Autonomous supply and distribution chain

Abstract

Methods and systems for an autonomous supply and distribution chain management network are disclosed. A server may control and coordinate the processes involved in distributing a product from suppliers to the customers, including generation of purchase orders and payment of invoices. A defined set of interactions may occur in a particular sequence and at designated times that may permit the chain to be synchronized between a customer and a supplier. Unlike a regular supply and distribution chain, in which human beings decide vehicle or asset compatibility types, the autonomous chain of the present invention may maintain a compatibility database within the platform, as well as detailed information about each asset and how it can function interactively with the others. The invention may also allow for dynamic modification of transit operations to alter one or more destinations of the inventory while it is in transit to a new location at any time.

What is claimed is: 
     
         1 . A machine-implemented method, comprising:
 coordinating at least one of a sourcing procedure, a procurement procedure, a conversion procedure, a logistic procedure, and a collaboration procedure,   wherein the coordinating is of an autonomous mode or a semi-autonomous mode,   wherein the autonomous mode does not include operator intervention,   wherein the semi-autonomous mode permit operator intervention;   receiving a customer demand;   analyzing the customer demand to determine whether it is valid,   wherein a valid customer demand comprises compliance with a contract term;   determining inventory within supplier capacity;   receiving an asset's telematics data;   calculating duration for one or more destinations;   calculating estimated arrival times of the destinations; and   determining one or more routes to the destinations based on at least one of an asset compatibility data, a financial constraint, an environment constraint and a geographic constraint.   
     
     
         2 . The method of  claim 1 , further comprising:
 wherein the asset compatibility data comprises operational constraints, and   wherein the operational constraints comprise a driver constraint, a vehicle constraint, a road constraint, a building constraint, and an environmental constraint.   
     
     
         3 . The method of  claim 1 , further comprising:
 wherein the asset compatibility data comprises mapping and analyzing one or more relational databases.   
     
     
         4 . The method of  claim 1 , further comprising:
 wherein determining the route comprises assigning the one or more destinations to the asset based on the duration of the one or more destinations.   
     
     
         5 . The method of  claim 1 , further comprising:
 wherein the collaboration process comprises mixing classes of vehicle assets.   
     
     
         6 . The method of  claim 5 , further comprising:
 wherein the mixing of vehicle asset classes comprises dynamically updating its mixture as the route is in progress.   
     
     
         7 . The method of  claim 1 , further comprising:
 re-routing the asset to a remaining assigned or unassigned destination after completion of each destination based on data of the remaining destination.   
     
     
         8 . The method of  claim 1 , further comprising:
 continually receiving the asset's telematics data, calculating service duration for one or more destinations, calculating estimated arrival times of the destinations, determining a delivery route to the destinations at predetermined intervals; and   altering the one or more destinations of the route while the route is in progress based on the telematics data, estimated arrival times, and delivery route.   
     
     
         9 . The method of  claim 1 , further comprising:
 incorporating a business intelligence data into the determining of the route, and   wherein the business intelligence data includes an additional amount of risk and cost assessment data.   
     
     
         10 . The method of  claim 1 , further comprising:
 using artificial intelligence to determine underperforming assets based on the telematics data.   
     
     
         11 . A machine-implemented method, comprising:
 coordinating at least one of a sourcing procedure, a procurement procedure, a conversion procedure, a logistic procedure, and a collaboration procedure,   wherein the coordinating is of an autonomous mode or a semi-autonomous mode,   wherein the autonomous mode does not include operator intervention,   wherein the semi-autonomous mode permit operator intervention;   receiving a customer demand;   analyzing the customer demand to determine whether it is valid;   determining inventory within supplier capacity; and   communicating the customer demand to a supplier if the demand is valid and the inventory is within supplier capacity.   
     
     
         12 . The method of  claim 11 , further comprising:
 wherein the collaboration process comprises permitting one or more network partners of a supply and distribution chain to collaborate.   
     
     
         13 . The method of  claim 11 , further comprising:
 comparing the customer demand to at least one of a previous buying pattern and a future forecast; and   determining whether the customer demand matches with one or more buying patterns or future forecasts.   
     
     
         14 . A machine-implemented method, comprising:
 coordinating at least one of a sourcing procedure, a procurement procedure, a conversion procedure, a logistic procedure, and a collaboration procedure,   wherein the coordinating is of an autonomous mode or a semi-autonomous mode,   wherein the autonomous mode does not include operator intervention,   wherein the semi-autonomous mode permit operator intervention;   receiving a customer demand; and   determining a route to the destinations based on at least one of an asset compatibility data, a financial constraint, an environment constraint and a geographic constraint.   
     
     
         15 . The machine-implemented method of  claim 14 , further comprising:
 wherein the semi-autonomous mode provide a simulation result of a transit operation to the operator.   
     
     
         16 . The machine-implemented method of  claim 14 , further comprising:
 attaching a tag to the asset to be tracked; and   collecting at least one of a location data and a transit characteristic data from the tag.   
     
     
         17 . The machine-implemented method of  claim 16 , further comprising:
 building a predictive model from the collected data, and   wherein building the predictive model comprises grouping the data based on a similarity criterion.   
     
     
         18 . The machine-implemented method of  claim 17 , further comprising:
 wherein the grouping of the data is an exact match or is within a predetermined threshold of difference.   
     
     
         19 . The machine-implemented method of  claim 17 , further comprising:
 comparing a subsequent data to the predictive model; and   determining whether the subsequent data falls within a parameter of the predictive model.   
     
     
         20 . The machine-implemented method of  claim 19 , further comprising:
 adding the subsequent data to the predictive model thereby generating another predictive model.