Statistical Software Helps Dow Reach Six Sigma Goals

Standardizing has simplified training and optimization efforts

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Typical analyses in JMP allow Dow researchers to explore relationships among variables, interactively and in several ways, and provide a means for optimizing processes.


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Data Pipeline Helps Hyperlast Access Batch Data
Like many chemical processing companies, Dow Chemical Co. embraced the Six Sigma standard for quality improvement. Dow launched its corporate Six Sigma program at the end of 1998. Statistical software supplied by JMP, a business unit of SAS, based in Cary, N.C., played an important role in helping the company achieve its quality goals across customer industries and manufacturing plants throughout the world. In fact, Dow says, the software was a key factor in allowing it to achieve its goal ," $1.5 billion in cumulative earnings in 2002,"one year earlier than originally planned.

In the mid-1990s, Dow realized that it needed to establish one standard statistics software package across the company. Using diverse packages made training a thorny issue, explains Dan Obermiller, Six Sigma master black belt and JMP administrator for Dow. The company had to customize training to fit the various programs in use at different manufacturing facilities. "We found that we were spending [more] time creating training than we were actually doing it," says Obermiller.

The company came up with a list of requirements for its statistical software. "We were looking for software that would meet the needs of 95 percent of the people 95 percent of the time," says Obermiller. "It needed to work under Windows and be affordable." Dow evaluated a number of Windows-based packages considering key criteria such as ease of use, accuracy, documentation, price and the ease with which it can be expanded or customized. JMP emerged as the best option.

First installed under Windows in 1995, JMP software is now used in Dow projects ranging from plant optimization to supply chain management. "The most common area is planning some design experiments in a plant, to find out how best to run that plant," says Jeff Sweeney, senior statistician for Dow. "Sometimes it's as simple as trying to find out where a problem is. For example, maybe we've got shipments that are being damaged. How are they being damaged? Is it our carrier's fault or was it due to a flawed packaging design?"

A number of Dow employees use the software to mine company data, using JMP's 5.0 partition models, PLS and neural networks, Obermiller says. Meanwhile, JMP's Design of Experiments capabilities allowed users to build complex models and pinpoint root causes of any failures or deficiencies. For example, the company used the software to optimize an olefin foam used to protect car passengers from serious head injuries during a crash. A design for five variables was created and studied in JMP. The design identified the significance of each variable, as well as interactions between variables. Then, a model was developed for predicting foam performance properties. As a result of the study, "Optimization of High-Efficiency Energy Absorbing Olefinic Foam Headliner Countermeasures via a Statistical Design of Experiment," automobile manufacturers and Tier 1 suppliers will be able to minimize design cycle times for foams of this type.

JMP has helped Dow realize some less-tangible benefits as well. "[JMP] has given people a standard way of looking at data, which is very helpful," Obermiller says. "It raised the level of statistical awareness throughout the company and provided a common language related to data analysis." With other software programs, a Dow employee making a presentation at the company might use graphs to compare key differences between corporate groups. JMP goes beyond visual differences, highlighting statistical differences, says Obermiller, who adds that the software is extremely user friendly. "It has given people a new way to play with data," he says. "It's very easy to just start clicking on bars in JMP." For more information on JMP, visit

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