The evolution of product cost management tools and the state of the art (Part 2): The 2nd revolution
Summary
The impetus for this was the sobering result of a study done in the 1960s by DARPA, which produced the oft-quoted maxim that 70-80% of cost is determined and frozen in the first 20% of the product development cycle. However, in the 3-D model, the engineer was spending hours creating a design artifact that was rich with information that could be tapped for many other analyses, including cost. However, when the use case was narrowed to a specific need (product costing), a problem that had been a science project in the past became a tractable reality. In 1996, Michael Philpott of the University of Illinois and I began collaborating with John Deere, building a methodology later dubbed “feature-based costing” and led to a company first called FBC Systems, and then aPriori. The two newest PCM players have extended the stochastic methods into the world of “Big (or bigger) Data.” One is easyKost, a French company that uses the “random forest” methods to analyze large amounts of past data from ERP, PLM and other sources to determine what portion of cost is driven by each input one provides in the data set.
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