Manufacturing by Paul Templin

ISE Magazine –Volume: 49, Number: 08

Wake up and behold the power of SPC

Few quality tools are as powerful, as simple and as poorly deployed as statistical process control (SPC). Developed by Walter Shewhart in the 1920s, SPC is one of the first quality tools promoted by W. Edwards Deming and other proponents of quality science in the 1930s. SPC then helped win the industrialization battle of World War II by using quality data to improve manufacturing processes in real time.

Yet decades later, effective SPC implementation continues to be a challenge, despite automation and statistical software that obviates the need to calculate control limits or plot trends manually.

Sadly, suppliers often view SPC as a customer requirement instead of a powerful tool to improve quality. On several occasions, the quality team’s SPC charts are scrutinized or displayed only during customer audits. In other cases, reaction to OOC (out of control) conditions is either nonexistent or ad hoc. Reaction plans are informal and root cause is rarely found. In fact, with the advent of software automation, the result is often the recalculation of SPC control limits to incorporate more random variation, effectively covering up the history of OOC conditions.

The explanation is both simple and complex. Readers of my column are no doubt familiar with the phrase, “There is no substitute for hard work,” and certainly the failure of many quality systems can be traced to a lack of discipline and determination. Like the larger quality journey, successful SPC deployment begins rather than ends when the last SPC chart is posted and data tracking begins. Effective SPC implementation is an iterative process that constantly scrutinizes data for meaning and significance. Initially tracked parameters don’t always prove to be the best indicators of product quality. As more is learned, new parameters are identified and others fall by the wayside. Likewise, reaction plans and the rules defining OOC conditions need to be updated as more is learned. In essence, the focus needs to be on the data, its significance, how it’s trending and its underlying meaning.

That said, one common mistake is to control chart everything. More than once in my career, the directive has come from on high to SPC chart everything. The result is an ocean of charts that undermine the purpose of SPC. Like any data collection project, you must take care and thought when selecting parameters to control chart. Use statistical tools like capability metrics (CPks) to find the most challenging parameters. Use institutional memory and common sense. The key is to build up experience with SPC slowly before rolling it out to the whole factory.

Another common mistake concerns roles and responsibilities. While everyone owns quality, SPC is most effective when it is owned by the operators and technicians who have day-to-day responsibility for the charted process. They are in the best position to react to OOC conditions and are the most aware of the changes in their process.

Keep in mind that responsibility without training is a recipe for disaster. These folks need to understand the mechanics and philosophy behind SPC. That said, robust reaction plans are not just the domain of production and should quickly escalate quality issues to engineering. Still, your operators and technicians are always your first line of defense.

Quality tools are neither a substitute for common sense nor hard work; rather, they are a supplement. Use SPC to identify and quickly respond to quality issues in your factory. Use robust reaction plans to respond to OOC conditions, but always keeps in mind that the people do the heavy lifting in any factory. Their commitment to quality tools like SPC is the most critical factor to success.