Wirtschaftsingenieurwesen und Management

An Application of Artificial Neural Network (ANN) Process to Assess Risk in Cement Industries in Bangladesh

Abstract

Syimun Hasan Mehidi, Nayan Chakrabarty, H.M. Mohiuddin

Today large and mid-size companies have more complex operations which involve different types of risk and are difficult to identify the risk. To extract these risks and analyze it scientifically, a professional risk assessment tool namely ANN is most appropriate and flexible network technology. This paper briefly presents Artificial Neural Networks (ANNs), universal and highly flexible functions, approximations for any data as an application to assess risk in cement industries for instance HOLCIM CEMENT BANGLADESH LIMITED has been selected to assess risks. To assess the risks a threelayer feed forward architecture of 10 risk factors which are independent and their strength of relationship was used as relative weight of input variables. Six neurons of hidden layer and two neurons of output layer with back propagation algorithm were designed using Neural Network Toolbox of MATLAB. The input data normalized to the range 0–0.9 and initial weights were randomly selected. The algorithm propagates the weights backwards and then controls the weights. The factors were inputted to a MATLAB-based application program and the numbers of iterations were set 9000. After implementation the overall process the adjustment weights were trained and by using adjusted weights actual output was found 0.63 in which predicted value was 0.66with 95.45% prediction success, the results very promising.