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

Авторы

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

Ключевые слова:

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

Аннотация

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

Биография автора

  • 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

Загрузки

Опубликован

2014-08-22

Выпуск

Том 4 № 2 (2014)

Раздел

Research Articles

Лицензия

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