Data mining methods have many origins, including drawing on insights into learning as it naturally occurs in humans (cognitive science), and advances in computer science and algorithm design on how to best detect patterns in unstructured data. Although traditional statistical methods for analyzing data, based on statistical theories and models, are now widely accepted throughout various industries, data mining methods have only been widely embraced in business for a decade or two. However, their effectiveness for root cause analysis, and for modeling, optimizing and improving complex processes, are making data mining increasingly popular--and even necessary--in many real-world discrete manufacturing, batch manufacturing, and continuous-process applications.

There is no single, generally agreed-upon definition of data mining. As a practical matter, whenever data describing a process are available, in manufacturing for example, then any systematic review of those data to identify useful patterns, correlations, trends, and so forth, could be called “data mining.” Put simply, data mining uncovers nuggets of information from a sometimes vast repository of data describing the process of interest.

In 1988, Motorola Corp. became one of the first companies to receive the Malcolm Baldrige National Quality Award. The award strives to identify those  excellent firms that are worthy role models for other businesses. One of Motorola's innovations that attracted a great deal of attention was its Six Sigma program. Six Sigma is, basically, a process quality goal. As such, it falls into the category of a process capability (Cp) technique.

The traditional quality paradigm defined a process as capable if the process's natural spread, plus and minus three sigma, was less than the engineering tolerance. Under the assumption of normality, this translates to a process yield of 99.73 percent. A later refinement considered the process location as well as its spread (Cpk) and tightened the minimum acceptable so that the process was at least four sigma from the nearest engineering requirement. Motorola's Six Sigma asks that processes operate such that the nearest engineering requirement is at least plus or minus six sigma from the process mean.

Motorola's Six Sigma program also applies to attribute data. This is accomplished by converting the Six Sigma requirement to equivalent conformance levels (see Figure 1).

What is quality? An academic definition of quality as it relates to business might be that quality is a product or service that consistently has zero defects, conforms to particular specifications, and is satisfactorily received by the customer. Another aspect of quality is that it is a thought process sought out by organizations to create an overall drive toward efficiency, the reduction of waste, and the continual creation of more streamlined management processes.

“Unlike twenty years ago, when the quality department was viewed as the creator of quality, now the whole concept is more ingrained into the culture of organizations,” says Ron Atkinson, past president of the American Society for Quality. “Quality is created by the people performing the function, whether it be assembling a Bluetooth device or filling out an intake form at a medical clinic. Therefore, a culture of quality is emerging in which the leadership of organizations is emphasizing that the functional areas are responsible for quality in the same way that they are responsible for manpower costs, etc.”

Manufacturers’ efforts to do more with less have resulted in purchasing departments sourcing cheaper products and parts, often from overseas. Such cost-cutting certainly makes purchasing look good to management. But the effect on quality professionals may be just the opposite: product or part defects, malfunctions or undesirable side effects, not to mention the challenge of producing high-quality end-products within narrow timelines and budgets. Many sleepless nights are a frequent outcome.

Because cost cutting and global sourcing are here to stay, how can quality professionals combat these monumental challenges? Root cause analysis (RCA), when fully utilized, can eliminate defects in your operations as well as defects that you inherit from suppliers, ultimately helping to maintain a satisfied and engaged customer base.