"Quality is the best business strategy of all." - John C. Maxwell.
Adopting technologies is not enough to successfully implement Statistical Process Control; culture has to change so that data becomes the language of improvement, and each employee is held responsible for upholding the highest quality standards.
· For an accurate quality report, the sampling has to be correct.
· For a precise statistical analysis report, the operators must correctly enter the quality analysis data in the system, which demands a cultural shift of the operators.
· To correctly interpret analysis data, the managers must be trained to apply analysis methodologies.
Integrating the workflow for statistical process control (SPC) is essential to attaining operational excellence and ongoing quality improvement.
Understanding Variation
Understanding the many types of variation in a process is crucial for effective statistical process control (SPC) monitoring, analysis, and improvement. Variations are of two main categories: common cause variation and special cause variation. They are characterized as shifts or oscillations in the outputs of a process. Let's look at the characteristics that set each of these groupings apart.
Common Cause Variation:
Common cause variation, or random variation, is inherent to every process and caused by consistent and expected factors. These are also known as chance causes and cannot be economically eliminated.
This type of variation is considered part of the standard operating conditions and is often represented by a bell-shaped curve when plotted on a graph. In simple terms, the variation of the data set within the control limits is called common cause variation.
Characteristics of Common Cause Variation:
§ Inherent to the Process: Common cause variation is present in every process due to intrinsic factors that are always present.
§ Stable and Predictable: The fluctuations caused by common causes are stable and predictable within a specific range.
§ Regular and Expected: These factors lead to a consistent variation that is expected over time.
§ Broad Impact: Common cause variation affects the entire process, leading to overall shifts in performance.
Examples:
o Normal machine wear and tear
o Minor variation in raw material properties
o Minor changes in operator performance
o Environmental factors such as temperature or humidity fluctuations
Special Cause Variation:
Special cause variation, also known as assignable cause variation, is caused by factors that are inconsistent or expected in the process. These factors are often sporadic and can lead to sudden shifts or spikes in process output. Special cause variation represents deviations from the normal operating conditions and requires investigation and corrective action. In simple terms, when data falls out of the control limits, the reasons for such variations are special cause variations that must be addressed immediately. These are easy to detect and eliminate.
Characteristics of Special Cause Variation:
§ Unpredictable and Unusual: Special cause variation is not part of the regular process and can occur unexpectedly.
§ Sporadic or Intermittent: These factors lead to occasional or irregular shifts in process performance.
§ Localized Impact: Special cause variation affects specific parts of the process or particular outputs.
§ Requires Investigation: When special cause variation occurs, it is essential to identify the main cause and take corrective action to prevent recurrence.
Examples:
o Equipment breakdowns or failures
o Sudden drastic changes in raw material quality
o Human errors or mistakes
o Power outages or other external disruptions
Suman Sarkar
July 22, 2024