Abstract: Examples provide a workforce manager that analyzes historical pharmacy transaction data to generate a set of forecasted future prescriptions for a selected pharmacy. The forecasted future prescriptions are divided into predicted new prescriptions and predicted refill prescriptions. The predicted refill prescriptions are classified into a set of high confidence refills and a set of low confidence refills. The labor demand associated with the set of high confidence refills are redistributed within a range of pickup dates associated with each high confidence refill prescription to smooth labor demand minimizing variation within a selected time-period. A number of personnel are identified for each day in the selected time-period based on the smoothed labor demand. A schedule is published assigning at least a portion of the forecasted future prescriptions and the number of personnel to each day in the selected time-period is output.

Abstract: The system and method described herein enable generating inventory placement recommendations for a store based on transaction data. Inventory data associated with the individual store is obtained. The inventory data includes container location data, item location data, item category data, and transaction data. A store layout model of the store is generated based on the container location data and item location data. Item category relationships are calculated based on the transaction data and the item category data. Inventory layouts are generated based on the store layout model and the calculated item category relationships. Average distance values for each inventory layout are calculated, and an inventory placement recommendation is generated based on the average distance values of the inventory layouts. The generated inventory placement recommendation enables arrangement of the inventory of the store based on past transactions to reduce the average travel distance of customers when shopping there.

Abstract: Examples provide an activity reward generator for generating customized activity-based rewards. A per-user activity threshold is generated for a user based on user data and/or transaction history data. A customized minimum transaction threshold is generated based on retail environment data and/or transaction history data associated with a retail environment. An amount of activity performed by a user within the retail environment is calculated based on activity data and an activity type for the user. If the calculated amount of activity exceeds the per-user activity threshold and a transaction total for a selected transaction completed by the user exceeds the customized minimum transaction threshold, reward point(s) corresponding to the amount of activity are given to the user. If the total number of reward points of the user exceeds a minimum rewards points threshold, providing a customized reward to the user.