{"id":3105,"date":"2015-11-13T04:01:06","date_gmt":"2015-11-13T09:01:06","guid":{"rendered":"https:\/\/demand-planning.com\/?p=3105"},"modified":"2015-11-13T04:01:06","modified_gmt":"2015-11-13T09:01:06","slug":"promotional-forecasting-a-new-smart-approach","status":"publish","type":"post","link":"https:\/\/demand-planning.com\/2015\/11\/13\/promotional-forecasting-a-new-smart-approach\/","title":{"rendered":"Promotional forecasting: a new, smart approach"},"content":{"rendered":"<p>As we know, promotional forecasting is a challenging area for all Demand Planning professionals.<\/p>\n<p>Few factors often responsible for that challenge are:<\/p>\n<ol>\n<li>Irregular demand \u2013 as many products are very seasonal and simply not available for sale through the whole year,<\/li>\n<li>Unprecedented discounts\/ price levels \u2013 in a promotional business, we often want to surprise our customers and position our offers as the \u201cbest they ever saw\u201d<\/li>\n<li>Much bigger contribution of new product sales to total revenue vs. traditional retail models<\/li>\n<li>A very short life cycle &amp; at the same time, very high number of new products introductions each year<\/li>\n<li>On top of that \u2013 our clients are getting used to an idea that they can cherry- pick among different promotions &amp; as a result, we observe very high cross-cannibalization between these promotions even in totally different products categories<\/li>\n<\/ol>\n<p>As a result of the above, a straight forward approach to a traditional statistical forecasting at a product level doesn\u2019t bring the result we would expect, even though, most of the IT companies selling Demand Planning software, are claiming that\u00a0<em>their<\/em>\u00a0set of algorithms is able to crack the problem.<br \/>\n[bar group=&#8221;content&#8221;]<\/p>\n<p>Have you been in that situation before, still frustrated with results and looking for a new or different approach?\u00a0 Yet, at the same time, a practical and cost\u2013effective approach on how to improve your forecast accuracy levels?<\/p>\n<p>Well, during IBF\u2019s Supply Chain Forecasting &amp; Planning Conference in Amsterdam, November 18-20, 2015, I will be discussing 2 algorithms which may solve your problems, while identifying up to 30% of your forecast outliers.\u00a0 Of course, if used properly, they can ultimately help to reduce your inventory levels and improve profitability too.<\/p>\n<p>During the IBF Session you will learn:<\/p>\n<ul>\n<li>A totally new statistical approach to predict and manage forecasting outliers, using logistic regression<\/li>\n<li>A new automated approach to prioritize products and planners time, based on the nature of an error (an alternative idea vs. FAV quadrants)<\/li>\n<\/ul>\n<p>These algorithms could later be modelled using MS Excel &amp; one of the free-ware statistical packages widely available on the internet. So you will not need to buy any expensive software to test this approach at your company.<\/p>\n<p><strong>As a bonus,<\/strong>\u00a0we will discuss, what skill are exactly required to run these types of modelling and how &amp; where to find them in a market.<\/p>\n<p>I look forward to meeting you at the IBF Conference!<\/p>\n","protected":false},"excerpt":{"rendered":"<p>As we know, promotional forecasting is a challenging area for all Demand Planning professionals. Few factors often responsible for that challenge are: Irregular demand \u2013 as many products are very seasonal and simply not available for sale through the whole year, Unprecedented discounts\/ price levels \u2013 in a promotional business, we often want to surprise [&hellip;]<\/p>\n","protected":false},"author":4874,"featured_media":3109,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[33],"tags":[],"class_list":{"0":"post-3105","1":"post","2":"type-post","3":"status-publish","4":"format-standard","5":"has-post-thumbnail","7":"category-forecasting-and-planning"},"_links":{"self":[{"href":"https:\/\/demand-planning.com\/wp-json\/wp\/v2\/posts\/3105"}],"collection":[{"href":"https:\/\/demand-planning.com\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/demand-planning.com\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/demand-planning.com\/wp-json\/wp\/v2\/users\/4874"}],"replies":[{"embeddable":true,"href":"https:\/\/demand-planning.com\/wp-json\/wp\/v2\/comments?post=3105"}],"version-history":[{"count":0,"href":"https:\/\/demand-planning.com\/wp-json\/wp\/v2\/posts\/3105\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/demand-planning.com\/wp-json\/wp\/v2\/media\/3109"}],"wp:attachment":[{"href":"https:\/\/demand-planning.com\/wp-json\/wp\/v2\/media?parent=3105"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/demand-planning.com\/wp-json\/wp\/v2\/categories?post=3105"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/demand-planning.com\/wp-json\/wp\/v2\/tags?post=3105"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}