Abstract: An index modeling system that generates index models that predict values of an attribute of a supply chain for a commodity is disclosed. The index models are generated from indicator data that includes data related to multiple indicators and a plurality of sub-indicators of the index arranged in a hierarchical structure. Accordingly, the index values can be predicted for different entities at different levels in the hierarchical structure. The predicted index values can be used to automatically generate a filtered list of suppliers who can be used for procurement based on comparisons of the predicted attribute values of the suppliers with a predetermined attribute threshold value.

Abstract: Implementations include receiving demand data representing types of material and quantities of material demanded for each of a plurality of locations within a geographical area; receiving vessel data representing availabilities and capacities of each of a plurality of vessels; processing the demand data and the vessel data through a capacity optimization model to provide a first output comprising initial voyage plans for the plurality of vessels; receiving weather data representing predicted weather conditions within the geographical area; and processing the first output and the weather data through a sequence optimization model to provide a second output comprising updated voyage plans. Each initial voyage plan and each updated voyage plan defines a type of material, quantity of material, vessel, and sequence of locations.

Abstract: Implementations include receiving, from a first system, resource request data indicating a first type of logistical resource to be reserved by a first enterprise of a plurality of enterprises; processing the resource request data using a forecast model to produce output including a forecasted demand for the first type of logistical resource; receiving, from a second system, resource availability data indicating availability of the first type of logistical resource; and based on the forecasted demand and on the resource availability data, providing, to the first system, suggested reservation data indicating available resources for accommodating the forecasted demand. Implementations include determining, based on the forecasted demand and on the resource availability data, a predicted cost of the available resources, and presenting, by the first system and through a graphical user interface, the suggested reservation data.

Abstract: This disclosure generally relates to devices, systems, and computer-implemented methods for simulating one or more inventories in a distribution network. Specifically, methods are described that comprise the operations of receiving inventory data for one or more inventories to be simulated, wherein the inventory data includes lead time data and demand data; determining, based on the inventory data, probability distributions for each of lead time and demand for the one or more inventories to be simulated; determining a lead time demand probability distribution of the one or more inventories to be simulated based on the determined probability distributions for the lead time and the demand; determining a predictive state of the one or more inventories to be simulated based on the lead time demand probability distribution; and outputting one or more evaluation parameters associated with the predictive state of the one or more inventories to be simulated.

Abstract: This disclosure generally relates to devices, systems, and computer-implemented methods for simulating one or more inventories in a distribution network. Specifically, methods are described that comprise the operations of receiving inventory data for one or more inventories to be simulated, wherein the inventory data includes lead time data and demand data; determining, based on the inventory data, probability distributions for each of lead time and demand for the one or more inventories to be simulated; determining a lead time demand probability distribution of the one or more inventories to be simulated based on the determined probability distributions for the lead time and the demand; determining a predictive state of the one or more inventories to be simulated based on the lead time demand probability distribution; and outputting one or more evaluation parameters associated with the predictive state of the one or more inventories to be simulated.