Leveraging Advanced Analytics for Backorder Prediction and Optimization of Business Operations in the Supply Chain

Abstract

Businesses can unlock valuable insights by leveraging advanced analytics techniques to optimize supply chain processes and address backorders. Backorders occur when a customer order cannot be fulfilled immediately due to lack of available supply. Root causes of backorders can range from supply chain complications and manufacturing miscalculations to logistical challenges. While a surge in demand might initially seem beneficial, backorders come with inherent costs, leading to supply chain disruptions, dissatisfied customers, and lost sales. This research aimed to assess the efficacy of predictive analytics in detecting early backorder signs and to understand how parameter tuning influences the performance of these predictive models. The foundation of this study was laid through an exhaustive literature review. In-depth Exploratory Data Analytics/ EDA was utilized to investigate datasets, followed by rigorous preprocessing steps, including data cleaning, feature engineering, scaling, and resampling. Machine learning models were subsequently trained, tuned, and assessed using appropriate evaluation metrics. Findings from this research underscored the value of predictive analytics in early backorder identification. They also offered a comparative analysis of machine learning algorithms and highlighted the significance of parameter tuning. Additionally, they established the necessity of multi-metric evaluations for imbalanced datasets. Thus, the study has provided a fundamental framework that can serve as a basis for future research endeavors.