東華大學圖書館 |

From Data to Decision Support in Manufacturing.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	From Data to Decision Support in Manufacturing./
作者:	Barring, Maja.
面頁冊數:	1 online resource (88 pages)
附註:	Source: Dissertations Abstracts International, Volume: 83-07, Section: B.
Contained By:	Dissertations Abstracts International83-07B.
標題:	Internet of Things. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28830351click for full text (PQDT)
ISBN:	9798496574976

From Data to Decision Support in Manufacturing.
Barring, Maja.

From Data to Decision Support in Manufacturing. - 1 online resource (88 pages)

Source: Dissertations Abstracts International, Volume: 83-07, Section: B.

Thesis (Ph.D.)--Chalmers Tekniska Hogskola (Sweden), 2021.

Includes bibliographical references

Digitalization is changing society, industry, and how business is done. For new companies that are more or less born digital, there is the opportunity to use and benefit from the capabilities offered by the new digital technologies, of which data-driven decision-making forms a crucial part. The manufacturing industry is facing the Fourth Industrial Revolution, but most manufacturing organizations are lagging behind in their digital transformation. This is due to the technical and organizational challenges they are experiencing. Based on this current state description and existing gap, the vision, aim, and research questions of this thesis are: Vision - future manufacturing organization to be driven by fact-based decision support based on data rather than of relying mainly on intuition and experience.Aim - to show manufacturing organizations the applicability of digital technologies in digitalizing manufacturing system data to support decision-making and how data sharing may be achieved.Research Question 1. How do manufacturing system lifecycle decisions influence the requirements of data collection towards interoperability? Research Question 2. What makes interoperability standardization applicable to sharing data in a manufacturing system's lifecycle?This research is applied, addressing real-world problems in manufacturing. For this reason, the main objective is to solve the problem at hand, and data collection methods will be selected that can help address it. This can best be explained by taking a pragmatic worldview and using mixed methods approach that combines quantitative and qualitative methods. The research upon which this thesis is based draws on the results of three research projects involving the active participation of manufacturing companies. The data collection methods included experiments, interviews (focus group and semi-structured), technical development, literature review, and so on.The results are divided into three areas: 1) connected factory, 2) standard representation of machine model data, and 3) the digital twin in smart manufacturing. Connected factory addresses the question of how a mobile connectivity solution, 5G, may be used in a factory setting and demonstrates how the connectivity solution should be planned and how new data from a connected machine may support an operator in decision-making. The standard representation of machine model data demonstrates how an international standard may be used more widely to support the sharing and reuse of information. The digital twin in smart manufacturing investigates the reasons why there are so few real-world examples of this.The findings reveal that a manufacturing system's lifecycle impacts data requirements, including a need for data accuracy in design, speed of data in operation (to allow operators to act upon events), and availability of historical data in maintenance (for finding root causes and planning). The volume of data was identified as important to all lifecycles. The applicability of standards was found to depend on: 1) the technology providers' willingness to adapt standards, 2) enforcement by OEMs and larger actors further down a supply chain, 3) the development of standards that consider the user, and 4) when standards are required for infrastructure reasons.Based on the results and findings obtained, it may be stated that it is important to determine what actual manufacturing problem should be addressed by digital technology. There is a tendency to view this change from the perspective of what the digital technology might solve (a technology push), rather than setting aside the solution and focusing on what problem should be solved (a technology pull). This work also reveals the importance of the collaboration between industry and academia making progress in the digital transformation of manufacturing. Proofs-of-concept and demonstrators of how digital technologies might be used are also important tools in helping industry in how they can address a digital transformation.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2023

Mode of access: World Wide Web

ISBN: 9798496574976Subjects--Topical Terms:

3538511
Internet of Things.
Index Terms--Genre/Form:

542853
Electronic books.

From Data to Decision Support in Manufacturing.
LDR:05274nmm a2200349K 4500 001 2357887
005 20230725053804.5
006 m o d
007 cr mn ---uuuuu
008 241011s2021 xx obm 000 0 eng d
020 $a 9798496574976
035 $a (MiAaPQ)AAI28830351
035 $a (MiAaPQ)Chalmers_SE525242
035 $a AAI28830351
040 $a MiAaPQ $b eng $c MiAaPQ $d NTU
100 1 $a Barring, Maja. $3 3549503
245 1 0 $a From Data to Decision Support in Manufacturing.
264 0 $c 2021
300 $a 1 online resource (88 pages)
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
500 $a Source: Dissertations Abstracts International, Volume: 83-07, Section: B.
500 $a Advisor: Johansson, Scarlett.
502 $a Thesis (Ph.D.)--Chalmers Tekniska Hogskola (Sweden), 2021.
504 $a Includes bibliographical references
520 $a Digitalization is changing society, industry, and how business is done. For new companies that are more or less born digital, there is the opportunity to use and benefit from the capabilities offered by the new digital technologies, of which data-driven decision-making forms a crucial part. The manufacturing industry is facing the Fourth Industrial Revolution, but most manufacturing organizations are lagging behind in their digital transformation. This is due to the technical and organizational challenges they are experiencing. Based on this current state description and existing gap, the vision, aim, and research questions of this thesis are: Vision - future manufacturing organization to be driven by fact-based decision support based on data rather than of relying mainly on intuition and experience.Aim - to show manufacturing organizations the applicability of digital technologies in digitalizing manufacturing system data to support decision-making and how data sharing may be achieved.Research Question 1. How do manufacturing system lifecycle decisions influence the requirements of data collection towards interoperability? Research Question 2. What makes interoperability standardization applicable to sharing data in a manufacturing system's lifecycle?This research is applied, addressing real-world problems in manufacturing. For this reason, the main objective is to solve the problem at hand, and data collection methods will be selected that can help address it. This can best be explained by taking a pragmatic worldview and using mixed methods approach that combines quantitative and qualitative methods. The research upon which this thesis is based draws on the results of three research projects involving the active participation of manufacturing companies. The data collection methods included experiments, interviews (focus group and semi-structured), technical development, literature review, and so on.The results are divided into three areas: 1) connected factory, 2) standard representation of machine model data, and 3) the digital twin in smart manufacturing. Connected factory addresses the question of how a mobile connectivity solution, 5G, may be used in a factory setting and demonstrates how the connectivity solution should be planned and how new data from a connected machine may support an operator in decision-making. The standard representation of machine model data demonstrates how an international standard may be used more widely to support the sharing and reuse of information. The digital twin in smart manufacturing investigates the reasons why there are so few real-world examples of this.The findings reveal that a manufacturing system's lifecycle impacts data requirements, including a need for data accuracy in design, speed of data in operation (to allow operators to act upon events), and availability of historical data in maintenance (for finding root causes and planning). The volume of data was identified as important to all lifecycles. The applicability of standards was found to depend on: 1) the technology providers' willingness to adapt standards, 2) enforcement by OEMs and larger actors further down a supply chain, 3) the development of standards that consider the user, and 4) when standards are required for infrastructure reasons.Based on the results and findings obtained, it may be stated that it is important to determine what actual manufacturing problem should be addressed by digital technology. There is a tendency to view this change from the perspective of what the digital technology might solve (a technology push), rather than setting aside the solution and focusing on what problem should be solved (a technology pull). This work also reveals the importance of the collaboration between industry and academia making progress in the digital transformation of manufacturing. Proofs-of-concept and demonstrators of how digital technologies might be used are also important tools in helping industry in how they can address a digital transformation.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2023
538 $a Mode of access: World Wide Web
650 4 $a Internet of Things. $3 3538511
650 4 $a Wireless networks. $3 1531264
650 4 $a Decision making. $3 517204
650 4 $a Electrical engineering. $3 649834
650 4 $a Engineering. $3 586835
655 7 $a Electronic books. $2 lcsh $3 542853
690 $a 0544
690 $a 0537
690 $a 0338
710 2 $a ProQuest Information and Learning Co. $3 783688
710 2 $a Chalmers Tekniska Hogskola (Sweden). $3 1913472
773 0 $t Dissertations Abstracts International $g 83-07B.
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28830351 $z click for full text (PQDT)