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

Quality Engineering & Management

Case Study: Descriptive Statistics

Dr. Holly Ott Production and Supply Chain Management

Chair: Prof. Martin Grunow TUM School of Management

Holly Ott Quality Engineering & Management Module 4.2 1

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

Descriptive Statistics

Empirical methods to describe populations: arranging and summarizing data to obtain useful information

Frequency Distribution: Histograms Box-and-Whisker Plots Measures of Location Measures of Dispersion

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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.

Quality Engineering & Management Module 4.2

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

Histogram The frequency distribution is the tool used to understand and describe the variation among units in a population. A histogram is the sample analog of the frequency distribution of a population

Holly Ott Quality Engineering & Management Module 4.2 3

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

The box-and-whisker (B&W) plot is another compact way of representing a population with variability, and it is especially useful when comparing several distributions with respect to their central value and dispersion.

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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.

Quality Engineering & Management Module 4.2

Box-and-Whisker Plots

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

Holly Ott Quality Engineering & Management Module 4.2

Example: Iron Foundry Casting Process

In an iron foundry that makes large castings, molten iron is carried from the furnace where iron is melted to where it is poured into molds over a distance of about 150 yards. This travel, as well as some time spent on checking iron chemistry and deslagging, causes cooling of the molten iron. [Disclaimer: This, example is taken from real processes, and the scenarios described represent true

situations. Some of the names of product characteristics and process parameters, however, have been changed so as to protect the identification or the source of data. The numbers that represent targets and specifications have also been occasionally altered to protect proprietary information.]

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.

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

Holly Ott Quality Engineering & Management Module 4.2

The process engineer had estimated that the temperature drop due to this cooling to be about 20F and, accordingly, had chosen the target temperature at which the iron is to be tapped out of the furnace as 2570F so that the iron would be at the required temperature of 2550F at pouring.

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.

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Example: Iron Foundry Casting Process

Target Temperature at Pouring = 2550F Estimated cooling between Tap-out and Pouring: 20F Target Temperature at Tap-out = 2550F + 20F = 2570F

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

Holly Ott Quality Engineering & Management Module 4.2

The final castings, however, showed burn-in defects that were attributed to the iron being too hot at pouring. The castings with such defects were absolutely not acceptable to the customer.

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.

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Example: Iron Foundry Casting Process

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

Data Analysis Pouring Temperature

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Problem: iron too hot at pouring,

Burn-in defects at customer site!

Estimate cooing by 20F from Tap-out to Pouring Target at Pouring = 2550F

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

Data Analysis Pouring Temperature

Holly Ott

Center of distribution near

2560F

Pouring Temperature Range

= 2595 2495 = 100F

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

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Target for tap-out temperature set to 2570F so that after 20F drop, the target pouring temperature is reached = 2550F

Data Analysis Tap-out Temperature

Quality Engineering & Management Module 4.2

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

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Data Analysis Tap-out Temperature

Pouring Temperature Range

= 2597 2537 = 60F

Center of distribution near

2570F

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

Box & Whisker Plot

Median TapoutTemperature =

2570F

Median PouringTemperature = 2560F

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

Holly Ott Quality Engineering & Management Module 4.2

Data Analysis

Final castings showing burn in defects due to too high pour in temps. Tap out temp is too high center about 2570F ;

Range = 2597 2537 = 60F Pour out temp is too high center about 2560F ;

Range = 2595 2495 = 100F it looks like the temperature drop is only 10F

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.

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

Holly Ott Quality Engineering & Management Module 4.2

Recommendation

1) Adjust target for tap out temperature to 2560F then -10F gives pour out at target temperature 2550F

2) Reduce variability of pour out temp. How?

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.

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