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Spares Inventory Prediction Using Back Propagation Neural Networks: A Case Study


Affiliations
1 National Institute of Technology, Tiruchirappalli, Tamilnadu, India
     

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Spares inventory is the key function in maintenance management and is a great challenge to estimate the current quantity with cost effectiveness. Modern manufacturing management invokes artificial intelligent techniques to enable faster and accurate decision making. This paper presents a novel method for machine spares inventory prediction using back propagation neural network. Traditional techniques have shown inadequacy in handling large data and limits the spares inventory prediction. This paper details on a back propagation neural network based spares inventory prediction for welding system. The results of the proposed system show that the developed model will be worth implementing in industries for quick breakdown resolution and for financial savings.

Keywords

Spares Inventory, BPNN, Prediction, Artificial Intelligence, Neural Networks.
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  • Cohen, M. A., Zheng, Y. S., & Agrawal, V. (1997). Service parts logistics a bench mark analysis. IIE Transactions. 29, 627-639.

  • Ehsan, R. M., Simon, S. P., & Venkateswaran, P. R. (2014). Artificial neural network predictor for grid-connected solar photovoltaic installations at atmospheric temperature. International Conference on Advances in Green Energy (ICAGE), 44-49. https://doi:10.1109/ICAGE.2014.7050142

  • Ehsan, R. M., Simon, S. P., & Venkateswaran, P. R. (2017). Day-ahead forecasting of solar photovoltaic output power using multilayer perceptron. Neural computing and applications. 28, 3981 - 3992. https://doi.org/10.1007/s00521-016-2310-z

  • Fortuin, L. (1980). The all-time requirement of spare parts for service after sales: theoretical analysis and practical results. International Journal of Operations & Production Management. 1(1), 59-70. https://doi.org/10.1108/eb054660

  • Fortuin, L., & Martin, H. (1999). Control of service parts. International Journal of Operations & Production Management. 19(9), 950-971. https://doi.org/10.1108/01443579910280287

  • He, W. (2013). An inventory controlled supply chain model based on improved BP neural network. Discrete Dynamics in Nature and Society. 1-7. https://doi.org/10.1155/2013/537675

  • Kozik, P., & Sep, J. (2012). Aircraft engine overhaul demand forecasting using ANN. Management and Production Engineering Review, 3(2), 21-26.

  • Kumar, S. (2005). Parts management models and applications: A Supply Chain System Integration Perspective. Springer.

  • Muckstadt, J. A., (2005). Analysis and algorithms for service parts supply chains. Springer. (Springer Series in Operations Research and Financial Engineering)

  • Nikolaos, K. (2013). Intermittent demand forecasts with neural networks. International Journal of Production Economics. 143(1), 198-206. https://doi:10.1016/j.ijpe.2013.01.009

  • Rojas, R. (1996). Neural Networks: A Systematic Introduction. Springer, Berlin, Heidelberg.


Abstract Views: 255

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  • Spares Inventory Prediction Using Back Propagation Neural Networks: A Case Study

Abstract Views: 255  |  PDF Views: 1

Authors

A. Andrew
National Institute of Technology, Tiruchirappalli, Tamilnadu, India
S. Kumanan
National Institute of Technology, Tiruchirappalli, Tamilnadu, India

Abstract


Spares inventory is the key function in maintenance management and is a great challenge to estimate the current quantity with cost effectiveness. Modern manufacturing management invokes artificial intelligent techniques to enable faster and accurate decision making. This paper presents a novel method for machine spares inventory prediction using back propagation neural network. Traditional techniques have shown inadequacy in handling large data and limits the spares inventory prediction. This paper details on a back propagation neural network based spares inventory prediction for welding system. The results of the proposed system show that the developed model will be worth implementing in industries for quick breakdown resolution and for financial savings.

Keywords


Spares Inventory, BPNN, Prediction, Artificial Intelligence, Neural Networks.

References