Optimizing e-Commerce Supply Chains With Categorical Boosting: A Predictive Modeling Framework

Managing various aspects of the Supply Chain (SC) has become increasingly challenging in today's complex business landscape. To improve profitability, boost sales, and enhance customer satisfaction, it is crucial to explore future possibilities by adjusting key relational parameters. However, t...

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Bibliographic Details
Published inIEEE access Vol. 12; pp. 134549 - 134567
Main Authors Sayyad, Javed K., Attarde, Khush, Saadouli, Nasreddine
Format Journal Article
LanguageEnglish
Published IEEE 2024
Subjects
Artificial intelligence
Business
Categorial boosting
e-commerce
Electronic commerce
Ensemble learning
Optimization methods
Prediction algorithms
predictive modeling
Predictive models
Supply chain management
supply chain management (SCM)
supply chain optimization (SCO)
Supply chains
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Summary:Managing various aspects of the Supply Chain (SC) has become increasingly challenging in today's complex business landscape. To improve profitability, boost sales, and enhance customer satisfaction, it is crucial to explore future possibilities by adjusting key relational parameters. However, traditional forecasting methods often fail to provide accurate insights and are time-consuming. These limitations can be overcome using Artificial Intelligence (AI) algorithms such as Machine Learning (ML) and Deep Learning (DL). CatBoost algorithm is an ensemble-based ML model that can handle categorical variables effectively in its architecture, whereas other ML and DL models fail and require explicit encoding techniques. In this study, a predictive modeling approach using CatBoost to optimize supply chain processes using a mathematical approach was proposed. CatBoost evaluates the model on an e-commerce dataset through empirical analysis by tuning various hyperparameters to enhance prediction efficiency. A computational time limit of ten minutes was used to ensure practicality. Using regression and classification frameworks, this approach involves predicting sales, profit, and delivery times, and identifying potential customers. Consequently, analyzing the behavior of the learning rate and its impact on the performance metrics indicated that increasing the learning rate can lead to improved model performance.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2024.3447756