Predict & Optimize Food Delivery Time Using Machine Learning & Analytics

While an on-demand food delivery model connects merchants, delivery riders, and end customers, machine learning clubbed with accumulated data from past food orders, and user-level consumption patterns help businesses enhance the customer experience. Rishabh Software blends innovation with elegance to help companies to optimize delivery times and gain maximum outcomes.

Food-tech companies are increasingly turning to machine learning (ML) and automation to drive their businesses.

It is driven by a surge in online orders enabling the food delivery companies to address the fast-changing consumer behavior, minimize delays, and enhance customer experiences.

However, the one common issue that food delivery businesses face is the dispatch of a delivery-partner to pick-up an order in time.

It becomes more challenging in situations like today, where the COVID-19 pandemic has led them to operate on a low workforce.

And, the use of machine learning in the food industry can help address this challenge via

• Quantifying the time spent on past deliveries
• Forecasting the time that would be spent on future deliveries

Rishabh Software offers advanced analytics solutions that enable food aggregators, cloud kitchens, and other businesses in this domain to build a sustainable ecosystem.

We create and implement a trip state model into the on-demand delivery applications. And, with that, businesses can better address the below scenarios during the delivery phase:

1. An early dispatch that makes a delivery-partner wait while the food is being prepared
2. A late dispatch leading to the food item losing its freshness and resulting in an unpleasant customer experience

Below are the three actionable elements that we put to practice around food delivery time prediction and to optimize the workforce involved while developing the trip state model.

We implement an ML-based architecture that helps with:

• Collection of motion data from sensors like accelerometers and gyroscopes from delivery partner’s mobile device
• Recognizing activities with labels like driving, cycling, walking, running, tilting, still, and the unknown using APIs

To feed the trip-level data into the model, we assemble the dataset as suggested below:
(restaurant1, rider1, timestamp1, parked)
(restaurant1, rider1, timestamp2, waiting_at_restaurant)
(restaurant1, rider1, timestamp3, walking_to_bike)
(restaurant1, rider1, timestamp4, enroute_to_eater)

The model further analyzes activity data such as routes, parking status, vehicle type, traffic conditions, food preparation time, & delivery time, and more from the previous orders & draw useful insights.

For instance,
Break pick-up & drop-off in stages like: On a trip – walk – service time – walk back

The detailed breakdown of trip state helps predict food delivery time using Machine Learning and optimize performance across stages, including:

Service Time

• Identify the busy service time window or the expected order preparation time at the pick-up location

On Trip

• Dispatch delivery-partners depending on the vehicle type, route restrictions, order preparation time, parking spot, and other conditions
• Notify drivers for the nearest available parking slot through the mobile app interface

On The Move

• Mobile phone’s GPS location helps calculate routing specific data, with distances between the restaurant and delivery location, as well as places in between
• Direction suggestions help the riders to navigate through the shortest route between the restaurant & drop-off place easily

To conclude, our food delivery data analytics model helps businesses reduce delivery time by dividing the on-demand delivery cycle into granular stages.

Rishabh Software’s team continues to create smart solutions that cater to the varied needs of food delivery businesses. We assist them with scalable, robust, reliable architecture to improve the experience for restaurant-partners, delivery-partners, and end-customers.