Semra Boran

An ERP software selection process with using artificial neural network based on analytic network process approach

An enterprise resource planning (ERP) software selection is known to be multi attribute decision making (MADM) problem. This problem has been modeled according with analytic network process (ANP) method due to fact that it considers criteria and sub criteria relations and interrelations in selecting the software. Opinions of many experts are obtained while building ANP model for the selection ERP then opinions are reduced to one single value by methods like geometric means so as to get desired results. To use ANP model for the selection of ERP for a new organization, a new group of experts opinions are needed. In this case the same problem will be in counter. In the proposed model, when ANP and ANN models are setup, an ERP software selection can be made easily by the opinions of one single expert. In that case calculation of geometric mean of answers that obtained from many experts will be unnecessary. Additionally the effect of subjective opinion of one single decision maker will be avoided. In terms of difficulty, ANP has some difficulties due to eigenvalue and their limit value calculation. An ANN model has been designed and trained with using ANP results in order to calculate ERP software priority. The artificial neural network (ANN) model is trained by results obtained from ANP. It seems that there is no any major difficulty in order to predict software priorities with trained ANN model. By this results ANN model has been come suitable for using in the selection of ERP for another new decision.

Development of a fuzzy decision support system for commodity acquisition using fuzzy analytic network process

Commodity acquisition is one of the most critical tasks in a firm especially for an accountancy department. Because of imprecise and uncertain product requirements, firm accountants have to make their best effort at this stage. Determining the most critical criteria for commodity acquisition process is a vital means for a firm to balance its limited budget. Therefore, firms have used different methods to cope with this time-consuming and mentally intensive process. This study develops a fuzzy analytic network process model which may help firm accountants in this process. Results derived from a data sample are presented to exemplify the established model. Briefly, this paper proposes an intelligent approach to vendor selection through a fuzzy ANP which takes into consideration quantitative and qualitative elements in evaluating vendor alternatives.

Evaluation of Spatial Risks of Nursing Homes by Fuzzy Risk Analysis Method

The elderly population in the world and Turkey is increasing rapidly. The proportion of elderly people in Turkey that has remained below 5% until the 1990s has reached its highest point in the past 15 years with a significant increase. This leads to an increase in the need for nursing homes. However, for those living in nursing homes, spatial risks can lead to serious hazards such as injury and death. To increase the quality of life for the elderly, these hazards should be assessed, and corrective actions should be taken to eliminate them. The Fine-Kinney risk analysis method is one of the most commonly used methods for risk assessment. In this method, the hazards are evaluated according to the probability, frequency and severity factors. However, the value assignments of these risk factors are often based on expert opinion. Experts often identify risk values in the context of incomplete information and uncertainty. Also, experts tend to use verbal expressions rather than numeric value assignments. In this study, a fuzzy Fine-Kinney risk analysis approach was developed to take advantage of fuzzy logic to remove this shortcoming. This developed approach was applied to the nursing homes located in Istanbul and the areas that need to be improved with high risk were determined first. The order of risk prioritization obtained with the fuzzy Fine-Kinney approach was compared with that of the conventional Fine-Kinney method and it was determined that the fuzzy Fine-Kinney approach gave better results.

Yapay Sinir Ağı Yaklaşımıyla Çinko Kalınlığının Tahminlenmesi

Since the competition level among the companies is increasing day by day, meeting customer demands with qualified products and cost reduction are primary goals of each company. And zinc, the main raw material in galvanization sector, is the most important cost item. So it is required to forecast the amount of zinc to be spent. In this study it is tried to forecast the amount of zinc consumption using the artificial neural network (ANN) method. To evaluate the convenience of values hypothesis tests are done; and the results showed that there is no significant difference between the predicted and real outputs statistically