Integrating fuzzy Delphi method with artificial neural network for demand forecasting of power engineering company

An organization has to make the right decisions in time depending on demand information to enhance the commercial competitive advantage in a constantly fluctuating business environment. Therefore, estimating the demand quantity for the next period most likely appears to be crucial. Manufacturing companies consider forecasting a crucial process for effectively guiding several activities, and research has devoted particular attention to this issue. The objective of the paper is to propose a new forecasting mechanism which is modeled by integrating Fuzzy Delhi Method (FDM) with Artificial Neural Network (ANN) techniques to manage the demand with incomplete information. Artificial neural networks has been applied as it is capable to model complex, nonlinear processes without having to assume the form of the relationship between input and output variables. The effectiveness of the proposed approach to the demand forecasting issue is demonstrated for a 20/25 MVA Distribution Transformer from Energypac Engineering Limited, a leading power engineering company of Bangladesh.