A system for providing an optimal vehicle configuration, the system comprising: at least one processor; a memory storage device including instructions that when executed by the at least one processor are configured to: obtain vehicle test data for various vehicle configurations exercised over a set of routes; calibrate a simulation model using the vehicle test data; obtain routes data including information associated with a plurality of routes; apply the calibrated simulation model over the plurality of routes; determine key performance indicators associated with various vehicle configurations for each of the plurality of routes; store simulation data including the determined key performance indicators in a simulations database; obtain real-world telematics data; and use the simulation data and real-world telematics data to train a predictive machine learning model to determine a vehicle configuration, wherein the system is further configured to: receive a request for a vehicle configuration that optimizes a customer performance priority for a representative route, wherein the customer performance priority includes a performance priority selected from: fuel economy, gradeability, acceleration, and freight efficiency; apply the predictive machine learning model to determine the vehicle configuration; provide the vehicle configuration for display; receive a selection of the vehicle configuration; and automatically provide the selected vehicle configuration to a provisioning system to initiate manufacture of a vehicle based on the vehicle configuration. at least one processor; a memory storage device including instructions that when executed by the at least one processor are configured to: obtain vehicle test data for various vehicle configurations exercised over a set of routes; calibrate a simulation model using the vehicle test data; obtain routes data including information associated with a plurality of routes; apply the calibrated simulation model over the plurality of routes; determine key performance indicators associated with various vehicle configurations for each of the plurality of routes; store simulation data including the determined key performance indicators in a simulations database; obtain real-world telematics data; and use the simulation data and real-world telematics data to train a predictive obtain vehicle test data for various vehicle configurations exercised over a set of routes; calibrate a simulation model using the vehicle test data; obtain routes data including information associated with a plurality of routes; apply the calibrated simulation model over the plurality of routes; determine key performance indicators associated with various vehicle configurations for each of the plurality of routes; store simulation data including the determined key performance indicators in a simulations database; obtain real-world telematics data; and use the simulation data and real-world telematics data to train a predictive machine learning model to determine a vehicle configuration, receive a request for a vehicle configuration that optimizes a customer performance priority for a representative route, wherein the customer performance priority includes a performance priority selected from: fuel economy, gradeability, acceleration, and freight efficiency; apply the predictive machine learning model to determine the vehicle configuration; provide the vehicle configuration for display; receive a selection of the vehicle configuration; and automatically provide the selected vehicle configuration to a provisioning system to initiate manufacture of a vehicle based on the vehicle configuration.