The role of artificial intelligence in predicting food delivery ETAs

Estimating food delivery time is extremely complex

LogiNext is working with several top QSR chains such as McDonald’s, Burger King, Starbucks, and KFC to ensure deliveries in less than 30 minutes. Photo Unsplash

Many times, while ordering food, one parameter a customer takes into account is the estimated time of arrival, which is known as ETA in food delivery jargon. At a rudimentary level, restaurants used to estimate the ETA depending on the type of food and where it has to be delivered. But these were times when reducing delivery times was not really a concern. 

In the modern, post-pandemic world, delivering food on time is crucial and a few extra seconds can be a deal-breaker in some cases. LogiNext is working with several top QSR (quick service restaurant) chains such as McDonald’s, Burger King, Starbucks, and KFC to ensure deliveries in less than 30 minutes. And pushing the envelope to under 15-minute deliveries. 

In such a scenario, it becomes important to predict the ETA before a customer places an order so that the entire supply chain is orchestrated in an efficient manner. And this is where artificial intelligence plays a critical role. Predictive ETAs can be calculated using two different modes – one is dependent on the delivery driver and the other on the best estimate. 

Best delivery driver mode

The objective of this mode is to identify the best delivery driver to whom the order can be assigned as per the specified profile and predict the ETA considering the delivery associate’s details. A few parameters are considered: average food preparation time, incremental preparation time (time taken to prepare each subsequent quantity of an item), load multiplier (includes peak hours), pickup time, transit time between the two locations considering traffic patterns, and other granular details. 

Best estimate mode

The objective of this mode is to calculate the best estimate of the pre-order ETA based on factors such as the availability of delivery associates, other orders waiting to be assigned at the branch, and so on. This is a more generic way of estimation and takes into account more parameters. The earlier one is laser-focused on optimizing delivery driver management. 

A generic formula for predicting ETAs would look like this:

Predicted ETA= Max {Pickup Time, (Order Preparation Time*Peak Hour Multiplier)} +

(Default Service Time Per Order*X) + (Delivery On Road Transit Time)

A complex problem

Calculating ETAs is a massively complex problem owing to possible on-ground situations that may arise. Key aspects in predicting ETAs are route planning and route optimization. Several planning properties contribute majorly to making this a reality:

  • Planning objective: When planning the orders, one would want to achieve a goal rather than just plan orders. The goal can be to optimize the overall trip cost or find the best possible route for a fleet or create trips that distribute the work among the selected fleet. 
  • Route constraints: To achieve the goal, one would take into account the route constraints to optimize the trips.
  • Fleet constraints: Along with route constraints, fleet constraints come into the picture.
  • Advanced: Apart from these, there could be other operational constraints that could be taken into account. 

Role of AI

At the base of these planning properties and delivery modes, is the power of artificial intelligence and machine learning. Tracking billions of location data points, all parameters can be predicted with greater accuracy and this makes the end customer experience ever richer. From an engineering perspective, this is an amazing problem where a data scientist gets to push the limits of how much machines can predict the future. The more variables we can account for, the higher will be the precision and accuracy. And this is why AI plays such a big role in the future of supply chain and logistics management all over the globe.

Pooja Patel, the author of this article, is vice-president, Product Delivery of LogiNext


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