Yönetim Bilişim Sistemleri

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A comparative analysis of employee and organization perspectives about “Bring Your Own Device” policies
(Thesis (M.A.) - Bogazici University. Institute for Graduate Studies in the Social Sciences, 2017., 2016.) Gökce, Kevser Gülnur.; Döğerlioğlu, Özgür.
Bring Your Own Device (BYOD), which allows employees to use their own mobile devices for work and connect to the corporate network, has getting more popular in many enterprises. Companies want to increase the efficiency and productivity of employees with decreasing the cost while employees prefer to use their own device in the work because they feel more comfortable and it eases the communication. BYOD has some advantages and disadvantages for both companies and employees. Organizations should think all of the effects of BYOD on their system and working environment. Although BYOD seems very attractive, companies and employees have some security concerns in different and various ways. The aim of this study is to explore employee and organization perspectives about BYOD. In this study, literature has been reviewed in order to develop a research model. Empirical part of the research has two parts: Qualitative and Quantitative. Manager’s opinions were determined through a series of interviews and then the findings were analyzed. In qualitative part, a questionnaire has been developed based on the research model. The English and Turkish versions of the questionnaire are in Appendix B and C, respectively.12 different interviews and 93 respondents were used in the analysis. It has been found that while organizations and employees believe BYOD having benefits in many ways, they also support the idea that security and privacy issues should not be ignored.
A decision support system for air cargo warehouse design
(Thesis (M.A.) - Bogazici University. Institute for Graduate Studies in the Social Sciences, 2013., 2013.) Karaismailoğlu, Adnan.; Sencer, Aslı.
Warehouse design is critical in the air cargo industry, where the service standards are high and the competition is getting harder. The designers of air cargo warehouses consider various criteria like costs, system failure risks, customer perception and marketing power to evc::Huate the alternative designs and find the optimal one. The issue of designing an air cargo warehouse is generally considered by design and consulting companies, which provide a broad experience in operational design but they lack in suggesting different alternatives based on a theoretical framework. The alternative warehouse designs are generated by allowing different combinations of resource capacities such as the number of gates, workstations and storage areas. The evaluation of these alternative designs requires the use of analytical methodologies for multi criteria decision making. At this point, simulation which is a popular tool to evaluate the operational performances and Analytical Hierarchy Process (AHP) which provides the ability to evaluate in accordance to qualitative as well as quantitative criteria, appear to be the common tools used in the literature for such problems. In this study, a flexible and user-friendly Decision Support System (DSS) is developed based on simulation and AHP approaches. The DSS is used to generate design alternatives, evaluate their performances, rank them according to a set of decision criteria and report the results. The graphical interfaces are designed in accordance with the consideration of the man-machine interaction to increase its functionality. The environment is applied with real-time data in one of the Europe's biggest air cargo carriers and the findings are discussed.
A decision support system for evaluating information technology projects
(Thesis (M.A.) - Bogazici University. Institute for Graduate Studies in the Social Sciences, 2017., 2017.) Atlı, Şeyhmus.; Mardikyan, Sona Kunuzyan.
The increasing pressures of global competition, the continuous stream of innovative technologies, and the introduction of new products to satisfy customer needs make Information Technology (IT) projects a key element of the business market. Due to the proactive nature of IT, its value is difficult to evaluate in advance, which means that organizations often select to implement IT projects that do not realize the intended benefits. A well-designed selection process for IT projects should decrease the failure rate of implemented solutions and increase the financial success of the companies. This study attempts to resolve the IT project selection problem in the companies by combining an ANP with fuzzy logic and strengthening the solution with a Monte Carlo simulation. While there have been previous attempts to combine any two of the three underlying methods, the combination of all three should lead to a method that gives optimal results for any given case. The study reviews existing literature on IT project selection. Every criteria that can be relevant to IT projects are obtained with literature review and currently significant ones are selected using feedback from experts. The most effective ones respectively finance, organizational goals, risk and technical are used for the study. Based on existing knowledge and analysis of problems facing the companies, a detailed theoretical model is developed and applied to a real-world case study. In this context, this study should be useful to IT project selection committee members and researchers in the field of decision making, but it should also be of interest to IT managers of companies.
A decision support system for generating optimal raw milk managment strategies in a dairy supply chain
(Thesis (M.A.) - Bogazici University. Institute for Graduate Studies in Social Sciences, 2011., 2011.) Gül, Burak.; Erdem, Aslı.
The main supply of the dairy business, raw milk, is one the most volatile supplies in fast-moving goods businesses since there are many areas that cause complexities. Raw milk has a high supply seasonality that contradicts with the demand; there is a harsh competition for supply that forces long-term contracts and, inventory alternatives are very limited due to perishability. Moreover, the demand is also volatile and highly sensitive to lost sales. Therefore, it is necessary to utilize an optimal mix of supply, production and inventory strategies that takes all of the items described into account; however, such optimization practices are not common and the strategy mix is usually determined by experience and intuition, being likely to provide a suboptimal solution. The goal of this study is to eliminate the trial-and-error methodology of strategy determination process by implementing a mathematical model of the whole raw milk management system and the relevant strategy options as the basis of a Decision Support System (DSS) to automate the strategic raw milk planning process in an actual dairy company. In addition to modeling and optimization, this study focuses on the user interface design in DSS development for practical use by providing an example with incorporating the new generation Microsoft Ribbon interface.
A decision support system for integrated optimization of production, transportation and pricing decisions in a supply chain
(Thesis (M.A.) - Bogazici University. Institute for Graduate Studies in the Social Sciences, 2016., 2016.) İzer, Murat Umut.; Sencer, Aslı.
In supply chains, the decisions regarding production, transportation and pricing are often handled separately. In this thesis study, a nonlinear programming model (NLP) is generated to optimize the production, transportation, and pricing decisions in a supply chain where substitute products are produced in multiple factories and sold at several markets. Under the cooperative competition of substitute products, these decisions are given centrally to maximize the total profit. Demands for the substitute products are realized as functions of their prices where the market shares are expressed as market share attraction models from the marketing literature. The NLP model is solved with different parameter settings and sensitivity analysis on the input parameters is made to provide managerial insights. Finally, a decision support system (DSS) is developed to provide an efficient, effective and flexible decision making environment. The DSS includes a relational database for input and output data, a model base that includes the generated NLP model and a graphical user interface that provides interaction between the user, the database, and the solver for the NLP model.
A decision support system for inventory management of information technology spare parts
(Thesis (M.A.) - Bogazici University. Institute for Graduate Studies in the Social Sciences, 2015., 2015.) Turan, Aycan.; Metin, Bilgin.
Rising operational costs have become a major issue in developing countries, causing many leading information technology (IT) companies to focus on inventory optimization. This thesis research concentrates on inventory policy optimization and decision making process. We develop a decision support system (DSS) that provides an optimal control of IT spare part inventory to minimize the total cost. The system supports a continuous review (Q, r) inventory policy and a periodic review (S, s) inventory policy options for managing the spare parts inventory. The DSS includes a forecast model to estimate the failure rates of different device types purchased in different time periods. It is also enhanced by a simulation environment which evaluates different inventory management scenarios and choses the optimum one. Next, the DSS is applied to a real system and optimum inventory management scenario is determined according to the cost and service performances. Experimental design analysis is performed to measure the sensitivity of optimal total cost with respect to input parameters such as inventory holding cost, part order cost and penalties. The DSS provides an efficient, effective and flexible decision making environment for the optimal control of IT spare parts.
A decision support system for production planning and scheduling in a smart factory environment
(Thesis (M.A.) - Bogazici University. Institute for Graduate Studies in the Social Sciences, 2017., 2017.) Cinkılıç, Haktan.; Sencer, Aslı.
With the industry 4.0 idea that emerged recently, manufacturing systems have been enhanced by advanced manufacturing technologies. These smart manufacturing systems can respond faster to the changes in the production plans which are mostly due to the updates in customer order quantities, order due dates, unexpected machine breakdowns, material supply problems, etc. This study aims to support the dynamic structure of smart factories by providing an efficient, effective and flexible platform for production planning and scheduling. In most of the studies in the literature, production control and scheduling plans have been handled separately and iteratively due their computational complexities. As an improvement, in this thesis a mixed integer quadratic programming (MIQP) model is developed to optimize the integrated production plans and daily schedules with minimum total cost. Then, this optimization model is linearized and a mixed integer linear programming (MILP) model is generated to improve its computational performance. Finally, the optimization model is embedded into a web based decision support system (DSS) together with a database and user interface to provide an efficient, effective and flexible decision making environment in a smarter cyber-physical system. The DSS is verified with different test scenarios and its computational performance is measured.
A decision support system for reducing empty run costs in a logistic service provider company
(Thesis (M.A.) - Bogazici University. Institute for Graduate Studies in the Social Sciences, 2019., 2019.) Turan, Murat.; Sencer, Aslı.
Two of the most important key performance indicators (KPI) in logistic service provider (LSP) companies are the cost of empty trailer transportations and the cost of outsourced trailer. Manufacturing companies are mostly cost-oriented while they are outsourcing their international transportation activities to the third-party logistic companies. This behavior of the manufacturers causes high demand fluctuations on the LSP company side. On the other hand, the volume of exports and imports are often imbalanced for an LSP. Hence the LSPs need to transport empty trailers between countries, or outsource trailers to fulfill the transportation needs in export and import directions. Within the scope of this study, a decision support system (DSS) is proposed for an LSP company to decrease the empty trailer movements and the number of outsourced trailers so that the total transportation cost is reduced. The generated model considers intermodal transportation of orders internationally. The DSS is implemented in one of the largest LSP companies in Turkey. The performance of the DSS is tested to show that it improves the effectiveness, efficiency, and the flexibility of the decision-making environment in logistic planning.
A decision support system for supplier evaluation and order allocation
(Thesis (M.A.)-Bogazici University. Institute for Graduate Studies in the Social Sciences, 2008., 2008.) Göçen, Emir.; Erdem, Aslı.
This study focuses on the improvement of supplier evaluation and order allocation decisions for one of the leaders of the white-goods manufacturers in Turkey. A decision support system (DSS) is developed to increase the quality and speed of decision making. In the current purchasing system, the decision maker evaluates the supplier candidates informally and after tough negotiations quota diversification is established. In the proposed system, a tool is developed to evaluate the suppliers with qualitative and quantitative criteria and allocate annual quota so as to optimize a set of purchasing goals.
A decision support system for workforce management in call centers
(Thesis (M.A.)-Bogazici University. Institute for Graduate Studies in Social Sciences, 2006., 2006.) Gedikoğlu, Burak.; Erdem, Aslı.
Shift design, workforce allocation and call allocation are the major problems in call center management. The aim of the call center manager is to allocate and dynamically update the workforce so that the incoming calls are answered in the shortest possible time, above certain service level measures. The software tools developed to aid decision making in these areas use models that are based on Erlang-C calculations. However, the strict assumptions of Erlang-C often lead to invaliddecisions. For this reason, especially at peak times during the day, dynamic updates in the applied design are inev itable. In this study, a framework for a decision supportsystem (DSS) is developed for designing the shifts and allocating the agent workforce to the shifts in a call center, so that target service levels are met. In theproposed system, shifts are designed by solving a linear optimization model. Using this solution as the input, a simulation model is developed to dynamically update theworkforce so that the minimum required service level is met at all times. The proposed DSS is applied to an existing call center system, alternative designs aregenerated and compared.
A deep learning-based extractive text summarization system for Turkish news articles
(Thesis (M.A.) - Bogazici University. Institute for Graduate Studies in the Social Sciences, 2020., 2020.) Gündeş, Özcan.; Durahim, Ahmet Onur.
The goal of this study is to develop an automated extractive summarization system for Turkish news using pre-trained language models. Pre-trained language models have been applied to wide range Natural Language Processing tasks and achieve state of the art performance results. In this thesis, pre-trained language models for Turkish are applied on extractive summarization task. The proposed model has a pre-trained language model and on top of it, Transformer layers are added to capture document level features and semantic relationships between the sentences in the news articles. Then, these sentences are scored with sigmoid function, which outputs a real value between 0 and 1. To train this model, 2076 news are collected from well-known Turkish news website. After the data collection, each sentence in the articles is labelled as 0 or 1 with a heuristic algorithm. By using these labels, an extractive model is trained. In the test time, Top-5 scoring sentences are combined to generate final summaries. Also, to investigate the effects of hyperparameters, 241 different models, which have different architecture and hyperparameter sets, are run. The best one has achieved 38.38 Rouge-1 F score, 26.8 Rouge-2 F score and 38.04 Rouge-L F score. These scores are promising since they are significantly greater than LEAD-5 baseline, which has 37.49, 26.4 and 37.12 Rouge F scores. For this study, LEAD-5 is very strong baseline since the most significant sentences are placed at the beginning of the news to capture the readers’ attention. Therefore, the proposed model shows a good performance for Turkish news dataset.
A digital innovations-driven regeneration model and corporate sustainability
(Thesis (Ph.D.)-Bogazici University. Institute for Graduate Studies in the Social Sciences, 2020., 2020.) Coşkun Setirek, Abide.; Tanrıkulu, Zuhal.
The traditional ways of doing business have been changed by digital innovations such as the Internet of things, blockchain and digital currency, data analytics, artificial intelligence, robots, additive manufacturing, etc. Firms can stay competitive using the benefits of digital technologies. The spread of the coronavirus disease in 2019 (COVID-19) all over the world has created a better understanding of the importance of organizations’ ability to keep up with digital innovations. In this study, a method for digital innovations-driven business model regeneration is developed and a dynamic business model, which can also be used in the business model regeneration process, to examine the effects of digital innovation strategies on the corporate sustainability is proposed. For this purpose, the existing literature on the business model innovation and system dynamic are examined, and the empirical data are collected from 44 managers using semi-structured interviews to complement gaps in the literature. Moreover, the digital innovations-driven business model regeneration method, which is proposed in this study, is applied to a real case. This study extends the literature on the business model innovation and the dynamic business model. The study can provide strategy analysts and managers with an opportunity to analyze the effects of potential digital innovation strategies on their current business models and to explore the most effective digital innovation strategies in order to regenerate their business model to gain a competitive advantage over their competitors or to sustain their business in light of technological developments.
A framework for integrating knowledge management and decision support systems by using knowledge discovery techniques: a case study forecasting financial time series
(Thesis (M.A.)-Bogazici University. Institute for Graduate Studies in Social Sciences, 2006., 2006.) Gülser, Zarife Gonca.; Badur, Bertan Yılmaz.
In a typical decision support environment, the decision-making process deploys asmall portion of the needed information that exists on computers and the structures of thedecision models are highly generalized for each of the problems. However, knowledge in the minds of the decision makers, called tacit knowledge, is needed to give convenient decisions and to construct specific models for problems. This situation coerces the development of anew decision support concept that integrates knowledge management and decision support systems by using knowledge discovery techniques. The purpose of the study is to develop a framework that applies knowledge discovery techniques for various types of knowledge conversion and generates specific decision models by utilizing previously defined models asmuch as possible. In order to prove the applicability of the proposed framework, anexperimental study is designed. The chosen problem domain forecasts the Turkish financialand macroeconomic time series. In addition to its main purpose, the study suggests to increase the effectiveness of decision support systems, to enhance the knowledge in the decision making process and the reby to improve the decision-making process.
A hybrid article recommendation system based on deep learning and co-publication network analytics
(Thesis (M.A.) - Bogazici University. Institute for Graduate Studies in the Social Sciences, 2019., 2019.) Atlanel, Büşra.; Durahim, Ahmet Onur.
In recent years, with the rapid development of world wide web, researchers are spending more effort and time to reach the most relevant academic work for their studies because of the information overload. Preventing users from being distracted by a tremendous amount of publications and simplification of the research process makes recommendation systems more valuable. Traditional recommendation systems generally suffer from limited coverage, data sparsity, and cold start problem. In order to tackle these problems and achieve better performance, many recommender systems started to use neural network models. Being an effective neural network model, deep learning technology can transform article titles and abstract information into text embeddings and capture non-linear relationships between these text embeddings. In addition to deep learning on text embeddings, the relationship between authors has a huge effect on their future preferences. The research of copublication relationship with social network analysis improves the performance of the recommendation systems. In this study, the aim is to propose a hybrid article recommendation system that incorporates deep learning for article text similarity using Deep Siamese BiLSTM and social network analysis through node embeddings using co-publication and citation networks to exploit the network structure to provide benefit for recommender systems. Experiments conducted in this research show that the proposed model achieved a prediction rate of 7% on average when the number of articles to be recommended is taken as 100.
A machine learning based capacity management system for mainframe resources
(Thesis (M.A.) - Bogazici University. Institute for Graduate Studies in Social Sciences, 2022., 2022.) Kürtül, Ekrem.; Özturan, Meltem.
The goal of this study is to design a capacity planning tool for resource consumption of application servers which are running on mainframes, also known as Z systems, by using machine learning algorithms. This tool is aimed to ensure adequate resources are available in order to meet current and future workload demands. The desired system is intended to have capability to determine and then forecast how much additional capacity will be needed based on increasing demands. In this study, IBM Cloud Pak for Data as a Service is used to create capacity planning model by using data analysis, data engineering, data governance and Artificial Intelligence modeling services which are provided by the platform. The data is prepared outside of the platform and imported to the platform in order to perform analysis and refinement. After the data refinement step is completed, machine learning models are trained by using several algorithms. Then, functional tests are performed in order to check accuracy and performance of the models by using the test interface of the platform. Results of these tests, comments and further research opportunities are also provided. It is observed that the designed capacity planning tool is capable of making consistent predictions with acceptable error rates.
A privacy paradox: the power of technological surveillance and Its effect on information technology usage behavior
(Thesis (M.A.) - Bogazici University. Institute for Graduate Studies in the Social Sciences, 2016., 2016.) Rençberoğlu, Emre.; Tanrıkulu, Zuhal.
Thanks to developing technologies, reaching information has become easier and the big data concept started to be used in various fields. Today, almost all of the technologies which are used widely, such as the internet, mobile phones, computers and smart TVs, are capable of collecting and storing data. The data gathering activity is a routine process for almost all private companies and governments, which can result in incidents of exploitation and misuse. Moreover, some sociological impacts, which include self-censorship and changing perceptions, are considered one of the results of increasing information privacy concerns. The aim of this thesis is to contribute to the literature by investigating the multidimensionality of information privacy concerns. For this purpose, a survey was conducted with 641 participants to measure the relationship between information privacy concerns with regard to news, regulations, user agreements, public beliefs and perceptions. Additionally, the association between information technology (IT) usage behavior and the dimensions of the information privacy concerns are examined. According to the analysis of survey data, demographic differences are important in terms of privacy concerns. News and regulations are highly associated with privacy concerns, but security perception is only related to the data collection dimension. Another finding of the research is that there is not a significant relationship between the information privacy concerns and IT usage behavior, except general IT tools.
A simulation based decision support system for supply chain management
(Thesis (M.A.)-Bogazici University. Institute for Graduate Studies in Social Sciences, 2006., 2006.) Sancar, Ayşe Ürün.; Erdem, Aslı.
This study focuses on the improvement of supply chain performance in one of the biggest commodity product manufacturers in Turkey. The aim is to generate a tool that runs as a Decision Support System (DSS) and procides an easy to use simulation environment for supply chain managers in decision-making. In the current supply chain system there is no demand information flow upwards in the chain and the manufacturer determines its manufacturing rate according to the orders faced in the last thirty days. On the other hand, the manufacturer offers a volume discount option if the orders placed by a distributor exceed a certain quota. In the "Monthly Quota" system, the distributors gain d discount for their unit-purchasing price, if they reach their quota at the end of the evaluation period. As an improvement of the current supply chain system, an information system is proposed to share the Point of Sale data among the members of the supply chain. Another improvement strategy may be applying the "Rolling Horizon" instead of "Monthly Quota" method where the quotas are checked every time a distributor places an order. Three simulation models are developed by using the software ARENA, a graphical user interface that uses MS-Excel as a database is generated and integrated into a DSS for the supply chain managers. The DSS environment is used to compare all three models with different performance measures.
A simulation based decision support system for workforce management in call centers
(Thesis (M.A.) - Bogazici University. Institute for Graduate Studies in Social Sciences, 2011., 2011.) Başarır, Birgül.; Erdem, Aslı.
Workforce Management is critical in the call centers where thousands of calls are handled by hundreds of agents everyday. In a call center, where the call arrival rates tend to flutuate during the day, the agent allocation plans are required to be planned flexible and the number of operating call center agents ought to be updated whenever needed, in order to keep the customer satisfaction level over a predefined level. Workforce plans are usually generated by the use of queuing models that are based on Erlang-C calculations. However, they have assumptions that oversimplify the real system and jeopardize the validation of the model. At this point, the simulation models, which do not have such restrictive assumptions, are becoming popular in calculating the required number of agents for each time period and measuring the performance of a given shift schedule. The combination of the advantages of simulation with a flexible and user-friendly decision support system environment provides more effective and efficient workforce planning and performance reporting in call centers. In this study, a simulation based decision support system, DSS is developed that runs on real time dta for one of the largest call centers in Turkey. The graphical user interfaces, GUIs are designed in accordance to the man-machine interaction consideration to increase the usability, functionality and effectiveness of the DSS.
A social media big data mining framework for detecting sentiments in multiple languages
(Thesis (Ph.D.)-Bogazici University. Institute for Graduate Studies in the Social Sciences, 2018., 2018.) Coşkun, Mustafa.; Özturan, Meltem.
The popularity of social media platforms has generated a new social interaction environment thus a new collaboration network among individuals. These platforms own tremendous amount of data about users’ behaviors and sentiments. One of these platforms is Twitter, which provides researchers data potential of benefit for their studies. Based on Twitter data, in this study a multilingual sentiment detection framework is proposed to compute European Gross National Happiness (GNH). This framework consists of a novel data collection, filtering and sampling method, and multilingual sentiment detection algorithm for social media big data, and tested with nine European countries (United Kingdom, Germany, Sweden, Turkey, Portugal, Netherlands, Italy, France and Spain) and their national languages over six-year period. The reliability of the data is checked with peak/troughs comparison for special days from Wikipedia. The validity is checked with a group of correlation analyses with OECD Life Satisfaction survey reports’, currency exchanges, and national stock market time series data. Then, the European GNH map is drawn for six years. Lastly, an exploratory study for determining the relationships between users’ Twitter account features (number of tweets, number of followers etc.) and happiness polarities are analyzed. Main aim of this study is to propose a novel multilingual social media sentiment analysis framework for calculating GNH for countries and change the way of OECD type organizations’ survey and interview methodology. Also, it is believed that this framework can serve more detailed results (e.g. daily or hourly sentiments of society in different languages).
A social network analytics based recommendation system
(Thesis (M.A.) - Bogazici University. Institute for Graduate Studies in the Social Sciences, 2019., 2019.) Payal, Sedat.; Durahim, Ahmet Onur.
In this thesis, different frameworks for a recommendation system based on social network analytics is investigated. In these frameworks, three different potential customer identification approaches are examined and corresponding successes are analyzed. In order to exploit the underlying network structure, three networks, restaurant-user, user-user and restaurant-restaurant, are generated. In the first approach, potential users are ranked and selected according to a combination of pagerank values and community scores of both restaurants and users. In the second approach, users are ranked according to the sentiments scores of their comments in conjunction with pagerank of restaurants. In the third approach, node embeddings for the restaurant-user network are computed and used to find the similarities between users and restaurants. Then, based on these similarities, potential users are ranked for a given focal restaurant. With the aim of comparing the successes of these three frameworks, dataset is splitted into three and success rates are calculated based on the percentage of the actual customers recommended by the generated models. Experiments in this research shows that Ranks framework utilizing the community structure together with the network ranking of both users and brands reached up to 50% and on average achieved 9.61% accuracy when the number of potential customers to be recommended is taken as 100. So, frameworks utilizing the underlying network structure can be exploited to improve the prediction capability of recommendation systems that find potential customers for a given company or brand.

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