Burcu Özcan

Fuzzy multi criteria decision making approach to innovative strategies based on Miles and Snow typology

Innovation which utilizes both competitive and co-operative relationships can be defined as the improvement of some new products, services or systems. Generation of an innovation strategy is an essential challenge in today’s competitive business world. Innovation provides a sustainable competitive advantage when the related activities become an indispensable element of the management within the company. In line with this issue, after a comprehensive literature review, five characteristic parameters of innovation are selected to analyze a company’s innovation strategy. The determined parameters are the strategic orientation of a firm, the abilities of a firm, the cultural structure, the attributes of products, and the environment of competition. The data belonging to the leading companies of their sector are evaluated by using these parameters and with the help of Fuzzy Analytic Hierarchy Process. Based on both the conceptual and empirical research, organizations are classified according to the Miles & Snow typology.

Thermal, viscoelastic and mechanical properties' optimization of polyphenylene sulfide via optimal processing parameters using the Taguchi method

ABSTRACT Thermal, viscoelastic and mechanical properties of polyphenylene sulfide (PPS) were optimized as a function of extrusion and injection molding parameters. For this purpose, design of experiments approach utilizing Taguchis L27 (37) orthogonal arrays was used. Effect of the parameters on desired properties was determined using the analysis of variance. Differential scanning calorimeter (DSC) tests were performed for the analysis of thermal properties such as melting temperature (Tm) and melting enthalpy (ΔHM). Dynamic mechanical analysis (DMA) tests were performed for the analysis of viscoelastic properties such as damping factor (tan δ) and glass transition temperature (Tg). Tensile tests were performed for the analysis of mechanical properties such as tensile strength and modulus. With optimized process parameters, verification DSC, DMA and tensile tests were performed for thermal, viscoelastic and mechanical properties, respectively. The Taguchi method showed that ‘barrel temperature’ and its level of ‘340°C’ were seen to be the most effective parameter and its level; respectively. It was suggested that PPS can be reinforced for further improvement after optimized thermal, viscoelastic and mechanical properties.

Artificial neural networks for the cost estimation of stamping dies

Abstract Although artificial neural networks (ANN) are more known in the field of image recognition and forecasting, cost estimation has become another emerging area in recent years. In this study, the establishment of an intelligent system was attempted for forecasting the total cost of sheet metal stamping dies. In this context, where the cost of stamping dies is estimated with a conventional approach which has been applied in the company up to now, the ANN and multiple regression analysis and the performance of the three cost-estimation models are examined. The examinations are based on the data of previous costs and use a number of critical criteria which are decided by experienced tool makers and engineers from every level of the organization of the seven companies which produce stamping dies. The comparative study reveals that the ANN system outperforms the traditional linear regression analysis model and conventional approach used for cost estimation. Thus, it is possible for firms which produce stamping dies to obtain a fairly accurate prediction with an ANN model and determined criteria.

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