Today, when you walk into a car dealer and order a new automobile, you pay the same price and get the same wait for delivery as every other customer. But in the future, as Tava Olsen sees it, instead you’ll select your price and delivery date from a dynamic menu of lead-times and prices, where you can pay more for quick delivery or get a better price for waiting.
While such options benefit the customer, they also pay bottom-line benefits for the retailer and manufacturer, says Olsen, associate professor of operations and manufacturing at Washington University’s Olin School of Business. To help companies reap those benefits, she’s engaged in groundbreaking theoretical research funded by a National Science Foundation (NSF) grant to tell them just how to do it.
Enabling lean production
“Dynamic control lets the retailer charge higher prices when capacity is tight and lower prices when there is little backlog,” says Olsen. “Additionally, it allows the manufacturer to expedite delivery, smooth demand, and create a stable demand pattern, which is the basis of lean production.” As a result an automaker, for example, can make cars to order instead of having thousands sitting on dealer lots, says Olsen.
Olsen’s research—now a year into the three-year grant—investigates how dynamic menus should be chosen and provides mathematical models to implement them. She heads a research team for the $360,000 project, co-authored by Hyun-soo Ahn, assistant professor of operations management at the University of Michigan, Ann Arbor.
“Our main research objective is to assess the efficacy of using dynamic operational strategies such as dynamic pricing, real-time delivery-time quotation, flexible inventory, subscriptions and more,” says Olsen. “These strategies can help a business’s long-run profit and its ability to attract and retain customers.”
Their initial approach is to study basic models that effectively capture the internal benefits of dynamic operational strategies, such as revenue improvement by considering the heterogeneity of demand, and the external benefits, such as competitive advantages gained over competitors through dynamic price and quality selection.
Ultimately the research will provide a better understanding of the impact of dynamic operational strategies in markets with different service-quality characteristics and expectations. That, in turn, will give managers useful solutions and insights to aid their decision making in pricing, scheduling, and delivery.
“When a business can differentiate among customers on the basis of service as well as price, it can attract new customers as well as dissatisfied customers from competitors,” says Olsen.
She envisions dynamic service-quality dependent pricing having the most impact in an environment of mass customization—such as custom product installation (e.g., home windows), high-end furniture, and technical-support services.
In oligopolistic markets, such as that for large virtual-storage systems, where HP and IBM are the only two major players, Olsen’s models may be of even greater value. They could enable a firm to quote the most profitable price for service while considering not only its own capacity but its competitor’s capacity as well.
Technology drives research
However, this research could not have taken place even just a few years ago, says Olsen. But the computer revolution, with its information-technology breakthroughs, changed everything. It enabled manufacturers and retailers to access accurate, real-time data on inventory, production, demand, and other variables—and is helping Olsen create three new mathematical models that can be applied to a wide range of businesses.
“None of this research and modeling would be possible without good information systems and accurate data,” says Olsen. “Which is why in the past we haven’t seen this sort of pricing except in select industries, such as shipping and construction. Real-time data allows a firm to control both price and perceived quality of service dynamically. That enables the firm to allocate its limited capacity to the right customers.”
Now, however, thanks to that good data, Olsen’s models will give the business community management tools previously unavailable.
“I’m not aware of other research that does this,” says Olsen. “And we’ve developed new research methodologies along the way.”
In addition to her substantial research, Olsen contributes to the academic and research community through her active professional service and extensive teaching. She holds four different editorial positions, including posts at Operations Research and Management Science, and serves on committees for conferences and student research-paper competitions. She has made numerous presentations at INFORMS* national conferences and other operations and manufacturing symposia. She teaches in Olin’s BSBA, MBA, PMBA, and PhD programs.
*Institute for Operations Research and the Management Sciences