ORDER DELIVERY TIME PREDICTION
In one aspect, an example methodology implementing the disclosed techniques includes receiving a corpus of historical order fulfillment data regarding a plurality of completed orders for one or more products, the historical order fulfillment data including an actual delivery time for each product in a completed order, and identifying, from the corpus of historical order fulfillment data, a plurality of features for a product, the plurality of features correlated with an actual delivery time for the product. The method also includes generating a training dataset using the identified plurality of features, the training dataset including a plurality of training samples, each training sample of the plurality of training samples corresponding to a product and including one or more identified features and the actual delivery time for the product. The method may include training the delivery time prediction module using the plurality of training samples.
1 . A computer implemented method to generate a delivery time prediction module to predict an expected delivery time for a product, the method comprising:
receiving a corpus of historical order fulfillment data regarding a plurality of completed orders for one or more products, the historical order fulfillment data including an actual delivery time for each product in a completed order;
identifying, from the corpus of historical order fulfillment data, a plurality of features for a product, the plurality of features correlated with an actual delivery time for the product;
generating a training dataset using the identified plurality of features, the training dataset including a plurality of training samples, each training sample of the plurality of training samples corresponding to a product and including one or more identified features and the actual delivery time for the product; and
training the delivery time prediction module using the plurality of training samples.
2 . The method of claim 1 , wherein the plurality of features includes a feature regarding a customer location that indicates a location at which a customer received the product.
3 . The method of claim 1 , wherein the plurality of features includes a feature regarding a manufacturing location that indicates a location at which the product is manufactured.
4 . The method of claim 1 , wherein the plurality of features of includes a feature regarding a supplier location that indicates a location of a supplier of a component of the product.
5 . The method of claim 4 , wherein the plurality of features includes a feature regarding a logistics provider that indicates a company that provided delivery of the product.
6 . The method of claim 1 , wherein the plurality of features includes a feature that indicates a quantity of the product.
7 . The method of claim 1 , wherein the plurality of features includes a feature indicating a time period associated with the order of the product.
8 . The method of claim 1 , wherein the delivery time prediction module includes a regression-based model.
9 . The method of claim 8 , wherein the regression-based model includes an input layer that includes a number of neurons that match the plurality of features included in a training sample of the plurality of training samples.
10 . The method of claim 8 , wherein the regression-based model includes a plurality of hidden layers, each hidden layer of the plurality of hidden layers including a number of neurons based on a number of neurons included in an input layer of the regression-based model.
11 . The method of claim 1 , further comprising:
receiving an order for at least one product;
generating a feature vector for the at least one product; and
predicting, by the delivery time prediction module, an expected delivery time for the at least one product based on the generated feature vector.
12 . A system comprising:
one or more non-transitory machine-readable mediums configured to store instructions; and
one or more processors configured to execute the instructions stored on the one or more non-transitory machine-readable mediums, wherein execution of the instructions causes the one or more processors to:
receive a corpus of historical order fulfillment data regarding a plurality of completed orders for one or more products, the historical order fulfillment data including an actual delivery time for each product in a completed order;
identify, from the corpus of historical order fulfillment data, a plurality of features for a product, the plurality of features correlated with an actual delivery time for the product;
generate a training dataset using the identified plurality of features, the training dataset including a plurality of training samples, each training sample of the plurality of training samples corresponding to a product and including one or more identified features and the actual delivery time for the product; and
train the delivery time prediction module using the plurality of training samples.
13 . The system of claim 12 , wherein the plurality of features includes a feature regarding one of a customer location that indicates a location at which a customer received the product, a manufacturing location that indicates a location at which the product is manufactured, or a supplier location that indicates a location of a supplier of a component of the product.
14 . The system of claim 12 , wherein the plurality of features includes a feature regarding a logistics provider that indicates a company that provided delivery of the product.
15 . The system of claim 12 , wherein the plurality of features includes a feature that indicates a quantity of the product.
16 . The system of claim 12 , wherein the plurality of features includes a feature indicating a time period associated with the order of the product.
17 . The system of claim 12 , wherein the delivery time prediction module includes a regression-based model.
18 . The system of claim 17 , wherein the regression-based model includes an input layer that includes a number of neurons that match the plurality of features included in a training sample of the plurality of training samples.
19 . The system of claim 17 , wherein the regression-based model includes a plurality of hidden layers, each hidden layer of the plurality of hidden layers including a number of neurons based on a number of neurons included in an input layer of the regression-based model.
20 . A computer program product including one or more non-transitory machine-readable mediums encoding instructions that when executed by one or more processors cause a process to be carried out to generate a delivery time prediction module to predict an expected delivery time for a product, the process comprising:
receiving a corpus of historical order fulfillment data regarding a plurality of completed orders for one or more products, the historical order fulfillment data including an actual delivery time for each product in a completed order;
identifying, from the corpus of historical order fulfillment data, a plurality of features for a product, the plurality of features correlated with an actual delivery time for the product;
generating a training dataset using the identified plurality of features, the training dataset including a plurality of training samples, each training sample of the plurality of training samples corresponding to a product and including one or more identified features and the actual delivery time for the product; and
training the delivery time prediction module using the plurality of training samples.