Prediction of probable backorder scenarios in the supply chain using Distributed Random Forest and Gradient Boosting Machine learning techniques

Prediction using machine learning algorithms is not well adapted in many parts of the business decision processes due to the lack of clarity and flexibility. The erroneous data as inputs in the prediction process may produce inaccurate predictions. We aim to use machine learning models in the area of the business decision process by predicting products' backorder while providing flexibility to the decision authority, better clarity of the process, and maintaining higher accuracy. A ranged method is used for specifying different levels of predicting features to cope with the diverse characteristics of real-time data which may happen by machine or human errors. The range is tunable that gives flexibility to the decision managers. The tree-based machine learning is chosen for better explainability of the model. The backorders of products are predicted in this study using Distributed Random Forest (DRF) and Gradient Boosting Machine (GBM). We have observed that the performances of the machine learning models have been improved by 20% using this ranged approach when the dataset is highly biased with random error. We have utilized a five-level metric to indicate the inventory level, sales level, forecasted sales level, and a four-level metric for the lead time. A decision tree from one of the constructed models is analyzed to understand the effects of the ranged approach. As a part of this analysis, we list major probable backorder scenarios to facilitate business decisions. We show how this model can be used to predict the probable backorder products before actual sales take place. The mentioned methods in this research can be utilized in other supply chain cases to forecast backorders.