Better forecasting by Machine Learning
A realistic forecast includes all the possible drivers of demand, from sales, promotions, price changes, local events, seasonal influences up to the weather. A highly accurate forecast uses Machine Learning to take into account both employee preferences and additional workforce data, spotting trends and disturbances from historical schedules.
Optimize for profit or service level
Make decisions about temporary work, hiring and layoffs to increase revenue when demand is high and reduce labor costs when demand is low.
Location-dependent forecasting
Use different methods for each location of your business to create precise forecasts. Different locations can have different demand drivers and conditions that need to be considered.
Uncover future labor demand by predicting how many people with the appropriate skills your organization will need several weeks from now. Improve your forecasts with Machine Learning and Optimization, and make pivoting business decisions in planning your workforce.
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Various demand signals and Machine Learning
By using various demand signals, such as sales, visitors, bookings, promotions, price changes, local events, ... we can accurately predict how many people you will need at any given time. We account for the season or the weather patterns that influence customer behavior and staff demand. In addition, you may know which employees are better at up-selling or work better under pressure - these are all data points you can just plug into OnShift to do the hard work for you.
Reach 20% higher accuracy in your forecasts by adding supervised Machine Learning regression techniques to predict labor demand.
- Machine Learning methods show an average 20% improvement over traditional techniques such as moving averages, historical predictors or rules of thumb, for a wide range of industries.
- Unlike using traditional statistical methods, Machine Learning is more expandable; as a result, new data features are easily incorporated that affect labor demand and no manual intervention or modification of mathematical formulations are required.
- By automatically retraining the model, our Machine Learning algorithm autonomously adapts to new customer data or new trends.
Predict Future Demand Features
Forecast with up to 98% accuracy your future demand
- Consider all possible demand variables and priorities
- Make accurate predictions and simulations to understand your future demand.
- Use you historical and current data, staffing requirements
ML features
- Granular prediction capabilities: from interval forecast as granular as 15 mins to daily, weekly, monthly or yearly forecasts
- All forecasts need to separately consider trend, level and seasonality but also consider events that don't follow a smooth pattern (eg: new product introduction, weather, equipment failures,... ). These must be understood in advance.
- Picks the best model in any given scenario: Compares all forecast models in a single generation and then the AI picks the best performing model for the specific objective relative to its environment.
