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.

中文翻译：

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使用分布式随机森林和梯度提升机器学习技术预测供应链中可能存在的缺货情况

由于缺乏清晰性和灵活性，使用机器学习算法进行的预测在业务决策过程的许多部分均无法很好地适应。在预测过程中作为输入的错误数据可能会产生不准确的预测。我们的目标是通过预测产品的缺货情况，在业务决策流程领域中使用机器学习模型，同时为决策机构提供灵活性，更好的流程清晰度并保持更高的准确性。远程方法用于指定不同级别的预测特征，以应对可能由于机器或人为错误而发生的实时数据的各种特征。该范围是可调的，为决策管理者提供了灵活性。选择基于树的机器学习可以更好地解释模型。使用分布式随机森林（DRF）和梯度提升机（GBM）预测了产品的缺货。我们已经观察到，当数据集由于随机误差而有很大偏差时，使用这种远程方法可以将机器学习模型的性能提高20％。我们使用五级度量标准来指示库存水平，销售水平，预测销售水平以及交货时间的四级度量标准。分析来自所构建模型之一的决策树，以了解远程方法的效果。作为此分析的一部分，我们列出了主要的可能的延期交货方案，以促进业务决策。我们展示了如何使用此模型在实际销售发生之前预测可能的缺货产品。