What could a plant manager from a process manufacturer that makes yarns and fabrics for industrial applications possibly learn from an automaker such as Toyota? Conventional wisdom says companies should benchmark against similar industries to gain knowledge relevant to their operations. But as we've seen with businesses ranging from medical institutions to governmental agencies adopting lean principals, one industry may have best practices that can be tailored to fit a completely different work environment.

The same could be said for process and discrete manufacturers. Generally speaking, process industries are characterized as businesses that make products in bulk quantities, such as chemicals, pharmaceuticals, gasoline, beverages and food products, which often undergo a chemical conversion. On the other hand, discrete manufacturers produce or assemble parts or finished products that are recognizable as distinct units, such as automobiles or computers, capable of being identified by serial numbers or labeling products and measurable as numerical quantities rather than by weight or volume.

Even with these stark differences, process and discrete manufacturers have a history of glomming on to one another's improvement methods. As lean and Six Sigma gained in popularity throughout the 1980s and '90s within discrete operations, process manufacturers began taking note, says Peter Martin, vice president of strategic ventures at automation technology provider Invensys Process Systems. Initially, the process industry's forays into the continuous improvement trend didn't fare so well. When process manufacturers tried to implement statistical analysis methods which are used primarily in discrete operations, such as Six Sigma, they didn't work because they focused too much on defects, Martin says.

"In the process industries we don't tend to do defect-oriented manufacturing," he explains. "[For example], if you charge a little too much pigment, you can just put a little more base in and fix it. The mindset in the process industry is direct, real-time control. The mindset in discrete manufacturing is after-the-fact statistical analysis to get continuous improvement. So the mindsets are very different."

Process manufacturers finally realized value from continuous improvement programs when they stopped applying methods that focused on statistics. "When the process industry started looking at what discrete was doing in continuous improvement, they started saying, 'We should be able to use different techniques, maybe not statistical techniques, to continuously improve our critical performance variables like contribution margin, or energy cost or production value,'" Martin says.

Lean Adaptations

Lean became a reality for several Milliken & Co. plants when the privately held textiles and chemicals manufacturer applied lean manufacturing techniques to optimize lead time and align itself with customer demand variations, says Chris Glover, Milliken Performance System practitioner. Like many other process manufacturers, the company dabbled in lean throughout the 1980s and '90s, but initially didn't fully understand how to gain significant improvements from the methodology, says Glover, who has worked in a variety of leadership roles within Milliken since 1985.

"We went to Japan as an organization and benchmarked many Japanese companies in the early '90s and [lean] was visible inside the Toyota Production System," he explains. "It was just part of their operating system, but when we brought it back we weren't really sure how to incorporate it in our activities, partly because we were process and couldn't figure out why you had to measure every millisecond a person was running a process machine. So we struggled a little bit with that type of diagnosis."

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