{"id":1406,"date":"2012-08-16T09:58:13","date_gmt":"2012-08-16T13:58:13","guid":{"rendered":"https:\/\/demand-planning.com\/?p=1406"},"modified":"2012-08-16T09:58:13","modified_gmt":"2012-08-16T13:58:13","slug":"sku-rationalization-improving-forecast-accuracy-and-profitability","status":"publish","type":"post","link":"https:\/\/demand-planning.com\/2012\/08\/16\/sku-rationalization-improving-forecast-accuracy-and-profitability\/","title":{"rendered":"SKU Rationalization: Improving Forecast Accuracy and Profitability"},"content":{"rendered":"

\"\"<\/a>IBF\u2019s LinkedIn discussion group<\/a> presently features a \u00a0lively conversation going on about SKU rationalization, a favorite topic of mine. Anthony Davidson<\/span><\/a> initiated the conversation by posting the question,\u201c\u2026what key factors should be considered in determining which SKUs should be eliminated from the mix?\u201d<\/span><\/a><\/span><\/p>\n

It is generally agreed upon that unchecked product proliferation will result in very negative consequences. This is due largely to the cost and complexity of managing an ever increasing product portfolio, and the self-c<\/span>annibalization of your own demand.<\/p>\n

As a first cut, several of the respondents suggested ranking products by gross margin dollars or percent. However, I\u2019m skeptical of financial metrics that purport to allocate costs (and compute the marginal profitability) of individual products. \u00a0As a result I prefer to stay away from those kinds of measures.<\/p>\n

Instead, a good first step is to create a Pareto chart ranking all products by unit volume or revenue.<\/p>\n

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As we can see in this example, the top 1\/3 of items generate about 90% of revenue, the middle third generates about 10% of revenue, and the bottom third generates less than 1%. A Pareto analysis of your own products should yield a similar result \u2013 the 80\/20 rule will likely apply. Two real-life examples are:<\/p>\n