{"id":180538,"date":"2023-03-08T13:39:28","date_gmt":"2023-03-08T05:39:28","guid":{"rendered":"https:\/\/www.grab.com\/sg\/?post_type=editorial&p=180538"},"modified":"2026-01-07T12:01:05","modified_gmt":"2026-01-07T04:01:05","slug":"cutting-down-wait-times-delivery-drivers","status":"publish","type":"editorial","link":"https:\/\/www.grab.com\/sg\/inside-grab\/stories\/cutting-down-wait-times-delivery-drivers\/","title":{"rendered":"Cutting down the time delivery-partners wait at food outlets"},"content":{"rendered":"\t\t
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It\u2019s a common sight to see a line of green jackets outside eateries at mealtimes, as our delivery-partners arrive and wait to pick up orders. But we\u2019re working to make pick-up more and more efficient for our merchant- and delivery-partners, to shorten those lines.<\/p>

From the moment a user orders a dish, our system optimises to assign the correct delivery-partner for pick-up. This doesn\u2019t simply mean assigning the nearest one to the job, because a delivery-partner waiting outside while the food isn\u2019t ready yet is a waste of time that could be used to pick up another order. Yet, we don\u2019t want a delivery-partner coming later while the food is sitting out there getting cold.<\/p>

The \u201cGoldilocks\u201d pick-up window<\/h5>

The business of getting our delivery-partners to arrive at restaurants at the right time requires our machine learning and predictive analytics engines to figure out when we should assign a delivery-partner to head towards the restaurant.<\/p>

Our engineers refer to this sweet spot the “just-in-time” window. The Grab system calculates this based on multiple factors such as the time taken for food preparation, an estimate of how long it would take to assign an order to a delivery-partner as well as the time taken for delivery-partners to get to the restaurant and pick up the order.<\/p>

Ideally, the order should be ready as soon as our delivery-partners arrive at the F&B outlet.<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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\n\t\t\t\tOur engineers call this sweet spot the \"just-in-time\" window.<\/b>\t\t\t<\/p>\n\t\t\t\t\t<\/blockquote>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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Previously, F&B merchants would only begin food preparation once a delivery-partner had been assigned to the order. But depending on the size and type of order, some merchants may need more time.<\/p>

The buffer time before allocation, which is calculated based on food preparation time, will give merchants the headstart that they need to prepare orders.\u00a0<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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Smaller orders that require less time to prepare will see a shorter buffer time before a delivery-partner is assigned.\u00a0On the other hand, there will be a longer buffer time before a delivery-partner is assigned to larger orders.<\/p>

This will ensure that delivery-partners arrive at the store as soon as the order is ready for pickup, rather than too early or late.\u00a0<\/p>

After we rolled out improvements to the system, the average wait time for delivery-partners across the region was trimmed to 5.6 minutes\u00a0in the third quarter of 2022, down from 6.4 minutes the previous quarter.<\/p>

Accurate predictions<\/h5>

But for our calculations to work, it was important for us to ensure that we are able to accurately predict food preparation times.\u00a0<\/p>

To do this, we turned to our merchants to help us gather more data on food preparation times. We introduced an in-app feature which prompts merchants to mark orders as \u201cready\u201d once food is ready for collection.\u00a0<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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The data gathered fine-tunes our system to better predict the actual time taken for food preparation across different types of orders.\u00a0<\/p>

The system also learns to differentiate orders that require a delivery-partner to come quickly from those that can wait a little longer.<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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