From Data to Decision Support in Manufacturing
Doktorsavhandling, 2021

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.

standards

interoperability

5G

digitalization

Industry 4.0

Smart Manufacturing

data-driven decision-making

manufacturing system lifecycle

Online (Zoom password: 151712)
Opponent: Thorsten Wuest, Assistant Professor, West Virgina University, USA

Författare

Maja Bärring

Chalmers, Industri- och materialvetenskap, Produktionssystem

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Digital Twin for Smart Manufacturing: the Practitioner's Perspective

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A Case Study for Modeling Machine Tool Systems Using Standard Representations

12th ITU Kaleidoscope Academic Conference,; (2020)

Paper i proceeding

Bärring, M., Shao, G., Hedberg, T. Jr., and Johansson, B. Identifying Use Cases and Requirements for Digital Twins in Supply Chains from Research and Practice

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 in the digital transformation. This thesis aims to show the manufacturing companies how the digital technologies may be used in digitalizing manufacturing systems data in order to support decision-making with data rather than relying mainly on intuition and experience.

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. 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, and availability of historical data in maintenance. 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.

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 important tools in helping industry in how they can address a digital transformation and become more data-driven in decision-making.

Morgondagens rymdproduktion

Nationellt rymdtekniskt forskningsprogram (NRFP) (4424064), 2016-08-01 -- 2018-12-31.

5G-Enabled Manufacturing II (5GEMII)

VINNOVA (2018-02820), 2018-06-21 -- 2019-09-01.

5G-Enabled Manufacturing (5GEM)

VINNOVA (2015-06755), 2015-11-16 -- 2018-07-13.

BOOST 4.0 – Big Data för industriell konkurrenskraft och uppkopplad smart industri

Europeiska kommissionen (EU) (780732), 2018-01-01 -- 2020-12-31.

Digitala Stambanan

VINNOVA (2018-04503), 2018-11-01 -- 2020-12-31.

Ämneskategorier

Produktionsteknik, arbetsvetenskap och ergonomi

Data- och informationsvetenskap

Styrkeområden

Produktion

ISBN

978-91-7905-536-3

Doktorsavhandlingar vid Chalmers tekniska högskola. Ny serie: 5003

Utgivare

Chalmers tekniska högskola

Online (Zoom password: 151712)

Online

Opponent: Thorsten Wuest, Assistant Professor, West Virgina University, USA

Mer information

Senast uppdaterat

2021-08-12