{"id":433,"date":"2009-10-15T07:47:29","date_gmt":"2009-10-15T14:47:29","guid":{"rendered":"https:\/\/demand-planning.com\/?p=433"},"modified":"2009-10-15T07:47:29","modified_gmt":"2009-10-15T14:47:29","slug":"is-regression-causal-modeling-for-forecasting-underutilized","status":"publish","type":"post","link":"https:\/\/demand-planning.com\/2009\/10\/15\/is-regression-causal-modeling-for-forecasting-underutilized\/","title":{"rendered":"Is Regression\/ Causal Modeling for Forecasting Underutilized?"},"content":{"rendered":"
\"Kevin

Kevin Gray<\/p><\/div>\n

As readers know, we basically have two ways of doing forecasting:<\/p>\n

1. <\/strong>Extrapolating from historical trends – univariate forecasting (ie. Time Series Forecasting)<\/strong><\/p>\n

2. <\/strong>Including independent variables such as price that we believe influence movements in sales – causal modeling<\/strong> or regression modeling<\/strong><\/p>\n

Comparing the two approaches, the chief advantage of univariate forecasting is that it is simpler. When historical patterns have been very regular for a long time, the univariate approach may be good enough. But we probably will only be able to speculate about what is causing these historical patterns, and usually the data aren\u2019t that easy on us.\u00a0 Sudden jumps or declines or other breaks with the past aren\u2019t unusual. Causal modeling can help us understand the key sales drivers and a good causal model will do better at forecasting future periods.<\/p>\n

Yet, according to Institute of Business Forecasting & Planning<\/a>, IBF\u2019s benchmarking studies, fewer than 20% of organizations use causal modeling for forecasting.\u00a0 Why is this?\u00a0 The surveys did not ask why causal modeling is or is not used but my own experience suggests several reasons, including:<\/p>\n