Factors of success in predictive supply chains
Summary
Having supply chains on autopilot through predictive technologies and achieving above human performance at scale remains a distant goal for nearly all companies, except the usual suspects (e.g. Amazon). Let’s take the recent worldwide Walmart demand forecasting competition: out of the two dozen “notable” supply chain vendors as listed by, say, Gartner, none of them is making it to the Top 100 out of 900+ teams. It’s not because people come to their senses and change their mind, but merely because companies stuck with inefficient methods gradually fade away and get replaced by their competitors - the creative destruction as identified by Schumpeter. Furthermore, it’s easy to derail an initiative by focusing on the wrong challenges such as factoring the weather because it’s cool, while dismissing tail risks because planning for the worse requires nerves and fortitude. Most of the elements that played a decisive role in improving the success rate of our initiatives for predictive supply chains turned out to be basic - fundamental even - concepts, such as revisiting the very notion of what a forecast should be expected to be, and reengineering our technology and our processes from scratch, based on the new understanding as many times as necessary.
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