Development and comparative performance evaluation of neural network classification techniques for manufacturing sector firms on the basis of new product growth rate

Vikas Bhatnagar and Ritanjali Majhi

International Journal of Business Information Systems, 2020, vol. 33, issue 2, 157-179

Abstract: Continuous changes and improvements in new product development and its process have brought competition to its peak among the manufacturing sector firms. Incessant improvement considers as the only option for sustainable development and growth. The new products have been launched by firms at regular interval of time to stay updated with present market trends and followings. Recognising current status of the firm with respect to its competitors is of utmost importance for better strategy formulation and decision making. In this study electronics, garment and metal and machinery industries are chosen for an in-depth analysis because of enormous growth shown by them in past few years. The firms are categorised into three classes termed as innovative, mediocre and traditional on the basis of their new product growth rate (NPGR). Adaptive exponential functional linked artificial neural network (AEFLANN) along with other neural network models and conventional methods of classification has been employed. An extension of AEFLANN model using derivative-based (RLS) and derivative free (PSO) algorithm has been proposed. Comparative performance analysis for neural network and conventional classification algorithms has been made. Neural network classification models outperform other employed models in terms of classification accuracy.

Keywords: new product growth rate; NPGR; discriminant analysis; support vector machine; SVM; K-nearest neighbour; K-NN; multilayer perceptron; MLP; AEFLANN; particle swarm optimisation; PSO. (search for similar items in EconPapers)
Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:ids:ijbisy:v:33:y:2020:i:2:p:157-179

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