Measurement System Analysis

A measurement system analysis (MSA) is a mathematical means of determining the amount of variation present in a measurement process.

Understanding a measurement system analysis

With businesses now reliant on more and more data to make important decisions, the data on which those decisions are based must be as accurate as possible.

The solution is an MSA, a resource-intensive component of the Six Sigma DMAIC process used to reduce defects, increase quality, and control costs.

Measurement system analysis is a formal, statistical method that evaluates measurement systems (devices or people) and assesses their potential to provide reliable data.

In other words, it enables the business to make sure that any variation in measurements is minimal when compared to variation in its processes.

Note that a measurement system is any system of related measures that result in the quantification of certain characteristics.

This also encompasses the validation or assessment of a particular unit of measure that is performed by personnel, software, fixtures, or gauges.

Measurement system analyses consider the accuracy, precision, and stability of the measurement system collecting the data.

Both the process variation and measurement (device) variation are quantified to define the total measurement system variation stems from multiple sources such as:

  • Subjective decision-making. For example, one factory worker may consider a machine to be close to failure while another may not.
  • The use of an improper tool to provide a numerical reading.
  • Systematic errors that result from a poorly calibrated device, such as an industrial scale that is always 2% off.
  • Sounding or recording errors that are caused by a person not using enough significant figures or incorrectly recording the number itself.
  • Environmental factors such as temperature, heat, humidity, and the like.

The five perspectives of a measurement system analysis

Once the two sources of variation have been examined, it is time to minimize the variation in the management system so that variation in the process can be understood better.

To that end, five perspectives of measurement error must be quantified before process capability can be established and data-based decisions are made. These perspectives relate to measurement precision and accuracy.

Precision

Precision describes the degree to which repeated measurements under the same conditions produce the same result. Put differently, it refers to how close two measurements are to each other. Precision-related perspectives include:

  1. Repeatability – the system is repeatable if the same person who measures the same object multiple times with the same device can obtain identical results.
  2. Reproducibility – the difference in the average measurements between different people using the identical characteristic or part of the same instrument. 

Accuracy

Think of accuracy as the discrepancy between the observed average and the true average.

Inaccurate systems are characterized by an average value that differs from the true average.

Accuracy-related perspectives include:

  1. Bias – a one-directional tendency or the difference between an average observed value and the true or reference value.
  2. Linearity – the difference in bias value over the standard operating range of a particular measuring instrument. For example, can a scale that measures an object weighing 1000 kilograms do so as accurately as when it is measuring an object that weighs 50 kilograms?
  3. Stability – the system is stable if the variation is more or less constant over time.

Measurement systems analysis best practices

To maximize the benefits of an MSA, consider these best practices:

  • Larger numbers of parts and repeat readings will produce results with a higher confidence level. But as always in business, the exhaustiveness of tests should be balanced with time, cost, and potential disruption to operations.
  • Where possible, those who routinely perform a measurement or are familiar with procedures and equipment should be involved in the analysis.
  • Ensure that the measurement procedure is documented and standardized among all MSA appraisers.
  • Select parts that best represent the entire process spread. If the process is not properly represented, the extent or severity of the measurement error may be exaggerated.

Key takeaways:

  • A measurement system analysis (MSA) is a mathematical means of determining the amount of variation present in a measurement process.
  • Measurement system analyses consider the accuracy, precision, and stability of the measurement system collecting the data. Total measurement system variation is comprised of process and device variation and can be caused by several factors such as poorly calibrated devices or environmental factors such as heat or humidity.
  • To minimize variation in the measurement system, five perspectives of measurement error must be quantified. Three relate to the precision of the system, while two relate to its accuracy.

Connected Analysis Frameworks

Cynefin Framework

cynefin-framework
The Cynefin Framework gives context to decision making and problem-solving by providing context and guiding an appropriate response. The five domains of the Cynefin Framework comprise obvious, complicated, complex, chaotic domains and disorder if a domain has not been determined at all.

SWOT Analysis

swot-analysis
A SWOT Analysis is a framework used for evaluating the business’s Strengths, Weaknesses, Opportunities, and Threats. It can aid in identifying the problematic areas of your business so that you can maximize your opportunities. It will also alert you to the challenges your organization might face in the future.

Personal SWOT Analysis

personal-swot-analysis
The SWOT analysis is commonly used as a strategic planning tool in business. However, it is also well suited for personal use in addressing a specific goal or problem. A personal SWOT analysis helps individuals identify their strengths, weaknesses, opportunities, and threats.

Pareto Analysis

pareto-principle-pareto-analysis
The Pareto Analysis is a statistical analysis used in business decision making that identifies a certain number of input factors that have the greatest impact on income. It is based on the similarly named Pareto Principle, which states that 80% of the effect of something can be attributed to just 20% of the drivers.

Failure Mode And Effects Analysis

failure-mode-and-effects-analysis
A failure mode and effects analysis (FMEA) is a structured approach to identifying design failures in a product or process. Developed in the 1950s, the failure mode and effects analysis is one the earliest methodologies of its kind. It enables organizations to anticipate a range of potential failures during the design stage.

Blindspot Analysis

blindspot-analysis
A Blindspot Analysis is a means of unearthing incorrect or outdated assumptions that can harm decision making in an organization. The term “blindspot analysis” was first coined by American economist Michael Porter. Porter argued that in business, outdated ideas or strategies had the potential to stifle modern ideas and prevent them from succeeding. Furthermore, decisions a business thought were made with care caused projects to fail because major factors had not been duly considered.

Comparable Company Analysis

comparable-company-analysis
A comparable company analysis is a process that enables the identification of similar organizations to be used as a comparison to understand the business and financial performance of the target company. To find comparables you can look at two key profiles: the business and financial profile. From the comparable company analysis it is possible to understand the competitive landscape of the target organization.

Cost-Benefit Analysis

cost-benefit-analysis
A cost-benefit analysis is a process a business can use to analyze decisions according to the costs associated with making that decision. For a cost analysis to be effective it’s important to articulate the project in the simplest terms possible, identify the costs, determine the benefits of project implementation, assess the alternatives.

Agile Business Analysis

agile-business-analysis
Agile Business Analysis (AgileBA) is certification in the form of guidance and training for business analysts seeking to work in agile environments. To support this shift, AgileBA also helps the business analyst relate Agile projects to a wider organizational mission or strategy. To ensure that analysts have the necessary skills and expertise, AgileBA certification was developed.

SOAR Analysis

soar-analysis
A SOAR analysis is a technique that helps businesses at a strategic planning level to: Focus on what they are doing right. Determine which skills could be enhanced. Understand the desires and motivations of their stakeholders.

STEEPLE Analysis

steeple-analysis
The STEEPLE analysis is a variation of the STEEP analysis. Where the step analysis comprises socio-cultural, technological, economic, environmental/ecological, and political factors as the base of the analysis. The STEEPLE analysis adds other two factors such as Legal and Ethical.

Pestel Analysis

pestel-analysis
The PESTEL analysis is a framework that can help marketers assess whether macro-economic factors are affecting an organization. This is a critical step that helps organizations identify potential threats and weaknesses that can be used in other frameworks such as SWOT or to gain a broader and better understanding of the overall marketing environment.

DESTEP Analysis

destep-analysis
A DESTEP analysis is a framework used by businesses to understand their external environment and the issues which may impact them. The DESTEP analysis is an extension of the popular PEST analysis created by Harvard Business School professor Francis J. Aguilar. The DESTEP analysis groups external factors into six categories: demographic, economic, socio-cultural, technological, ecological, and political.

Paired Comparison Analysis

paired-comparison-analysis
A paired comparison analysis is used to rate or rank options where evaluation criteria are subjective by nature. The analysis is particularly useful when there is a lack of clear priorities or objective data to base decisions on. A paired comparison analysis evaluates a range of options by comparing them against each other.

Related Strategy Concepts: Go-To-Market StrategyMarketing StrategyBusiness ModelsTech Business ModelsJobs-To-Be DoneDesign ThinkingLean Startup CanvasValue ChainValue Proposition CanvasBalanced ScorecardBusiness Model CanvasSWOT AnalysisGrowth HackingBundlingUnbundlingBootstrappingVenture CapitalPorter’s Five ForcesPorter’s Generic StrategiesPorter’s Five ForcesPESTEL AnalysisSWOTPorter’s Diamond ModelAnsoffTechnology Adoption CurveTOWSSOARBalanced ScorecardOKRAgile MethodologyValue PropositionVTDF FrameworkBCG MatrixGE McKinsey MatrixKotter’s 8-Step Change Model.

Other strategy frameworks

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