This technology is an algorithm that uses prediction models and theoretical foundations to determine staffing needs based on both historical and real-time data.
Determining the number of employees to schedule for a work shift can be extremely difficult, especially in sectors where need and demand waxes and wanes, such as in healthcare and retail. Although various types of software exist for the allocation of shifts, staffing decisions are still made heuristically, relying on common sense and the employer’s own experience. This strategy has weak predictive power and leaves businesses vulnerable to sudden surges in demand.
This technology is an algorithm that integrates prediction models with theoretical foundations to provide staffing recommendations. It uses historical data to determine the number of staff that should be scheduled weeks in advance, and also analyzes real-time data to determine the number of additional surge staff needed during periods of rising demand. Using this data-driven approach, users can cut costs by minimizing over-staffing and improve quality of service by ensuring adequate staffing during demand surges.
IR CU22096
Licensing Contact: Joan Martinez