Predicting performance measures using linear regression and neural network: A comparison

Abstract

The need to make judicious use of organizational resources has put a lot of pressure on production managers and demand planners; thus, it is necessary to accurately predict what resources will yield what output. Well planned activities result in improved performance of organizational goals among which are productivity, price recovery and profitability. This work uses artificial neural network, Back Propagation Artificial Neural Network (BP-ANN), as an alternative predictive tool to multi-linear regression, for establishing the interrelationships among productivity, price recovery and profitability as performance measures. A 2-20-20-1 back propagation artificial neural network was proposed. Productivity and price recovery served as independent variables while profitability was used as the dependent variable in the BP-ANN architecture. It was observed that BA-ANN model has Mean Square Error (MSE) of 0.02 while Multiple Linear Regression (MLR) has MSE of 0.036. This study concluded that artificial neural network is a more efficient tool for modeling interrelationships among productivity, price recovery and profitability. This approach can be applied in predicting performance measures of firms.

Keywords: Sigmoid function, artificial neural network, multiple linear regressions, profitability.