Supply chain flexibility assessment by multivariate regression and neural networks - Jeeva, Ananda; Guo, W. (Springer-Verlag, 2010)
This paper compares two vastly different methods of analysis - multiple regression and neural networks, in supply chain flexibility assessment. Data of manufacturing firms evaluating their prominent suppliers were analysed by multiple regression and simulated using three-layer multilayer perceptron(MLP) neural networks. Our study shows that NN can accurately determine a supplier's flexibility capability within an error of 1% The incorporation of these two methods can lead to better understanding and dynamic prediction of supply chain flexibility for buyers.