Real-Time Data, Analytics Drive Better Forecasts

Manufacturers and retailers lead the way in using up-to-the-minute point-of-sale data to avoid stock-outs and overproduction.
Once you've established that demand data is available, start with simple reporting and alerting. It isn't hard to detect low stock levels and send out alerts, but some of the specialized apps get into sophisticated statistical and predictive analytics. Before retailers or manufacturers get carried away, though, they must make sure they know what problems they're trying to solve--and whether there are corrective actions they can take.

If the problem is with the retailer, a supplier has to know whether it can change the business process or get the retailer to act differently. "If you can't address the root cause of the problem, you might as well not bother with the analysis," says Cedric Guyot, VP of marketing at Retail Solutions.

Executing better on promotions and avoiding stock-outs are the big opportunities, because they increase revenue without hiking the fixed costs of manufacturing, distributing, and advertising the goods. But that's where the science of analysis and the art of experience come in. With promotions, the challenge is recognizing when retailers aren't executing on plan, and with stock-outs, the trick is avoiding false alerts that can compound stocking errors (see 5 Dos And Don'ts For Using Demand Data).

Demand-signal analysis may provide earlier and more granular insights, but the problems manufacturers and retailers are trying to solve have been around a long time. It's essential to have people steeped in industry knowledge leading this effort. "The companies that are in the best shape have someone from the line of business who owns the data and who's working on how to transition to using point-of-sale information," says AMR's Cecere.

Phrases like "real-time data" and "analytic insight" have been kicking around in lots of industries in recent years, but they're just buzzwords until somebody figures out how to use emerging technology trends to solve a real business problem.

In this economy, the hazards of getting demand wrong multiply. The risk is clear in a consumer who clips coupons and waits for sales, only to give the store brand a try when the name-brand manufacturer's out of stock, possibly switching to the cheaper brand for good.

But that kind of scenario can play out in any number of industries, making demand-based management a prescription for these times. With consumption patterns shifting, sometimes wildly, the predictive value of historical information is suspect.

Health care companies need to manage their bed space not just based on admissions, but on the conditions patients have as they come in and their progress as they're treated. Banks can train staff on new products in close line with demand, rather than en masse. There's a serious risk of procuring, staffing, and training out of sync with the real market--whether for raw steel, pickups, railcars, energy, hospital beds, airline seats, or loans. And it's highest for those still using rear-view-mirror methods to anticipate demand.