Adnan Aktepe

Customer satisfaction and loyalty analysis with classification algorithms and Structural Equation Modeling

The model proposed bridges the gap in post analysis of customer satisfaction (CS) and customer loyalty (CL) research.An integrated approach is presented using data mining and structural models together.We contribute to customer classification literature by adapting decision tree results to CS-CL matrix.CS-CL matrix is created by integrating group and criteria evaluation results using the advantage of structural models.A customer strategy development tool is offered according to matrix-based model outcomes. Businesses can maintain their effectiveness as long as they have satisfied and loyal customers. Customer relationship management provides significant advantages for companies especially in gaining competitiveness. In order to reach these objectives primarily companies need to identify and analyze their customers. In this respect, effective communication and commitment to customers and changing market conditions is of great importance to increase the level of satisfaction and loyalty. To evaluate this situation, level of customer satisfaction and loyalty should be measured correctly with a comprehensive approach. In this study, customers are investigated in 4 main groups according to their level of satisfaction and loyalty with a criteria and group based analysis with a new method. We use classification algorithms in WEKA programming software and Structural Equation Modeling (SEM) with LISREL tools together to analyze the effect of each satisfaction and loyalty criteria in a satisfaction-loyalty matrix and extend the customer satisfaction and loyalty post-analysis research bridging the gap in this field of research. To convert developed conceptual thought to experimental study, white goods industry is exemplified. 15 criteria are used for evaluation in 4 customer groups and a satisfaction-loyalty survey developed by experts is applied to 200 customers with face-to-face interviews. As a result of the study, a customer and criteria grouping method is created with high performance classification methods and good fit structural models. In addition, results are evaluated for developing a customer strategy improvement tool considering method outcomes.

WELDING PROCESS OPTIMIZATION WITH ARTIFICIAL NEURAL NETWORK APPLICATIONS

Correct detection of input and output parameters of a welding pro- cess is significant for successful development of an automated welding operation. In welding process literature, we observe that output parameters are predicted according to given input parameters. As a new approach to previous efforts, this paper presents a new modeling approach on prediction and classification of welding parameters. 3 different models are developed on a critical welding process based on Artificial Neural Networks (ANNs) which are (i) Output parameter prediction, (ii) Input parameter prediction (reverse application of output prediction model) and (iii) Classification of products. In this study, firstly we use Pareto Analysis for determining uncontrollable input parameters of the welding process based on expert views. With the help of these analysis, 9 uncontrollable parameters are determined among 22 potential parameters. Then, the welding process of ammu- nition is modeled as a multi-input multi-output process with 9 input and 3 output parameters. 1st model predicts the values of output parameters according to given input values. 2nd model predicts the values of correct input parameter combina- tion for a defect-free weld operation and 3rd model is used to classify the products whether defected or defect-free. 3rd model is also used for validation of results obtained by 1st and 2nd models. A high level of performance is attained by all the methods tested in this study. In addition, the product is a strategic ammunition in the armed forces inventory which is manufactured in a limited number of countries in the world. Before application of this study, the welding process of the product could not be carried out in a systematic way. The process was conducted by trial- and-error approach by changing input parameter values at each operation. This caused a lot of costs. With the help of this study, best parameter combination is found, tested, validated with ANNs and operation costs are minimized by 30%.

Backpropagation Neural Network Applications for a Welding Process Control Problem

The aim of this study is to develop predictive Artificial Neural Network (ANN) models for welding process control of a strategic product (155 mm. artillery ammunition) in armed forces’ inventories. The critical process about the production of product is the welding process. In this process, a rotating band is welded to the body of ammunition. This is a multi-input, multi-output process. In order to tackle problems in the welding process 2 different ANN models have been developed in this study. Model 1 is a Backpropagation Neural Network (BPNN) application used for classification of defective and defect-free products. Model 2 is a reverse BPNN application used for predicting input parameters given output values. In addition, with the help of models developed mean values of best values of some input parameters are found for a defect-free weld operation.

AN INVENTORY CLASSIFICATION APPROACH COMBINING EXPERT SYSTEMS, CLUSTERING, AND FUZZY LOGIC WITH THE ABC METHOD, AND AN APPLICATION

The classification of inventories requires using several criteria to control different functions of inventory management. In this study, a new classification algorithm, called the FNS (functional, normal, and small) algorithm, is developed that combines classical ABC classification with a new grouping strategy. In the algorithm, handling frequency, lead time, contract manufacturing process, and specialty are used as input criteria, and the outputs are new classes for the inventories. The algorithm is applied in a large company operating in the defence industry. The main problem in the company is not being able to manage and track inventories effectively. The company has previously used the Pareto analysis approach, but this no longer met the company’s inventory management needs. In our study, the ABC classification method is enriched and combined with the proposed FNS algorithm to create nine different classes for inventories. To achieve this, the classical ABC classification method is integrated with expert systems, clustering, and fuzzy logic methods. Now, inventories can be classified in more detail, and useful counting strategies can be created. The classification system developed is currently being used by the company, and is integrated into its enterprise resources planning (ERP) system.

Design of a Tracking Welding Robot Automation System for Manufacturing of Steam and Heating Boilers

For satisfying customers companies want to respond to customer requests on time. At the same time, they expect production process to be completed with low cost and low loss. For this reason, the importance of mechanization and automation in production sector has increased. As a result, companies have begun to give more importance to robotic systems, which are the basic components of automation systems. Despite the likelihood of mistakes caused by physiological and mental states of humans, these systems can perform operations precisely without any variability. In this study, an application was carried out for the automation of welding process of industrial type boilers in different sizes and features. For products of which standard measurements or welding operations are difficult to perform manually, a robotic system was proposed in which measurement and welding operations can be performed automatically. In addition, operators are prevented from exposure to gas and light via the proposed system which enables a safer working condition.

Creating alternative layout plans with simulated annealing and data mining

When studies in literature are examined, it is seen that different approaches have been used to solve facility layout problems. The relationship between departments in layout is always important. In this study, data mining technique is used for analyzing relations among departments and then association rules are obtained. Determining closeness relationships between the departments in facility are often ambiguous and require expert opinions. In such cases, a fuzzy component emerges in the facility layout problem. Hence, fuzzy logic is widely used to address ambiguous problems. Association rules is converted by using defuzzification approach to crisp values used facility layout problem solution in this study. Facility layout problems are considered to be NP-Hard (Nondeterministic-Polynomial-Hard) optimization problems. That is, definite solution approaches are limited in solving large-scale problem examples. For heuristic approaches are frequently used to improve the layout, simulated annealing approach is used in this study. To improve facility layout planning, simulated annealing approach is carried out via code written in Visual Basic 2012. In conclusion, 17% improvement is achieved with alternative layout plan obtained.

Internet Based Intelligent Hospital Appointment System

In today’s competitive service industry, the technology in service systems is used in a wide range of areas. The service companies are now providing service via internet or via other computer based systems in an increasing trend day by day. Expert systems are good examples of these applications. Today, expert systems are used in various fields such as design, planning, imaging, diagnosis, etc. For practical use, the expert systems are also used through internet. One of the most important service system institutions is the hospital. Increasing the service quality level in hospitals, internet based appointment systems are used in several hospitals in Turkey. There are also several internet based expert system applications today contributing the improvement of service levels in several industries. In this study, an internet based expert system is created that is used in outpatient department/polyclinic direction. The system architecture, algorithm and the role of such an expert system are discussed in this p...

KALİTE FONKSİYON YAYILIMINDA (KFY) BİR VERİ ZARFLAMA ANALİTİK AĞ SÜRECİ (VZAAS) UYGULAMASI

Bu calismada coklu musteri gruplarinin degerlendirilmesi durumunda Kalite Fonksiyon Yayilimi (KFY)’de ilkadimi olusturan musteri gereksinimlerinin onemini on plana cikaran bir cozum yaklasimi gelistirilmistir. AnalitikAg Sureci (AAS) teknigi kullanilarak gelistirilen bir KFY algoritmasina, musteri grubunun birden fazla oldugudurumlarda musteri beklentilerini onem derecelerine gore siralamak yerine etkin olanlari tespit etmek amaciylaVZA adimlari eklenmistir. Veri Zarflama Analizi (VZA) ve Analitik Ag Sureci (AAS)’yi birlestiren bu yaklasimVZAAS algoritmasi olarak adlandirilmistir. Urun teknik gereksinimlerinin goreceli onem degerleri bu yontemlehesaplanmis ve Turkiye’de beyaz esya ureticisi olan bir firmada uygulanmistir. VZAAS algoritmasi ile eldeedilen sonuclarin AAS teknigi ile elde edilen sonuclardan daha iyi oldugu pazarlama uzmanlari tarafindan dadogrulanmistir.