chain management gets importance for service quality and patient satisfaction. Thus, there are many studies about supply chain management in healthcare to emphasize its importance. In this study, it is aimed to put forward a literature review of supply chain management in healthcare in order to give a perspective by expressing the important cases and researches which are parallel to the latest studies. Furthermore, this literature review will assess a perspective in order to understand how to manage a complex supply chain in healthcare sector by investigating studies in the literature.

The principal aim of this study is to investigate the direct and indirect impacts of Knowledge Management (KM) and Enterprise Resource Planning (ERP) usage with the mediating effect of Supply Chain Orientation (SCO) on operational performance drawing on a sample of 200 Turkish manufacturing companies. Universal Structure Modelling (USM) were applied to examine the direct and indirect effects of variables on performance. In the path analysis seven hypothesis were evaluated with USM. Data analysis reveals that SCO affects operational performance positively with a degressive nonlinear relationship. Also it is found that there is a positive degressive nonlinear relationship between operational performance (OPER) and financial performance (FIN). The study shows that all relationships other than above are linear. In addition, no support has been found between ERP usage relations with OPER.

Advances in technology continue to affect our lives day by day. The new technological developments gaining momentum with the 4th Industrial Revolution have an impact on the working life, employment and unemployment in the 4th industrial revolution as in every industrial revolution. Every new technology that has emerged may adversely affect some sectors and business areas and cause unemployment. The ongoing industrial revolution is expected to contribute to the formation of new businesses and new jobs. This change necessitates the preparation of the individuals for the needs of the new age as well as the equipping of individuals for entrepreneurship activities. There is no doubt that the dynamos of the preparatory process will be universities. This situation increases the importance of monitoring the developments in entrepreneurship and innovation in the field of technology and in gaining competence in this field for university students.As a requirement of competition, state authorities care about the role of universities in entrepreneurship and have serious expectations. Public authorities support and promote the universities in this area significantly. The questions such as ‘‘How are the students, who are the main target groups of universities, taken into account in the university entrepreneurship ecosystem?’’ and ‘‘What is the impact of universities on students through training, incentives and support activities in the field of entrepreneurship?’’ have become a topic to be studied in the field of higher education. While looking for answers to these questions, determining entrepreneurial competences of university students has emerged as an important requirement. The aim of this methodological study is to develop a tool to measure the entrepreneurial competencies of university students based on Kailer’s Entrepreneurship Competence Model. For the study, a sample was used consisting of 415 students from Selcuk University Faculty of Economics and Administrative Sciences and Konya Technical University Faculty of Engineering and Natural Sciences in Konya, Turkey. Content validity, construct validity and reliability studies were conducted at the development stage of Entrepreneurial Competences Scale of Students. The Data were analyzed on the computer using SPSS and LISREL package programs. According to the results of EFA, 25-item and two-dimensional Entrepreneurial Competency Scale was developed. Cronbach’s alpha coefficient of the scale was calculated as .90 and the percentage of variance explanatory was measured as 42.28%. Subsequent CFA results showed that the scale had good fit values (X2/df: 2.39, RMSEA: .073, CFI: .94, NNFI: .94). It is concluded that the scale developed in this study is a valid and reliable measurement tool that can be used to measure the entrepreneurship competencies of university students. It can be stated that the Entrepreneurship Competence Scale of University Students will be important for the universities that want to improve their student-based entrepreneurial-innovative features, public athorities and for the whole economy at macro level.

One of the issues frequently encountered in the digitalization of higher education is the transformation of universities to the University 4.0 with the Industry 4.0. University 4.0 refers to the characteristics that universities must have in the Industry 4.0 era. The adaptation of the workforce that will be most affected by the Industry 4.0 process and training it for the new competency needs will be carried out by universities and research centers to a great extent. The evaluation of the studies to be carried out within this framework in a wide range, not only in the fields of engineering but also in the fields of social sciences, medicine and even law, will accelerate the integration process of the Industry 4.0 at universities. In this way, with the change of the objectives and learning outcomes of the academic programs developed based on competency, it is naturally expected that the industry will have an impact on the educational programs and techniques. As a requirement of competition, state authorities care about the role of universities in entrepreneurship and have serious expectations. Public authorities support and promote universities in this area significantly. It is observed that universities have recently put forward their entrepreneurial and innovative university feature in marketing activities. However, how much are the students who are the real target group of the universities taken into consideration in the university entrepreneurship ecosystem? The questions such as “How can a university be measured and assessed as an entrepreneurial-innovative university?” have become a subject to be studied in the field of higher education. While looking for answers to these questions, evaluation of entrepreneurial-innovative university conditions and activities from the university students’ point of view in the context of Industry 4.0 and University 4.0 emerged as an important requirement. The aim of this methodological study is to develop a tool to measure the internal environmental conditions and activities of the university students and entrepreneurial-innovative university based on Koyuncuoglu and Tekin (2019)’s Entrepreneurial University Model. For the study, a sample was used consisting of 528 students from Selçuk University Faculty of Economics and Administrative Sciences and Konya Technical University Faculty of Engineering and Natural Sciences, in Konya Province in Turkey. Content validity, construct validity and reliability studies were conducted at the stage of development of the Entrepreneurial-Innovative University Conditions Scale. Data were analyzed on the computer using SPSS and LISREL package programs. According to the results of the explanatory factor analysis (EFA), 31-item and three-dimensional Entrepreneurial-Innovative University Conditions Scale was developed. The Cronbach’s alpha coefficient of the scale was calculated as .95 and the explanatory percentage was calculated as 51.29%. As a result of confirmatory factor analysis (CFA), it was seen that the scale had good fit values (X2/df: 2.47, RMSEA: .075, CFI: .96, NNFI: .96).It was concluded that the scale developed in this research is a valid and reliable measurement tool that can be used to measure the internal environmental conditions and activities of the Entrepreneurial-Innovative universities. It can be said that the entrepreneurial-innovative university conditions scale will be important for the universities which want to improve their student-oriented entrepreneurial-innovative features, public authorities and for the entire economy in macro level.

Due to the increase in globalization, higher customer expectations and environment volatility, supply chains are exposed to risks more easily. Therefore, supply chain risk management become a crucial issue for companies and countries in last decades. Supply chain risk management (SCRM) involves all type of risks such as supplier risks, locational risks, natural risks. Hazardous goods transport is a very destructive type of transport which causes very risky accidents. Hazardous goods transport risks can have serious consequences for the environment, property or people. The importance of these substances in daily life and industrialization and the increasing demand for these products are important for minimizing and assessing the risks associated with Dangerous Goods Transportation (DGT). Assets that may be affected by these risks are classified under 5 main criteria in this research. As a result of the Analytic Hierarchy Process (AHP) method, it is aimed to help the selection by establishing a risk-based superiority between alternative ways and tunnels while selecting the route in the pilot area.

Hybrid flow shop environment generally refers to the flow shop with multiple parallel machines per stage. Hybrid flow shop scheduling problem (HFSP) is a complex combinatorial optimization problem that came across in many real-life problems. In this study, a real-life HFSP of a lubricant company is considered, where the aim is to minimize total weighted completion time of the jobs. Apart from classical HFSPs, the studied problem has additional constraints such as machine eligibility, sequence-dependent setup times and machine capacities. Due to the additional constraints in the system, a novel mixed integer linear programming model is proposed for the studied HFSP with three stages. As the problem is NP-hard, two constructive heuristic algorithms and an improvement heuristic algorithm are also developed. The performance of the proposed heuristic algorithms is evaluated by comparisons with the optimal results obtained from the mathematical model. The extensive computational results show that proposed heuristic algorithms find near optimal results in reasonable computational times. Sensitivity analysis is also performed for the weight parameter of the problem, which indicates that the proposed heuristic algorithms also perform very well for different weight parameter values. Finally, the proposed heuristic algorithms are integrated into a user-friendly decision support system using Microsoft Excel VBA interface to provide an efficient scheduling tool for the company.

This study deals with a daily shipment plan for a bedding company in Turkey. We develop a decision support system optimizing total travel distance of vehicles completing daily shipment plans. In detail, the developed models both assign the shipment goods to capacitated vehicles as well as minimize the total travel distance of these vehicles by considering an open vehicle routing algorithm. The decision support system is coded by using the MS excel-macro and a user friendly tool is developed for users’ convenience. In the developed tool, there are two different solution approaches: a mixed integer mathematical programming and a heuristic solution approaches. For the small size plans (i.e. less than 25 demand points) mathematical modelling guaranteeing the optimal solution can be utilized. For the large size cases the heuristic algorithm providing a near-optimal result promptly can be utilized. The macro-based tool is designed such a flexible way that the user can make any revisions on the provided plan whenever necessary.

One of the most costly operations in logistics is the distribution of goods. Inefficient vehicle routes increase distribution costs, especially for companies performing distribution operations daily. Vehicle Routing Problem (VRP) addresses this inefficiency and optimizes the distribution routes of vehicles. In this study, we developed a decision support system to solve the Vehicle Routing Problem with Time Windows and Split Delivery and applied it to a real-life case company. The data of the problem were obtained by a real logistic company, which is one of the leading Turkish logistics companies located in Izmir, Turkey. The company distributes goods to the customers located in various cities in Turkey and currently does not use any decision-making tool to optimize the routes of its trucks. We formulated the mathematical model as Mixed Integer Linear Programming (MILP) and solved it by using IBM OPL CPLEX. Our proposed decision support system clusters the customers into geographical groups and then optimizes the routes within the clusters. The results of the decision support system can be manually adjusted by the decision maker to fine-tune the routes. We demonstrated the efficiency of our proposed methodology on the regional distribution of the company. The results of the study showed that our proposed model decreases the total distribution distance by 16% and total distribution time by approximately 13%.

The focus of this study is on developing a decision support system (DSS) in order to forecast spare parts demand for a company producing high technology products in Turkey. The company is one of the world’s leading original design manufacturers in the field of consumer electronics and white goods. Accurate forecasts of customer demand for preliminary products and spare parts play an important role in order to reduce costs and increase customer satisfaction. Currently, the company’s forecasting system is based on personnel experience and a statistical approach, which lacks the ability of capturing demand data behaviour. The approach followed results in an increased forecasting error, thus increases production costs, results in lack of spare parts, and decreases customer satisfaction. The aim of this project is to develop a DSS to minimize the forecasting error; therefore help the company develop a policy for optimizing the stock levels kept, reducing costs and increasing customer satisfaction. In order to understand the behaviour of customer demand of spare parts, the company’s television products are chosen for the pilot study, since these products are highly influenced by rapid technological changes and changes in the product models. The spare parts are classified into different groups, using ABC analysis, in order to develop a forecasting model for each group. In the solution methodology part, three different statistical methodologies for the forecasting process were respectively studied; Winter’s, Double Exponential Smoothing and Moving Average Methods. Winter’s Method is used for the data which exhibit trend and seasonality, Double Exponential Smoothing is used for the data which exhibit trend and Moving Average Method is used for the data which exhibit stationary behaviour. In the DSS developed, the above-mentioned methodologies are coded, using Excel VBA programming language, historical data’s behaviour is analysed and forecasts for future spare parts demand are made. The forecasting results are compared based on the minimum error (PAE), to decide upon which is the most appropriate forecasting methodology to use according to the specific spare parts past data behaviour.

In container shipping industry, due to the strategic alliances and vessel sharing, the services given to the customers have become the same each other. High competition remains but differentiation among carriers now totally depends on understanding customers and getting their loyalties. Therefore, the aim of this study is to find how and which service attributes of a container shipping company affect the shipper satisfaction and loyalty. Additionally, because there are thousands of shippers with different characteristics, such as product, organizational structure, trade region, and capacity, in this study it is also aimed to find the basic differences among shippers toward container shipping service attributes. Structural equation modelling (SEM) applied to assess data collected by a questionnaire survey. Data assessment achieved through hypotheses testing and with the help of some other statistical analyses such as ANOVA and T-Test. In this study, the factors affecting the choice of carriers were determined by using the literature. Factors are divided into subheadings such as rates, operation, and customer service. Data analyses were conducted by using LISREL and SPSS.

In Turkey, Pınar Et is one of the leading companies in meat sector since 1985. Within the scope of senior year project, this paper considers the optimization of lot sizes of auxiliary and packaging materials, purchased by the company. Aim of the study is the minimization of total inventory costs, which include unit purchasing, setup, and holding costs subject to a service level. For this purpose, multi-item dynamic lot sizing models are formulated with time-varying demand, deterministic lead time, with backorders allowed. The first model is formulated by considering Joint Replenishment of items supplied from the same suppliers. The second model is formulated to reflect Vendor-Based minimum lot sizes and rounding quantities to the replenishment quantity. Although these models produce an optimal solution in a reasonable time, a Wagner-Whitin based heuristic algorithm is developed as an additional solution to the company. All of the models run according to Rolling Horizon (RH) approach, which updates the existing plan at the beginning of each planning period in order to cope with changes in demand and other parameters. As a result of the study, potential savings in holding cost and reduction in planning duration are obtained and are reported.

In this study, it is aimed to analyze the logistics service quality provided by freight forwarder logistics company in Izmir, Turkey. As in every sector, competition between companies is increasing day by day in the logistics sector. Therefore, all companies in the service sector must provide good and quality service in the face of increasing competition. In addition to the cost factor to attract limited customers in the market, logistics companies can also come to the fore with the service quality they offer. The service quality of a business depends on what the customer expects from that service and the extent to which their expectations are met. In the study, firstly, the expectations of the customers are analyzed from the quality service perspective and secondly, the quality of the company, which serves for the same quality indicators, has been tried to be measured. To achieve these aims, service quality (SERVQUAL) method will be used. Also, the Best Worst Method (BWM) will be used to find the weight of each logistics service quality dimensions with a mathematical model.

In this study, the empty container repositioning problem in a liner shipping company is considered. The research includes necessary information about the company and maritime transportation, highlighting the importance of empty container storage. As the worldwide containerized trade increases, the container traffic increases. Consequently, the surplus containers are repositioned to locations where they are required, which causes high costs. Therefore, in the existing system, the company deals with higher prices. In this study, the storage location of the empty containers, which are coming from the vessels to the ports, are determined according to the different cost policies of the warehouses. Thus, our problem is defined as an empty container repositioning problem. The primary objective of this study is to build an operations-research based decision-making system in order to minimize the total cost, regarding changing prices of warehouses and transportation cost of containers. In the decision-making process, this study will help the company to overcome the complexity about repositioning empty containers regarding the container size, storage day of the containers, changing inventory holding costs of the warehouses, and the transportation costs of the containers from port to the corresponding warehouses. Taking into consideration all these parts of the problem, this study will eventually help in reducing inventory holding and transportation costs of the empty container storage. As a solution methodology, the mixed integer linear programming model is proposed, and optimal solutions are obtained in a concise time.

Izmir based Information Technology (IT) solution provider develops supply chain management software tools for many customers from fast moving consumer goods sector (FMCG). Customers of the FMCG sector needs to be visited a certain number of times in a given period by sales representatives. The company must decide which customers must be visited in which sequence by each sales representative while obeying visit frequency and time windows requirements. These decisions have significant impact on total cost. For this reason, finding an optimal route for every sales representative for each day of the planning horizon is important. This practically challenging and technically important problem can be described as periodic multiple depot traveling salesman problem with time windows (PMDTSPTW). We propose a novel mathematical model for the optimal solution of this problem. The proposed model minimizes the total distance traveled by sales representatives by deciding which sales representative will visit which customer on which day while following the time windows to collect demand data. The proposed model is applicable to any company from FMCG sector. The solution approach in this study is implemented and tested with real life including coordinates and location data of customers and sales representatives from Turkey’s largest beer distributor. The proposed model is solved using IBM ILOG CPLEX Optimization Studio version 12.8. The results show significant improvement over the current situation. To ensure efficient usage of the proposed approach, a user-friendly decision support system (DSS) is constructed and implemented.

In Bosch’s Research and Development (R&D) laboratories, several performance tests are executed to check whether the targeted quality and reliability values of the products are attained. Each product has to go through several tests on several test machines with different processing times. The specified problem in Bosch Thermotechnology entirely fits the Flexible Job Shop Scheduling (FJSS) Problem with machine and operation dependent setup times. The most crucial factor while performing these schedules of the products is to complete them as soon as possible to provide customer satisfaction as well as test machine utilization. Hence, our objective function is to minimize the makespan. Additionally, a decision support system (DSS) for the company on MS Excel is developed.

This project is related to the standardization of the packaging of variable-size products, which belongs to base, headboard, marquise and cupboard (nightstand) product categories, for a bed products company. The company cares importance to special size demands of their customers and produces products in a wide range of sizes. This causes complexity in the stock-tracking of cardboard plates, which are the most important packaging materials in the packaging process, and high waste of cardboards. Therefore, this study aims to improve the packaging system of the company and to reduce the packaging costs by decreasing the amount of waste related to the materials used for packaging. The main objective of the study is to create a standardized packaging plan by considering all product sizes the company has produced in the last three years in the product categories of base, headboard, marquise and cupboard. To achieve this goal, after the completion of literature search, forecasts for the next year’s sales were done, and a mathematical model that has two conflicting objectives was formulated. Then, the mathematical model was solved by using IBM ILOG CPLEX program for each product category and optimal package sizes were determined. A useful Decision Support System (DSS) which enables to prepare weekly packaging plans that reduce the extra packaging costs and the amount of waste has been created at the end of the project.

Growing complexity in supply chains has increased the importance of warehousing activities more and more with each passing year. This study focuses on textile warehouses, where fluctuations in demand is high, and sustainability concerns are growing due to high production and consumption rates. With this view, aim is to present social and environmental sustainability criteria for textile warehouses and present the relationship between them in order to guide sustainability transformation of textile warehouses. Social and environmental aspects of sustainability are considered as a core subject based on company needs that are determined in the light of meetings performed with company experts, and the literature review made on environmental and social sustainability perspective. After that, interviews were held with the company to specialize the criteria for textile warehouses. Finally, fuzzy DEMATEL method was used to present the importance order and causal relationship between criteria for implications. At the end of the implementations, metrics were determined for each criterion, and recommendations were made to create a roadmap for the company.

In global logistics, the trade imbalances between the exporting and importing countries (and regions) cause the empty container repositioning problem. While export dominant countries face the difficulty to find empty containers to be loaded and shipped, import dominant countries have a surplus of empty containers and try to get rid of them because they are stacked at storage areas of the seaports. Transporting empty containers to the export dominant countries is as costly as transporting the loaded ones because they keep the same storage space and require the same time and handling operations. In this study, we formulated a novel Mixed-Integer Linear Programming (MILP) model considering a multi-period and multi-region shipping network to minimize the total cost for empty container repositioning operations. We investigated the impacts of foldable containers, street-turn and depot-direct strategies on the container repositioning cost. To test our proposed model, a hypothetical case study has been developed. The total costs of different strategies (i.e., street-turn, depot-direct, foldable containers) and their combinations are compared and the results are discussed.

This study aims to modify the 5th lean assembly line in order to include a new product model in a combi boiler producer company. After reviewing customers’ needs, company decides to produce a new product. New product model will be produced in the one of the existing lines in the plant. Most suitable line determined according to other product models job sequences’ similarity with new product models’. After the analysis of the production process, the changes and possible constraints for the 5th assembly line are determined. After that, solution procedures are developed, the required calculations are made and implemented. The solutions are developed by various techniques and approaches such as Bubble diagram, Value Stream Mapping, Lean Production, Line Balancing. Particularly, mathematical model, which is solved by IBM ILOG Optimization Studio programmed comprehensive analysis with a simulation model, which is conducted by Arena Simulation software, are presented in this study. The outcomes of the solutions are simulated, compared and automatized. At the end, three options are provided for the 5th lean line to include a new product type while continuing the production process with the current products.

In this study, the uniform parallel machine scheduling problem with non-common due dates and sequence-dependent setup times is addressed for a real-life problem in the dye house of a hood manufacturer company. The aim of this study is to create an efficient scheduling tool for the company, which minimizes lateness (earliness and tardiness) in the system and reduces the buffer stock caused by the lateness. A mathematical model is developed for the problem and optimal results are obtained for the small-sized instances. As the studied problem is NP-hard, three heuristic algorithms are also proposed to solve larger instances. The performance of the proposed algorithms is evaluated with a detailed computational experiment. Furthermore, a user-friendly decision support system (DSS) is developed using Excel VBA interface and proposed solution approaches are embedded in the DSS. The developed DSS enables users to make an efficient scheduling in very short computational time and provides the results with detailed schedule reports and Gantt charts. As this problem can be faced in various industrial areas, the proposed solution approaches can also be applied to different sectors and factories.