Measurement System Variation | Different Types of Variation

What is Measurement System Variation?

→ Measurement systems variation refers to the inconsistencies or differences that arise when measuring identical characteristics multiple times.

→ The observed variation in process output is not simply due to the process.

→ But this might be due to multiple factors, such as the measuring instrument, the person taking the measurement, environmental conditions, or the method used.

→ Sometimes, variation is described as spread or dispersion.

Table of Contents:

• What is Measurement System Variation?
• What is a Measurement System?
• Components of a Measurement System
• Purpose of a Measurement System
• Common Issues in a Measurement System
• Classification of Measurement System Variation
• Types of Measurement System Variation
• Systematic Variation
• Random Variation
• Causes of Measurement Variation
• Common Error in Measurement Variation
• Conclusion

What is a Measurement System?

→ A Measurement System is a combination of measuring instruments, methods, and procedures used to collect data about a physical characteristic of a product or a process.

→ It includes everything involved in measuring something.

→ In simple words, it involves the instrument used, the person taking the measurement, the procedure, and the conditions under which it is taken.

→ A good measurement system should be accurate, precise, stable, and repeatable

Components of a Measurement System:

→ Refer to the different components mentioned below.

→ Measuring Instrument: The tool or device used to measure (e.g., calipers, micrometers, thermometers, scales).

→ Operator (Appraiser): The person performing the measurement.

→ Method: The procedure or technique followed.

→ Environment: The conditions affecting the measurement (e.g., temperature, humidity, lighting).

→ Standard Reference: The known standard used for calibration and comparison.

→ Measured Object (Part or Sample): The item being measured.

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Purpose of a Measurement System:

• To ensure product quality and consistency
• To monitor process performance
• To support decision-making based on accurate data
• To maintain compliance with industry standards

Common Issues in a Measurement System:

→ Bias: Difference between measured value and true value.

→ Repeatability Issues: A difference due to the same operator measuring the same item multiple times.

→ Reproducibility Issues: A difference when different operators measure the same item.

→ Resolution Limitations: The smallest measurable change detectable by the instrument.

Classification of Measurement System Variation:

→ It can be classified into different categories based on source and nature.

→ Observed overall variation is classified into measurement system variation and process variation.

→ The actual process variation is caused by different parameters such as cycle time, dimension data, number of defects, some other factor, etc.

→ It can be further classified into more specific types.

→ Refer to the below picture for a better understanding.

Types of Measurement System Variation:

→ There are two types are mentioned below.

1. Systematic Variation and
2. Random Variation

1. Systematic Variation:

→ This is also known as predictable or correctable.

→ It occurs predictably and consistently affects measurements in the same direction.

→ It can often be identified and corrected.

→ This variation is related to accuracy, or we can say that it is related to a central location.

→ Accuracy is the closeness between the true value and the measured value.

⏩Types of Systematic Variation:

• Bias
• Linearity
• Stability (Drift)
• Calibration Error

👉 For better understanding, read out Bias, Linearity, and Stability Study with Examples

2. Random Variation:

→ It is also known as indeterminate unpredictable or inherent to the process.

→ This occurs unpredictably due to uncontrollable factors.

→ It can be reduced but never eliminated completely.

→ This variation is related to precision or related to variability.

→ Precision is the closeness between the measured value, how close all values are to each other.

⏩Types of Random Variation:

• Repeatability
• Reproducibility
• Resolution
• Environmental Factors
• Instrument Resolution Error

👉 Refer to our detailed explanation of Repeatability vs Reproducibility

⏩Summary of Measurement Variation Source and Example:

 → Refer to the table below, in which we have summarized the different types, their sources, and examples.

Causes of Measurement System Variation:

 → The purpose of the system is to validate before they are considered true data and used as a basis for decision-making.

 → Measurement variation occurs due to multiple factors related to the instrument, operator, environment, method, and object being measured.

Common Error in Measurement Variation:

→ There are many possible causes for measurement variation.

⏩Most Common Errors are:

1. Equipment Error (Instrument Error)
2. Operator Error (Appraiser Error)
3. Environmental Factors
4. Method Related Errors
5. Part-to-Part Variation

→ Now, we will discuss the different causes with examples.

1. Instrument Error Related Causes:

→ Instrument errors arise due to issues with the measuring instrument itself.

→ Calibration Errors - If an instrument is not properly calibrated, it can lead to biased measurements.

→ Wear and Tear - Over time, the mechanical components of an instrument may degrade, causing inaccurate readings.

→ Resolution Limitations - Some instruments may not be able to measure a small detection of change.

→ Hysteresis - Some instruments do not return to zero or have different readings when measuring increasing vs. decreasing values.

→ Faulty equipment or instruments are another major factor that results in getting inaccurate readings.

→ If the equipment or instrument is not calibrated, then we can not rely on that. It is not recommended.

→ Regular maintenance and calibration of the instrument are essential.

2. Operator Error Related Causes:

→ These errors occur due to human factors.

→ Inconsistent Technique - Different operators may use different techniques.

→ Reading Errors - Misinterpretation of analog scales, parallax errors, or digital display misreading.

→ Fatigue or Distraction - Operators may record incorrect readings due to lack of focus.

→ Personal Bias - Some operators may unconsciously adjust readings to match expectations.

→ Due to the repetitive nature of data collection, it becomes a monotonous task, and sometimes, errors are made.

→ Also, sometimes employees intentionally avoid work for some other reason and manipulate the readings.

3. Environmental Factors:

→ External factors can influence accuracy.

→ Temperature - Materials expand or contract with temperature changes.

→ Humidity and Moisture - High humidity can affect electronic measuring devices and material properties.

→ Vibration and External Forces - Instruments in high-vibration environments may produce inconsistent readings.

→ Lighting Conditions - Poor lighting can lead to reading errors, especially for analog instruments.

4. Method-Related Causes:

→ Inconsistencies in procedures.

→ Improper Measuring Technique - Incorrect positioning, incorrect application of force, or wrong methods.

→ Lack of Standardization - Differences in procedures between operators or locations.

→ Sampling Plan - Differences in the way samples are selected or prepared can affect end results.

→ Sometimes, due to complex calculations, the possibility of error is due to a lack of proper communication or selection of a proper data set.

→ It is important that the standard operating procedure be available for any operation.

→ Also, it should be communicated to every person who needs to know it.

5. Part-to-Part Variation:

→ Surface Roughness - Variations in texture can affect contact-based measurements.

→ Material Properties - Some materials may change shape, size, or properties due to stress, heat, or aging.

→ Shape and Size Differences - Even identical parts may have small variations due to manufacturing tolerances.

⏩Summary of Causes of Measurement Variation:

 → Refer to the table below, which summarizes the different causes.

Conclusion:

→ Variation in a measurement system is an unavoidable reality.

→ By understanding sources, we can minimize impact.

→ The key contributors to measurement variation include instrument errors, operator errors, environmental factors, method-related issues, and part-to-part variation.

→ These variations can be categorized as systematic and random errors.

→ To ensure measurement reliability, it is crucial to conduct different Measurement System Analysis techniques, such as a Gauge R&R Study, bias assessment, stability analysis, and linearity evaluation.

→ Proper calibration, standardized procedures, operator training, and environmental controls can reduce errors and improve accuracy.

→ By addressing these sources of variation, industries can enhance product quality, process control, and data-driven decision-making.

→ It leads towards more consistent and reliable outcomes.