An embodiment method of operating a cell-based mobility production system for producing various types of mobilities includes assigning works required in each cell of a plurality of cells connected in series or in parallel that vehicle bodies need to go through based on the type of the mobility to be produced, determining a feeding order of the vehicle bodies based on the assigned works, and reassigning the works required in each cell that the vehicle bodies to be fed need to go through based on the determined feeding order, wherein each of assigning the works, determining the feeding order, and reassigning the works is performed by a processor.

Methods and systems determine at least one production constraint for a material loading system and a material processing system; estimate an effect of entropy on a cycle time of a material conveying system to produce a future cycle time estimate; estimate an effect of entropy on a material processing time to produce a future material processing time estimate; predict whether a delay will occur during the operation of the material loading, material conveying, and material processing systems; estimate a duration of the predicted delay; determine a loading system capacity and a conveying system capacity required to meet the defined production target based on the production constraint and estimates of the future cycle time, the future material processing time, and the duration of the predicted delay; and deploy one or more material loading and conveying systems to meet the determined loading and conveying system capacities.

Methods of deploying shovels and haul trucks to meet a defined production target may include: Determining at least one production constraint for a shovel and a material processing system; estimating an effect of entropy on a cycle time of the haul trucks to produce a future cycle time estimate; estimating an effect of entropy on a material processing time of the material processing system to produce a future material processing time estimate; predicting whether a delay will occur during the operation of the shovel, the haul trucks, and the material processing system; estimating a duration of the predicted delay; determining a number of shovels and a number of haul trucks required to meet the defined production target based on the determined production constraint, the future cycle time estimate, the future material processing time estimate, and the duration of the predicted delay; and deploying the determined number of shovels and the determined number of haul trucks.

Methods and systems determine at least one production constraint for a material loading system and a material processing system; estimate an effect of entropy on a cycle time of a material conveying system to produce a future cycle time estimate; estimate an effect of entropy on a material processing time to produce a future material processing time estimate; predict whether a delay will occur during the operation of the material loading, material conveying, and material processing systems; estimate a duration of the predicted delay; determine a loading system capacity and a conveying system capacity required to meet the defined production target based on the production constraint and estimates of the future cycle time, the future material processing time, and the duration of the predicted delay; and deploy one or more material loading and conveying systems to meet the determined loading and conveying system capacities.

The disclosure is directed to a system for integrating and centralizing multiple manufacturing software types into a consolidated platform. The system interfaces with third party software and performs data collection, data analytics, factory controls, virtual modeling, and checklist creation, as well as many other manufacturing applications. Artificial intelligence and machine learning are also integrated into the platform to assist with root cause analysis and increasing production efficiency.

The disclosure is directed to a system for integrating and centralizing multiple manufacturing software types into a consolidated platform. The system interfaces with third party software and performs data collection, data analytics, factory controls, virtual modeling, and checklist creation, as well as many other manufacturing applications. Artificial intelligence and machine learning are also integrated into the platform to assist with root cause analysis and increasing production efficiency.

Methods and systems determine at least one production constraint for a material loading system and a material processing system; estimate an effect of entropy on a cycle time of a material conveying system to produce a future cycle time estimate; estimate an effect of entropy on a material processing time to produce a future material processing time estimate; predict whether a delay will occur during the operation of the material loading, material conveying, and material processing systems; estimate a duration of the predicted delay; determine a loading system capacity and a conveying system capacity required to meet the defined production target based on the production constraint and estimates of the future cycle time, the future material processing time, and the duration of the predicted delay; and deploy one or more material loading and conveying systems to meet the determined loading and conveying system capacities.

The disclosure is directed to a system for integrating and centralizing multiple manufacturing software types into a consolidated platform. The system interfaces with third party software and performs data collection, data analytics, factory controls, virtual modeling, and checklist creation, as well as many other manufacturing applications. Artificial intelligence and machine learning are also integrated into the platform to assist with root cause analysis and increasing production efficiency.

The disclosure is directed to a system for integrating and centralizing multiple manufacturing software types into a consolidated platform. The system interfaces with third party software and performs data collection, data analytics, factory controls, virtual modeling, and checklist creation, as well as many other manufacturing applications. Artificial intelligence and machine learning are also integrated into the platform to assist with root cause analysis and increasing production efficiency.

Methods of deploying shovels and haul trucks to meet a defined production target may include: Determining at least one production constraint for a shovel and a material processing system; estimating an effect of entropy on a cycle time of the haul trucks to produce a future cycle time estimate; estimating an effect of entropy on a material processing time of the material processing system to produce a future material processing time estimate; predicting whether a delay will occur during the operation of the shovel, the haul trucks, and the material processing system; estimating a duration of the predicted delay; determining a number of shovels and a number of haul trucks required to meet the defined production target based on the determined production constraint, the future cycle time estimate, the future material processing time estimate, and the duration of the predicted delay; and deploying the determined number of shovels and the determined number of haul trucks.