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Resource optimisation through artificial neural network for handling supply chain constraints

Author

Listed:
  • C.G. Sreenivasa
  • S.R. Devadasan
  • N.M. Sivaram
  • S. Karthi

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.

Suggested Citation

  • C.G. Sreenivasa & S.R. Devadasan & N.M. Sivaram & S. Karthi, 2012. "Resource optimisation through artificial neural network for handling supply chain constraints," International Journal of Logistics Economics and Globalisation, Inderscience Enterprises Ltd, vol. 4(1/2), pages 5-19.
    • Handle: RePEc:ids:injleg:v:4:y:2012:i:1/2:p:5-19
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    References listed on IDEAS

    as
    1. Simon S.M. Yuen, 2010. "Development of electronic marketplace for collaborative supply chain: a conceptual framework," International Journal of Enterprise Network Management, Inderscience Enterprises Ltd, vol. 4(1), pages 59-67.
    2. P. Sasikumar & A. Noorul Haq, 2010. "A multi-criteria decision making methodology for the selection of reverse logistics operating modes," International Journal of Enterprise Network Management, Inderscience Enterprises Ltd, vol. 4(1), pages 68-79.
    3. Tersine, Richard J. & Barman, Samir, 1995. "Economic purchasing strategies for temporary price discounts," European Journal of Operational Research, Elsevier, vol. 80(2), pages 328-343, January.
    4. Caputo, Antonio C. & Pelagagge, Pacifico M., 2008. "Parametric and neural methods for cost estimation of process vessels," International Journal of Production Economics, Elsevier, vol. 112(2), pages 934-954, April.
    5. Wang, Qing, 2007. "Artificial neural networks as cost engineering methods in a collaborative manufacturing environment," International Journal of Production Economics, Elsevier, vol. 109(1-2), pages 53-64, September.
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