TUM School of Management Production and Supply Chain Management Prof Martin Grunow Technische Universitt Mnchen

Statistics for Quality

Why statistics in quality? The science of statistics provides: Means for describing populations with variability.

Methods for estimating population quality from samples.

Holly Ott Quality Engineering & Management Module 2.1 11

2012 from "A First Course in Quality Engineering: Integrating Statistical and Management Methods of Quality" by K.S. Krishnamoorthi. Reproduced by permission of Taylor and Francis Group, LLC, a division of Informa plc.

TUM School of Management Production and Supply Chain Management Prof Martin Grunow Technische Universitt Mnchen

Major statistical methods used in quality engineering Control charts:

used for controlling processes so they produce products of uniform quality

Sampling plans: used for determining acceptability of lots based on samples taken from them

Designed experiments: used for determining the best combination of process parameter levels to obtain desired levels of quality characteristics

Holly Ott Quality Engineering & Management Module 2.1 12

2012 from "A First Course in Quality Engineering: Integrating Statistical and Management Methods of Quality" by K.S. Krishnamoorthi. Reproduced by permission of Taylor and Francis Group, LLC, a division of Informa plc.

TUM School of Management Production and Supply Chain Management Prof Martin Grunow Technische Universitt Mnchen

Major statistical methods used in quality engineering (contd.) Regression and Correlation Analysis:

used for determining which cause variables affect, to what extent, which quality characteristics

Reliability Engineering: used in understanding the factors that affect the life of parts and assemblies in order to increase their life

Tolerancing: used for determining allowable variability in product and process variables so the products can be produced economically while meeting customer needs.

Holly Ott Quality Engineering & Management Module 2.1 13

2012 from "A First Course in Quality Engineering: Integrating Statistical and Management Methods of Quality" by K.S. Krishnamoorthi. Reproduced by permission of Taylor and Francis Group, LLC, a division of Informa plc.

TUM School of Management Production and Supply Chain Management Prof Martin Grunow Technische Universitt Mnchen

Numerical methods for describing populations Population: collection of all items that are of interest in a given

situation

Sample: subset chosen from the population. Random Sample: each item has equal chance of being included

Holly Ott Quality Engineering & Management Module 2.1 14

2012 from "A First Course in Quality Engineering: Integrating Statistical and Management Methods of Quality" by K.S. Krishnamoorthi. Reproduced by permission of Taylor and Francis Group, LLC, a division of Informa plc.

Population Sample

Subset

TUM School of Management Production and Supply Chain Management Prof Martin Grunow Technische Universitt Mnchen

Numerical methods for describing populations Two types of data:

Measurement data: measurements of characteristics (length, width, strength)

"Quantitative" data Attribute data: inspection data (small, color, taste, fit)

"Qualitative" data Nominal Ordinal

Holly Ott Quality Engineering & Management Module 2.1 15

2012 from "A First Course in Quality Engineering: Integrating Statistical and Management Methods of Quality" by K.S. Krishnamoorthi. Reproduced by permission of Taylor and Francis Group, LLC, a division of Informa plc.

TUM School of Management Production and Supply Chain Management Prof Martin Grunow Technische Universitt Mnchen

Numerical methods for describing populations

The mean () represents the center point around which the data (population) is distributed

The standard deviation () represents the amount of variability (dispersion) in the data about the center point

The square of the standard deviation is the variance (2)

Holly Ott Quality Engineering & Management Module 2.1 16

2012 from "A First Course in Quality Engineering: Integrating Statistical and Management Methods of Quality" by K.S. Krishnamoorthi. Reproduced by permission of Taylor and Francis Group, LLC, a division of Informa plc.

Population

TUM School of Management Production and Supply Chain Management Prof Martin Grunow Technische Universitt Mnchen

Numerical methods for describing populations When a sample is taken, where n is the sample size, then the sample average and sample standard deviation can be calculated: Sample Average, X-bar: Sample Standard Deviation, S :

n

XX i

i=

( )1

2

=

nXXi

S

Holly Ott Quality Engineering & Management Module 2.1 17

2012 from "A First Course in Quality Engineering: Integrating Statistical and Management Methods of Quality" by K.S. Krishnamoorthi. Reproduced by permission of Taylor and Francis Group, LLC, a division of Informa plc.

Sample

X-bar

S

TUM School of Management Production and Supply Chain Management Prof Martin Grunow Technische Universitt Mnchen

Population Parameters vs. Sample Statistics

and are population parameters

X-bar and S are sample statistics

Sample

X-bar S

Population

Holly Ott Quality Engineering & Management Module 2.1 18

2012 from "A First Course in Quality Engineering: Integrating Statistical and Management Methods of Quality" by K.S. Krishnamoorthi. Reproduced by permission of Taylor and Francis Group, LLC, a division of Informa plc.

TUM School of Management Production and Supply Chain Management Prof Martin Grunow Technische Universitt Mnchen

Who is the better marksman?

Quality vs. Variability

To reduce defects we need to reduce variability and target the mean It is typically easier to change the mean than it is to reduce the

variation Holly Ott Quality Engineering & Management Module 2.1 19

TUM School of Management Production and Supply Chain Management Prof Martin Grunow Technische Universitt Mnchen

Quality vs. Variability

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Excessive variability in a population causes poor quality and waste.

TUM School of Management Production and Supply Chain Management Prof Martin Grunow Technische Universitt Mnchen

Coming Up

Lecture 2.2: Random Variables and Probability Distributions

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