Systems for optimizing a delivery process according to various aspects of the present technology may comprise a user device, a ranking engine, a delivery driver management platform communicatively linked to the ranking engine, a database, a server, and a display. The user device may be configured to receive customer data and delivery driver data. The ranking engine may be configured to compute a plurality of rank scores according to the customer data and the delivery driver data. The ranking engine may also be configured to generate a plurality of customer profiles and a plurality of delivery driver profiles according to the customer data and the delivery driver data, respectively. The ranking engine may be further configured to store the plurality of delivery driver profiles and the plurality of rank scores in the database. The server may be communicatively linked to the database over a communication network, wherein the server may be provided with access to the plurality of customer profiles, the plurality of delivery driver profiles, and the plurality of rank scores. The display may be configured to present the plurality of delivery driver profiles according to the plurality of rank scores.

A system and method for building or optimizing a supply chain is presently provided. The method comprises registering and inputting provided services or capabilities by at least one supply chain service provider, registering and inputting desired services or desired capabilities by a supplier of goods, and selecting, or presenting to the supplier of goods for the selecting with the system, at least one of the registered supply chain service providers, by the supplier of goods or the system. Each of the selected supply chain service providers is not associated with the supplier of goods.

Systems and methods for using machine learning to dynamically assess the likelihood that a shipping agreement will be successfully fulfilled. According to certain aspects, an electronic device may receive order data associated with the shipping agreement, wherein the electronic device may input the order data into a machine learning model which outputs a likelihood that the shipping agreement will be successfully fulfilled. The electronic device may enable a customer computing device to access this information and facilitate communications or corrective actions.

Methods and systems for controlling production resources in a supply chain are described. The system automatically generates predicted supply chain operational metrics across a nodes of a supply chain. The system automatically infers causal factors that impact the predicted supply chain operational metrics. The causal factors include a change to a utilization of the production resource. The system communicates a user interface including production runs being scheduled on the production resource including a user interface element representing the scheduling of the production run associated with a value at risk. The system receives input causing a change to the utilization of the production resource. The change to the utilization of the production resource impacts the predicted supply chain operational metrics including the value at risk associated with the scheduling of the production run.

An example operation may include one or more of receiving transport data of a multi-party transport process, identifying documents and events that are associated with the multi-party transport process based on the received transport data, dynamically determining read and write permissions for the documents and the events of the multi-party transport process based on predefined roles, and storing an identifier of the multi-party transport process and the dynamically determined read and write permissions in a block on a blockchain.

Systems and methods for processing shipment request by using a multi-carrier shipping services platform.

A method for prediction model determination can include: determining a set of models, training each model, determining package transit data, evaluating the set of models, selecting a model from the set of models, predicting package transit data using the selected model, and/or any other suitable element.

A system includes one or more processors to obtain a transportation event and a transportation event time of a vehicle system at a current location on a route from an origin to a destination. The one or more processors determine transportation event conditions based on historical transportation data and predict, by mathematical optimization methods, optimal transportation routes based on one or more of historical transportation routes, contractual routes, contractual junctions, and station master data. The one or more processors cluster from the historical transportation data, by a machine learning classification method, transportation event data clusters and match at the current location the transportation event data to historical transportation data machine learning classification clusters. The one or more processors predict, by a machine learning model, an estimated time of arrival (ETA) of the vehicle system to the destination.

An example operation may include one or more of receiving, by a retailer node, an encrypted inventory of goods data from a plurality of supplier nodes over a blockchain network, computing, by the retailer node, an ordering proportion based on the encrypted inventory of goods data, generating, by the retailer node, an ordering policy based on the ordering proportion, and executing a smart contract to order goods from the plurality of the supplier nodes based on the ordering policy.

Disclosed herein are system, method, and computer program product embodiments for estimating shipping costs of items purchased in an online market using machine learning techniques. By determining in real-time the dimensional weight of an item with reference to a machine learning model describing past transactions, a buyer and seller in the online market can finalize the transaction in real-time. The machine learning model further implements a bias within the machine learning algorithm towards heavier estimation to avoid undercharging the market participants for shipping costs. One embodiment involves using these cost-estimation techniques in the context of a local shipping feature, which allows buyers and sellers to schedule a same-day delivery by seamlessly involving a local carrier.