On many occasions, people do not like to eat out and prefer to order in. Therefore, most of the restaurateurs who want to make a sustainable income rely on food delivery. People prefer to order from restaurants with short wait times because they do not like to wait when they are hungry. So, restaurants need to focus on faster delivery times while hiring a web developer for food delivery app development.
Food delivery application development involves developers, delivery personnel, and even customers. Using machine learning, a good delivery app can optimize food delivery with faster delivery by analyzing traffic, bottlenecks, quantity of food portions, obstacles in the way, etc.
As AI and machine learning provide real-time information about all requirements, this is made possible. Still skeptical about leveraging machine learning and AI in food delivery? This article will help you learn how these technologies can help optimize food delivery time and cost for profitable results.
Predicting and optimizing food delivery with machine learning and analytics identification of potential hazards
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A food delivery app developer has to build the app keeping in mind the possible factors that can affect the speed of delivery.
What is the quantity to be delivered and how far does it need to be delivered? Can it be reached by walking? Or does it require a bike? If it is heavy, can a four wheeler be arranged?
Where is the delivery coming from? Is it a catering service, a cafe, a restaurant, or a hotel?
Our delivery crews are around the area and will they be able to reach the supplier's location on time?
How long can one reach a supplier's location if no delivery personnel are around? And what if the food is cooked quickly, it will get cold before it reaches the customer. If the food reaches the customer late, they will lose interest in re-ordering from the business.
Collection of data
The development of food ordering apps is made easier by machine learning. This allows the app to collect data to optimize delivery:
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Motion data from the accelerometer and gyroscope from the delivery worker's phone.
Be able to identify whether the delivery person is walking, running, biking or driving a car with the API.
This information becomes valuable when we have this set of data:
What did the customer order? Could you tell me who the supplier is and when the delivery is expected?
Who placed the order and what is the delivery location? Do they have any specific preferences?
In turn, this data helps understand past delivery performance, which can help calculate average and best times. Collectively, all this data can be combined with other variables that can be fed into trial versions of the app.
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