Supplier evaluation and selection is a kind of problem which includes multiple criteria of qualitative and quantitative properties. From different alternatives it requires to find the best option using different criteria and opinions of the decision makers. Because of the judgments or the bias of the decision makers, sometimes classical methods cannot be precise. In this paper, a relatively new decision method called TODIM (an acronym in Portuguese for iterative multi-criteria decision-making -"Tomada de Decisão Iterativa Multicritério") is improved with fuzziness to prevent the above mentioned problems of the classical methods. A real life case study for a furniture manufacturing company is also be solved.

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The purpose of this paper is to forecast housing prices in Ankara, Turkey using the artifi cial neural networks (ANN) approach. The data set was collected from one of the biggest real estate web pages during April 2013. A three-layer (input layer -one hidden layer -output layer) neural network is designed with 15 different inputs to forecast the future housing prices. The proposed model has a success rate of 78%. The results of this paper would help property investors and real estate agents in developing more effective property pricing management in Ankara. We believe that the artifi cial neural networks (ANN) proposed here will serve as a reference for countries that develop artifi cial neural networks (ANN) method-based housing price determination in future. Applying the artifi cial neural networks (ANN) approach for estimation of housing prices Journal of Marketing and Consumer Behaviour in Emerging Markets 1(5)2017Olgun Kitapci, Ömür Tosun, Murat Fatih Tuna, is relatively new in the fi eld of housing economics. Moreover, this is the fi rst study that uses the artifi cial neural networks (ANN) approach for analyzing the housing market in Ankara/Turkey. JEL classifi cation: C15, D14, R31

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Purpose -The purpose of this paper is to provide a structured methodology to permit the optimal selection of the best-suited computerized maintenance management system (CMMS) software within maintenance information technologies. Design/methodology/approach -The analysis has been executed adopting a multi-attribute decision-making methodology, namely the technique for order preference by similarity to an ideal solution (TOPSIS). For the selection process, 17 criteria under five main heading have been defined. Data obtained from questionnaires and interviews with the company's maintenance managers have been used in fuzzy TOPSIS. Findings -The application of the proposed approach allows the maintenance practitioners to concentrate on a limited subset of CMMS applications and to compare their actual capabilities in order to select the right one, rather than considering only their purchase cost. Research limitations/implications -Comparisons with other multi-attribute decision-making techniques such as AHP (analytic hierarchy process) and ELECTRE (elimination and choice expressing reality) under fuzzy conditions can be done for further research. Practical implications -This paper is a very useful source of information both for maintenance managers and stakeholders in making decisions about the selection of CMMS software. Originality/value -This paper addresses CMMS software evaluation and selection criteria for practitioners and proposes a new multi-attribute decision-making methodology, hierarchical fuzzy TOPSIS, for the problem.