Abstract: Disclosed are techniques for modeling costs per pallet in a facility. A computer system can: receive, from a warehouse management system (WMS) and/or a refrigeration control system (RCS), time series data for a period of time for pallets in a facility, retrieve, from a data store, pallet movement data, changes in temperature data, labor usage data, and energy consumption data for each pallet amongst the pallets, determine costs per pallet over the period of time based on correlating the time series data with the retrieved data for each pallet amongst the pallets, the costs per pallet including at least one of energy costs per pallet or labor costs per pallet, determine projected costs per pallet based on modeling the costs per pallet over the period of time, the projected costs being determined for future time periods, and generate output indicating the projected costs per pallet for the future time periods.

Abstract: Disclosed are techniques for modeling costs per pallet in a facility. A computer system can: receive, from a warehouse management system (WMS) and/or a refrigeration control system (RCS), time series data for a period of time for pallets in a facility, retrieve, from a data store, pallet movement data, changes in temperature data, labor usage data, and energy consumption data for each pallet amongst the pallets, determine costs per pallet over the period of time based on correlating the time series data with the retrieved data for each pallet amongst the pallets, the costs per pallet including at least one of energy costs per pallet or labor costs per pallet, determine projected costs per pallet based on modeling the costs per pallet over the period of time, the projected costs being determined for future time periods, and generate output indicating the projected costs per pallet for the future time periods.

Abstract: Described are systems and methods for determining a blast freezing optimization schedule. A system can include: a blast cell to receive items for cooling, a refrigeration control system (RCS) to control the blast cell, and a computer system that can perform operations including: receiving, from at least one of the RCS and a warehouse management system (WMS), storage facility data, determining a blast schedule for the blast cell based on applying a simulation model to the storage facility data that was trained to generate the blast schedule based on determining that a blast cell cost per pallet maximizes a blast profit margin for the storage facility by at least a threshold amount, and returning the blast schedule to cause the RCS to automatically control the blast cell according to the blast schedule.

Abstract: Disclosed are techniques for modeling costs per pallet in a facility. A computer system can: receive, from a warehouse management system (WMS) and/or a refrigeration control system (RCS), time series data for a period of time for pallets in a facility, retrieve, from a data store, pallet movement data, changes in temperature data, labor usage data, and energy consumption data for each pallet amongst the pallets, determine costs per pallet over the period of time based on correlating the time series data with the retrieved data for each pallet amongst the pallets, the costs per pallet including at least one of energy costs per pallet or labor costs per pallet, determine projected costs per pallet based on modeling the costs per pallet over the period of time, the projected costs being determined for future time periods, and generate output indicating the projected costs per pallet for the future time periods.

Abstract: Disclosed is a system for interpolating ambient conditions across a facility, the system including: sensors to generate sensor data indicating ambient conditions in a facility and a computer system that can: receive the sensor data from the sensors, determine real-time temperature information for different locations of the facility based on processing the received sensor data, retrieve a machine learning model that was trained using historic facility data to interpolate ambient conditions across a facility using temperature information that corresponds to a portion of the facility, apply the model to the real-time temperature information for the different locations of the facility, determine, based on applying the machine learning model, real-time temperature information for the facility, and return the real-time temperature information for the facility.

Abstract: Disclosed are systems and methods for detecting phase state information of objects in a facility. An apparatus can include a case with sensors. The sensors can detect environmental conditions related to an object and generate signals based on the detected conditions. The sensors can include temperature sensors, acoustic sensors, ultrasonic transducers, and/or humidity sensors. The apparatus can include a wireless transceiver to transmit the signals to a computer system. The computer system can process the signals using machine learning models to determine temperature and/or phase state information of the object, such as food products that are retained in/by the object. The apparatus can be removably affixed to the object. Sometimes, the apparatus can be positioned amongst many objects, such as multiple item cases on a pallet to determine an overall temperature and/or phase of the items on the pallet. The temperature/phase information can be used to optimize facility cooling operations.