東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Increasing the Value of Data in Prod...

Barring, Maja.

FindBook

Google Book

Amazon

博客來

Increasing the Value of Data in Production Systems.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Increasing the Value of Data in Production Systems./
作者:	Barring, Maja.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2019,
面頁冊數:	70 p.
附註:	Source: Dissertations Abstracts International, Volume: 81-09, Section: B.
Contained By:	Dissertations Abstracts International81-09B.
標題:	Industrial engineering. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=27556649
ISBN:	9781085757577

Increasing the Value of Data in Production Systems.
Barring, Maja.

Increasing the Value of Data in Production Systems. - Ann Arbor : ProQuest Dissertations & Theses, 2019 - 70 p.

Source: Dissertations Abstracts International, Volume: 81-09, Section: B.

Thesis (Licentiate)--Chalmers Tekniska Hogskola (Sweden), 2019.

This item must not be sold to any third party vendors.

A digital transformation is taking place, where information is available about almost anything, changing how work is performed and anticipated. More digitized information enabled by the digital technologies is supporting businesses to measure more about their processes and thereby also to know more. The same transformation is taking place in the manufacturing domain, which is referred to as the fourth industrial revolution. There are numerous national initiatives to approach the fourth industrial revolution and the aim is to make the manufacturing industry more digitalized and increase competitiveness. Digitalization is making more information about processes available, but it is first when data is informing decisions in an organization it will add value. Along with all the benefits and potential values of the digital transformation, much of the attention has been on the technologies and systems that can enable the digitalization. Less focus has been spent on how the technologies and systems should be put into practice in the organizations to fulfill the needs of the manufacturing domain. New knowledge about digital technologies in combination with already existing expertise about manufacturing processes is needed. The aim of the thesis is to identify the value of data for decision-making. The approach outlined in this thesis will identify the values gained from the raw data itself and from the further processed data to provide decision support. The distinction between these two forms of data, raw data and further processed data, is important because it is believed that these can provide different values and that they involve different challenges for the organization. 5G telecommunication and 3D laser scanning serve as digital technologies in this thesis to enable more data in digital form on a production system. 5G was used for connecting a machine enabling the collection of data about critical machine components. 3D laser scanning was used to collect the spatial data in a factory environment. The results show that more data available about the connected machine provide values to the organization to know the status of the machine, be able to compare the designed system against the behavior in the real-world setting, a better understanding of the process and to learn from data. Spatial data provide values by being able to represent the production system as-is in a very accurate and photorealistic way. The values identified from having more data available for the decision support were in the daily operations to know the condition of the machine, for the manufacturing organization to plan proactive actions, and for the production engineer to understand the behavior of the designed system in the real-world context. The spatial data could both support when making changes to the physical setup and when planning the design of the factory environment in an offline mode.The initial studies presented in this thesis supported to build the understanding of the current practice of data as decision support in the production organization. The understanding that data should support decision-making was high, but the data availability in the current state was scarce or of poor quality. This strengthens the aim of the thesis, to provide results that can show the value of data for decision-making. "To measure more is to know more" (McAffee and Brynjolfsson, 2012) is a statement serving as a cornerstone throughout this thesis and has also been justified by the results presented to answer research question 1 and 2. Data enabled by digital technologies can support multiple roles in the manufacturing organization throughout the different phases of the production system, for example in daily operations and maintenance.

ISBN: 9781085757577Subjects--Topical Terms:

526216
Industrial engineering.
Subjects--Index Terms:

Digital technologies

Increasing the Value of Data in Production Systems.
LDR:04905nmm a2200361 4500 001 2272079
005 20201102111701.5
008 220629s2019 ||||||||||||||||| ||eng d
020 $a 9781085757577
035 $a (MiAaPQ)AAI27556649
035 $a (MiAaPQ)Chalmers_SE507782
035 $a AAI27556649
040 $a MiAaPQ $c MiAaPQ
100 1 $a Barring, Maja. $3 3549503
245 1 0 $a Increasing the Value of Data in Production Systems.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2019
300 $a 70 p.
500 $a Source: Dissertations Abstracts International, Volume: 81-09, Section: B.
502 $a Thesis (Licentiate)--Chalmers Tekniska Hogskola (Sweden), 2019.
506 $a This item must not be sold to any third party vendors.
520 $a A digital transformation is taking place, where information is available about almost anything, changing how work is performed and anticipated. More digitized information enabled by the digital technologies is supporting businesses to measure more about their processes and thereby also to know more. The same transformation is taking place in the manufacturing domain, which is referred to as the fourth industrial revolution. There are numerous national initiatives to approach the fourth industrial revolution and the aim is to make the manufacturing industry more digitalized and increase competitiveness. Digitalization is making more information about processes available, but it is first when data is informing decisions in an organization it will add value. Along with all the benefits and potential values of the digital transformation, much of the attention has been on the technologies and systems that can enable the digitalization. Less focus has been spent on how the technologies and systems should be put into practice in the organizations to fulfill the needs of the manufacturing domain. New knowledge about digital technologies in combination with already existing expertise about manufacturing processes is needed. The aim of the thesis is to identify the value of data for decision-making. The approach outlined in this thesis will identify the values gained from the raw data itself and from the further processed data to provide decision support. The distinction between these two forms of data, raw data and further processed data, is important because it is believed that these can provide different values and that they involve different challenges for the organization. 5G telecommunication and 3D laser scanning serve as digital technologies in this thesis to enable more data in digital form on a production system. 5G was used for connecting a machine enabling the collection of data about critical machine components. 3D laser scanning was used to collect the spatial data in a factory environment. The results show that more data available about the connected machine provide values to the organization to know the status of the machine, be able to compare the designed system against the behavior in the real-world setting, a better understanding of the process and to learn from data. Spatial data provide values by being able to represent the production system as-is in a very accurate and photorealistic way. The values identified from having more data available for the decision support were in the daily operations to know the condition of the machine, for the manufacturing organization to plan proactive actions, and for the production engineer to understand the behavior of the designed system in the real-world context. The spatial data could both support when making changes to the physical setup and when planning the design of the factory environment in an offline mode.The initial studies presented in this thesis supported to build the understanding of the current practice of data as decision support in the production organization. The understanding that data should support decision-making was high, but the data availability in the current state was scarce or of poor quality. This strengthens the aim of the thesis, to provide results that can show the value of data for decision-making. "To measure more is to know more" (McAffee and Brynjolfsson, 2012) is a statement serving as a cornerstone throughout this thesis and has also been justified by the results presented to answer research question 1 and 2. Data enabled by digital technologies can support multiple roles in the manufacturing organization throughout the different phases of the production system, for example in daily operations and maintenance.
590 $a School code: 0419.
650 4 $a Industrial engineering. $3 526216
650 4 $a Materials science. $3 543314
653 $a Digital technologies
653 $a Digitalization
653 $a Decision support
653 $a Manufacturing
653 $a Smart manufacturing
690 $a 0794
690 $a 0546
710 2 $a Chalmers Tekniska Hogskola (Sweden). $3 1913472
773 0 $t Dissertations Abstracts International $g 81-09B.
790 $a 0419
791 $a Licentiate
792 $a 2019
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=27556649