Staying Up to Speed with Non-Stop Demand

The demand side of the supply chain continues to undergo major changes in strategies and technologies. The scale of consumer demand—and the need for both accurate forecasting and a fast response to demand—acts as the driving force. Fast response time is king of the market.

While the Internet has been given a lot of credit for fostering this change, other forces and solutions are at work.

One of these solutions centers on the scientific work of Dr. Hau Lee of Stanford University, Calif. He has developed an analytical methodology—a master algorithm or group of algorithms—that can sort out 150 variables per product. His demand chain science makes predictions that relate to the entire value chain of suppliers, plants, manufacturers, warehouses, stores, distribution centers, and most significantly, consumers. Lee’s system offers up-to-the-day sales predictions in a marketplace that has often worked from data that is weeks or months old. Such dated information is counterproductive, if not useless, in the market of fast and faster.

Dr. Lee’s work has come to rest at Nonstop Solutions, San Francisco, Calif. Homer Dunn, co-founder, along with Dr. Lee, and the chairman of Nonstop Solutions have positioned the company as an analysis provider instead of a software provider. Their statistical analysis of the demand for a vendor’s product anticipates the variations of customer behavior. The object of Nonstop’s service is to cut the cost of doing business by determining the most cost-effective ways to ship stock, how often to order stock, and how much stock to carry in any one day.

Nonstop Solutions has penetrated the healthcare and pharmaceutical industries with its demand science, according to Dunn. One customer, Longs Drug Store, uses Nonstop’s San Francisco-based servers to regularly obtain data over the Internet from hundreds of Longs terminals. This system allows Longs to free labor from unnecessary stocking and save on static inventory. The Longs system uses the Nonstop algorithm, applying it to two years of inventory data—including transportation costs, handling costs, seasonal variability, and vendor performance. As a result of this program, Longs Drug Stores reduced inventory significantly and saved millions of dollars, freeing capital for other uses.

“What differentiates Nonstop from other companies is an optimization across multiple functions,” says Dunn. “While there are good solutions that optimize transportation, warehouse handling, cross docking, automation, and inventory management, replenishment is a combination of transportation, handling, inventory, and the cost of the product. If you try to optimize any single function you are likely to sub-optimize the total.

“One of my first customers had just finished a project and had received a bonus. The company had reduced transportation costs by almost 40 percent from the year before,” Dunn recalls. “Later we found out the company had spent five times that in total cost—five times what it thought it had saved.

“What we offer is cross-functional optimization, which is complex to do,” he adds. “There is a lot of data involved. Think of a large retailer or wholesaler with tens of thousands of products flowing from 1,000 vendors to thousands of stores that are moving thousands of items a day. The target is constantly moving, with huge quantities of transactional data to handle.”

Rather than act as a consultant, recommending changes in business processes, Nonstop markets directly to corporate management. Its presentation shows how, based on public data available on the company, Nonstop can improve the balance sheet and flow of products, both inbound and outbound. Nonstop is prepared to guarantee that the customer will achieve the level of savings projected, or they don’t pay.

“One of our clients jokingly refers to us not as consultants but as resultants,” says Dunn.

Nonstop’s theory is based on Dr. Lee’s “bullwhip effect,” developed after studying Procter & Gamble, which manufactures Pampers diapers. Pampers sell consistently to demand, and 3M provides part of the raw materials. Orders from P&G were once unpredictable—huge orders would flow in, then nothing. Essentially, Lee determined, the farther away you get from the demand, the more the order variation grows—similar to looking at the end of a bullwhip in motion.

“Everyone in the chain tries to figure out what their customers will need,” says Dunn. “Adding safety stock all the way through creates a growing variability. To solve this problem, you need data on the critical needs of customers.

“We are able to tell each player in the value chain what they need to do over the next few months from data that is updated daily,” he says.

You can reach Nonstop Solutions at