Model Answer
0 min readIntroduction
Statistical Quality Control (SQC) is a crucial aspect of modern manufacturing and service industries, ensuring products and processes meet predefined quality standards. Within SQC, two primary approaches are employed: ‘control by variables’ and ‘control by attributes’. These methods differ fundamentally in how quality characteristics are measured and analyzed. Control by variables focuses on measurable characteristics, while control by attributes deals with the presence or absence of defects. Understanding these distinctions is vital for selecting the appropriate control techniques and maintaining process stability. This answer will delineate the differences between these two approaches and detail the popular control charts used in each.
Control by Variables vs. Control by Attributes
Both ‘control by variables’ and ‘control by attributes’ aim to monitor and control process variation, but they differ significantly in their approach.
| Feature | Control by Variables | Control by Attributes |
|---|---|---|
| Data Type | Continuous (e.g., length, weight, temperature) | Discrete (e.g., good/bad, pass/fail) |
| Measurement Scale | Ratio or Interval | Nominal or Ordinal |
| Focus | Measuring the actual value of a characteristic | Determining if a characteristic conforms to specifications |
| Sample Size | Typically small (e.g., 5-10 units) | Can be larger, often based on inspection of entire lots |
| Complexity | More complex statistical analysis | Simpler statistical analysis |
| Examples | Monitoring the diameter of a shaft, the viscosity of a liquid | Counting the number of defective items in a batch, classifying products as acceptable or unacceptable |
Control Charts for Control by Variables
Control charts for variables are used to monitor characteristics that can be measured on a continuous scale. The most commonly used charts include:
X-bar and R Chart
- X-bar Chart: Monitors the average of sample values over time. It detects shifts in the process mean.
- R Chart: Monitors the range (difference between the highest and lowest values) within each sample. It detects changes in process variability.
- Application: Used together to control processes where both the mean and variability are important.
X-bar and S Chart
- X-bar Chart: Same as above.
- S Chart: Monitors the standard deviation within each sample. It’s more sensitive to changes in variability than the R chart, especially for larger sample sizes.
- Application: Preferred over X-bar and R charts when sample sizes are larger (n > 10).
Individual and Moving Range Chart
- Individual Chart (X Chart): Plots individual measurements over time.
- Moving Range Chart (MR Chart): Plots the range between consecutive individual measurements.
- Application: Used when data is collected infrequently or when only one item is measured at a time.
Control Charts for Control by Attributes
Control charts for attributes are used to monitor characteristics that can be classified as conforming or non-conforming. The most popular charts are:
p-Chart
- Purpose: Monitors the proportion of defective items in a sample.
- Application: Used when the sample size varies.
- Example: Tracking the percentage of rejected circuit boards in batches of varying sizes.
np-Chart
- Purpose: Monitors the number of defective items in a sample.
- Application: Used when the sample size is constant.
- Example: Tracking the number of errors in a fixed number of processed invoices.
c-Chart
- Purpose: Monitors the number of defects per unit.
- Application: Used when the number of defects can be counted on a single unit (e.g., scratches on a car).
- Example: Counting the number of blemishes on a painted surface.
u-Chart
- Purpose: Monitors the number of defects per unit when the unit size varies.
- Application: Used when inspecting different-sized areas or units.
- Example: Counting the number of flaws per 100 meters of fabric.
Conclusion
In conclusion, ‘control by variables’ and ‘control by attributes’ represent distinct yet complementary approaches to statistical quality control. The choice between them depends on the nature of the quality characteristic being monitored and the type of data available. Variables charts are suited for continuous measurements, while attributes charts are ideal for assessing conformance to specifications. Effective implementation of these control charts, coupled with continuous process improvement efforts, is essential for maintaining high-quality standards and enhancing operational efficiency.
Answer Length
This is a comprehensive model answer for learning purposes and may exceed the word limit. In the exam, always adhere to the prescribed word count.