{"id":1136,"date":"2011-03-17T16:17:06","date_gmt":"2011-03-17T20:17:06","guid":{"rendered":"https:\/\/demand-planning.com\/?p=1136"},"modified":"2011-03-17T16:17:06","modified_gmt":"2011-03-17T20:17:06","slug":"proof-positive-sticking-to-the-basics-in-forecasting-and-planning-works","status":"publish","type":"post","link":"https:\/\/demand-planning.com\/2011\/03\/17\/proof-positive-sticking-to-the-basics-in-forecasting-and-planning-works\/","title":{"rendered":"Proof Positive, Sticking to the Basics in Forecasting and Planning Works"},"content":{"rendered":"<div id=\"attachment_1012\" style=\"width: 160px\" class=\"wp-caption alignleft\"><a href=\"https:\/\/demand-planning.com\/wp-content\/uploads\/2010\/11\/Lora.jpg\"><img loading=\"lazy\" decoding=\"async\" aria-describedby=\"caption-attachment-1012\" class=\"size-thumbnail wp-image-1012\" title=\"Lora Cecere - Altimeter Group\" src=\"https:\/\/demand-planning.com\/wp-content\/uploads\/2010\/11\/Lora-150x150.jpg\" alt=\"Lora Cecere - Altimeter Group\" width=\"150\" height=\"150\" \/><\/a><p id=\"caption-attachment-1012\" class=\"wp-caption-text\">Lora Cecere - Altimeter Group<\/p><\/div>\n<p><strong>Trading Places<\/strong><\/p>\n<p>The storyline is an old one. It was the theme of the 1983 American comedy titled <strong><em>Trading Places<\/em><\/strong> starring Dan Aykroyd and Eddie Murphy.\u00a0 You may remember it; It is one of my favorite funny movies in which an upper class commodities broker and a homeless street hustler switch roles when they are unknowingly made part of an elaborate bet.<\/p>\n<p>It is an ageless theme where someone less fortunate trades places with a more fortunate person.\u00a0 As a child, I was enthralled as I saw it play out in Mark Twain\u2019s <strong><em>Prince and the Pauper<\/em><\/strong> and Disney\u2019s <strong><em>Parent Trap.<\/em> <\/strong>While these are fictional stories, this week, I found a story where it happened in real life. Some of my favorite <a href=\"http:\/\/ibf.org\/conferences.cfm?fuseaction=conferenceDetail&amp;conID=307\">supply chain management leaders,<\/a> organizations that I have worked with over the past seven years, had traded places in their <a href=\"http:\/\/www.ibf.org\">organizational capabilities to forecast demand,<\/a> and it was not a conscious choice.<\/p>\n<p><strong>Prelude<\/strong><\/p>\n<p>Before I tell the story, let me share a quick perspective on <a href=\"http:\/\/ibf.org\/books.cfm?fuseaction=bookdetail&amp;bkID=98\">benchmarking<\/a> demand metrics.\u00a0 I have been working in this area for seven years.\u00a0 It is one of the hardest area of the supply chain to benchmark.\u00a0 I would like to take this opportunity to share my personal experience.<\/p>\n<p>While companies eagerly want the data that <a href=\"http:\/\/ibf.org\/books.cfm?fuseaction=bookdetail&amp;bkID=98\">benchmarking reports <\/a>provide, benchmarking forecast accuracy is tricky.\u00a0\u00a0 Why is it so hard?\u00a0 Let\u2019s start with two major reasons:<\/p>\n<ul>\n<li><strong>It\u2019s Hard to get Apples to Apples.\u00a0 It is a Fruit Basket. <\/strong>The first reason that makes it tough to benchmark forecast accuracy is that every company does it differently.\u00a0 When doing this type of work, it is essential to have an \u201cApples to Apples\u201d comparison.\u00a0 To do this, you need to look closely at five variables:\u00a0 frequency of \u00a0planning, granularity of the planning or does the organization use \u00a0monthly, weekly or daily planning, the construct of the data model, the input into the data model (E.g. shipments, orders, channel data), and the drivers of demand forecasting variance such as promotions, seasonal builds, etc. To get it right, the data must be scrubbed and normalized to ensure an \u201cApple to Apple\u201d comparison.\u00a0 As a result, companies should never accept data from self-reported sources.<\/li>\n<li><strong>The Apple doesn\u2019t fall far from the Tree.<\/strong> The second reason that benchmarking forecast accuracy can be difficult is the fact that the data can be hard to get.\u00a0 To be useful, and since market conditions change, the data set needs to represent a like peer group from the same point in time.\u00a0 Since many companies have multiple supply chains, and competitors tend to not want to share data directly with their competitor, getting the data is quite a feat.<\/li>\n<\/ul>\n<p><strong>Prologue<\/strong><\/p>\n<p>I ran into the, CEO of a major forecasting software developer earlier this week, and I was excited to find that he had just finished a project to benchmark demand data for consumer products companies to be deployed with his software solution.\u00a0 Five of the companies were organizations that I had benchmarked in 2003 and worked with over the past five years.\u00a0 While, neither he nor I can share the names of the companies, I would like to share my insights on their journey. It is truly a story of Trading Places. (In table 1, I have made up fictional names to hide the identity of the companies involved in the case study.)<\/p>\n<p><strong>The Story<\/strong><\/p>\n<p>While this story may not be as much fun as the original <strong><em>Trading Places<\/em><\/strong> movie, it is a real story where a focus on supply chain basics made a difference. In Table 1, I show the relative positions of the companies in the two analyses:<\/p>\n<p>Table 1: Comparison of Five Consumer Products Companies Forecast Accuracy<\/p>\n<table border=\"1\" cellspacing=\"0\" cellpadding=\"0\">\n<tbody>\n<tr>\n<td width=\"141\" valign=\"top\">Monthly Forecasting at an Item\/Ship From Level at a 30 Day Lag<\/td>\n<td width=\"132\" valign=\"top\">2003Relative Ranking of Forecast Accuracy<\/td>\n<td width=\"132\" valign=\"top\">2011Relative Ranking of Forecast Accuracy<\/td>\n<td width=\"137\" valign=\"top\">Technology Used<\/td>\n<td width=\"97\" valign=\"top\">Organizational:\u00a0 Regional vs. Global Focus<\/td>\n<\/tr>\n<tr>\n<td width=\"141\" valign=\"top\">A<\/td>\n<td width=\"132\" valign=\"top\">1<\/td>\n<td width=\"132\" valign=\"top\">5<\/td>\n<td width=\"137\" valign=\"top\">SAP APO<\/td>\n<td width=\"97\" valign=\"top\">Matrix Organization with a change in Reporting through Go-to-Market Teams<\/td>\n<\/tr>\n<tr>\n<td width=\"141\" valign=\"top\">B<\/td>\n<td width=\"132\" valign=\"top\">2<\/td>\n<td width=\"132\" valign=\"top\">2<\/td>\n<td width=\"137\" valign=\"top\">SAP APO<\/td>\n<td width=\"97\" valign=\"top\">Centralized with a Strong Focus on Analysis<\/td>\n<\/tr>\n<tr>\n<td width=\"141\" valign=\"top\">C<\/td>\n<td width=\"132\" valign=\"top\">3<\/td>\n<td width=\"132\" valign=\"top\">4<\/td>\n<td width=\"137\" valign=\"top\">JDA\/Manugistics<\/td>\n<td width=\"97\" valign=\"top\">Strong Regional Focus<\/td>\n<\/tr>\n<tr>\n<td width=\"141\" valign=\"top\">D<\/td>\n<td width=\"132\" valign=\"top\">4<\/td>\n<td width=\"132\" valign=\"top\">1<\/td>\n<td width=\"137\" valign=\"top\">JDA\/Manugistics<\/td>\n<td width=\"97\" valign=\"top\">Matrix\u2019d Organization with Global Reporting through Supply Chain<\/td>\n<\/tr>\n<tr>\n<td width=\"141\" valign=\"top\">E<\/td>\n<td width=\"132\" valign=\"top\">5<\/td>\n<td width=\"132\" valign=\"top\">3<\/td>\n<td width=\"137\" valign=\"top\">SAP APO<\/td>\n<td width=\"97\" valign=\"top\">Centralized with Strong IT\/Line of Business Partnering<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>&nbsp;<\/p>\n<p><strong>Progress? <\/strong>For the group of companies that were benchmarked, the average monthly Mean Absolute Percentage Error (MAPE) for a one month lag was 31% <span style=\"text-decoration: underline;\">+<\/span> 12%.\u00a0 Data eight years ago for the same companies was an average of 36% + 10% MAPE.\u00a0 What was the result?\u00a0 This group of consumer products leaders has gotten slightly; but not significantly <a href=\"http:\/\/ibf.org\/index.cfm?fuseaction=showObjects&amp;objectTypeID=301\">better in demand forecasting<\/a>.\u00a0 They have weathered the storm of market changes that could have made the forecast much worse.\u00a0 The industry has experienced major shocks including shorter product lifecycles, product proliferation, higher levels of promotions, changes in competitive behavior, and global expansion.<\/p>\n<p><strong>Better Math? <\/strong>Consistent with other industry studies over the last ten years (E.g. IBF, AMR Research, etc); the data from the present study shows that we have not made progress to <a href=\"http:\/\/ibf.org\/conferences.cfm?fuseaction=conferenceDetail&amp;conID=297\">improve forecasting &amp; planning processes<\/a> in leaps and bounds through the use of statistical models.\u00a0 In the new benchmarking study, the use of statistical modeling software improved the forecast 3% on average (on a MAPE level with a 1 month lag) when compared to a naive forecast where this month\u2019s volume planning is based on what was shipped last month.\u00a0 In the top quartile of customers, the impact was 2X or a 6% improvement in MAPE.\u00a0 A fact that was consistent in both studies showed that when the forecasting group reports to sales, the forecast bias is higher.<\/p>\n<p>What is a 6% improvement in <a href=\"http:\/\/ibf.org\/index.cfm?fuseaction=showObjects&amp;objectTypeID=301\">forecast accuracy <\/a>worth?\u00a0 Based on AMR Research correlations, a 6% forecast improvement could improve the perfect order by 10% and deliver a 10-15% reduction in inventory. Slow moving items on the tail of the supply chain are most greatly impacted by this.\u00a0 Unfortunately, most companies let their supply chain tail whip them around.<\/p>\n<p><strong>It doesn\u2019t just happen.\u00a0 Basics matter. <\/strong>For me, the interesting story can be found beneath \u00a0the data. I am referring to the switch in position of the players over the course of the past eight years.\u00a0 In this time period, the best in Class Company from 2003 became the worst performer and two lowest performers propelled themselves upward.\u00a0 As I thought about why, and recounted my many experiences with these companies, several ideas came to mind:<\/p>\n<ul>\n<li><strong>Moving down. <\/strong>The company that showed the worst performance in current benchmarking and the best performance in 2003 had a very high bias.\u00a0 Why do you think this is the case?\u00a0 The company made a decision shortly after the benchmarking in 2003 to have the forecasting group report through sales where there was a pervasive belief in the organization that if the company over-forecasted that sales would be higher.\u00a0 This decision increased bias and cast a cloud over the forecasting &amp; planning process.\u00a0 The lack of a \u201ctrue North\u201d in the organization became a stumbling block to <a href=\"http:\/\/ibf.org\/index.cfm?fuseaction=showObjects&amp;objectTypeID=301\">improving forecast accuracy.<\/a><\/li>\n<li><strong>Moving up.<\/strong> The companies that moved up in the analysis, focused hard on the basics. This included efforts to clean data, frequently tune supply chain planning software, a strong corporate demand planning team that reports through supply chain and the use of the statistics.<\/li>\n<\/ul>\n<p><strong>Thoughts on tactical forecasting: <\/strong>While technology vendors like to brag that the use of their technology will make a difference in supply chain leadership, the data here is inconclusive to that point.\u00a0 Instead, what made a difference in relative position was the process, data, and organizational reporting.\u00a0 I know this may \u00a0not be the sexy stuff, but the basics matter.<\/p>\n<p><strong>Wrapping it Up<\/strong><\/p>\n<p>I commend this software developer for spending the energy and the manpower to benchmark their client base.\u00a0 This type of commitment to ones client base differentiates and creates long-term relationships.\u00a0 It is my hope that this type of analysis will be able to be part of continuous efforts for supply chain leaders.<\/p>\n<p>I look forward to sharing these journeys and many other lessons that I have learned from my experience at IBF\u2019s Demand Planning &amp; Forecasting: Best Practices Conference w\/ Demand Management Forum in Dallas, Texas USA this coming May 2011.<\/p>\n<p>Please let me know your thoughts!<\/p>\n<p>Lora Cecere,<br \/>\nPartner<br \/>\n<strong>Altimeter Group<\/strong><\/p>\n<p style=\"text-align: center;\"><span style=\"color: #ff0000;\"><strong>Hear Lora Speak At: <\/strong><\/span><\/p>\n<p style=\"text-align: center;\"><strong><a href=\"http:\/\/ibf.org\/conferences.cfm?fuseaction=conferenceDetail&amp;conID=307\"><img loading=\"lazy\" decoding=\"async\" class=\"size-full wp-image-1140 aligncenter\" title=\"dallas_banner\" src=\"https:\/\/demand-planning.com\/wp-content\/uploads\/2011\/03\/dallas_banner.jpg\" alt=\"\" width=\"448\" height=\"191\" \/><\/a><\/strong><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Trading Places The storyline is an old one. It was the theme of the 1983 American comedy titled Trading Places starring Dan Aykroyd and Eddie Murphy.\u00a0 You may remember it; It is one of my favorite funny movies in which an upper class commodities broker and a homeless street hustler switch roles when they are [&hellip;]<\/p>\n","protected":false},"author":448,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[33],"tags":[213,60,61,62,64,65,66,145,34,67,39,69,70,35,42,43,44,36,48,81,37,73,74,38],"class_list":{"0":"post-1136","1":"post","2":"type-post","3":"status-publish","4":"format-standard","6":"category-forecasting-and-planning","7":"tag-benchmarking-forecast-accuracy","8":"tag-best-practices","9":"tag-business-forecasting","10":"tag-collaborative-forecasting","11":"tag-demand-forecast","12":"tag-demand-forecasting","13":"tag-demand-management","14":"tag-demand-management-forum","15":"tag-demand-planning","16":"tag-demand-planning-and-forecasting-conference","17":"tag-economic-forecasting","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\/1136"}],"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\/448"}],"replies":[{"embeddable":true,"href":"https:\/\/demand-planning.com\/wp-json\/wp\/v2\/comments?post=1136"}],"version-history":[{"count":0,"href":"https:\/\/demand-planning.com\/wp-json\/wp\/v2\/posts\/1136\/revisions"}],"wp:attachment":[{"href":"https:\/\/demand-planning.com\/wp-json\/wp\/v2\/media?parent=1136"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/demand-planning.com\/wp-json\/wp\/v2\/categories?post=1136"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/demand-planning.com\/wp-json\/wp\/v2\/tags?post=1136"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}