A monitoring unit tracks inventory at a work site. The inventory is stored in a plurality of storage locations. The method inlcudes receiving at least one first measurement result of the inventory from at least one transporting vehicle. The at least one first measurement result is indicative of at least part of the inventory transported to, from, and/or between the plurality of storage locations in the work site. The at least one first measurement result is measured by an on-board sensor of the at least one transporting vehicle. The at least one first measurement result comprises at least one volume measurement result, and/or at least one weight measurement result. The method further comprises estimating an amount of the inventory based on the received at least one first measurement result.

A data prediction subsystem stores hourly event potential data indicating an expected amount of removal events as a function of time of day. Based at least in part on event data, it is determined that a first item associated with a first location has an empty status at a time after a start of a day. For the day, an anticipated event value is determined for the first item at the first location. Using the anticipated event value and the hourly event potential data, a time-adjusted event value is determined. Based at least in part on the time-adjusted event value, a prediction value is determined that corresponds to a recommended amount of the first item to request for a future time.

Methods and systems are described for scale-based inventory management and monitoring at a location, such as hospitals. Scales may be configured to receive a tray/holder for a given type of supply item, e.g., bandages or syringes. A weight determined by the scale may be associated with a given quantity of the respective item. Data around supply delivery and restocking is collected and can be used in an AI/ML model to optimize delivery routes, labor options for delivery, delivery speed, or other factors useful in optimizing supply and logistics in any location with logistics challenges.

Systems and methods for autonomous device procurement are disclosed. A system can obtain point-of-sale (POS) terminal demand data and execute a machine-learning model to generate a forecasted inventory demand for POS terminal devices. The system can allocate the forecasted demand among devices based on ratios, simulate demand instances, calculate losses, and modify the allocated demand based on the losses. The system can generate a procurement plan based on the modified demand. The system may autonomously procure devices according to the plan. The forecasted demand may comprise a probabilistic distribution for multiple future time periods. The system may calculate a conditional value at risk (CVaR) based on the losses and generate the plan based on the CVaR.

An example computer-implemented method for temporal data analysis and forecasting utilizes topological hierarchical decompositions to process historical and future time windows. The method receives sales data and purchase data for at least one item and generates multiple sets of historical time subsets with varying lengths, where information in shorter subsets is duplicated in longer ones. Future time windows are also generated in a similar manner. Future time windows are chronologically after a given initial time. The method creates past and future topological hierarchical decompositions and directed graph adjacency arrays. Customer attention matrices are generated for past and future windows, and matrix multiplications are performed to create self-attention arrays. These arrays are then multiplied together. The method culminates in providing a dashboard for forecasting after an initial time point, enabling comprehensive temporal data analysis and prediction.

Aspects of the present disclosure relate to a demand forecasting system. The demand forecasting system may include components for developing forecasting models, generating demand forecasts, and handling outputs of demand forecasting models. In some embodiments, the demand forecasting system may include a model training system and one or more components that can be used by the model training system to improve model performance.

An apparatus includes at least one processor and at least one memory in electronic communication with the at least one processor. The at least one memory has instruction stored thereon which, when executed by the at least one processor, direct the at least one processor to communicate to a communication device of at least one user with regard to a plurality of parts in an inventory. The processor is operable to receive a current on hand inventory for a plurality of parts, receive a safety inventory that is defined to ensure a safety margin of parts on hand, receive an expected consumption within a first time period, and receive an expected receipts of parts within the first period of time and identify a gap by subtracting the safety amount on hand and the consumption amount from a sum of the current on hand inventory and the expected receipts. A display device is communicated to to provide the gap on the display device. A factory and a method are also disclosed.

A tire stock management apparatus (10) that calculates a stock quantity of tires during a specific time in future, the tire stock management apparatus including: a warehoused quantity acquiring unit (131) configured to acquire a predicted quantity of tires to be warehoused in connection with the specific time; an issued quantity acquiring unit (132) configured to acquire a predicted quantity of tires to be issued in connection with the specific time; and a stock quantity calculation unit (133) configured to calculate a stock quantity of tires at the specific time, based on the predicted quantity to be warehoused and the predicted quantity be issued, wherein the predicted quantity to be warehoused is calculated using warehouse data generated based on tire operation time data indicating a removal time, a usage time, and/or a delivery time of tires prior to the specific time.

A production plannings of products and goods or shop floor logistics in producing, trading or distributing products/goods is provided in which it is addressed to maintain or keep data about the production planning of the products and goods or the shop floor regarding the logistics in producing, trading or distributing the products and goods up to date, and at least one mobile logistics robot to automate a logistic transport of the products, the goods or production material for the products and goods within a shop floor is equipped each with a sensor technology appropriate to measure or capture and control a current state or changes of the shop floor by sensor data, when each the robot is managed by a fleet management system for distributing or scheduling logistic tasks among the at least one robot executing transport tasks of a transport task queue maintained by an automated logistics planning system.