Incentive Budget Optimization for Workforce Scheduling
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Solution Overview
Problem
Current systems for distributing incentive budgets in work schedules are inefficient as they rely solely on current net staffing data, leading to incentives being wasted on time intervals that would have been accepted without incentives or those with low agent demand, rather than prioritizing intervals with higher demand.
Innovation Solution
A computer-implemented method using machine learning algorithms to forecast future net staffing and agent demand, optimizing the distribution of incentives across a multi-week schedule by classifying understaffed intervals and calculating a combination of incentives to maximize agent uptake in high-demand intervals.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If incentive budget is distributed based solely on current net staffing data, then staffing shortages can be addressed, but incentive waste increases because incentives are given to time intervals that would have been accepted without incentives or have low agent demand
Solution Approach 1:
The system implements feedback loops by continuously monitoring agent response to incentive offers and using this information to adjust future incentive distributions. The machine learning model learns from historical data about which agents respond to incentives under different conditions, creating a closed-loop system that optimizes incentive spending over time based on actual outcomes rather than static staffing data alone.
Solution Approach 2:
The system changes the parameters used for incentive distribution from static net staffing data to dynamic predictions that include forecasted net staffing, predicted agent demand, and forecasted incentive response. This multi-parameter approach allows the system to identify time intervals where incentives will be most effective, transforming the decision-making process from reactive to predictive.
2Ease of operation
If incentive budget is distributed to maximize agent demand, then agent satisfaction improves, but staffing coverage may deteriorate if incentives are focused on low-priority time intervals
Solution Approach 1:
The system applies local quality by treating different time intervals differently based on their specific characteristics. Instead of uniform incentive distribution, the model identifies which specific intervals have both high staffing needs and high predicted agent demand, then concentrates incentives on those specific locations in the schedule. This allows simultaneous optimization for both staffing coverage and agent satisfaction by matching incentives to locally optimal intervals.
Solution Approach 2:
The system performs preliminary action by forecasting future net staffing and agent demand before distributing incentives. The machine learning model predicts which time intervals are likely to be understaffed and which agents are likely to respond to incentives, allowing the system to proactively allocate incentives to prevent staffing shortages rather than reacting after problems occur.
3Measurement precision
If machine learning models forecast both net staffing and agent demand, then incentive distribution accuracy improves, but system complexity increases
Solution Approach 1:
The system segments the complex forecasting task into separate machine learning models for different functions: one model forecasts net staffing requirements, another predicts agent demand for specific intervals, and a third predicts agent response to incentives. This segmentation allows each model to specialize in a specific aspect, improving overall accuracy while making the system more manageable and interpretable compared to a single monolithic model.
Data Source
AI summary
A computer-implemented method for optimizing distribution of incentive-budget for additional time interval allocation in a multi-week work schedule is provided herein. The computer-implemented method comprising: (i) training a model to forecast future net staffing; (ii) generating a multi-week work schedule; (iii) using the model to forecast for each time interval a net staffing value; (iv) classifying time intervals as understaffed; (v) displaying the understaffed time intervals to suggest agents to take as additional time interval; (vi) providing a tier incentive structure and an incentive budget to be updated by a user; (vii) training the model to forecast a degree of elasticity of agents demand for each time interval based on historical agents schedule changes; and (viii) calculating a combination of incentives of each tier of the tier incentive structure in the incentive-budget to accommodate understaffed time intervals and maximize agents demand for time intervals based on forecasted degree of elasticity.