Systems and methods for production and logistics management
Systems and methods are disclosed for providing a product to consumers by receiving the product from a manufacturer located in a first country; matching the products with a seller in the second country; managing delivery logistics by matching available third-party truckers and third-party warehouses in a second country based on proximity; forecasting demand using one or more neural networks for the manufacturer, the logistics organization, and the warehouses; and managing logistics for the manufacturer through a dashboard populated by the forecasted demand from the one or more neural networks.
1. A method for delivering a product, comprising:
Receiving, at a manufacturer processor, a customer order for the product specified using a computer or a smart phone of the customer;
extracting, by the manufacturer processor, order information from the customer order comprising a geographic location of the customer and mailing address;
capturing, using a digital camera, a plurality of images forming a three-dimensional (3D) representation of the product;
training, by the manufacturer processor, using machine learning to generate a 3D model of the product with the plurality of images;
transmitting, from the manufacturer processor, the trained 3D model to the customer computer or smart phone;
executing the 3D model on the customer computer or smart phone to calibrate and measure dimensions of a customer environment;
scaling the 3D model to the dimensions of the customer environment at a size range adjusting by the customer;
rendering, by the manufacturer processor, the scaled 3D model over a view of the customer environment and allowing the customer to virtually try the product using an augmented reality display;
forecasting, using one or more neural networks, demand of products fulfilling the orders comprising recommending a number of products for each product type including brands, styles, and sizes according to the customer environment;
managing logistics for the manufacturer through a dashboard populated by the forecasted demand from the one or more neural networks; and
managing delivery logistics by matching available third-party truckers and third-party warehouses based on proximity.
2. The method of claim 1 , further comprising:
creating a codebook of deformable body models;
shape matching the 3D model by matching points on the body to the deformable model;
generating a match score with information about clothing using a product distance function with an exact match of brand, clothing type, style and size assigned a distance of zero and a match for only brand, clothing type and size is assigned a positive number and differences in size produce proportionally increased positive numbers; and
wherein recommending a number of products comprises one or more of: clothing, footwear, headgear, and furniture based on the match score.
3. The method of claim 1 , further comprising: forecasting, by a third-party processor using the one or more neural networks, demand of products fulfilling the orders including recommending a number of products for each product type including brands, styles, and sizes according to the customer environment.
4. The system of claim 3 , wherein the product includes clothing or furniture.