{"id":36146,"date":"2018-11-13T19:42:49","date_gmt":"2018-11-14T00:42:49","guid":{"rendered":"https:\/\/digital.hbs.edu\/platform-rctom\/submission\/doordash-and-machine-learning\/"},"modified":"2018-11-15T18:16:00","modified_gmt":"2018-11-15T23:16:00","slug":"doordash-and-machine-learning","status":"publish","type":"hck-submission","link":"https:\/\/d3.harvard.edu\/platform-rctom\/submission\/doordash-and-machine-learning\/","title":{"rendered":"DoorDash and Machine Learning"},"content":{"rendered":"

Doordash and the Food Delivery Industry<\/strong><\/p>\n

DoorDash is a triple-sided marketplace and logistics platform that enables consumers to order food on-demand from restaurant merchants that are then delivered by a fleet of delivery people, called \u201cDashers\u201d. Consumers are Doordash\u2019s B2C customers, merchants are Doordash\u2019s B2B customers, and Dashers are contractors employed by Doordash who deliver meals from merchants to consumers in their own time.<\/p>\n

The food delivery space is highly competitive[1]<\/a>. Many large players, such as GrubHub, UberEats, Yelp\u2019s Eat24 and DoorDash, vie for dominance, while the threat of new entrants from companies like Google and Amazon looms large. To stay competitive in the food delivery space, DoorDash needs to be better than its competitors at delivering against what the merchants and consumers care about. This includes selecting and surfacing the right restaurants to consumers, minimizing delivery times and making sure that the supply of Dashers meets the consumer demand for food deliveries at every given point in time. Machine learning is a critical tool that enables DoorDash to deliver against all of these key objectives.<\/p>\n

Present Uses of Machine Learning<\/strong><\/p>\n

DoorDash uses machine learning to present a personalized selection of recommended restaurants for users based on their past search and order history. Like traditional recommender systems (e.g. Amazon\u2019s product recommendations), DoorDash has to handle cases where new stores or consumers enter the system and manage the tradeoff between relevance versus diversity. Unlike traditional recommender systems, DoorDash\u2019s personalization mechanism faces the issue of sparsity: not every consumer can see every store[2]<\/a>. By showing users restaurants they are likelier to order from, DoorDash has lifted its conversion rate from search to checkout by 25%[3]<\/a>.<\/p>\n

Unsurprisingly, consumers care about both getting their food quickly and getting a reliable estimate of their delivery time. Predicting the delivery time enables DoorDash to assign their Dashers optimal routes and orders, which drives efficiency as measured by delivery time per order and Dasher utilization (the number of deliveries a Dasher performs per unit of time). Variability complicates the problem: DoorDash needs to take into account real-world conditions such as traffic and weather; Dasher supply and delivery demand are both constantly in flux; restaurants also vary in the time it takes them to prepare an order depending on how busy they are. The supply of Dashers available to make deliveries at a given point in time also affects delivery times. To ensure that the supply of Dashers meets consumers\u2019 demand for deliveries at any given point in time, DoorDash uses machine learning to forecast delivery demand.<\/p>\n

In the medium term, DoorDash is investing in autonomous robots to deliver food to customers. These robots are eventually meant to complement, rather than replace, the activity of human Dashers. While robots perform actions primarily based on reinforcement learning algorithms (separate from classic machine learning), machine learning is still a critical component of robots. Similar to autonomous vehicles, these robots run on LIDAR technology. LIDAR takes raw input from the robot\u2019s camera and sensors and uses machine learning to recognize and classify objects in the robot\u2019s surroundings. Having a fleet of autonomous delivery vehicles at hand serves as a great backup for when the supply of Dashers fails to meet consumer demand, which reduces the variability caused by inconsistent Dasher supply and dramatically cuts delivery times[4]<\/a>.<\/p>\n

Untapped Opportunities<\/strong><\/p>\n

One short-term application of machine learning that DoorDash should do is empowering their local merchants with insights from machine learning to fuel operational efficiency. DoorDash can, for example, use machine learning to help merchants forecast demand, price more optimally, increase labor utilization, reduce food waste by optimizing inventory levels and select the right menu items to offer diners.<\/p>\n

DoorDash can also use machine learning to prioritize the restaurants they choose to partner with. Consumers want to find their favorite restaurants on DoorDash. With machine learning, DoorDash can predict the types of restaurants attributes that will be popular with users and rank these restaurants accordingly.<\/p>\n

In the medium term, DoorDash should continue developing their delivery robots to make them ready for mass adoption: the robots need to be able to reliably navigate themselves to the proper destinations, interface with local merchants (who are often not very tech-savvy) and recognize the correct orders. Moreover, DoorDash’s ability to grow its user base is currently limited by its Dasher capacity: if DoorDash grows too quickly, it runs the risk of failing to fulfill every order. Autonomous robots solve this problem and allow DoorDash to aggressively grow its user base.<\/p>\n

Open Questions<\/strong><\/p>\n

One of the biggest open questions for DoorDash is an ethical one: the rise of delivery robots could eliminate the need for human Dashers, which runs counter to DoorDash\u2019s mission of economically empowering the masses. DoorDash, then, will need to think about how exactly these robots should complement, rather than replace the work that Dashers do<\/p>\n

References<\/strong><\/p>\n

[1] DoorDash. \u201cPowering 性视界 & Recommendations at DoorDash \u2013 DoorDash.\u201d DoorDash<\/i>, DoorDash, 6 July 2017, blog.doordash.com\/powering-search-recommendations-at-doordash-8310c5cfd88c.<\/p>\n

[2] DoorDash. \u201cWelcoming Our Newest Robots to the DoorDash Fleet with Marble.\u201d DoorDash<\/i>, DoorDash, 16 Aug. 2017, blog.doordash.com\/welcoming-our-newest-robots-to-the-doordash-fleet-with-marble-e752a85d6602.<\/p>\n

[3] Hirschberg, Carsten, et al. \u201cThe Changing Market for Food Delivery.\u201d McKinsey & Company<\/i>, www.mckinsey.com\/industries\/high-tech\/our-insights\/the-changing-market-for-food-delivery.<\/p>\n

[4] Marshall, Matt. \u201cDoorDash Sees 25% Lift from AI Recommendations.\u201d VentureBeat<\/i>, VentureBeat, 18 May 2017, venturebeat.com\/2017\/05\/17\/doordash-sees-25-lift-from-ai-recommendations\/.<\/p>\n

 <\/p>\n

(790 words)<\/p>\n

 <\/p>\n","protected":false},"excerpt":{"rendered":"

How DoorDash uses machine learning to power its logistics platform<\/p>\n","protected":false},"author":11147,"featured_media":0,"comment_status":"open","ping_status":"closed","template":"","categories":[346],"class_list":["post-36146","hck-submission","type-hck-submission","status-publish","hentry","category-machine-learning","hck-taxonomy-organization-doordash","hck-taxonomy-industry-technology","hck-taxonomy-country-united-states"],"connected_submission_link":"https:\/\/d3.harvard.edu\/platform-rctom\/assignment\/rc-tom-challenge-2018\/","yoast_head":"\nDoorDash and Machine Learning - Technology and Operations Management<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/d3.harvard.edu\/platform-rctom\/submission\/doordash-and-machine-learning\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"DoorDash and Machine Learning - Technology and Operations Management\" \/>\n<meta property=\"og:description\" content=\"How DoorDash uses machine learning to power its logistics platform\" \/>\n<meta property=\"og:url\" content=\"https:\/\/d3.harvard.edu\/platform-rctom\/submission\/doordash-and-machine-learning\/\" \/>\n<meta property=\"og:site_name\" content=\"Technology and Operations Management\" \/>\n<meta property=\"article:modified_time\" content=\"2018-11-15T23:16:00+00:00\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data1\" content=\"4 minutes\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\\\/\\\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\\\/\\\/d3.harvard.edu\\\/platform-rctom\\\/submission\\\/doordash-and-machine-learning\\\/\",\"url\":\"https:\\\/\\\/d3.harvard.edu\\\/platform-rctom\\\/submission\\\/doordash-and-machine-learning\\\/\",\"name\":\"DoorDash and Machine Learning - Technology and Operations Management\",\"isPartOf\":{\"@id\":\"https:\\\/\\\/d3.harvard.edu\\\/platform-rctom\\\/#website\"},\"datePublished\":\"2018-11-14T00:42:49+00:00\",\"dateModified\":\"2018-11-15T23:16:00+00:00\",\"breadcrumb\":{\"@id\":\"https:\\\/\\\/d3.harvard.edu\\\/platform-rctom\\\/submission\\\/doordash-and-machine-learning\\\/#breadcrumb\"},\"inLanguage\":\"en-US\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https:\\\/\\\/d3.harvard.edu\\\/platform-rctom\\\/submission\\\/doordash-and-machine-learning\\\/\"]}]},{\"@type\":\"BreadcrumbList\",\"@id\":\"https:\\\/\\\/d3.harvard.edu\\\/platform-rctom\\\/submission\\\/doordash-and-machine-learning\\\/#breadcrumb\",\"itemListElement\":[{\"@type\":\"ListItem\",\"position\":1,\"name\":\"Home\",\"item\":\"https:\\\/\\\/d3.harvard.edu\\\/platform-rctom\\\/\"},{\"@type\":\"ListItem\",\"position\":2,\"name\":\"Submissions\",\"item\":\"https:\\\/\\\/d3.harvard.edu\\\/platform-rctom\\\/submission\\\/\"},{\"@type\":\"ListItem\",\"position\":3,\"name\":\"DoorDash and Machine Learning\"}]},{\"@type\":\"WebSite\",\"@id\":\"https:\\\/\\\/d3.harvard.edu\\\/platform-rctom\\\/#website\",\"url\":\"https:\\\/\\\/d3.harvard.edu\\\/platform-rctom\\\/\",\"name\":\"Technology and Operations Management\",\"description\":\"MBA Student Perspectives\",\"potentialAction\":[{\"@type\":\"性视界Action\",\"target\":{\"@type\":\"EntryPoint\",\"urlTemplate\":\"https:\\\/\\\/d3.harvard.edu\\\/platform-rctom\\\/?s={search_term_string}\"},\"query-input\":{\"@type\":\"PropertyValueSpecification\",\"valueRequired\":true,\"valueName\":\"search_term_string\"}}],\"inLanguage\":\"en-US\"}]}<\/script>\n<!-- \/ Yoast SEO plugin. -->","yoast_head_json":{"title":"DoorDash and Machine Learning - Technology and Operations Management","robots":{"index":"index","follow":"follow","max-snippet":"max-snippet:-1","max-image-preview":"max-image-preview:large","max-video-preview":"max-video-preview:-1"},"canonical":"https:\/\/d3.harvard.edu\/platform-rctom\/submission\/doordash-and-machine-learning\/","og_locale":"en_US","og_type":"article","og_title":"DoorDash and Machine Learning - Technology and Operations Management","og_description":"How DoorDash uses machine learning to power its logistics platform","og_url":"https:\/\/d3.harvard.edu\/platform-rctom\/submission\/doordash-and-machine-learning\/","og_site_name":"Technology and Operations Management","article_modified_time":"2018-11-15T23:16:00+00:00","twitter_card":"summary_large_image","twitter_misc":{"Est. reading time":"4 minutes"},"schema":{"@context":"https:\/\/schema.org","@graph":[{"@type":"WebPage","@id":"https:\/\/d3.harvard.edu\/platform-rctom\/submission\/doordash-and-machine-learning\/","url":"https:\/\/d3.harvard.edu\/platform-rctom\/submission\/doordash-and-machine-learning\/","name":"DoorDash and Machine Learning - Technology and Operations Management","isPartOf":{"@id":"https:\/\/d3.harvard.edu\/platform-rctom\/#website"},"datePublished":"2018-11-14T00:42:49+00:00","dateModified":"2018-11-15T23:16:00+00:00","breadcrumb":{"@id":"https:\/\/d3.harvard.edu\/platform-rctom\/submission\/doordash-and-machine-learning\/#breadcrumb"},"inLanguage":"en-US","potentialAction":[{"@type":"ReadAction","target":["https:\/\/d3.harvard.edu\/platform-rctom\/submission\/doordash-and-machine-learning\/"]}]},{"@type":"BreadcrumbList","@id":"https:\/\/d3.harvard.edu\/platform-rctom\/submission\/doordash-and-machine-learning\/#breadcrumb","itemListElement":[{"@type":"ListItem","position":1,"name":"Home","item":"https:\/\/d3.harvard.edu\/platform-rctom\/"},{"@type":"ListItem","position":2,"name":"Submissions","item":"https:\/\/d3.harvard.edu\/platform-rctom\/submission\/"},{"@type":"ListItem","position":3,"name":"DoorDash and Machine Learning"}]},{"@type":"WebSite","@id":"https:\/\/d3.harvard.edu\/platform-rctom\/#website","url":"https:\/\/d3.harvard.edu\/platform-rctom\/","name":"Technology and Operations Management","description":"MBA Student Perspectives","potentialAction":[{"@type":"性视界Action","target":{"@type":"EntryPoint","urlTemplate":"https:\/\/d3.harvard.edu\/platform-rctom\/?s={search_term_string}"},"query-input":{"@type":"PropertyValueSpecification","valueRequired":true,"valueName":"search_term_string"}}],"inLanguage":"en-US"}]}},"_links":{"self":[{"href":"https:\/\/d3.harvard.edu\/platform-rctom\/wp-json\/wp\/v2\/hck-submission\/36146","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/d3.harvard.edu\/platform-rctom\/wp-json\/wp\/v2\/hck-submission"}],"about":[{"href":"https:\/\/d3.harvard.edu\/platform-rctom\/wp-json\/wp\/v2\/types\/hck-submission"}],"author":[{"embeddable":true,"href":"https:\/\/d3.harvard.edu\/platform-rctom\/wp-json\/wp\/v2\/users\/11147"}],"replies":[{"embeddable":true,"href":"https:\/\/d3.harvard.edu\/platform-rctom\/wp-json\/wp\/v2\/comments?post=36146"}],"version-history":[{"count":0,"href":"https:\/\/d3.harvard.edu\/platform-rctom\/wp-json\/wp\/v2\/hck-submission\/36146\/revisions"}],"wp:attachment":[{"href":"https:\/\/d3.harvard.edu\/platform-rctom\/wp-json\/wp\/v2\/media?parent=36146"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/d3.harvard.edu\/platform-rctom\/wp-json\/wp\/v2\/categories?post=36146"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}