Machine Learning In The Digital Supply Chain Isn’t New

Forbes, Steve Banker

Machine learning has become hot this year and suppliers are investing research and development into using machine learning in the supply chain. But in a conversation with Michael Farlekas and John Lash – the CEO and VP of Product Marketing at E2open – I was reminded of the fact that machine learning in the supply chain is not a new technology.  Last year E2open acquired Terra Technology.  Terra has been using machine learning to power their demand management and demand sensing applications since 2004. Their customers include Procter & Gamble, Unilever, General Mills and several other global, multinational consumer goods companies.

For machine learning to work well, it needs to be a big data application.  In this case in addition to doing forecasting based on historical sales, consumer goods companies leverage other data sets such as their retail customer’s point of sale, recent shipments of products from their warehouses to their stores, the retailer’s orders, syndicated data, and store inventory. Many of these data sets are accessed daily, or even several times a day, so the dynamic nature of demand is captured to a much higher degree than traditional forecasting techniques.

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