# Applications of Industrial Statistics

##### Kankaanranta, Jarno (2012-12-21)

Kankaanranta, Jarno

Turun yliopisto Annales Universitatis Turkuensis A I 450

21.12.2012

**Julkaisun pysyvä osoite on:**

http://urn.fi/URN:ISBN:978-951-29-5230-4

#### Kuvaus

Siirretty Doriasta

##### Tiivistelmä

This dissertation examines knowledge and industrial knowledge creation

processes. It looks at the way knowledge is created in industrial processes

based on data, which is transformed into information and finally into

knowledge. In the context of this dissertation the main tool for industrial

knowledge creation are different statistical methods.

This dissertation strives to define industrial statistics. This is done using an

expert opinion survey, which was sent to a number of industrial statisticians.

The survey was conducted to create a definition for this field of applied statistics

and to demonstrate the wide applicability of statistical methods to industrial

problems. In this part of the dissertation, traditional methods of industrial

statistics are introduced. As industrial statistics are the main tool for

knowledge creation, the basics of statistical decision making and statistical

modeling are also included.

The widely known Data Information Knowledge Wisdom (DIKW) hierarchy

serves as a theoretical background for this dissertation. The way that data is

transformed into information, information into knowledge and knowledge

finally into wisdom is used as a theoretical frame of reference. Some scholars

have, however, criticized the DIKW model. Based on these different perceptions

of the knowledge creation process, a new knowledge creation process,

based on statistical methods is proposed.

In the context of this dissertation, the data is a source of knowledge in industrial

processes. Because of this, the mathematical categorization of data into

continuous and discrete types is explained. Different methods for gathering

data from processes are clarified as well. There are two methods for data

gathering in this dissertation: survey methods and measurements.

The enclosed publications provide an example of the wide applicability of

statistical methods in industry. In these publications data is gathered using

surveys and measurements. Enclosed publications have been chosen so that

in each publication, different statistical methods are employed in analyzing

of data. There are some similarities between the analysis methods used in the

publications, but mainly different methods are used.

Based on this dissertation the use of statistical methods for industrial

knowledge creation is strongly recommended. With statistical methods it is

possible to handle large datasets and different types of statistical analysis results

can easily be transformed into knowledge.

processes. It looks at the way knowledge is created in industrial processes

based on data, which is transformed into information and finally into

knowledge. In the context of this dissertation the main tool for industrial

knowledge creation are different statistical methods.

This dissertation strives to define industrial statistics. This is done using an

expert opinion survey, which was sent to a number of industrial statisticians.

The survey was conducted to create a definition for this field of applied statistics

and to demonstrate the wide applicability of statistical methods to industrial

problems. In this part of the dissertation, traditional methods of industrial

statistics are introduced. As industrial statistics are the main tool for

knowledge creation, the basics of statistical decision making and statistical

modeling are also included.

The widely known Data Information Knowledge Wisdom (DIKW) hierarchy

serves as a theoretical background for this dissertation. The way that data is

transformed into information, information into knowledge and knowledge

finally into wisdom is used as a theoretical frame of reference. Some scholars

have, however, criticized the DIKW model. Based on these different perceptions

of the knowledge creation process, a new knowledge creation process,

based on statistical methods is proposed.

In the context of this dissertation, the data is a source of knowledge in industrial

processes. Because of this, the mathematical categorization of data into

continuous and discrete types is explained. Different methods for gathering

data from processes are clarified as well. There are two methods for data

gathering in this dissertation: survey methods and measurements.

The enclosed publications provide an example of the wide applicability of

statistical methods in industry. In these publications data is gathered using

surveys and measurements. Enclosed publications have been chosen so that

in each publication, different statistical methods are employed in analyzing

of data. There are some similarities between the analysis methods used in the

publications, but mainly different methods are used.

Based on this dissertation the use of statistical methods for industrial

knowledge creation is strongly recommended. With statistical methods it is

possible to handle large datasets and different types of statistical analysis results

can easily be transformed into knowledge.

##### Kokoelmat

- Väitöskirjat [2017]