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The project aims to develop a predictive model that can forecast backorders based on historical data from inventories, supply chain, and sales. By utilizing machine learning algorithms and data from ERP systems, the model will classify products as either going into backorder or not. This will help streamline planning, prevent unexpected strain on production and logistics, and improve inventory management.
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umangtank
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umangtank08@gmail.com
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Predictive Model for Backorder Forecasting
Description
The project aims to develop a predictive model for forecasting backorders based on historical data from inventories, supply chain, and sales. By utilizing data from ERP systems, the model will classify products as either going into backorder (Yes) or not (No). This will help streamline planning and prevent unexpected strain on production, logistics, and transportation by anticipating which products are likely to be backordered.
The project will involve data collection, preprocessing, feature selection, model training, and evaluation. Various machine learning algorithms, such as decision trees, random forests, or gradient boosting algorithms, can be explored for the classification task. The goal is to create an accurate predictive model that can effectively forecast backorders and aid in planning and inventory management.
umangtank
commented
1 year ago
Project Request
Define You
Predictive Model for Backorder Forecasting
Description
The project aims to develop a predictive model for forecasting backorders based on historical data from inventories, supply chain, and sales. By utilizing data from ERP systems, the model will classify products as either going into backorder (Yes) or not (No). This will help streamline planning and prevent unexpected strain on production, logistics, and transportation by anticipating which products are likely to be backordered.
The project will involve data collection, preprocessing, feature selection, model training, and evaluation. Various machine learning algorithms, such as decision trees, random forests, or gradient boosting algorithms, can be explored for the classification task. The goal is to create an accurate predictive model that can effectively forecast backorders and aid in planning and inventory management.