Columbia Technology Ventures

This technology is an algorithm that uses prediction models and theoretical foundations to determine staffing needs based on both historical and real-time data.

Unmet Need: Algorithm using historical data to predict staffing needs

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.

The Technology: Prediction-based algorithm to identify staffing and surge staffing needs

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.

Applications:

  • Predict staffing and surge staffing for healthcare systems and retail stores
  • Predict customer demand for products at certain times of year to assist with production planning
  • Predict resource demand (electricity, water) at certain times of day/year
  • Predict online traffic to avoid website downtime or prepare for periods of higher traffic
  • Analyze consumer demand over time to adjust pricing of products (airplane tickets)

Advantages:

  • Provides a tailored, data-driven approach to staffing management
  • Removes heuristic approach to staffing
  • Uses historical data of staffing demand
  • Able to predict staffing needs weeks in advance
  • Able to respond to surge demands and predict surge staffing needs in real time
  • Can help increase staffing efficiency and reduce staffing costs without impacting service

Lead Inventor:

Carri Chan, Ph.D.

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