Flow time and product cost estimation by using an artificial neural network (ANN): A case study for transformer orders

In electromechanical industrial corporations, determining the production cost of the orders according to the technical specifications demanded by the customer has great importance in giving an accurate price offer. Labor cost is one of the important and most variable cost components that must be estimated in order to give an accurate price offer. In this study, a feed-forward back-propagation artificial neural network (FF-BPN) is used to predict the flow times of power transformer orders of a transformer producer according to the technical specifications given by the customer. The results of this study show that the prediction capability of an artificial neural network is very good for this type of problem and results in better cost estimation than current company practice. A case study is carried out for a manufacturer of electrical transformers in Turkey.     

Emerging market multinationals’ role in facilitating developed country multinationals’ regional expansion: A critical review of the literature and Turkish MNC examples

The recent expansion of emerging market multinationals into world markets has generated a rich literature. While this literature has addressed the potential motivations, behaviors, and strategic implications of these firms’ moves abroad, their possible role as facilitating agents in regional expansion has not been adequately explored. In this paper, we explore this question through a critical review of the literature and examples from Turkish multinationals. We also offer questions for future research.     

The contribution of 3D computer modeling education to twenty-first century skills: self-assessment of secondary school students

International Journal of Technology and Design Education - In this study, the contribution of the 3D computer modeling to the twenty-first century’s common competencies was evaluated from the...