{"id":474,"date":"2018-03-20T16:16:54","date_gmt":"2018-03-20T20:16:54","guid":{"rendered":"http:\/\/blog.richmond.edu\/mgmt340-03\/?p=474"},"modified":"2018-03-20T16:16:54","modified_gmt":"2018-03-20T20:16:54","slug":"forecasting-and-ai","status":"publish","type":"post","link":"https:\/\/blog.richmond.edu\/mgmt340-03\/2018\/03\/20\/forecasting-and-ai\/","title":{"rendered":"Forecasting and AI"},"content":{"rendered":"<p class=\"p1\"><span class=\"s1\"> In forecasting future demand, the most important aspect in the creation of these estimates is accuracy.<span class=\"Apple-converted-space\">\u00a0 <\/span>With such a wide range of factors contributing to potential variation, it can be difficult for companies to consider all possible variables, along with the additional issue of constantly incorporating real-time data.<span class=\"Apple-converted-space\">\u00a0 <\/span>This is where AI can play a crucial role, with the emergence of, what retail companies are calling, \u201cForecasting 2.0.\u201d<\/span><\/p>\n<p class=\"p1\"><span class=\"s1\"> In the past, traditional forecasting relied primarily on historical data, using Excel spreadsheets or other programs to find future estimates from past results.<span class=\"Apple-converted-space\">\u00a0 <\/span>A frequent issue was uncertainty with the release of new products; often times, such forecasts were based on what companies considered \u201clike\u201d items or goods.<span class=\"Apple-converted-space\">\u00a0 <\/span>This can become problematic as the calculated forecasts would be extremely limited and simplistic, with only available data factoring into the estimate.<span class=\"Apple-converted-space\">\u00a0 <\/span>Forecasting 2.0 fixes this problem, as not only is it able to process much larger amounts of data, but, by using complex machine learning algorithms, it can factor in any sort of erratic event or circumstance.<span class=\"Apple-converted-space\">\u00a0 <\/span>AI algorithms can take weather patterns, seasonality, multiple channel exposure, and more variables into consideration, and are then capable of providing multiple \u201cwhat-if\u201d scenarios depending on different inputs.<span class=\"Apple-converted-space\">\u00a0 <\/span>Also, the AI are able to analyze all old product data, and then decide which existing goods\u2019 sales are closely related enough in making forecasts of the new product.<span class=\"Apple-converted-space\">\u00a0 <\/span>Perhaps the most significant aspect about Forecasting 2.0 that makes it so dynamic is the ability of AI to constantly adapt to changes in the product mix, changes within the company, or changes in the markets.<span class=\"Apple-converted-space\">\u00a0 <\/span>In other words, the algorithms are self-learning and as new data comes in, the algorithms are able to forecast future results with greater and greater accuracy.<\/span><\/p>\n<p class=\"p1\"><span class=\"s1\"> Ultimately, this development is obviously a significant implementation for all companies forecasting demand.<span class=\"Apple-converted-space\">\u00a0 <\/span>As previously stated, the flexibility and learning nature of AI have the ability to make a range of forecasts for any sort of spontaneous event as well as the insertion of newly acquired data into the future estimates.<span class=\"Apple-converted-space\">\u00a0 <\/span>Looking at all aspects of the production process, this development would have extremely positive effects on the company.<span class=\"Apple-converted-space\">\u00a0 <\/span>There would be less chance of surpluses or shortages of products and as a result, companies would see greater profits.<span class=\"Apple-converted-space\">\u00a0 <\/span>Looking at some reasonings as to the importance of forecasts we discussed in class, greater accuracy would be correlated with better supply-chain relations and better management of the number of employee within the company or how<span class=\"Apple-converted-space\">\u00a0 <\/span>to delegate work to different products.<span class=\"Apple-converted-space\">\u00a0 <\/span>It should be noted however, that the second article related to AI notes the importance of still needing a human presence in the forecasting process.<span class=\"Apple-converted-space\">\u00a0 <\/span>Managers within the company can use these scenarios obtained from AI algorithms to perform analyses to make more educated internal decisions, better than simply relying on historical data.<span class=\"Apple-converted-space\">\u00a0 <\/span>AI is emotionless, and there are many matters within a company that deal with intangibles (or factors with no easily calculated numeric value) that an AI couldn\u2019t process wholly into algorithms or estimates.<span class=\"Apple-converted-space\">\u00a0 <\/span>Nonetheless, utilizing AI in forecasting is an extremely powerful asset that, as technology progresses further, will be more and more prevalent within companies.<span class=\"Apple-converted-space\">\u00a0 <\/span>Still, it is not a singular solution to the inevitable uncertainty that comes with forecasts, and human analysts are still needed to help make the best estimates and any resulting decisions stemming from this data.<\/span><\/p>\n<p>&nbsp;<\/p>\n<p class=\"p1\"><span class=\"s1\">Sources: <\/span><\/p>\n<p class=\"p1\"><span class=\"s2\"><a href=\"http:\/\/multichannelmerchant.com\/ecommerce\/making-sense-of-forecasting-2-0-and-the-role-of-ai\/\">http:\/\/multichannelmerchant.com\/ecommerce\/making-sense-of-forecasting-2-0-and-the-role-of-ai\/<\/a><\/span><\/p>\n<p class=\"p1\"><span class=\"s2\"><a href=\"https:\/\/www.salesforce.com\/quotable\/articles\/how-AI-is-transforming-forecasting-for-the-better\/\">https:\/\/www.salesforce.com\/quotable\/articles\/how-AI-is-transforming-forecasting-for-the-better\/<\/a><\/span><\/p>\n","protected":false},"excerpt":{"rendered":"<p>In forecasting future demand, the most important aspect in the creation of these estimates is accuracy.\u00a0 With such a wide<\/p>\n","protected":false},"author":3711,"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-474","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\/474","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\/3711"}],"replies":[{"embeddable":true,"href":"https:\/\/blog.richmond.edu\/mgmt340-03\/wp-json\/wp\/v2\/comments?post=474"}],"version-history":[{"count":0,"href":"https:\/\/blog.richmond.edu\/mgmt340-03\/wp-json\/wp\/v2\/posts\/474\/revisions"}],"wp:attachment":[{"href":"https:\/\/blog.richmond.edu\/mgmt340-03\/wp-json\/wp\/v2\/media?parent=474"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/blog.richmond.edu\/mgmt340-03\/wp-json\/wp\/v2\/categories?post=474"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/blog.richmond.edu\/mgmt340-03\/wp-json\/wp\/v2\/tags?post=474"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}