Title: Resource optimisation through artificial neural network for handling supply chain constraints

Authors: C.G. Sreenivasa; S.R. Devadasan; N.M. Sivaram; S. Karthi

Addresses: Department of Industrial and Production Engineering, University BDT College of Engineering, Davangere-577004, Karnataka, India. ' Department of Production Engineering, PSG College of Technology, Coimbatore-641004, Tamil Nadu, India. ' Department of Production Engineering, PSG College of Technology, Coimbatore-641004, Tamil Nadu, India. ' Department of Production Engineering, PSG College of Technology, Coimbatore-641004, Tamil Nadu, India

Abstract: In today's dynamic market environment, the organisations are enforced to optimise their supply chain constraints. The objective of this paper is to identify the supply chain constraints and propose/develop methods to optimise it. Accordingly, two constraints namely, temporary price discount and anticipated price increase has been identified. Subsequently, two models namely, mathematical and artificial neural network (ANN) models are developed. The results obtained from the mathematical models have been correlated with ANN models. This paper has been concluded that the developed ANN model shall be beneficial for the contemporary companies for handling the supply chain constraints.

Keywords: agile manufacturing; supply chain management; SCM; supply chain constraints; resource optimisation; artificial neural networks; ANNs; economic order quantity; EOQ; temporary price discounts; anticipated price increases.

DOI: 10.1504/IJLEG.2012.047211

International Journal of Logistics Economics and Globalisation, 2012 Vol.4 No.1/2, pp.5 - 19

Received: 08 May 2021
Accepted: 12 May 2021

Published online: 03 Jun 2012 *

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