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Name: Amitabh Modi
Type: User
Location: India
Name: Amitabh Modi
Type: User
Location: India
It is challenging to build useful forecasts for sparse demand products. If the forecast is lower than the actual demand, it can lead to poor assortment and replenishment decisions, and customers will not be able to get the products they want when they need them. If the forecast is higher than the actual demand, the unsold products will occupy inventory shelves, and if the products are perishable, they will have to be liquidated at low costs to prevent spoilage. The overall objective of the model is to use the retail data which provides us with historic sales across various countries and products for a firm. We use this information given, and make use of FM’ s to predict the sparse demand with missing transactions. The above step then enhances the overall demand forecast achieved with LSTM analysis. As part of the this project we answered the following questions: How well does matrix factorization perform at predicting intermittent demand How does matrix factorization approach improve the overall time-series forecasting
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