We implemented the AI-powered demand forecasting feature in UP 2023.2 three months ago, but the staffing predictions are consistently inaccurate and causing significant scheduling inefficiencies. The forecasts are either dramatically over-predicting or under-predicting our actual staffing needs, sometimes by 30-40%.
We’re a retail operation with 15 locations, and we configured the AI demand forecasting to analyze historical sales data, foot traffic, and scheduling patterns from the past two years. The historical data quality appears solid - we have complete records with no major gaps. However, the business rule configuration may need adjustment as we’re not sure if we’ve properly weighted the various input factors.
The staffing optimization recommendations seem disconnected from our actual operational patterns. For example, the system recommended cutting staff by 25% during what turned out to be our busiest weekend of the quarter. Our managers have lost confidence in the AI recommendations and are reverting to manual scheduling.
Are there specific configuration parameters or data quality checks we should be reviewing to improve the AI forecasting accuracy?
I had similar issues with AI forecasting in our environment. The problem was that we hadn’t properly configured the business rules to account for promotional events and holidays. The AI model was treating every day as a normal business day, so it couldn’t predict the demand spikes during sales events or the drops during holidays. Make sure you’ve defined all your special events and business exceptions in the calendar configuration. The AI model needs this context to make accurate predictions.
Check the model training logs in the AI Forecasting module. You can see which features the model is weighing most heavily and whether certain data sources are being excluded due to quality issues. Also, look at the prediction confidence scores - if they’re consistently low, it indicates the model doesn’t have enough signal in your data to make reliable predictions. You might need to add more relevant variables or adjust the feature engineering settings.
We loaded two full years of historical data initially, which should cover all seasonal patterns. All our data sources are connected - POS systems, time and attendance, and scheduling history. But I’m wondering if the data mapping is correct. How can we verify that the AI model is actually using all these data points in its calculations?