Reducing the Bullwhip Effect in a Supply Chain Using Artificial Intelligence Technique

Autori

  • Om Prakash
  • Vijay Pandey Department of Production Engineering, Birla Institute of Technology, Mesra, Ranchi, INDIA

Parole chiave:

Bullwhip effect, Forecasting, Artificial intelligence techniques, artificial neural network

Abstract

The bullwhip effect in a supply chain leads to various inefficiencies like excessive inventory, quality problems, higher raw material costs, and poor customer service, which can be reduced by coordination and collaboration among partners of a supply chain with time bound information sharing; it requires full integration. However, full integration of the organizations of a supply chain is not possible in real case scenario due to existing differences of functions, strategies and goals of partner organizations. Hence, in absence of full integration among partners, bullwhip effect can be reduced by increasing forecast accuracy. The results of this paper show that the artificial intelligence technique, artificial neural network forecasts accuracy is better than that of the traditional forecasting approaches including exponential smoothing, moving average and naïve forecasts.Keywords: Bullwhip effect, Forecasting, Artificial intelligence techniques, Artificial neural network

Biografia autore

  • Vijay Pandey, Department of Production Engineering, Birla Institute of Technology, Mesra, Ranchi, INDIA
    Associate Professor, Department of Production Engineering, Birla Institute of Technology, Mesra, Ranchi, INDIA

Dowloads

Pubblicato

2014-08-22

Fascicolo

V. 4 N. 2 (2014)

Sezione

Research Articles

Licenza

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