Abstract: Provided is a system, method, and computer program product for predicting product temperatures. The system includes at least one processor programmed or configured to collect, from temperature probes, temperature data of the plurality of different perishable products during a time period, collect, from at least one environmental sensor, environmental data of the at least one environment during the time period, generate a machine-learning model based on the temperature data and the environmental data, collect second environmental data from the at least one environmental sensor and/or from a separate data source while at least one package of at least one perishable product is in the at least one environment, and generate a predicted temperature of the at least one perishable product based on inputting the second environmental data into the machine-learning model.

Abstract: Provided is an automated loading dock detection and management system, method, and apparatus. The system includes a plurality of vehicle sensors arranged with respect to a plurality of loading docks, a plurality of door sensors arranged with respect to a plurality of doors associated with the plurality of loading docks, and at least one processor in communication with the plurality of vehicle sensors and the plurality of door sensors, the at least one processor configured to receive a plurality of signals from at least a subset of vehicle sensors of the plurality of vehicle sensors and at least a subset of door sensors from the plurality of door sensors, start and/or stop at least one timer based on the plurality of signals, and generate a graphical interface based on the at least one timer.

Abstract: Provided is a system, method, and computer program product for predicting product temperatures. The system includes at least one processor programmed or configured to collect, from temperature probes, temperature data of the plurality of different perishable products during a time period, collect, from at least one environmental sensor, environmental data of the at least one environment during the time period, generate a machine-learning model based on the temperature data and the environmental data, collect second environmental data from the at least one environmental sensor and/or from a separate data source while at least one package of at least one perishable product is in the at least one environment, and generate a predicted temperature of the at least one perishable product based on inputting the second environmental data into the machine-learning model.