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In today's understanding, the universities are considered as service providers besides their institutional functions. Because the universities shape the future of the country via the services they provide, it is a necessity that their service quality must be assessed by using scientific analyses, and their service quality must be improved based on such scientific findings. The Generation Z, whose members are currently receiving university education carries unique features that distinguish them from the previous generations. When this fact is considered, it is understood that the constant research and monitoring of the learning environment of the Generation Z is important. In this study, as a result of a detailed literature search, a scale consisting of 7 dimensions and 36 indicators was developed in order to measure the higher education service quality of the Z generation. The validity and reliability tests of this scale are completed via the convergent and divergent validity analyses, Exploratory Factor Analysis (EFA), and Confirmatory Factor Analysis (CFA). Because the answers provided to the surveys reflect the personal evaluation of the participants, the Fuzzy Logic is employed, and the study is conducted by using the fuzzy modelling and fuzzy ranking. As a result of this study, the General Satisfaction Index is created, and improving recommendations are carried out based on the scores. [ABSTRACT FROM AUTHOR]

Today companies continue to examine their costs in business life where competition is hard. In this sense, warehouse site selection, which is significantin terms of logistics costs, becomes a critical process. Warehouse site selection decision making model proposed in this study is developed for contributing the efffiency of distribution network and minimizing logistics costs. In this study, to solve warehouse site selection problem 3 different methods are synthesised, which are Analytical Hierarchy Process (AHP), Vise Kriterijumska Optimizacija I Kompromisno Resenje (VIKOR) and with Multi- Objective Optimization by Ratio Analysis (MOORA). AHP method is used for determining the criteria of alternatives and VIKOR and MOORA methods are used for ranking of alternatives.Using real data instead of classical pairwise comparison scales for AHP and VIKOR applications and the use of MOORA method in warehouse site selection problem have been important for considering a new approach and solution method in this field. The application is carried out for the decision problem of determining 3 best alternatives of a casting factory among 11 alternatives. In the study, comparisons are conducted for results of both of the methods and final decision is made according to application results. [ABSTRACT FROM AUTHOR]

In this study, we propose a performance management model based on employee performance evaluations. Employees are clustered into 4 different groups according to a job satisfaction-performance model and strategic plans are derived for each group for an effective performance management. The sustainability of this business process improvement model is managed with a control mechanism as a Plan-Do-Check-Act (PDCA) cycle as a continuous improvement methodology. The grouping model is developed with a data mining clustering algorithm. Firstly 4 different performance groups are determined with a two-step k-means clustering approach. Then the clustering model developed is testified with an Artificial Neural Network (ANN) model. Necessary data for this study are collected with a questionnaire application composed of 25 questions, first 13 variables measuring job satisfaction level and last 12 variables measuring performance characteristics where evaluators are employees themselves. With the help of model developed, human resources department is able to track employees' job satisfaction and performance levels and strategies for different performance groups are developed. Application of the model is conducted in a manufacturing company located in Istanbul, Turkey [ABSTRACT FROM AUTHOR]

In this paper a solution method is proposed for emphasizing the importance of costumer needs at the starting of Quality Function Deployment (QFD) application when there are multi costumer groups. Data Envelopment Analysis (DEA) steps are added to an algorithm in which an Analytic Network Process (ANP) approach is used in QFD. The need for adding DEA steps is determining the efficient customer requirements when there are multi customer groups. This solution approach is called DEANP which aggregates data DEA to ANP. The relative importance values of product technical requirements are calculated with this algorithm and application is performed at a manufacturer of white goods company in Turkey. The fact that using DEANP instead of ANP produced more powerful solutions is also confirmed by marketing specialists. [ABSTRACT FROM AUTHOR]

An expert system is a computer program that imitates the procedures by which experts solve problems. This paper describes an expert system application which provides advice to primary school students who are seeking assistance in determining high school type. The system gathers information about student grade point average (GPA), centralized exam score, personality type and socio-economic factors. It assesses student qualifications for seven different school types in Turkey. The expert system recommends a school type for the students and produces a short report explaining the reasons of recommendation. Student GPA and centralized exam score are considered as technical criteria. In addition, social and socio-economic factors such as personality type, parental involvement etc. affecting the decision process are also considered and included in the model. [ABSTRACT FROM AUTHOR]

The sustainability of enterprises in an increasingly competitive environment is proportional to their ability to use the resources efficiently. Maintenance departments are critical to ensure that resources are ready and operational. Increasing the efficiency of maintenance departments depends on reducing the number of failures, performing planned maintenance on time and sustaining availability of spare parts needed. Therefore, it is vital that businesses consider predictive maintenance and spare parts jointly. In this study, predictive maintenance and spare parts integration studies are carried out using gearbox failure data and spare parts consumption data of a bus fleet. This study aims to contribute to the reduction of failure costs by finding failure patterns and predicting the subsequent failures and spare parts to be used. The sequential pattern mining approach was used to determine failure patterns and the traditional frequent itemset mining approach was used to predict spare parts. As a result, 45 failure patterns were found. Rules with a reliability of up to 79% were obtained. In addition, spare part clusters with a support value of approximately 40% were created. With this valuable information, businesses are able to investigate root causes, take precautions against future failures, make predictions about the spare parts that will be needed, and develop joint maintenance planning and inventory management policies. [ABSTRACT FROM AUTHOR]

The wide usage of information technologies in production has led to the Fourth Industrial Revolution, which has enabled real data collection from production tools that are capable of communicating with each other through the Internet of Things (IoT). Real time data improves production control especially in dynamic production environments. This study proposes a decision support system (DSS) designed to increase the performance of dispatching rules in dynamic scheduling using real time data, hence an increase in the overall performance of the job-shop. The DSS can work with all dispatching rules. To analyze its effects, it is run with popular dispatching rules selected from the literature on a simulation model created in Arena®. When the number of jobs waiting in the queue of any workstation in the job-shop falls to a critical value, the DSS can change the order of schedules in its preceding workstations to feed the workstation as soon as possible. For this purpose, it first determines the jobs in the preceding workstations to be sent to the current workstation, then finds the job with the highest priority number according to the active dispatching rule, and lastly puts this job in the first position in its queue. The DSS is tested under low, normal, and high demand rate scenarios with respect to six performance criteria. It is observed that the DSS improves the system performance by increasing workstation utilization and decreasing both the number of tardy jobs and the amount of waiting time regardless of the employed dispatching rule. [ABSTRACT FROM AUTHOR]

In job-shop production systems, orders are assigned to work centers according to their routes, and their operations are performed in this order. Production is becoming more and more complex with the increasing number of product lines and work centers with different routes. Decisions to be made according to the real-time monitoring of a dynamic production environment have become important. With the Fourth Industrial Revolution, information technologies are widely used in industries. A large amount of data is obtained from production tools that are capable of communicating with each other by means of Industry 4.0 and the internet of things. In this study, a simulation model of a production system that can collect data in real-time via sensors in work centers has been created and operation conditions have been determined. Then, work center / machine loading strategies were compared according to the delay periods of the jobs. The simulation model with the best loading strategy was run according to three different demand rates. Then data related with the delay status of the orders and the status of the work centers was obtained. The data were evaluated with data mining classification algorithms and rules were determined for delayed jobs. These rules were added to the simulation model as a decision mechanism. When an order is received in this model, the expert system estimates whether or not there will be a delay, and makes a decision to outsource the order's production if needed. This approach further reduces the number of delayed orders [ABSTRACT FROM AUTHOR]