AI Driven Predictive Staffing and Scheduling Workflow Guide

Predictive Staffing and Scheduling Workflow

1. Data Collection

1.1 Gather Historical Data

Collect historical sales data, customer footfall, and staffing levels from the restaurant’s Point of Sale (POS) system.

1.2 Integrate External Data Sources

Incorporate external factors such as local events, weather forecasts, and holidays that may influence customer traffic.

2. Data Analysis

2.1 Implement AI Algorithms

Utilize machine learning algorithms to analyze the collected data and identify patterns in customer behavior and staffing needs.

2.2 Predictive Analytics Tools

Examples of AI-driven products include:

• Forecastly: Provides predictive analytics for staffing based on historical sales data.
• 7shifts: Offers AI-driven scheduling tools that adapt to predicted customer volume.

3. Staffing Optimization

3.1 Generate Staffing Recommendations

Use AI insights to generate optimal staffing schedules that align with predicted demand.

3.2 Automate Scheduling

Implement automated scheduling tools that adjust staff schedules in real-time based on changing predictions.

4. Implementation

4.1 Communicate Schedules to Staff

Utilize employee management platforms to disseminate schedules to staff efficiently.

4.2 Monitor Compliance

Track adherence to the generated schedules and make adjustments as necessary based on real-time data.

5. Continuous Improvement

5.1 Gather Feedback

Collect feedback from staff and management on the effectiveness of the staffing schedules.

5.2 Refine AI Models

Continuously refine AI models based on feedback and new data to enhance predictive accuracy.

5.3 Evaluate Performance Metrics

Analyze key performance indicators (KPIs) such as labor costs, customer satisfaction, and service speed to assess the effectiveness of the predictive staffing process.