![]() ![]() An inventory level dependent ordering model discusses the relationship between inventory level and demand which reflects how negative inventory level can affect the demand. The causes and effects of negative inventory are well understood in the supply chain industry. The negative inventory level suggests that the current stock level of the product is less than zero. Our dataset contains the number of negative records related to the inventory. There are very high negative and positive values in several predicting features of this dataset. This issue is a challenge to analyze the dataset of this study. Machine learning models may misclassify many records if the dataset contains misleading or missing information. Nowadays, some companies predict the backorders of products by applying machine learning prediction processes to overcome the associated tangible and intangible costs of backorders. Moreover, the uncertainty in customers’ demands causes difficulty in forecasting the demand which makes the traditional supply chain management systems less effective in many ways such as inaccurate demand forecasting or misclassifying of back-ordered products. On the other hand, the prompt actions to satisfy backorders put enormous pressure on different stages of the supply chain which may exhaust the supply chain processes or may appear with extra labor and/or production costs, and associated shipment expenses. If backorders are not handled promptly, they will have high impacts on the respective company’s revenue, share market price, customers’ trust, and may end up losing the customer or sale order. When a customer orders a product, which is not available in the store or temporary out of stock, and the customer decides to wait until the product is available and promised to be shipped, then this scenario is called backorder of that specific product. The mentioned methods in this research can be utilized in other supply chain cases to forecast backorders. We show how this model can be used to predict the probable backorder products before actual sales take place. As a part of this analysis, we list major probable backorder scenarios to facilitate business decisions. A decision tree from one of the constructed models is analyzed to understand the effects of the ranged approach. 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. 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. The backorders of products are predicted in this study using Distributed Random Forest (DRF) and Gradient Boosting Machine (GBM). The tree-based machine learning is chosen for better explainability of the model. The range is tunable that gives flexibility to the decision managers. 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. ![]() 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. The erroneous data as inputs in the prediction process may produce inaccurate predictions. 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. ![]()
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