Abstract: A disclosed system for transport asset monitoring, for example monitoring truck trailer unloading progress at large retail locations, includes an artificial intelligence (AI) solution for managing operations at a facility. The AI solution analyzes current load percentage and other data to predict availability for moving the transport asset and ability to accept a new incoming transport asset. Predictions of availability can reduce response times, resulting in higher utilization rates for assets, thereby improving efficiency. An exemplary system includes a sensor configured to sense operation progress parameter data for a transport asset; and logic to receive the operation progress parameter data from the sensor; determine, using the AI solution and based at least on the operation progress parameter data, a predicted milestone parameter; and report the predicted milestone parameter to a remote node.

Abstract: A mobile assembly apparatus and a method of assembling a retail product utilizing a retail assembly are discussed. The mobile assembly apparatus may include a cabinet structure, an expandable work surface, a frame, wheels, an articulated arm, outrigger supports, a power supply, sensors, a hardware dispenser, and a computing device. The mobile assembly apparatus scans a machine readable identifier and retrieves assembly instructions for an item associated with the identifier. The computing device controls the outrigger supports and extendable surface based on the instructions. The computing device monitors the progress of the assembly of the time and validates the assembly based on assembly data from the sensors.

Abstract: A disclosed system for transport asset monitoring, for example monitoring truck trailer unloading progress at large retail locations, includes an artificial intelligence (AI) solution for managing operations at a facility. The AI solution analyzes current load percentage and other data to predict availability for moving the transport asset and ability to accept a new incoming transport asset. Predictions of availability can reduce response times, resulting in higher utilization rates for assets, thereby improving efficiency. An exemplary system includes a sensor configured to sense operation progress parameter data for a transport asset; and logic to receive the operation progress parameter data from the sensor; determine, using the AI solution and based at least on the operation progress parameter data, a predicted milestone parameter; and report the predicted milestone parameter to a remote node.

Abstract: A mobile assembly apparatus and a method of assembling a retail product utilizing a retail assembly are discussed. The mobile assembly apparatus may include a cabinet structure, an expandable work surface, a frame, wheels, an articulated arm, outrigger supports, a power supply, sensors, a hardware dispenser, and a computing device. The mobile assembly apparatus scans a machine readable identifier and retrieves assembly instructions for an item associated with the identifier. The computing device controls the outrigger supports and extendable surface based on the instructions. The computing device monitors the progress of the assembly of the time and validates the assembly based on assembly data from the sensors.

Abstract: A disclosed system for transport asset monitoring, for example monitoring truck trailer unloading progress at large retail locations, includes an artificial intelligence (AI) solution for managing operations at a facility. The AI solution analyzes current load percentage and other data to predict availability for moving the transport asset and ability to accept a new incoming transport asset. Predictions of availability can reduce response times, resulting in higher utilization rates for assets, thereby improving efficiency. An exemplary system includes a sensor configured to sense operation progress parameter data for a transport asset; and logic to receive the operation progress parameter data from the sensor; determine, using the AI solution and based at least on the operation progress parameter data, a predicted milestone parameter; and report the predicted milestone parameter to a remote node.

Abstract: A mobile assembly apparatus and a method of assembling a retail product utilizing a retail assembly are discussed. The mobile assembly apparatus may include a cabinet structure, an expandable work surface, a frame, wheels, an articulated arm, outrigger supports, a power supply, sensors, a hardware dispenser, and a computing device. The mobile assembly apparatus scans a machine readable identifier and retrieves assembly instructions for an item associated with the identifier. The computing device controls the outrigger supports and extendable surface based on the instructions. The computing device monitors the progress of the assembly of the time and validates the assembly based on assembly data from the sensors.