Optimizing labor and controlling costs is critical
A 1% improvement in labor optimization can result in millions in savings. Transforming labor operations and controlling costs starts with accurately and precisely predicting demand for each location and customer touchpoint by creating optimal schedules.
Because of Legion’s AI and machine learning functionality, it forecasts an ideal headcount per hour per day based on the operating hours and the traffic that’s anticipated using all historical data. Additionally, the tool continues to learn over time as we add more data. It's been hugely impactful and has changed our approach to work.
Director, human resources
Optimize productivity. Minimize costs.
Minimize unplanned OT and premiums while optimizing performance.
Precisely predict demand
Legion WFM enables intelligent automation to precisely predict demand across all customer touchpoints, every 15 minutes.
Automatically adjust to market changes
Self-learning demand forecasting automatically learns from subtle patterns to continuously improve and adjust to changing conditions – without human intervention.
Improve forecast accuracy
Automatically synthesizes massive volumes of historical and ongoing demand driver data; incorporates future activities, such as sporting events, weather, and local events for each location.
Automatically create the optimal schedule
Based on precise demand forecasts, Legion WFM automatically creates the optimal labor plan that incorporates staffing rules, budget constraints, and compliance policies.
Improve labor efficiency
Automatically prepare for peak periods and augment staff with employees from other locations in order to meet demand.
Reduce overstaffing
Legion WFM automatically computes the minimum labor needed to meet forecasted demand and includes labor not directly related to customer service, such as restocking and inventory.
Legion by the Numbers
Nation-wide US Convenience Store
10M
in savings
56x
return on cost per store
500bp
improvement in workload scheduling efficiency
2x
improvement in forecast accuracy