Ferhan Çebi

An integrated approach for supplier selection

Competitive international business environment has forced many firms to focus on supply chain management to cope with highly increasing competition. Hence, supplier selection process has gained importance recently, since most of the firms have been spending considerable amount of their revenues on purchasing. The supplier selection problem involves conflicting multiple criteria that are tangible and intangible. Hence, the purpose of this study is to propose an integrated model for supplier selection. In order to achieve this purpose, supplier selection problem has been structured as an integrated lexicographic goal programming (LGP) and analytic hierarchy process (AHP) model including both quantitative and qualitative conflicting factors. The application process has been accomplished in a food company established in Istanbul, Turkey. In this study, the model building, solution and application processes of the proposed integrated model for supplier selection have been presented.

Multi-Criteria and Multi-Stage Facility Location Selection under Interval Type-2 Fuzzy Environment: A Case Study for a Cement Factory

AbstractThe study proposes a comprehensive and systematic approach for multi-criteria and multi-stage facility location selection problem. To handle with high and more uncertainty in the evaluation and selection processes, the problem is solved by using multi-criteria decision making technique with interval Type-2 fuzzy sets. The study contributes the facility location selection literature by introducing the application of fuzzy TOPSIS method with interval Type-2 fuzzy sets. Finally, the suggested approach is applied to a real life region and site selection problem of a cement factory.

An Application of Expert System Approach for Supplier Evaluation and Selection

The conditions of global competition are becoming more difficult in all the industries. All companies are competing with global and local rivals. Suppliers have played a crucial role for companies to outperform their rivals in competitive markets. This increases the importance of the supplier evaluation and selection process which is a multi-dimensional decision making process containing various variables, quantitative and qualitative criteria, heuristics and experiences of managers. Development of expert systems simulating this multidimensional problem solving process of a human being, has gained importance in the past years. The aim of this study is to point out the importance of supplier evaluation and selection in the buying process and also to develop an expert system for supplier evaluation and selection. The proposed expert system is called as ESforSES (An Expert System for Supplier Evaluation and Selection). ESforSES was applied in a large-scale electronic company. The results of the application shows that ESforSES is a reliable and objective system for evaluating and selecting of the suppliers and it may be utilized by small and medium sized companies by making some modifications and improvements according to their requirements and strategies

Benefits of Knowledge Management in Banking

Knowledge management has been increasingly one of the most important practices for organizations to achieve better performance and long-term competitive advantage. This study aims to investigate the relationships of the benefits obtained from knowledge management implementation based on research in a knowledge management implementing banking institution, which is among one of the five largest banks in Turkey. The study is based on one of the specific applications of knowledge management to achieve process efficiency. The results of the study show that knowledge management implementation has provided various interrelated benefits to the instituation, rendering to utilize its resources efficiently and effectively. Although the study is based on a specific application in one instituation, it provides useful insight for understanding the contribution of knowledge management implementation to organizations for improving their performance and competitiveness.

A multiattribute ABC classification model using fuzzy AHP

ABC analysis is one of the most widely used techniques in organizations to classify inventory items. This classification is based on the Pareto principle. The main limitation of the Pareto principle comes from its one dimensional analysis. To overcome this limitation, an ABC analysis based on a multiattribute classification can be used. Many attributes are hard to define precisely in this analysis. The fuzzy set theory can overcome this problem by incorporating imprecision and subjectivity into a multiattribute ABC classification model. In this paper, Zengs fuzzy analytic hierarchy process is used for classifying inventory items by taking care of conflicting attributes like demand, unit cost, substitutability, payment terms, and lead time. A real case study in a Turkish firm distributing fast moving consumer goods is realized. The obtained results show that this multiattribute fuzzy methodology can be effectively used in classifying inventory items.

A fuzzy integrated approach for project selection

Purpose – The purpose of this paper is to develop a systematic and comprehensive project selection model utilizing fuzzy multi-objective linear programming (FMOLP) that deals with the imprecise data in IS projects and uncertain judgment of decision makers. Design/methodology/approach – First, projects are prioritized by considering both quantitative and qualitative factors. A fuzzy analytical hierarchical process (FAHP) is used in order to obtain weights of each project that indicates their priorities. At the second step, project selection decision is completed by using FMOLP. Then, the sensitivity analysis is performed to evaluate the robustness of the proposed integrated model. Findings – The result of this study indicates that an integrated approach utilizing FAHP and FMOLP can be used as a supportive tool for project selection in IS context. It decreases the uncertainty caused from uncertain judgment of decision makers. Research limitations/implications – Future studies are suggested to design models ...

An integrated decision analytic framework of machine learning with multi-criteria decision making for multi-attribute inventory classification

A multi-criteria inventory classification method was developed.Machine learning algorithms are integrated with multi-criteria decision making.A case study at an automotive company validates the model with its high accuracy.The proposed method yields significantly better results than others in literature.It is flexibly applicable to other multi-criteria inventory classification cases. The purpose of this study is to develop a hybrid methodology that integrates machine learning algorithms with multi-criteria decision making (MCDM) techniques to effectively conduct multi-attribute inventory analysis. In the proposed methodology, first, ABC analyses using three different MCDM methods (i.e. simple-additive weighting, analytical hierarchy process, and VIKOR) are employed to determine the appropriate class for each of the inventory items. Following this, naive Bayes, Bayesian network, artificial neural network (ANN), and support vector machine (SVM) algorithms are implemented to predict classes of initially determined stock items. Finally, the detailed prediction performance metrics of algorithms for each method are determined. The comprehensive case study executed at a large-scale automotive company revealed that the best classification accuracy is achieved by SVMs. The results also revealed that Bayesian networks, SVMs and ANNs are all capable of successfully dealing with the unbalanced data problems associated with Pareto distribution, and each of these algorithms performed well against all examined measures, thus validating the fact that machine learning algorithms are highly applicable to inventory classification problems. Therefore, this study presents uniqueness in that it is the first and foremost of its kind to effectively combine MCDM methods with machine learning algorithms in multi-attribute inventory classification and is practically applicable in various inventory settings. Furthermore, this study also provides a comprehensive chronological overview of the existing literature of machine learning methods within inventory classification problems.

A fuzzy multi-objective model for solving project network problem with bonus and incremental penalty cost

We develop multiobjective linear mathematical model for project network problem under fuzziness.We design a model supporting project managers to evaluate bonus and incremental penalty cost simultaneously.We compare the findings with different methods under various sets of weights.We demonstrate a real life case study to check the feasibility of the model.We observed that the trade-off between time & cost is significant and implementation of the proposed method may provide substantial savings. Project management decisions require considering several conflicting constraints and objectives as well as uncertainties in the information over the planning period. The study aims to design a multi-objective linear programming model for solving project network problem under fuzzy environment. The developed model consists of three objectives such as minimizing total project completion time, minimizing total project completion cost and minimizing the earliest time of an event requiring special attention by taking into account several factors such as crash time, normal time, normal cost, crash cost, indirect cost as well as financial bonus and incremental penalty cost. In the study, a case study based on a real life problem is conducted to illustrate the validity and feasibility of the model. The study contributes to the project network literature by developing the fuzzy goal programming model and allows the project managers simultaneously evaluate financial bonus and incremental penalty cost with respect to total project time. In addition, the results of the sensitivity analysis highlight that the developed model can be used for helping the contractors make effective decisions.

A fuzzy long-term investment planning model for a genco in a hybrid electricity market considering climate change impacts

We study the long-term generation capacity investment problem of an independent power generation company (GenCo) that functions in an environment where GenCos perform business with both bilateral contracts (BC) and transactions in the day-ahead market (DAM). A fuzzy mixed integer linear programming model with a fuzzy objective and fuzzy constraints is developed to incorporate the impacts of imprecision/uncertainty in the economic environment on the calculation of the optimal value of the GenCo’s objective function. In formulating the fuzzy objective function we also include the potential impacts of climate change on the energy output of hydroelectric power plants. In addition to formulating and solving the capacity planning/investment problem, we also performed scenario-based (sensitivity) analysis to explore how investment decisions of the GenCos change when fuzziness (tolerance) in the maximum energy output of hydroelectric units and/or drought expectation increases. The proposed model is novel and investigates the effects of factors like drought expectations of climate changes, hydroelectric power plant investments, and other power generation technology investment options.

A multiobjective fuzzy mathematical model for a supply chain problem with the forward and reverse flows

Reverse logistics has been one of the complex and popular topics drawing attentions of the researchers and practitioners. Recovery of products has many advantages to companies in reducing costs and protecting the environment. In this study, one of the recovery options, namely reuse, is analysed. For this pursuit, a mathematical model is developed to plan production and distribution of the reusable products as well as the new products by considering the forward and reverse flows of the products. The model is a multi-echelon supply chain model composed of multiple customers, multiple distributors, multiple transshipment points, and a factory. Due to vagueness, ambiguity and lack of information in the reverse logistics, the problem is constructed and solved under the fuzzy environment. The model is implemented on a hypothetical supply chain network based on an industrial case, and the results of the model are compared with the results of the other methods.