What is the key difference between Bias and Linearity (MSA)?

The difference between ordinary and extraordinary is that little extra: Jimmy Johnson

Introduction

As we grow in age, our preferences, taste and choices changes. We do not realize but the skin of our body changes over time. We always have a fondness for one person over another irrespective of whether there is any logic or not. While shopping, we invariably have a bias over one shop/brand to another.  

Objective

Measurement Systems Analysis is a key step to any process improvement effort. By understanding existing measurement systems, a team can better understand the data provided by those systems and make better business decisions.

To understand Measurement System Analysis (MSA) effectively, the understanding of keywords like Bias, Linearity, Stability is important. Once we have clarity about it, it becomes easy to understand the importance of selecting an appropriate measurement system and how it can impact our decision making (Type I and Type II error).

Definitions: MSA (Measurement System Analysis) 4th Edition

Bias: The difference between the observed average of measurements and the reference value.

Linearity: The change in bias over the normal operating range. A systematic error component of the measurement system.

Repeatability: Variations in the measurements obtained with one measuring instrument when used several times by an appraiser while measuring the identical characteristic on the same part.

Reproducibility: Variation in the average of the measurements made by different appraisers using the same gauge when measuring a characteristic on a part.

Detailed Information

The purpose of Measurement System Analysis is to qualify a measurement system for use by quantifying its accuracy, precision, and stability.

In any measurement system, there can be 5 different errors. They are

The key purpose of conducting the study:

  • Whether your measurement system variability is small compared with the process variability.
  • Whether your measurement system is capable of distinguishing between different parts.

When Linearity study should be conducted?

Whenever we are using a measuring system for a long operating range. But if we are measuring only in a narrow operating range, conducting a Linearity study will not serve any purpose. Example: In a Vernier calliper (0 – 200 mm), if the operating range is from 50 mm to 150 mm, conducting a linearity study can give some surprising result but if the operating range is 50 to 55 mm, it will not yield any desired result.

Possible Causes of Bias:

  • Instrument needs calibration
  • Worn instrument/fixture
  • Worn or damaged Master
  • Improper calibration
  • Poor quality instrument
  • Measuring the wrong characteristic

Possible Causes of Linearity error:

  • Environment: Temperature / Humidity / Vibration / Cleanliness
  • Instrument needs calibration
  • Worn instrument/fixture
  • Worn or damaged Master
  • Improper calibration
  • Poor quality instrument
  • Wrong gauge for the application

Key difference between Bias and Linearity:

S.No. Bias Linearity
1 Difference between the reference value and the observed average of the measurement on the same characteristic, on the same part. Change of bias with respect to the size
2 Bias indicates how accurate the gage is when compared to a reference value. Linearity examines how accurate your measurements are through the expected range of the measurements.
3 A positive bias indicates that the gauge overestimates. A negative bias indicates that the gauge underestimates. Linearity indicates whether the gage has the same accuracy across all reference values.
4 The %Bias value indicates the magnitude of the bias as a %age of the process variation (usually 6 sigma). When the slope is small, the gage linearity is good.

 

Present Challenges:

  • How often the organization is clear about the difference between both?
  • How often MSA study is conducted to understand the contribution of measurement error or just to fulfil the IATF requirement?
  • How often factual data is recorded to get the actual results?

References:

IATF 16949: 2016

MSA 4th Edition (Measurement System Analysis)- AIAG

Industry Experts

This is the 99th article of this Quality Management series. Every weekend, you will find useful information that will make your Management System journey Productive. Please share it with your colleagues too.

Your genuine feedback and response are extremely valuable. Please suggest topics for the coming weeks.

             

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Siva Sankar GR
Siva Sankar GR
1 year ago

The explanations given are very easily understandable, thank you so much for your good work sir.

Purushottam Patil
Purushottam Patil
2 months ago

Truly nice explanation sir