{"id":478,"date":"2018-03-20T16:50:55","date_gmt":"2018-03-20T20:50:55","guid":{"rendered":"http:\/\/blog.richmond.edu\/mgmt340-03\/?p=478"},"modified":"2018-03-20T16:50:55","modified_gmt":"2018-03-20T20:50:55","slug":"disney-uses-big-data-to-forecast","status":"publish","type":"post","link":"https:\/\/blog.richmond.edu\/mgmt340-03\/2018\/03\/20\/disney-uses-big-data-to-forecast\/","title":{"rendered":"Disney Uses Big Data to Forecast"},"content":{"rendered":"

Having just gotten back from spring break in Orlando, Florida, I thought it would be nice to reminisce on one of the\u00a0<\/span>\"\"most memorable places I visited while there: Disney World. Specifically, my friends and I went to the most popular park, Magic Kingdom, and experienced the great anticipation of the spring break rush. Magic Kingdom was ready for us, through the use of forecasting techniques, analysis, and experience. It\u2019s well known that the most important factor most people think about when going to Disney World is the season and how many other people will most likely be there. Although there are always people going, whether it be people going in off-season or local Florida residents, the demand is mostly seasonal. The highest peak times are definitely in the summer and near the winter holidays such as Christmas.<\/p>\n

As discussed in class, forecasting plays a strategic role in supply chain management, human resources, and capacity. These components are key to providing the products and experiences that have made Disney World as successful and alluring as it has been. The article discusses the intelligent method which Disney uses \u201cbig data\u201d and data analytics to understand trends and predict visitor patterns. Disney employed the use of Magic Bands wristbands as a part of the \u201cMy Magic Plus Program\u201d that acts as hotel keys, credit cards, tickets, and more. User-friendly and kid appealing, these bands are a simple way to keep track of everything in one device on your wrist. Moreover, it gathers large amounts of data for Disney analysts and managers to be able to forecast and anticipate demands. One of the biggest challenges Disney faces is how to minimize wait times for rides and attractio<\/span>ns. Through data collection, decisions are able to be made about adding staff or attracting guests to other attractions within parks to ease wait times. Big data also opens up possibilities to how Disney can make visits more personable and guest-centric. Future possibilities could entail families pre-ordering a meal at a restaurant, which the system could alert wait staff and not only instantly deliver the meal, but have the staff greet the family by name. Another potential could include the system compensating guests if their wait time was too long with a voucher, or a Disney character personally greeting children based on location and scheduled times.<\/p>\n

In this way, Disney employs strategic approaches and has room to grow in regards to innovation. The company is able to forecast strategi<\/span>cally to accommodate and adapt to different seasonal arrivals of guests, as well as internally forecast to cater to daily operations and services within the parks. Anticipation through data analytics is paramount to allowing the parks to optimize guest experience and create that unique Disney experience everyone wishes.<\/p>\n

What are your thoughts? What other aspects could Disney use forecasting approaches to improve? Do you think the big data innovations are too lofty to achieve with the grand amount of people that visit?<\/span><\/p>\n

https:\/\/www.forbes.com\/sites\/bernardmarr\/2017\/08\/24\/disney-uses-big-data-iot-and-machine-learning-to-boost-customer-experience\/#b3d083533876<\/p>\n","protected":false},"excerpt":{"rendered":"

Having just gotten back from spring break in Orlando, Florida, I thought it would be nice to reminisce on one<\/p>\n","protected":false},"author":3718,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"colormag_page_layout":"default_layout","footnotes":""},"categories":[71164],"tags":[],"class_list":["post-478","post","type-post","status-publish","format-standard","hentry","category-forecasting-managing-sc-inventory"],"jetpack_featured_media_url":"","_links":{"self":[{"href":"https:\/\/blog.richmond.edu\/mgmt340-03\/wp-json\/wp\/v2\/posts\/478","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/blog.richmond.edu\/mgmt340-03\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/blog.richmond.edu\/mgmt340-03\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/blog.richmond.edu\/mgmt340-03\/wp-json\/wp\/v2\/users\/3718"}],"replies":[{"embeddable":true,"href":"https:\/\/blog.richmond.edu\/mgmt340-03\/wp-json\/wp\/v2\/comments?post=478"}],"version-history":[{"count":0,"href":"https:\/\/blog.richmond.edu\/mgmt340-03\/wp-json\/wp\/v2\/posts\/478\/revisions"}],"wp:attachment":[{"href":"https:\/\/blog.richmond.edu\/mgmt340-03\/wp-json\/wp\/v2\/media?parent=478"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/blog.richmond.edu\/mgmt340-03\/wp-json\/wp\/v2\/categories?post=478"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/blog.richmond.edu\/mgmt340-03\/wp-json\/wp\/v2\/tags?post=478"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}