level of finished goods inventory as a function of product demand, setup, holding, and material
costs. The model selects a feed-forward back-propagation ANN with four inputs, ten hidden
neurons and one output as the optimum network. The model is tested with a manufacturing
industry data and the results indicate that the model can be used to forecast finished goods
inventory level in response to the model parameters. Overall, the model can be applied for
optimization of finished goods inventory for any manufacturing enterprise in a competitive
business environment.
How to cite this paper
Paul, S & Azeem, A. (2011). An artificial neural network model for optimization of finished goods inventory.International Journal of Industrial Engineering Computations , 2(2), 431-438.
Refrences
Au, K. F., Choi, T.M., & Yu, Y., (2008). Fashion retail forecasting by evolutionary neural networks. International Journal of Production Economics, 114, 615-630.
Chang, H. C., (2004). An application of fuzzy sets theory to the EOQ model with imperfect quality items. Computers & Operations Research, 31, 2079–2092.
Chat, S., Konak, A., & Smith, A. E., (2002). Estimation of all-terminal network reliability using an artificial neural network. Computers and Operations Research, 29, 849-868.
Chiu, Y. P., (2003). Determining the optimal lot size for the finite production model with random defective rate, the rework process, and backlogging, Engineering Optimization, 35(4), 427–437.
Fonseca, D. J., & Navaresse, D., (2002). Artificial neural networks for job shop simulation. Advanced Engineering Informatics, 16, 241-246.
Gutierrez, R. S., Solis, A. O., & Mukhopadhyay, S., (2008). Lumpy demand forecasting using neural networks. International Journal of Production Economics, 111, 409-420.
Koh, S. G., Hwang, H., Sohn, K. I., and Ko, C. S., (2002). An optimal ordering and recovery policy for reusable items. Computers and Industrial Engineering, 43, 59–73.
Lin, Y. H., Shie, J. R., and Tsai, C. H., (2009). Using an artificial neural network prediction model to optimize work-in-process inventory level for wafer fabrication. Expert Systems with Applications, 36, 3421–3427.
Lotfi, K. G., & Choueiki, M. H., (2000). A neural network model for solving the lot sizing Problem. Omega, 28, 175-184.
Maiti, M. K., and Maiti, M., (2006). Fuzzy inventory model with two warehouses under possibility constraints. Fuzzy Sets and Systems, 157, 52–73.
Ntuen, C. A., (1991). A neural network model for a holistic inventory system. Proceedings of the International Industrial Engineering Conference, 435-444.
Partovi, F. Y., & Anandarajan, M., (2002). Classifying inventory using an artificial neural network approach. Computers and Industrial Engineering, 41(4), 389-404.
Wee, H. M., & Chung, C. J., (2009). Optimizing replenishment policy for an integrated production inventory deteriorating model considering green component-value design and remanufacturing. International Journal of Production Research, 47(5), 1343 – 1368.
Wee, H. M., Yu, J., and Chen, M. C., (2007). Optimal inventory model for items with imperfect quality and shortage backordering. Omega, 35, 7–11.
Yildirim, L. B., Cakar, T., Doguc, U., & Meza J. C., (2006). Machine number and the due date determination in flexile manufacturing systems using artificial neural networks. Computers and Industrial Engineering, 50, 185-194.
Chang, H. C., (2004). An application of fuzzy sets theory to the EOQ model with imperfect quality items. Computers & Operations Research, 31, 2079–2092.
Chat, S., Konak, A., & Smith, A. E., (2002). Estimation of all-terminal network reliability using an artificial neural network. Computers and Operations Research, 29, 849-868.
Chiu, Y. P., (2003). Determining the optimal lot size for the finite production model with random defective rate, the rework process, and backlogging, Engineering Optimization, 35(4), 427–437.
Fonseca, D. J., & Navaresse, D., (2002). Artificial neural networks for job shop simulation. Advanced Engineering Informatics, 16, 241-246.
Gutierrez, R. S., Solis, A. O., & Mukhopadhyay, S., (2008). Lumpy demand forecasting using neural networks. International Journal of Production Economics, 111, 409-420.
Koh, S. G., Hwang, H., Sohn, K. I., and Ko, C. S., (2002). An optimal ordering and recovery policy for reusable items. Computers and Industrial Engineering, 43, 59–73.
Lin, Y. H., Shie, J. R., and Tsai, C. H., (2009). Using an artificial neural network prediction model to optimize work-in-process inventory level for wafer fabrication. Expert Systems with Applications, 36, 3421–3427.
Lotfi, K. G., & Choueiki, M. H., (2000). A neural network model for solving the lot sizing Problem. Omega, 28, 175-184.
Maiti, M. K., and Maiti, M., (2006). Fuzzy inventory model with two warehouses under possibility constraints. Fuzzy Sets and Systems, 157, 52–73.
Ntuen, C. A., (1991). A neural network model for a holistic inventory system. Proceedings of the International Industrial Engineering Conference, 435-444.
Partovi, F. Y., & Anandarajan, M., (2002). Classifying inventory using an artificial neural network approach. Computers and Industrial Engineering, 41(4), 389-404.
Wee, H. M., & Chung, C. J., (2009). Optimizing replenishment policy for an integrated production inventory deteriorating model considering green component-value design and remanufacturing. International Journal of Production Research, 47(5), 1343 – 1368.
Wee, H. M., Yu, J., and Chen, M. C., (2007). Optimal inventory model for items with imperfect quality and shortage backordering. Omega, 35, 7–11.
Yildirim, L. B., Cakar, T., Doguc, U., & Meza J. C., (2006). Machine number and the due date determination in flexile manufacturing systems using artificial neural networks. Computers and Industrial Engineering, 50, 185-194.