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All Journal International Journal of Supply Chain Management
Gazi Md Daud Iqbal
College of Business, Coppin State University
Decision Sciences, Operations Research & Management Engineering Environmental Science Industrial & Manufacturing Engineering Transportation

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Classification and Regression Tree Model to Predict the Probability of a Product being Backordered in Supply Chain Gazi Md Daud Iqbal; Matthew Rosenberger; Lidan Ha; Sadie Gregory; Emmanuel Anoruo
International Journal of Supply Chain Management Vol 12, No 4 (2023): International Journal of Supply Chain Management (IJSCM)
Publisher : ExcelingTech

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59160/ijscm.v12i4.6199

Abstract

Supply chain uncertainties pose a massive and ever-present challenge for modern companies. These uncertainties can manifest in two contrasting scenarios: supply surplus, where companies have excess items, and supply shortages, where there is an insufficient quantity of goods. Each situation demands a different approach from businesses to adapt to the varying outcomes and maintain a competitive edge in the market. Product backordering is one of the important things that companies need to deal with in an uncertain supply chain. A backorder occurs when a customer-ordered product or service is not in stock or cannot be supplied immediately, and the customer has to wait. Companies striving for a balance in managing backorders. Machine learning models can help to determine the probability of a product being backordered. In this research, we develop Classification and Regression Tree (CART) model that uses previously known parameters to predict the likelihood of a product being backordered. We also use different model parameters to evaluate the accuracy of the model.
Co-Authors Emmanuel Anoruo Lidan Ha Matthew Rosenberger Sadie Gregory