Product backorder prediction using deep neural network on imbalanced data

Md Shajalal · Petr Hájek · Mohammad Zoynul Abedin

89 citations0/0 files storedInternational Journal of Production Research

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Supply Chain and Inventory Management Forecasting Techniques and Applications Imbalanced Data Classification Techniques

Abstract

Taking backorders on products is a common scenario in inventory and supply chain management systems. The ability to predict the likelihood of backorders can surely minimise a company's losses. Because the number of backorders is much lower than the number of orders that ship on time, applying a predictive model for this domain is a challenging task. This paper proposes a model that uses a deep neural network to predict backorders; it handles the data imbalance between backorders and filled orders with efficient techniques. To make the dataset balanced, we employ different techniques that include minority class weight boosting, randomised oversampling, SMOTE oversampling, and a combination of oversampling and undersampling. The balanced training data are used in our proposed, fully connected deep neural networks model to train the predictive model. The predictive model learns the likelihood of product backorders by using the training samples. We conduct experiments on a large benchmark dataset to test the performance of our proposed deep neural network–based model. The experimental results achieve a new state-of-the-art performance and outperform some prominent classification models in terms of standard evaluation metrics and expected profit measure.

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References

2017Big Data Analytics in Operations Management 2016Deep Learning for Finance: Deep Portfolios 2019Deep learning in business analytics and operations research: Models, applications and managerial implications 2018Exploring the use of deep neural networks for sales forecasting in fashion retail 2019Two-stage consumer credit risk modelling using heterogeneous ensemble learning 2014Development and application of consumer credit scoring models using profit-based classification measures 2020Prediction of probable backorder scenarios in the supply chain using Distributed Random Forest and Gradient Boosting Machine learning techniques 2018Applying machine learning to the dynamic selection of replenishment policies in fast-changing supply chain environments

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