Schedule for shorter delivery times and maximum driver productivity

Automated Employee Scheduling For Food Delivery

The challenges in Food Delivery

Higher and volatile demand in different areas

Different customers make different orders at different times. As order volumes differ per hour and location, it’s a complex puzzle to solve how many drivers you need where and when to make sure deliveries are done when food is still warm.

Unexpected changes and disruptions

Common trends might be that there are more orders and deliveries in the evening and on the weekends, but exceptions make the rule. Ranging from extra orders on Tuesday morning to full cancellations, how do you handle disruptions?

Low productivity and high idle time

Your drivers are ready to go, but how do you make sure that they are always on the road? And when you have deliveries, who do you assign to them that makes most sense economically and geographically? 

AI services that fit within the planning flow

Step 1 - Accurately forecast next week’s orders

With Labour Demand Forecasting

Predict future demand per location and demand driver one week out while taking local factors into account so you can staff and schedule strategically.

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Step 2 - Create shifts and tasks to meet demand

With Shift Creation

Group deliveries based on time and location and assign them to shifts to meet delivery windows while adhering to break rules and other local labour laws and regulations.

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Step 3 - Assign shifts to maximise employee happiness

With Shift Filling

Automatically assign shifts to drivers based on their availability, location and contract to maximise productivity and minimise costs. Take labour laws and their personal scheduling preferences into account to cater to their private lives and improve employee loyalty.

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Step 4 - Update schedules for even better service

With Re-optimisation

The world changed between the initial forecast and today. Make new forecasts on the day before day of operation using the newest data to ensure even higher forecasting accuracy and handle accordingly. Downsize shifts if demand is lower and create open shifts or overtime if demand unexpectedly jumps up.