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How We Can Use Machine Learning And Analytics To Optimize Food Delivery Time

On many occasions, people do prefer not to dine outside and prefer to order in. Hence, the majority of restaurants that wish to make a sustainable income thrive on food deliveries. People prefer to order from restaurants with short waiting times as they dislike waiting while they are hungry. Hence, restaurants need to focus on faster delivery times when hiring a web developer for food delivery app development.

Food delivery application development involves everyone from the developers, delivery personnel, and even the customer. Using machine learning, a good delivery app can optimize food delivery with faster deliveries by analyzing traffic, obstacles, the quantity of the food portions, obstacles along the route, etc. 

AI and machine learning make this possible as it provides real-time information about all the requirements. Still doubtful about how to leverage machine learning and AI in food delivery? This article will help you know how these technologies can help optimize food delivery time and cost for profitable outcomes.

5 Ways Machine Learning And Analytics Can Predict And Optimize Food Delivery

Identification of Potential Hazards

A food delivery app developer needs to create the app, keeping in mind potential factors that could affect the speed of delivery. These factors could include situations such as:

  • What is the quantity of the delivery and how far does it need to be delivered? Can it be delivered by walking? Or does it require a bike? If it is bulky, can a four-wheeler be arranged?
  • Where is the delivery coming from? Is it from a catering service, café, restaurant, or hotel?
  • Our delivery personnel around the area and will they be able to reach the supplier’s location on time?
  • If no delivery personnel is around, how long till someone can reach the supplier’s location? and what If the food is cooked early, it will get cold before it reaches the customer. If the food reaches the customer late, they will lose interest in reordering from the business.

Collection of Data

Machine learning is a boon for food ordering app development. With its help, the app can collect data to optimize delivery:

  • Motion data from accelerometers and gyroscopes from the delivery personnel’s phone.
  • Be able to identify if the delivery personnel is walking, running, riding a bike, or driving a car with APIs.

This information becomes useful when we have this set of data:

  • What did the customer order? Who is the supplier, and what is the estimated delivery time?
  • Who placed the order, and what is the delivery location? Do they have any specific preferences?

This data, in turn, helps understand the performance of previous deliveries, which can help calculate the average and best times. Collectively, all of this data can be combined with other variables, which can be fed into the test versions of the app. 

Essentials of Food Delivery App Development

These are some factors which you should take into consideration during food ordering app development:

  • Food pick-up from the supplier.
  • The route from the supplier to the destination address.
  • Delivering the package.

All of these collectively form the total delivery time. Other factors to consider when developing the app are: 

Pick-up From The Supplier Travel Time Delivery Point
Is parking available? Using Google Maps APIs to identify locations. Difficult to access locations.
Late or early preparation of food. Different levels of traffic. Security issues causing the delivery personnel to be stuck at the gate. 
Food that might have been misplaced or lost. Road Closures because of accidents or work in progress.

 

Real-time Analysis By Implementing Datasets

Machine learning and AI can help the food delivery app developer to implement intelligent datasets for real-time analysis. We can analyze and classified activity data into various tangents such as available routes, traffic conditions, how much time it will take to prepare and deliver the food, etc.

Reduce Wait Times to A Minimum

Delivery estimates and wait times can be minimized when the app has a detailed breakdown report from previous orders and deliveries. We can use Machine learning to optimize food delivery timings and reduce them greatly during all these important stages:

  • Service during peak hours: The food delivery app can understand and identify peak hours for the business. This way, it can immediately identify the accurate food pick-up time from the supplier’s location.
  • Preparations for the delivery: Depending on the quantity of food, through machine learning and AI, the app can alert the nearest delivery personnel to reach the restaurant at the earliest.
    It will even identify the fastest route, restrictions on the route, time till the order are getting prepare, etc. It can even alert the delivery personnel of the nearest parking location available to the food supplier.
  • During the delivery: Using APIs, the food delivery app can use the GPS signals of the mobile phone to ascertain and calculate the fastest route available between the supplier and the customer’s delivery location. If there are any obstacles, AI can alert the driver, which will enable them to change their route at short notice. 

Catch Up With The Rapid Pace of Food Delivery

A food delivery app developer can create a smart and intelligent food delivery app with the help of Machine Learning and Artificial Intelligence. They can build it with a smooth user interface and easy navigation. This way, businesses, and customers will be able to use the food delivery app efficiently.

With the help of machine learning, the food delivery app developer has the potential to build an app that is not only efficient and robust but is also reliable and scalable to improve the user experience of suppliers and customers ultimately.

Final Words

Efficient use of machine learning analytics can assist with food delivery app development by enabling real-time tracking of food preparation time, the delivery location, tracking the GPS location of the delivery personnel, etc. This data can be very useful in locating and dispatching the nearest delivery agent on the shortest route available without any restrictions.

This way, with efficient food delivery application development, we can optimize food delivery timings for the customers to ensure repeat business.

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