The Perils and Promise of Real-Time Data

As the demand for real-time data increases, as more and more information flows into the enterprise and its systems, the challenge of understanding and managing it grows proportionately. And sometimes, more is just too much.

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The Sock Market

International Legwear Group makes a lot of socks. As the third-largest sock-maker in the United States, ILG runs on a weekly cycle for processing orders from its customers, which range from Wal-Mart on down to Mom-and-Pop stores. And for the most part, ILG's planners and forecasters are able to satisfy their retailers' demands for socks and sync up those demands with ILG's global suppliers.

But if you know anything about the sock market, you know how difficult ILG's planners' jobs can be. "Sock sales are very dependent on the weather," says Alex Moore, ILG's CIO. "If it stays hot into the fall, our sales do not start climbing." Or, if a cold snap hits the South, sock sales will jump for as long as it lasts. "I don't care how good your software forecasting system is, you can't predict when it's going to get cold," Moore says.

The unpredictability and randomness that can throw off a company's supply chain system is called noise—whether it's an ice storm, earthquake, e.coli outbreak or terrorist attack. And what noise does is make computer systems, especially real-time systems, nervous. That, in turn, amplifies the perils of demand and inventory planning. "A nervous system is one in which you try to react to every little thing," says Moore. "If you change your plan with every thing little thing that happens, that's a bad thing."

It therefore becomes critical that companies develop some type of strategy that can filter out noise and nervousness. Overreacting to sudden and random upticks in sales can produce a deadly chain reaction in the supply chain, with each supplier downstream from the first increasing its orders and supply requirements because it wants to have enough inventory to comply with the illusory rising demand. This is called the bullwhip effect.

In 1997, Stanford's Lee cowrote the seminal article on it—"Information Distortion in a Supply Chain: The Bullwhip Effect" —and the now-famous example of the variability (and challenges) in demand planning for P&G's Pampers product remains the best-known example. "You have to be able to distinguish between noise versus a real systematic shift," cautions Lee.

The Beer Facts

The fifth business day of every month used to be a significant day for Heineken USA. On that day, employees got their first glimpse of the previous month's sales data, which provided a snapshot of how well the U.S. arm of the Dutch brewer was doing. "This company lived and died by monthly data," says Director of IT Schillat. That the business users had to wait a full month to view Heineken's key performance indicators didn't bother the staff. It's just the way the beer industry operated, they thought.

But by 2000, Schillat knew differently. Industry heavyweights Budweiser and Miller had begun investing millions in building real-time connections to their distributors. Schillat turned to Vermont Information Processing, which had already made inroads in the marketspace with its supply chain products. In less than a year Schillat could stream distributors' daily sales (prices, quantities and which retail stores the beer had been shipped to) to the business. "This was huge for us," she recalls. "All of the sudden, we had daily sales."

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