optimising labour and controlling costs is critical

A 1% improvement in labour optimisation can result in millions in savings. Transforming labour operations and controlling costs starts with accurately and precisely predicting demand for each location and customer touchpoint by creating optimal schedules.

Legion Wfm optimise Labour Costs
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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

optimise productivity. minimise costs.

minimise unplanned OT and premiums while optimising 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 synthesises 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 labour plan that incorporates staffing rules, budget constraints, and compliance policies.

Improve labour 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 labour needed to meet forecasted demand and includes labour 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