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A Product Backorder Predictive Model Using Recurrent Neural Network
Subject area: Science,Engineering and Technology · Area of research: Deep Learning
Abstract
Increasing demand of products is a common cause of out of product inventory, and the adoption of backordering to satisfy outstanding customer orders after its occurrence cannot be undermined. However, wrong management of backorders incurs several issues such as delay in product delivery, low customer satisfaction, and many more. Therefore, it is necessary to ascertain products with high tendencies of shortage beforehand in order to undertake proactive measures and potentially mitigate both tangible and intangible costs. Hence, this paper proposes a backorder predictive model using recurrent neural network (RNN) on large and imbalanced inventory dataset. The data was preprocessed using Min-Max Scaler, while three data balancing methods (ADASYN, SMOTE, and Random Under Sampling)were applied on the imbalanced data simultaneously and their output were fed into RNNto predict whichitem goes on backorder . The evaluation of the result obtained showed ADASYN+ RNN had performed better with 0.901 precision, 0.879 recall, and 0.889 F1-Score. The proposed model when compared with other machine learning algorithms shows significant impact on prediction of product backorder.
How to cite this paper
@article{1702592,
author = {Akintola K.G, Lawal S.O},
title = {A Product Backorder Predictive Model Using Recurrent Neural Network},
journal = {Iconic Research And Engineering Journals},
year = {2021},
volume = {4},
number = {8},
pages = {49-57},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/17025921.pdf},
abstract = {Increasing demand of products is a common cause of out of product inventory, and the adoption of backordering to satisfy outstanding customer orders after its occurrence cannot be undermined. However, wrong management of backorders incurs several issues such as delay in product delivery, low customer satisfaction, and many more. Therefore, it is necessary to ascertain products with high tendencies of shortage beforehand in order to undertake proactive measures and potentially mitigate both tangible and intangible costs. Hence, this paper proposes a backorder predictive model using recurrent neural network (RNN) on large and imbalanced inventory dataset. The data was preprocessed using Min-Max Scaler, while three data balancing methods (ADASYN, SMOTE, and Random Under Sampling)were applied on the imbalanced data simultaneously and their output were fed into RNNto predict whichitem goes on backorder . The evaluation of the result obtained showed ADASYN+ RNN had performed better with 0.901 precision, 0.879 recall, and 0.889 F1-Score. The proposed model when compared with other machine learning algorithms shows significant impact on prediction of product backorder.},
month = {February},
}