When farm and construction-equipment manufacturer John Deere-Horicon experienced a larger-than-expected leap in sales of golf course fairway and greens mowers, the pleasant surprise elicited a scheduling and supply dilemma: Should the company strive to increase flexibility by cross-training workers to operate both types of manufacturing lines?
Business managers daily deal with such questions: Should I outsource or manufacture everything in house? Should I implement quality control charts? Should I contract with multiple suppliers for input materials?
Today those managers often make important corporate decisions based on traditional “net-present-value” analysis. They consider such variables as demand, interest rate and investment outlay. However, for many high-risk or highly uncertain projects, they often hesitate to chance a big loss — especially in today’s volatile market. Those who are a little more daring might use predictions, forecasts or gut feeling: their best guesses.
But uncertainties may offer businesses an advantage if they know how to manipulate them, says Leyuan Shi, an associate professor of industrial engineering. “We want to develop tools to tell those in a company, ‘If you have uncertainty, don’t be afraid of it. You can take advantage of it if you know how to do it.'”
Simply, “how to do it” is to be prepared for uncertainty by keeping a variety of alternatives available — a concept of real-options theory.
The theory forms one of the cornerstones of a hot new area called financial engineering, which combines engineering skills with economics, business, finance, computer sciences, math and statistics. People with training in this area have a good background to address the uncertainty and ever-changing needs of manufacturing operations, says IE Assistant Professor Harriet Black Nembhard.
For the second spring in a row, Shi and Nembhard have offered #ac, IE 691: Financial Engineering: A View of Manufacturing Operations, to teach students some of those strategies.
Such universities as Columbia, Michigan and Princeton offer courses and degree programs that focus on overall financial engineering, but this seminar-style course is more specific and tailored to the pair’s research interests, says Shi. “We focus on manufacturing,” she says.
The course’s objective is to investigate a real-options modeling framework for manufacturing transitions. Students review financial-option research results, learn ways to quantify manufacturing activities, and analyze optimal business strategies based on real-option models.
In a portion of the course, students also participate in teams on an actual project. Last year, Nembhard and Shi directed three teams of students on industry case studies at John Deere, Horicon; Milsco Manufacturing Co., Milwaukee; and Springs Window Fashions, Middleton.
In the John Deere project, students concluded the company could benefit from scheduling production of both farm and golf equipment concurrently and tried to determine what advantages it would have if it invested in more workforce flexibility.
Some three-quarters of a century ago, Milsco began life making horse harnesses and saddles, but shifted to motorcycle seats after Harley-Davidson enthusiastically received the company’s initial design. Recently, Milsco adopted lean manufacturing strategies such as flexible cells and employee involvement. To carry out its new vision, the company faced several decisions, including whether to replace an old 200-ton press, and if so, when; if not, whether to outsource the affiliated parts. Students evaluated what flexibility Milsco would gain or lose with each option.
The Springs project addressed another manufacturing issue. A maker of home window blinds, the company wondered whether it should introduce statistical process control (SPC) charts to monitor overall product quality. Implementing the charts involved costs such as computers, monitoring software and labor, and the financial impact of those factors varied depending on the products’ current sales price. Students determined how Springs could reap greater economic benefits from introducing SPC charts as products command a higher price and the company produces them in larger runs.
As they are teaching, Shi and Nembhard hope to learn more, too. “Previously people have focused on using real-options analysis for commodities. Now they’re just beginning to develop the theory for other fields like pharmaceuticals, software and computer chips,” says Nembhard.
Recently, Nembhard, Shi and Auburn University Professor Chan Park published the paper, “Real Option Models for Managing Manufacturing System Changes in the New Economy” in The Engineering Economist. With the help of IE PhD student Mehmet Aktan, they also have developed a working paper that details a real-options design for statistical process control. “We’re really getting down to how we can use real options in product design, how we can use them for scheduling, and how we can use them to evaluate decisions for quality improvement,” says Nembhard. “This aspect really is fairly new.”
And that’s for certain.