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Food Delivery Optimization

How food delivery companies can benefit from better employee scheduling 

The food delivery market has grown rapidly over the last few years. Now, due to COVID-19, more people are being limited to meals in their homes and the popularity of this market has surged even more. Restaurants of all sizes have started offering food delivery, and consequently, digital restaurant orders have increased by 63%. Similarly, home delivery has increased by 67% in comparison to March 2019. 

With demand at this all-time high, food delivery companies have to ask themselves: How do we meet that new demand? And how do we make the fastest food deliveries? The key component is optimal driver scheduling. Our expertise tells us that the answer does not lie in simply hiring more drivers, but in improving their schedules to drive productivity and efficiency. Read on to find out more… 

The Complexity of Food Deliveries 

Now we must ask ourselves: What factors make food delivery quite complicated? And how can efficiency be improved in those factors through intelligent scheduling? 

1. Order volumes differ per hour and location, making it hard to estimate how many drivers are needed.

Different customers make different orders at different times and you don’t want to miss any of them. More lunch deliveries in business areas and more evening deliveries in residential areas are common patterns. However, it remains difficult to schedule the right amount of drivers at the right time and place, because even taking general patterns into account, the numbers and types of orders can differ greatly. 

Working with diverse demand over many areas and coordinating drivers with different contract types, preferences, and starting locations makes schedules almost naturally prone to under or overstaffing, especially when done manually. With understaffing, you can cause late deliveries. With overstaffing, the competition between your drivers will be so tight that the quality of the service and employee happiness will decrease.  

So, how do you fix this? By accurately forecasting demand. This allows companies to pinpoint peak hours and slower periods of each area. Having accurate demand forecasts enables you to create shifts that perfectly cover demand, even if that varies per area or during the day. 

2. We always try to have drivers on the road continuously, but we can’t seem to avoid idle time. 

Maximising productivity and minimizing idle time is important. Employees want to keep themselves busy and employers want to make effective use of their time. With orders at different times and different locations, the challenge of ‘who do you send where and for how long are they working?’ arises. You must consider:

  • The location of your drivers with respect to the delivery location.
  • The number of deliveries to be made. 
  • Travel time between deliveries.
  • Fitting in the rest breaks drivers are legally entitled to.

Balancing these core factors requires a decision-making process. Automation of the decision process for the drivers will output the optimal solution, every time.

3. Disruptions are common and no week is ever the same. 

No matter how established the pattern of your orders is, there will always be unexpected outliers. Moreover, external factors, like weather or external events like COVID-19 can significantly influence demand. 

To cope with disruptions, companies have to update their demand outlook based on available data close to the day of operations. Normally, when forecasting, it makes sense to take data from a longer time horizon, i.e. previous month and even years. This allows you to identify common demand patterns over time. However, you then risk overlooking recent trends. When coping with disruptions, we recommend shortening the scope and looking at the newest available data as well. This might show significantly different patterns, allowing you to downsize shifts, maneuver new or different tasks between employees, or create more open shifts as necessary. 

4. Compliance and employee happiness

Food delivery companies also face a problem recognizable to every big employer: building schedules that are compliant with local labor laws. The employer has to consider that employees should have meal breaks, be paid for all their hours, and more. Law violations can result in hefty fees, unsatisfied employees, and damage to your company’s reputation.

On top of that, employees also want fair and workable schedules. Failing to provide this will lead to higher turnover and employees seeking jobs at competitors. With the goal of providing quality service, we understand that sometimes food comes first. However, you can meet demand and keep your employees happy with a few key steps

  1. Accurately forecast your demand
  2. Correctly staff against that demand
  3. Layer breaks in when demand allows it

By doing this, you will not only make sure all food is delivered in time, but you also ensure your employees aren’t over or under pressured at work and they get the rest and breaks they deserve.   

The role of technology 

Technology has enabled people from all over the world to get food delivered to their doorstep with just a few clicks. We advocate that your employee scheduling can be just as easy.  On-time deliveries allow you to win business. Creating optimal schedules (that meet demand) allows you to win over employees.

Finding solutions that solve both challenges while meeting your business goals requires harnessing the power of AI-driven scheduling. By automating your scheduling process, you’re sure to have accurate demand forecasts. This enables you to create schedules in advance, improve service levels with optimal shift or task alignment, consider employee preferences, and adhere to labor laws. 

Want to learn more? 

With AI-driven scheduling, you solve the most painful issues and thus open a huge window of opportunity to become market leaders. Contact us to learn more about how you can deal with your current and future scheduling challenges right away. Or, request a demo down below to see our scheduling solutions in action.

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