{"id":2078,"date":"2013-09-10T09:36:29","date_gmt":"2013-09-10T13:36:29","guid":{"rendered":"https:\/\/demand-planning.com\/?p=2078"},"modified":"2013-09-10T09:36:29","modified_gmt":"2013-09-10T13:36:29","slug":"simple-tools-for-evaluating-the-forecasting-process","status":"publish","type":"post","link":"https:\/\/demand-planning.com\/2013\/09\/10\/simple-tools-for-evaluating-the-forecasting-process\/","title":{"rendered":"Simple Tools for Evaluating the Forecasting Process"},"content":{"rendered":"
\"Michael<\/a>

Michael Gilliland<\/p><\/div>\n

In the movie Slingblade<\/i>, there is a great scene where they bring an apparently broken lawnmower to Karl (Billy Bob Thornton): \u201cKarl, see if you can figure out what\u2019s wrong with this. It won\u2019t crank up and everything seems to be put together right.\u201d \u00a0After a brief inspection, Karl responds \u201cIt ain\u2019t got no gas in it.\u201d<\/p>\n

Sometimes it\u2019s the simplest things that are most effective. And this certainly holds true in business forecasting.<\/strong><\/a><\/p>\n

There are several easy to understand, and easy to implement tools for evaluating the forecasting process. These tools utilize data you should already have.<\/p>\n

One of these useful tools is the \u201ccomet chart,\u201d which illustrates the relationship between demand volatility and forecast accuracy. As you would expect, when products have smooth and stable demand, we tend to forecast them more accurately than products with wild, erratic demand.<\/p>\n

By creating a scatterplot of all products, showing their volatility and the achieved forecast accuracy (or error), you get a quick sense of the magnitude of your forecasting challenge. While volatility is not a perfect indicator of forecastability (there are volatile (yet well behaved) patterns that can be forecast accurately), it is of practical value in assessing your organization\u2019s performance.<\/p>\n

In a \u201cforecastability matrix,\u201d products are segmented into categories that are more (or less) forecastable, and more (or less) profitable. Since forecasting resources are always limited (no organization can afford an army of forecast analysts), forecasters can deliver more value by first focusing on those items that are more profitable and more forecastable. Those items that are difficult to forecast and have little contribution to profits have the lowest priority, and should receive little or no attention from the forecasting staff.<\/p>\n

Forecast Value Added (FVA) analysis is another simple tool that has gained wide industry adoption. FVA looks at each step in the forecasting <\/strong><\/a>process, to make sure forecasting activities are \u201cadding value\u201d by making the forecast more accurate and less biased.<\/p>\n

FVA identifies the waste and worst practices in a forecasting process. Many organizations have found things they are doing that just made the forecast worse! Such non- (or negative-) value adding steps can be eliminated, resulting in more effective use of company resources, and potentially more accurate forecasts.<\/p>\n

Finally, a new approach for determining the \u201cavoidability\u201d of forecast error seems to be showing promise. This concept was proposed by Steve Morlidge, forecasting thought leader from the UK. \u00a0This approach seeks to determine the smallest amount of forecast error you can reasonably expect. This is another easy to apply tool, that can save you a lot of time by knowing when to stop trying to improve a forecast that has reached its limit of accuracy.<\/p>\n

Michael Gilliland<\/strong>
\n Product Marketing Manager – Forecasting<\/strong>
\n SAS Institute<\/strong><\/p>\n

Hear Michael speak on simple, yet effective tools for evaluating the forecasting process at IBF’s\u00a0Business Planning & Forecasting: Best Practices Conference<\/a><\/strong> in Orlando Florida, November 4-6, 2013.<\/p>\n

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In the movie Slingblade, there is a great scene where they bring an apparently broken lawnmower to Karl (Billy Bob Thornton): \u201cKarl, see if you can figure out what\u2019s wrong with this. It won\u2019t crank up and everything seems to be put together right.\u201d \u00a0After a brief inspection, Karl responds \u201cIt ain\u2019t got no gas […]<\/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":[82,271,263,62,41,65,66,34,68,273,35,133,36,236,37,73,74,270,258,38],"class_list":{"0":"post-2078","1":"post","2":"type-post","3":"status-publish","4":"format-standard","6":"category-forecasting-and-planning","7":"tag-apics","8":"tag-apo","9":"tag-certification","10":"tag-collaborative-forecasting","11":"tag-data-cleansing","12":"tag-demand-forecasting","13":"tag-demand-management","14":"tag-demand-planning","15":"tag-executive-sop","16":"tag-forecast-value-added","17":"tag-forecasting","18":"tag-fva","19":"tag-ibf","20":"tag-metrics","21":"tag-sop","22":"tag-sales-operations-planning","23":"tag-sales-forecasting","24":"tag-sap","25":"tag-siop","26":"tag-supply-chain"},"_links":{"self":[{"href":"https:\/\/demand-planning.com\/wp-json\/wp\/v2\/posts\/2078"}],"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=2078"}],"version-history":[{"count":0,"href":"https:\/\/demand-planning.com\/wp-json\/wp\/v2\/posts\/2078\/revisions"}],"wp:attachment":[{"href":"https:\/\/demand-planning.com\/wp-json\/wp\/v2\/media?parent=2078"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/demand-planning.com\/wp-json\/wp\/v2\/categories?post=2078"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/demand-planning.com\/wp-json\/wp\/v2\/tags?post=2078"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}