Intelligent Enterprise

In the perfume business, it's new products, like last year's U.S. launch of Kate, a fragrance Coty branded for supermodel Kate Moss, that can make or break a company's year. But big hits also can lead to big problems.

When a product takes off, Coty must respond quickly to keep shelves full. But its ability to ramp up is dependent upon glass, packaging, and other suppliers."If we can't meet demand ... it annoys the retailers, the consumers lose interest, and we lose sales," says Dave Berry, CIO at Coty Fragrance, whose other brands include Jennifer Lopez, Kenneth Cole, and Vera Wang.

Empty shelves are the scourge of manufacturing and retail. Just look at the annual shortages of the Christmas season's hottest toys or the rain checks stores must write regularly on sale items. At any given time, 7% of all U.S. retail products are out of stock, says AMR Research analyst Lora Cecere. Goods on promotion are out of stock more than 15% of the time.

That's why manufacturers and retailers are pushing for the next breakthroughs in demand forecasting, what has emerged as the discipline of "demand-signal management." Instead of just relying on internal data such as order and shipment records, manufacturers are analyzing weekly and even daily point-of-sale data from retailers so they can better see what's selling where. This sort of timely, detailed data lets manufacturers spot trends much sooner by region, product, retailer, and even individual store.

Predicting demand is particularly hard in a recession. Just last week, home improvement chains Lowe's and Home Depot came out with falling sales, forcing Lowe's to rein back its expansion plans. In November, after same-store sales fell 7.6% in one month, Best Buy CEO Brad Anderson described "rapid, seismic changes in consumer behavior." Intel isn't even forecasting revenue this quarter because global economic uncertainty makes it "particularly difficult to predict product demand."

While the need for demand management is playing out most dramatically among manufacturers and retailers, its use is spreading to other industries. Raw materials suppliers and logistics companies are applying it at the beginning of the supply chain, monitoring real-time consumption and inventory data at manufacturing plants rather than relying solely on orders. Utilities are doing finer-grained analysis of consumption patterns by tapping gas and electric meter data previously used just for billing. Even health care providers are exploring the use of detailed, real-time patient information rather than just admissions stats to better forecast occupancy levels and set requisite staffing.

But it's in retail and consumer goods where such demand analysis could quickly play a much more important role in separating winners from losers. In an economic downturn, people buy more based on discounts and promotions, so stock-outs on advertised goods could be even costlier to companies. The most sophisticated users of demand data stand to gain a competitive edge through the recession and into the upturn.

Handling demand-signal data presents the same problems real-time data causes in any industry: how to access and integrate high volumes of data, and then combine and analyze it alongside historical information.

With the advent of highly scalable data warehouses, low-latency integration techniques, and faster, deeper query and analysis capabilities, the technology is finally here, at a price most can afford. And with easier-to-use business intelligence tools, manufacturers and retailers are pushing analytic tools into the hands of front-line decision makers, most often field sales and marketing people involved in planning, merchandising, and supply chain management.

The payoff from early efforts by Coty, Goodyear, and Kimberly-Clark has been more accurate forecasting, higher on-shelf availability, and more effective promotions. With faster and more detailed insight into demand, manufacturers as a whole can ratchet up revenue by 2% to 7%, says AMR's Cecere.

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