At Coca-Cola Bottling, flash memory energizes big data efforts

Coca-Cola may have once declared itself “the official soft drink of summer,” but as summer approached in 2013, clouds of uncertainty hung over its largest independent U.S. bottler. Mounting volumes of data were testing the technology in place there, challenging the company’s ability to keep Coke products on store shelves.
At the time, Charlotte, North Carolina-based Coca-Cola Bottling Co. Consolidated  COKE 1.30% had just recently begun upgrading its supply chain software. With new capabilities, the company hoped to improve its demand forecasting—specifically by narrowing the focus from warehouses down to individual customers to better predict how much of which products was needed on any given day at any particular store or vending machine.
It’s a formidable challenge. CCBCC has five production centers and 47 distribution centers and serves 11 states, mostly in the southeastern U.S. The company rolls out some 18,000 cases of beverage products every hour. Making sure the right ones get to the right places has never been a trivial matter, but the decision to narrow forecasting brought with it a gigantic leap in the amount of data that had to be processed each day, from 100,000 to 3.5 million data points—”demand forecasting units,” in supply chain speak.

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