{"id":1350,"date":"2012-04-20T13:54:15","date_gmt":"2012-04-20T17:54:15","guid":{"rendered":"https:\/\/demand-planning.com\/?p=1350"},"modified":"2012-04-20T13:54:15","modified_gmt":"2012-04-20T17:54:15","slug":"using-shipment-history-a-deadly-sin","status":"publish","type":"post","link":"https:\/\/demand-planning.com\/2012\/04\/20\/using-shipment-history-a-deadly-sin\/","title":{"rendered":"Using Shipment History: A Deadly Sin?"},"content":{"rendered":"<p><a href=\"https:\/\/demand-planning.com\/wp-content\/uploads\/2012\/04\/trucks.jpg\"><img loading=\"lazy\" decoding=\"async\" class=\"alignleft size-thumbnail wp-image-1351\" title=\"Shipping \" src=\"https:\/\/demand-planning.com\/wp-content\/uploads\/2012\/04\/trucks-150x150.jpg\" alt=\"\" width=\"150\" height=\"150\" \/><\/a>In his article titled \u201c<a href=\"http:\/\/view.apics-email.org\/?j=fe971570746c077477&amp;m=fe971570746d037874&amp;ls=fdfb1577746c037c75107072&amp;l=ff2a12707c62&amp;s=fe521078726300797c16&amp;jb=ff971775&amp;ju=fe5d1672776100747d16&amp;utm_source=eMail_Extra_utm_medium=eMail_utm_campaign=Extra__2012328&amp;r=0\">Seven Deadly Sins of Sales Forecasting<\/a>\u201d in the March 28 edition of <em>APICS extra<\/em>, Fred Tolbert compiled a useful list of bad practices than can worsen our forecasting, inventory management, and customer service results. I particularly liked <em>Deadly Sin #5: Senior Management Meddling<\/em>, and wrote about it on <a href=\"http:\/\/blogs.sas.com\/content\/forecasting\/2012\/03\/29\/deadly-sin-5-senior-management-meddling\/\">The Business Forecasting Deal blog<\/a>.\u00a0 However, I did have some issue with <em>Deadly Sin #1, Using Shipment History<\/em>, which we will discuss here.<\/p>\n<p>The historical \u201cdemand\u201d we feed into our <a href=\"http:\/\/www.ibf.org\/membership.cfm?fuseaction=Online_Training_Outline_detail#IBF4\">statistical forecasting<\/a> models play a role in the appropriateness of the forecasts we generate. This history should represent what our customers wanted, and when they wanted it, so any patterns of demand behavior can be projected into the future.<\/p>\n<p>We often misrepresent demand history by attributing demand to the wrong time bucket, or in the wrong quantity. Tolbert shows how easy this can be if we use shipment history to represent demand.<\/p>\n<p>Suppose you receive an order for 1000 units for delivery in July, but are unable to ship until September. If we say that Demand=0 in July (because nothing was shipped) and Demand=1000 in September (when the shipment was made), this doesn\u2019t seem right. The shipments don\u2019t seem to represent the \u201ctrue demand\u201d of the customer.<\/p>\n<p>Tolbert states, \u201cThe appropriate response is to post the 1,000 units as July history for sales forecasting purposes.\u201d But this assumes that Order = Demand, and I\u2019m not convinced this is correct. There are many situations where an order does not represent what the customer truly demands, for example:<\/p>\n<ul>\n<li>An unfillable order may be rejected by the company or cancelled by the customer (so no \u201cdemand\u201d appears in the history).<\/li>\n<li>An unfilled order may be rolled ahead into future time buckets so \u201cdemand\u201d is overstated, re-appearing in each time bucket until the order is filled or cancelled.<\/li>\n<li>If customers anticipate a shortage, they may inflate their orders in hopes of capturing a larger share of what\u2019s available so \u201cdemand\u201d appears higher than it really is.<\/li>\n<li>If customers anticipate a shortage they may withhold orders, change orders to different (substitute) products, or redirect their orders to alternative suppliers so \u201cdemand\u201d appears less than it really is.<\/li>\n<\/ul>\n<p>\u201cTrue demand\u201d is a nebulous concept that can be very difficult to capture with the data readily available to us. Unless we service our customers perfectly, in which case Orders = Shipments = Demand, then neither orders nor shipments are a perfect indicator.<\/p>\n<p>Perhaps this Deadly Sin should be restated to read \u201cAssuming you can know true demand\u201d \u2013 because you probably can\u2019t. However, as a<a href=\"http:\/\/ibf.org\/conferences.cfm?fuseaction=upcoming\"> practical matter for forecasting purposes<\/a>, it should be <em>good enough<\/em> to feed our systems with \u201cdemand history\u201d that is <em>reasonably close<\/em> to what true demand really is. When you consider that the typical SKU forecasting error is 30%, 40%, 50% or even more, does it really matter that your history is off by a few percentage points? Probably not.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>In his article titled \u201cSeven Deadly Sins of Sales Forecasting\u201d in the March 28 edition of APICS extra, Fred Tolbert compiled a useful list of bad practices than can worsen our forecasting, inventory management, and customer service results. I particularly liked Deadly Sin #5: Senior Management Meddling, and wrote about it on The Business Forecasting [&hellip;]<\/p>\n","protected":false},"author":6,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[33],"tags":[60,61,62,41,64,65,66,34,67,39,68,69,70,35,42,43,44,36,48,81,37,73,74,38],"class_list":{"0":"post-1350","1":"post","2":"type-post","3":"status-publish","4":"format-standard","6":"category-forecasting-and-planning","7":"tag-best-practices","8":"tag-business-forecasting","9":"tag-collaborative-forecasting","10":"tag-data-cleansing","11":"tag-demand-forecast","12":"tag-demand-forecasting","13":"tag-demand-management","14":"tag-demand-planning","15":"tag-demand-planning-and-forecasting-conference","16":"tag-economic-forecasting","17":"tag-executive-sop","18":"tag-forecast-accuracy","19":"tag-forecast-error","20":"tag-forecasting","21":"tag-forecasting-metrics","22":"tag-forecasting-models","23":"tag-forecasting-system","24":"tag-ibf","25":"tag-institute-of-business-forecasting-and-planning","26":"tag-inventory-management","27":"tag-sop","28":"tag-sales-operations-planning","29":"tag-sales-forecasting","30":"tag-supply-chain"},"_links":{"self":[{"href":"https:\/\/demand-planning.com\/wp-json\/wp\/v2\/posts\/1350"}],"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\/6"}],"replies":[{"embeddable":true,"href":"https:\/\/demand-planning.com\/wp-json\/wp\/v2\/comments?post=1350"}],"version-history":[{"count":0,"href":"https:\/\/demand-planning.com\/wp-json\/wp\/v2\/posts\/1350\/revisions"}],"wp:attachment":[{"href":"https:\/\/demand-planning.com\/wp-json\/wp\/v2\/media?parent=1350"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/demand-planning.com\/wp-json\/wp\/v2\/categories?post=1350"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/demand-planning.com\/wp-json\/wp\/v2\/tags?post=1350"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}