How is ETA for food delivery calculated? 

 

Many times, while ordering food, one parameter a customer takes into account is the estimated time of delivery (ETA in food delivery jargon). At a rudimentary level, restaurants used to estimate this 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 this modern, post-pandemic world, delivering food in time is of critical importance and a few extra seconds can be a deal breaker in some cases. At LogiNext, we’ve been working with several top QSR (Quick Service Restaurant) chains like McDonald’s, BurgerKing, Starbucks and KFC to ensure under 30 minute deliveries (download QSR case study). And the envelope is being pushed to under 20 minute deliveries. 

 

In such a scenario, it becomes extremely important to estimate the ETA even 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.  There are two engines that kick into effect with respect to ETAs. 

The first is the ETA calculation engine where the system calculates considering various factors like traffic conditions, weather patterns, capacity availability, time of the day and week, and the restaurant’s SLA requirements (which can be configured on the LogiNext platform).

 

In technical jargon, the LogiNext engine calculates ETA based on:
a) Time to load orders in a trip
b) Travel time to reach customer location

 

 

 

How does the app show delivery time before a customer places an order? 

ETA calculation is one thing and then there is another engine on the LogiNext platform which calculates the estimated time of delivery even before a customer places an order. The LogiNext platform does this via two ways: Best Delivery Driver Mode and the Best Estimate Mode.

 

The best delivery driver mode identifies one delivery associate for transporting the order according to the auto-assignment settings that the user has configured. And the ETA will be determined based on that one driver’s parameters. While the Best Estimate mode does not zero down on the one driver that the auto-assignment engine would find. It takes into account the supply and demand of drivers available to service the order.

 

A generic formula for calculating ETAs before an order is placed would look like this:

 

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

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

 

 

Configuration is the key

 

Calculating ETAs is a massively complex problem owing to all the possible on the ground situations that can arise. One of the major strides when it comes to predicting ETAs, is the route planning and route optimization. There are several planning properties which contribute majorly to make the above 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 overall trip cost or find the best possible route for the fleet or to create trips which distribute the amount of work among the selected fleet. 

 

  • Route Constraints: To achieve the goal, one may want to modify the various route constraints that can be applied to optimize the trips.

 

 

  • Fleet Constraints: Along with the route constraints, one can also apply fleet constraints which are applied on the selected fleet to optimize the trips.

 

 

  • Advanced: There are certain other operational constraints that one may want to apply and a configurable system allows for this. 

 

Role of Artificial Intelligence

 

At the base of all these planning properties and delivery modes is the power and beauty of Artificial Intelligence and Machine Learning. Tracking billions of location data points, all the 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 can machines 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 over the globe.

 

 

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