But in a conversation with Michael Farlekas and John Lash, the CEO and vice president 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.

E2open also acquired Orchestro last year. This is a demand signal repository solution which harmonizes retail Point of Sale (POS), syndicated, internal ERP, and 3rd party data into a common view of demand. In other words, the Orchestro solution makes it much easier to access demand sensing retail data sets.

Machine learning for demand management works this way. The engine is making many forecasts simultaneously in different planning horizons. So, there can be a forecast for demand for liquid detergent in a 100-ounce container to the Wal-Mart store in Tuscaloosa tomorrow, one week out, and one month out. There can also be a forecast for how much of that detergent will be needed at the distribution center in Cullman, Alabama tomorrow, one week, and one month from now. Other forecasts are being done for other big retail customers and channels.

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