Yigit Kazancoglu

A fuzzy hybrid MCDM approach for professional selection

Highlights? A novel hybrid model is created for the professional selection problem. ? The weights of criteria related with sniper are calculated by fuzzy ANP. ? Fuzzy TOPSIS is used to determine the most suitable candidates. ? Final ranking is obtained with fuzzy ELECTRE. Personnel selection is an important process in management. Sniper selection as a subset of personnel selection contains different characteristics compared to selection of other personnel. The multi criteria nature and the presence of both qualitative and quantitative factors make it considerably more complex. This study proposes a fuzzy hybrid multicriteria decision making approach enabling the combination of both qualitative and quantitative factors. The use of a combination of Fuzzy ANP, Fuzzy TOPSIS, Fuzzy ELECTRE techniques, proposing a MCDM approach for sniper selection, and applying these to a real case are the unique features of this study.

ERP software selection with MCDM: application of TODIM method

Any ERP software in the market cannot fully meet the needs and expectations of manufacturing companies, because each company, which looks for implementing ERP system, runs its business with different strategies and goals. Therefore, enterprise resource planning ERP software selection is an important and critical decision process. Another aspect of this problem is that multi-disciplinary content reveals the multi-criteria decision making as the appropriate field of study. In this study, by employing TODIM method, which allows the usage of both qualitative and quantitative data, an example, which involves ERP software selection process of a steel forming and hot dip-galvanising firm located in Izmir, Turkey, is provided by using a proposed framework. A new path toward ERP software selection is designed for the decision makers in various industries include manufacturing companies. In order to deal with the complex calculations in the decision making process, the proposed framework is formed as an applicable tool for all decision makers in various industries.

E-government website evaluation with hybrid MCDM method in fuzzy environment

E-government websites play an important role in the knowledge society not only by accelerating bureaucratic procedures but also enhancing citizen commitment to democracy. The acceptance of democracy (in its fullest sense) means considering the citizen as customer, making citizen satisfaction the ultimate target. Therefore, in addition to web design characteristics, e-service and e-democracy are taken into account in e-government website evaluation. Fuzzy logic is employed due to the properties of the evaluation process. Experts evaluated the criteria weights using fuzzy AHP, and the e-government websites from a group of selected EU countries using fuzzy VIKOR as a multi-criteria decision making (MCDM) technique, and a ranking was obtained. The unique properties of the paper can be listed as the formation of criteria using two kinds of fuzzy numbers, combining two MCDM methods, and applying this methodology to the problem of website evaluation for the first time.

Fuzzy DEMATEL-based green supply chain management performance: Application in cement industry

Purpose Performance assessment of green supply chain management (GSCM) requires a systematic approach because of its interdisciplinary and multi-objective nature. The purpose of this paper is to propose a model to the performance assessment of GSCM. Design/methodology/approach A model is proposed, grounded on a literature review on GSCM performance, after which the causal relationships and prioritization of the sub-criteria are analyzed by fuzzy Decision Making Trial and Evaluation Laboratory technique in a company operating in the cement industry. Findings An integrated holistic performance assessment model incorporating specifically six criteria and 21 sub-criteria is applied, which represents causal relationships and prioritization of sub-criteria. Research limitations/implications The proposed model can be generalized, because an integrative framework can be used in future empirical studies to analyze performance of GSCM. However, the causal relationships and prioritization among sub-criteria are analyzed based on the needs and capabilities of the individual company; therefore, the causal relationships found are company specific. Practical implications The proposed model can be hired and implemented by companies striving for GSCM. This model allows companies to assess their current GSCM performance, analyze causal relationships, and prioritize sub-criteria. Originality/value Several studies have analyzed performance assessment in green supply chains; however, to the best of the authors’ knowledge, no study has taken an approach to performance assessment in GSCM that combines environmental, economics/financial, logistics, operational, organizational and marketing in the same framework. In addition, the cause-effect relationships identified will be the base for performance improvement.

Predictive Modelling of Ball Burnishing Process Using Regression Analysis and Neural Network

Abstract The present paper focuses on two techniques, namely regression and neural network techniques, for predicting surface roughness in ball burnishing process. Values of surface roughness predicted by the two techniques were compared with experimental values. Also, the effects of the main burnishing parameters on surface roughness have been determined. Surface roughness (Ra) was taken as response (output) variable and burnishing force, number of passes, feed rate, and burnishing speed were taken as input parameters. Relationship between the surface roughness and burnishing parameters was found out for direct measurement of the surface roughness. Results showed the application of the regression and neural network models to accurately predict the surface roughness.

Process Capability Analysis in Machining for Quality Improvement in Turning Operations

Abstract Process capability indices are effective tools for both, process capability analysis and quality assurance. In quality assurance programs, process capability indices reflect the performance of key quality characteristics for a control process. Quality assurance in mass production is enabled by using statistical process control techniques. In this study, various statistical process control techniques were carried out using the measured values taken from the workpieces that represent the whole process in the medium sized company. The chances for using statistical techniques for quality estimation processes have been discussed. For this purpose, normal probability plots and histograms were prepared and the process capability indices were calculated. As a result of this study, it turned out that the process capability for the whole process was inadequate and the mass production was unstable. Some actions must be taken by engineers to improve the quality level by shifting the process mean to target value and reducing the process variation.

Grey-based fuzzy algorithm for the optimization of the ball burnishing process

Abstract In the present study, Grey based fuzzy algorithm was used for the optimization of complex multiple performance characteristics of the ball burnishing process. Experiments have been planned according to Taguchis L16 orthogonal design matrix. Burnishing force, number of passes, feed rate and burnishing speed were selected as input parameters, whereas surface roughness and microhardness were selected as output responses. Using Grey relation analysis (GRA), Grey relational coefficient (GRC) and Grey relation grade (GRG) were obtained. Then, Grey-based fuzzy algorithm was applied to obtain Grey fuzzy reasoning grade (GFRG). Analysis of variance (ANOVA) was carried out to find the significance and contribution of parameters on multiple performance characteristics. Finally, a confirmation test was applied at the optimum level of GFRG to validate the results. The results also show the feasibility of the Grey-based fuzzy algorithm for continuous improvement in product quality in complex manufacturing processes.

Regression Based Neural Network Modeling for Forecasting of the Metal Volume Removal Rate in Turning Operations

Abstract The present paper focuses on two techniques, namely regression and neural network, for predicting tool wear. Predicted values of tool wear by both techniques were compared with experimental values. Also, the effects of the main machining variables on tool wear have been determined. The metal volume removed (MVR) was taken as response (output) variable and cutting speed, feed rate, depth of cut and hardness were taken as input parameters, respectively. The relationship between tool wear and machining parameters was found out by direct measurement of the tool wear by MVR. The results showed the ability of regression and neural network models to predict the tool wear, accurately.

Determining the causal relationship among the research streams of the operations management course

Abstract Due to the rapid changes in technology, there have been dramatic changes in manufacturing systems and techniques in almost all industries. In this sense, higher education institutions have become more important as they play a key role in ensuring the labor force in the industry. In the present study, the research streams for operations management course were established, then fuzzy total interpretive structural modelling technique was employed to analyze the causal relations and influences among the factors. The purpose of the study was to determine the relationships among the research streams of operations management course.

Efficiency Analysis in Retail Sector: Implementation of Data Envelopment Analysis in a Local Supermarket Chain

Increased competitiveness leads the importance of efficiency analysis for organizations. There are many tools and techniques for performance measurement. Data Envelopment Analysis is one of the well-known nonparametric, direct programming technique that can deal with multiple inputs and outputs and used for efficiency analysis. In this study, data envelopment analysis is used in retail sector. This study is based on a real life problem and the aim is to measure the store performance of a local retail chain in Turkey. While, size of the store, number of employees, number of deliveries and total cost are used as inputs; number of customers, sales, and store evaluation of customers are used as outputs in this study. Main contribution of this study is using store evaluation of customers as an output by conducting an interview with each stores’ customers. Due to the dynamic environment of the retail sector, model that allows variable return to scale while maximizing inputs; input-oriented BCC is used in the study. At the end of the study, numerical results are obtained from a software called Frontier Analyst, and managerial suggestions are presented in the conclusion part.