Basic Civil Engineering first year Notes- Chapter 4 Building.pptx
2.2 Mesure Phase (1).pptx
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Presented By
Dr. Niraj Chaudahri
Assistant Professor,
Sanjivani College of Engineering ,
Dept.of MBA,
Kopargaon
1
Sanjivani College of Engineering, Kopargaon
Department of MBA
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307 – Six Sigma For Operation
Measure Phase
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Goals of Measure Phase
• Establish baseline performance of the
process
• Identification of process performance
indicators
• Develop a data collection plan and then
collect data.
• Validating the measurement system
• Determine the process capability
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Measure Phase Deliverables
• Detailed process map
• Data collection plan and collected data
• Results of Measurement system analysis
• Graphical analysis of data
• Process capability and sigma baseline
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Measure Phase of DMAIC Overview
• The Measure phase is approximately 2 to 3
weeks process based on the project inputs. In
particular, all the relevant stakeholders’
involvement is key in getting the quality data.
• The measure phase is all about the baseline of
the current process, data collection, validating
the measurement system, and also
determining the process capability. There are
multiple tools and concepts available in the
Measure phase of six sigma.
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Data Collection
• In fact, the measure phase is all about collecting as
much data as possible to get the actual picture of the
problem. Hence, the team has to ensure the
measurement process for data collection is accurate
and precise.
• Data is a set of values of qualitative or quantitative
variables. It may be numbers, measurements,
observations or even just descriptions of things.
Below are the types of Quantitative Data
• .
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Data Type
• Discrete data: The data is discrete if the
measurements are integers or counts. For
example, Number of customer complaints,
weekly defects data etc.
• Continuous data: The data is continuous if
the measurement takes on any value, usually
within some range. For example, Stack
height, distance, cycle time etc.
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Data Collection Plan
• Data collection plan is a useful tool to focus
your data collection efforts on. This directed
approach helps to avoid locating &
measuring data just for the sake of doing so.
• Identify data collection goals
• Develop operational definitions
• Create a sampling plan
• Select & validate data collection methods
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Measurement System Analysis
• Measurement System Analysis (MSA) is an
experimental and mathematical method of
determining how much the variation within
the measurement process contributes to
overall process variability.
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Measurement System Analysis
• Accuracy: It is a difference between the true
average and observed average. If the average
value differs from the true average, then the
system is not accurate. This is an indication
of an inaccurate system.
• Precision: Precision refers to how close the
data points falls in relation to each other. In
other words, a high-precision process will
have little variance between the individual
measurement point
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R&R
• Repeatability: Repeatability is the variation
between successive measurements of the
same part, same characteristic, by the same
person using the same gage.
• Reproducibility: Reproducibility is the
difference in the average of the
measurements made by different people
using the same instrument when measuring
the identical characteristic on the same part.
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Determine the process Stability
• Process Stability tells us how well a process
meets a set of specification limits based on a
sample of data taken from a process. The
process capability study helps to establish
the process baseline and measure the future
state performance. Revisit the operational
definitions and specify what are defects and
which are opportunities.
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Linearity
• Linearity can be determined by choosing parts or
standards that cover all or most of the operating
range of the measurement instrument.
• Bias is determined at each point in the range and
a linear regression analysis is performed.
• Linearity is defined as the slope times the process
variance or the slope times the tolerance,
whichever is greater. A scatter diagram should
also be plotted from the data.
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Process Capability Analysis
• An important technique used to determine how well
a process meets a set of specification limits is called
a process capability analysis. A capability analysis is
based on a sample of data taken from a process and
usually produces:
1. An estimate of the DPMO (defects per million
opportunities).
2. One or more capability indices.
3. An estimate of the Sigma Quality Level at which
the process operates.
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Characteristics of Index Numbers
• Index numbers are a special type of average that
provides a measurement of relative changes in the level
of a certain phenomenon from time to time. It is a
special type of average because it can be used to
compare two or more series which are composed of
different types of items or even expressed in different
types of units.
• Index numbers are expressed in terms of percentages to
show the extent of relative change.
• Index numbers measure relative changes. They measure
the relative change in the value of a variable or a group
of related variables over a period of time or between
places.
