{"id":35995,"date":"2018-11-13T19:57:55","date_gmt":"2018-11-14T00:57:55","guid":{"rendered":"https:\/\/digital.hbs.edu\/platform-rctom\/submission\/anticipatory-shipping-retails-crystal-ball\/"},"modified":"2018-11-15T15:37:52","modified_gmt":"2018-11-15T20:37:52","slug":"anticipatory-shipping-retails-crystal-ball","status":"publish","type":"hck-submission","link":"https:\/\/d3.harvard.edu\/platform-rctom\/submission\/anticipatory-shipping-retails-crystal-ball\/","title":{"rendered":"Anticipatory shipping\u2014retail\u2019s crystal ball?"},"content":{"rendered":"<p>You\u2019ve run out of toilet paper.\u00a0 You go online to order a 12 pack of your favorite kind\u2014Charmin\u2019s ultra-soft 3-ply rolls.\u00a0 You hover over the \u201cAdd to cart\u201d button, but then you remember that you have 2 rolls stored away in another cabinet.\u00a0 Little do you know that Amazon has already begun shipping Charmin ultra-soft toilet paper to its hub in Stoughton, Massachusetts about 23 miles from your apartment in Cambridge<a href=\"#_ftn1\" name=\"_ftnref1\">[1]<\/a>.\u00a0 When you go back online in 4 days to order your toilet paper, it arrives the same day.\u00a0 Boom!\u00a0 As the customer, you\u2019re impressed that Amazon has beat its 2-day prime delivery promise, while Amazon has simultaneously managed to reduce its fulfillment and shipping costs through machine learning<a href=\"#_ftn2\" name=\"_ftnref2\">[2]<\/a>.<\/p>\n<p>Amazon and a number of other online retailers are using machine learning to predict consumer demand, in hopes of more accurately forecasting demand and, in the case of Amazon, reducing costs<a href=\"#_ftn3\" name=\"_ftnref3\">[3]<\/a>.\u00a0 As Amazon continues to challenge brick-and-mortar retailers by cutting prices for consumers, it faces strong pressures to simultaneously reduce its cost-to-serve and maintain its margins.\u00a0 For instance, the company\u2019s logistics costs have skyrocketed in recent years.\u00a0 In 2017, the company\u2019s fulfillment and shipping expenses amounted to $25.2 billion and $21.7 billion respectively<a href=\"#_ftn4\" name=\"_ftnref4\">[4]<\/a>.\u00a0 In total, that represents 26.4% of Amazon\u2019s net retail sales and has been growing since 2009 when the figure stood at just above 15%.\u00a0 Rising fulfillment and shipping costs have become such a critical risk factor for Amazon that in its 10-K annual filing, it notes:<\/p>\n<p>\u201cWe rely on a limited number of shipping companies to deliver inventory to us and completed orders to our customers. If we are not able to negotiate acceptable terms with these companies or they experience performance problems or other difficulties, it could negatively impact our operating results and customer experience.\u201d<a href=\"#_ftn5\" name=\"_ftnref5\">[5]<\/a><\/p>\n<p>It has become evident that this is a major challenge that must be dealt with rapidly.\u00a0 Tackling the rise in fulfillment and shipping costs of this magnitude will enable prices to continue to be held as low as they have been for consumers.<\/p>\n<p>In the short-term, Amazon is investing in what it calls \u201canticipatory shipping\u201d to reduce both its fulfillment and shipping costs.\u00a0 Data from a customer\u2019s previous purchases; items added to carts, but not yet purchased; items in the wishlist, and even data from a customer\u2019s cursor movements are used in machine learning to predict what the customer will likely order<a href=\"#_ftn6\" name=\"_ftnref6\">[6]<\/a>.\u00a0 This allows Amazon to use standard shipping, which is typically a third of the cost of 2-day expedited shipping, to send such items to its hub closest to the customer.\u00a0 In this way, once the customer actually places his\/her order for the items, the items are much closer and less expensive to deliver (i.e., ground vs. air freight delivery).<\/p>\n<p>Looking beyond the next two years, Amazon must address the biggest weakness in its value proposition in comparison to traditional brick-and-motor, which continues to be delivery time; the former typically takes 2 days to ship, while the latter works almost instantaneously.\u00a0 In order to be more convenient and as quick as its brick-and-mortar counterparts, Amazon must be able to deliver most products within hours and, eventually, minutes from the time an order is placed.\u00a0 To do so, it will need to augment its anticipatory shipping machine learning algorithm with its own delivery fleet that is able to transport products for short 25-50 mile distances, allowing last-mile deliveries to be made from its hubs to customers within minutes.\u00a0 Amazon has already taken initiative to bridge its speed gap relative to its brick-and-motor counterparts by testing drones and smart door locks inside customer homes<a href=\"#_ftn7\" name=\"_ftnref7\">[7]<\/a>.\u00a0 But these technologies have to be paired with predictive analytic tools and anticipatory shipping to reduce delivery times to minutes<a href=\"#_ftn8\" name=\"_ftnref8\">[8]<\/a>.\u00a0 In this way, the Charmin ultra-soft toilet paper that would have typically taken 2-days to be delivered, can be at your door within 60 minutes because it can be dispatched from Amazon\u2019s hub in Stoughton, Massachusetts.<\/p>\n<p>What does machine learning and anticipatory shipping ultimately mean for the customer?\u00a0 At least in theory, it should mean they\u2019ll receive items faster, but will it also mean reduced shipping costs for Amazon\u2019s customers?<\/p>\n<p>As for Amazon, will it have to go a step beyond simply delivering to a hub closest to its customer, and actually ship to its customer the products that it anticipates he\/she will want before a purchase is made?\u00a0 Will it have to risk occasionally leaving customers with free products they didn\u2019t pay for?\u00a0 How far does Amazon need to go to truly eliminate the need for its brick-and-mortar counterparts?<\/p>\n<p>[797 words]<\/p>\n<p><a href=\"#_ftnref1\" name=\"_ftn1\">[1]<\/a> Alvalara TrustFile. (2018).\u00a0<em>Amazon Fulfillment Center Locations | Avalara TrustFile<\/em>. [online] Available at: https:\/\/www.avalara.com\/trustfile\/en\/resources\/amazon-warehouse-locations.html [Accessed 14 Nov. 2018].<\/p>\n<p><a href=\"#_ftnref2\" name=\"_ftn2\">[2]<\/a> Boone, T. and Ganeshan, R. (2013). Exploratory analysis of free shipping policies of online retailers.\u00a0<em>International Journal of Production Economics<\/em>, 143(2), pp.627-632.<\/p>\n<p><a href=\"#_ftnref3\" name=\"_ftn3\">[3]<\/a> Ulanoff, L. (2018).\u00a0<em>Amazon Knows What You Want Before You Buy It<\/em>. [online] Mashable. Available at: https:\/\/mashable.com\/2014\/01\/21\/amazon-anticipatory-shipping-patent\/#Ryy4twKmRiqb [Accessed 14 Nov. 2018].<\/p>\n<p><a href=\"#_ftnref4\" name=\"_ftn4\">[4]<\/a> Bernard, Z. (2018).\u00a0<em>Amazon is spending more and more on shipping out your orders<\/em>. [online] Business Insider. Available at: https:\/\/www.businessinsider.com\/amazons-logistics-costs-are-growing-really-fast-charts-2018-2 [Accessed 14 Nov. 2018].<\/p>\n<p><a href=\"#_ftnref5\" name=\"_ftn5\">[5]<\/a> Services.corporate-ir.net. (2018).\u00a0<em>SEC FILING | Amazon.com Inc Form 10-K<\/em>. [online] Available at: http:\/\/services.corporate-ir.net\/SEC.Enhanced\/SecCapsule.aspx?c=97664&amp;fid=15414896 [Accessed 14 Nov. 2018].<\/p>\n<p><a href=\"#_ftnref6\" name=\"_ftn6\">[6]<\/a> Nichols, M. (2018).\u00a0<em>Amazon Wants to Use Predictive Analytics to Offer Anticipatory Shipping<\/em>. [online] SmartData Collective. Available at: https:\/\/www.smartdatacollective.com\/amazon-wants-predictive-analytics-offer-anticipatory-shipping\/ [Accessed 14 Nov. 2018].<\/p>\n<p><a href=\"#_ftnref7\" name=\"_ftn7\">[7]<\/a> Yu, D., Cheong, T. and Sun, D. (2017). Impact of supply chain power and drop-shipping on a manufacturer\u2019s optimal distribution channel strategy.\u00a0<em>European Journal of Operational Research<\/em>, 259(2), pp.554-563.<\/p>\n<p><a href=\"#_ftnref8\" name=\"_ftn8\">[8]<\/a> Bishop, T. (2018).\u00a0<em>Jeff Bezos explains Amazon\u2019s artificial intelligence and machine learning strategy<\/em>. [online] GeekWire. Available at: https:\/\/www.geekwire.com\/2017\/jeff-bezos-explains-amazons-artificial-intelligence-machine-learning-strategy\/ [Accessed 14 Nov. 2018].<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Can products be on their way to you even before you think of ordering them?  Can a company accurately anticipate your order and ship before you even place it?  Amazon and other online retailers are investing in machine learning to more accurately forecast consumer demand and reduce fulfillment and shipping costs<\/p>\n","protected":false},"author":11597,"featured_media":35996,"comment_status":"open","ping_status":"closed","template":"","categories":[5079,200,893,49,2156,346],"class_list":["post-35995","hck-submission","type-hck-submission","status-publish","has-post-thumbnail","hentry","category-costs","category-delivery","category-department-store","category-e-commerce","category-machine","category-machine-learning","hck-taxonomy-organization-amazon","hck-taxonomy-industry-retail","hck-taxonomy-country-united-states"],"connected_submission_link":"https:\/\/d3.harvard.edu\/platform-rctom\/assignment\/rc-tom-challenge-2018\/","yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.3 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Anticipatory shipping\u2014retail\u2019s crystal ball? - 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\/anticipatory-shipping-retails-crystal-ball\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Anticipatory shipping\u2014retail\u2019s crystal ball? - Technology and Operations Management\" \/>\n<meta property=\"og:description\" content=\"Can products be on their way to you even before you think of ordering them? 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