Abstract: Apparatus and associated methods relate to an inventory management system having a machine learning engine trained to (a) dynamically predict a demand amount of the inventory in response to a set of near-future data and (b) generate a signal to order the amount of inventory as needed. In an illustrative example, the machine learning engine may be trained by a hotel's historical room booking data, the hotel's historical occupancy data, and corresponding inventory consumption data stored in a database. Various embodiments may enable, for example, the hotel to have better management on the status of inventories at various stages. For example, sufficient inventory may be available. In addition, providing the proper amount of inventory may also advantageously reduce the cost spent on unused products.

Abstract: Apparatus and associated methods relate to an inventory management system having a machine learning engine trained to (a) dynamically predict a demand amount of the inventory in response to a set of near-future data and (b) generate a signal to order the amount of inventory as needed. In an illustrative example, the machine learning engine may be trained by a hotel's historical room booking data, the hotel's historical occupancy data, and corresponding inventory consumption data stored in a database. Various embodiments may enable, for example, the hotel to have better management on the status of inventories at various stages. For example, sufficient inventory may be available. In addition, providing the proper amount of inventory may also advantageously reduce the cost spent on unused products.

Abstract: Apparatus and associated methods relate to an inventory management system having a machine learning engine trained to (a) dynamically predict a demand amount of the inventory in response to a set of near-future data and (b) generate a signal to order the amount of inventory as needed. In an illustrative example, the machine learning engine may be trained by a hotel's historical room booking data, the hotel's historical occupancy data, and corresponding inventory consumption data stored in a database. Various embodiments may enable, for example, the hotel to have better management on the status of inventories at various stages. For example, sufficient inventory may be available. In addition, providing the proper amount of inventory may also advantageously reduce the cost spent on unused products.

Abstract: Apparatus and associated methods relate to an inventory management system having a machine learning engine trained to (a) dynamically predict a demand amount of the inventory in response to a set of near-future data and (b) generate a signal to order the amount of inventory as needed. In an illustrative example, the machine learning engine may be trained by a hotel's historical room booking data, the hotel's historical occupancy data, and corresponding inventory consumption data stored in a database. Various embodiments may enable, for example, the hotel to have better management on the status of inventories at various stages. For example, sufficient inventory may be available. In addition, providing the proper amount of inventory may also advantageously reduce the cost spent on unused products.