{"id":112,"date":"2009-06-01T11:39:18","date_gmt":"2009-06-01T18:39:18","guid":{"rendered":"https:\/\/demand-planning.com\/?p=112"},"modified":"2009-06-01T11:39:18","modified_gmt":"2009-06-01T18:39:18","slug":"models-alone-are-not-enough-for-improving-forecasts","status":"publish","type":"post","link":"https:\/\/demand-planning.com\/2009\/06\/01\/models-alone-are-not-enough-for-improving-forecasts\/","title":{"rendered":"Models Alone are Not Enough for Improving Forecasts"},"content":{"rendered":"
\"jain-chaman_v4\"

Chaman L. Jain, Ph.D<\/p><\/div>\n

Statistical models are important, but they are not the be all and end all of forecasting. Forecasts can be further improved if:<\/p>\n

1) Data is properly analyzed and treated before using a model<\/p>\n

2) Have a process in place for obtaining data\/information\/feedback from various stakeholders, both within and outside the organization<\/p>\n

3) Have a procedure that is well established for monitoring and revising forecasts<\/p>\n

4) Set goals and metrics that are well defined for measuring performance<\/p>\n

5) Ensure that the people involved are awarded for their performance.<\/p>\n

At times, intuitive adjustments in statistical forecasts are needed to account for elements, which have a bearing on forecasts, but cannot be quantified.\u00a0 This is especially so for information that is not available at the time forecasts were generated, as well as when forecasts just dont make sense. For example, if a major competitor goes out of business or a natural disaster occurred in one of your major markets?\u00a0 This information would not be incorporated within a statistical forecast.\u00a0 Furthermore, it is not unusual for a forecaster to find certain computer generated forecasts that don’t make sense. They are either too high or too low, and the forecaster knows from his or her experience the numbers are far from reality. To take care of them, he or she has to adjust them intuitively. So, statistical models are fine, but they would never replace human judgment. They must work together.<\/p>\n

Nike, for example, in 2000 implemented a new forecasting system, i2 Technologies, where nine months later it issued a press release saying it lost $400 million because of poor forecasts attributed to the system. It badly over-forecasted their products, and under-forecasted others. In this case, you cannot just blame the forecasting system and models residing within the system? If a proper process was in place that called for monitoring forecasts every month and efforts were made to find the source of error, Nike would have detected the problem much earlier, and mitigated the damages. It appears it used the forecasting system as a black box, and took the forecasts at the face value.\u00a0 This is a dangerous approach.\u00a0 You can read more about forecasting & planning in the IBF’s Journal of Business Forecasting<\/a>.\u00a0 Your thoughts and experiences on this topic or other topics are welcome!<\/p>\n","protected":false},"excerpt":{"rendered":"

Statistical models are important, but they are not the be all and end all of forecasting. Forecasts can be further improved if: 1) Data is properly analyzed and treated before using a model 2) Have a process in place for obtaining data\/information\/feedback from various stakeholders, both within and outside the organization 3) Have a procedure […]<\/p>\n","protected":false},"author":4,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[33],"tags":[41,42,43,44,45,46],"class_list":{"0":"post-112","1":"post","2":"type-post","3":"status-publish","4":"format-standard","6":"category-forecasting-and-planning","7":"tag-data-cleansing","8":"tag-forecasting-metrics","9":"tag-forecasting-models","10":"tag-forecasting-system","11":"tag-i2","12":"tag-nike"},"_links":{"self":[{"href":"https:\/\/demand-planning.com\/wp-json\/wp\/v2\/posts\/112"}],"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\/4"}],"replies":[{"embeddable":true,"href":"https:\/\/demand-planning.com\/wp-json\/wp\/v2\/comments?post=112"}],"version-history":[{"count":0,"href":"https:\/\/demand-planning.com\/wp-json\/wp\/v2\/posts\/112\/revisions"}],"wp:attachment":[{"href":"https:\/\/demand-planning.com\/wp-json\/wp\/v2\/media?parent=112"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/demand-planning.com\/wp-json\/wp\/v2\/categories?post=112"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/demand-planning.com\/wp-json\/wp\/v2\/tags?post=112"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}