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International Journal of Scientific and Engineering Research
ISSN Online 2229-5518
ISSN Print: 2229-5518 5    
Website: http://www.ijser.org
scirp IJSER >> Volume 3,Issue 5,May 2012
Modified s-S Inventory Model Using Artificial Neural Network
Full Text(PDF, )  PP.996-995  
Author(s)
Samuel S. Udoh, Okure U. Obot, Uduak D. George, Etebong B. Isong
KEYWORDS
Artificial neural network, Backpropagation algorithm, Sigmoid transfer function, Stochastic inventory model.
ABSTRACT
The use of Artifical Neural Network (ANN) in modifying and improving the output of existing scientific and economic models has been pragmatic in recent time. In this study, we modify the traditional s-S stochastic inventory model by adding the ANN predicted customers demand at lead time to the re-order quantity. The ANN was designed and implemented using Visual Basic 6.0 programming tools and Microsoft Access as the database. Data collected from petrol mega station were standardized to fit the [0,1] ANN sigmoid function domain. Backpropagation algorithm was used in training the Network. At every re-order point the expected period (days) of arrival of goods was inputted into the system and the quantity of goods to be demanded (Dp ) by the customers before the arrival of new stock was predicted and added to the s-S re-order quantity. The correlation coefficient of 0.97 and 0.58 were obtained for the modified s-S and traditional s-S inventory models respectively. The modified s-S model was found to be 97% efficient in preventing stock out of petrol in a distribution depot.
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