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Critical Factors for Selecting and Integrating Digital Technologies to Enable Smart Production: A Data Value Chain Perspective
Mälardalen University, School of Innovation, Design and Engineering, Innovation and Product Realisation.
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.ORCID iD: 0000-0003-3469-1834
Mälardalen University, School of Innovation, Design and Engineering, Innovation and Product Realisation.ORCID iD: 0000-0002-5963-2470
Mälardalen University, School of Innovation, Design and Engineering, Innovation and Product Realisation.ORCID iD: 0000-0002-7512-4425
2023 (English)In: IFIP Advances in Information and Communication Technology, Springer Science and Business Media Deutschland GmbH , 2023, p. 311-325Conference paper, Published paper (Refereed)
Abstract [en]

With the development towards Industry 5.0, manufacturing companies are developing towards Smart Production, i.e., using data as a resource to interconnect the elements in the production system to learn and adapt accordingly for a more resource-efficient and sustainable production. This requires selecting and integrating digital technologies for the entire data lifecycle, also referred to as the data value chain. However, manufacturing companies are facing many challenges related to building data value chains to achieve the desired benefits of Smart Production. Therefore, the purpose of this paper is to identify and analyze the critical factors of selecting and integrating digital technologies for efficiently benefiting data value chains for Smart Production. This paper employed a qualitative-based multiple case study design involving manufacturing companies within different industries and of different sizes. The paper also analyses two Smart Production cases in detail by mapping the data flow using a technology selection and integration framework to propose solutions to the existing challenges. By analyzing the two in-depth studies and additionally two reference cases, 13 themes of critical factors for selecting and integrating digital technologies were identified.

Place, publisher, year, edition, pages
Springer Science and Business Media Deutschland GmbH , 2023. p. 311-325
Keywords [en]
Digital Transformation, Industry 5.0, Production Development, Smart Manufacturing, Technology Integration, Technology Selection, Data integration, Engineering education, Critical factors, Data values, Digital technologies, Value chains, Life cycle
National Category
Production Engineering, Human Work Science and Ergonomics
Identifiers
URN: urn:nbn:se:mdh:diva-64438DOI: 10.1007/978-3-031-43662-8_23Scopus ID: 2-s2.0-85172421353ISBN: 9783031436611 (print)OAI: oai:DiVA.org:mdh-64438DiVA, id: diva2:1803347
Conference
IFIP Advances in Information and Communication Technology
Available from: 2023-10-09 Created: 2023-10-09 Last updated: 2023-11-16Bibliographically approved
In thesis
1. Digital Technologies for Enabling Smart Production: Examining the Aspects of Selection and Integration
Open this publication in new window or tab >>Digital Technologies for Enabling Smart Production: Examining the Aspects of Selection and Integration
2023 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

With the development towards Industry 5.0, manufacturing companies are developing towards smart production. In smart production, data is used as a resource to interconnect different elements in the production system to learn and adapt to changing production conditions. Common objectives include human-centricity, resource-efficiency, and sustainable production. To enable these desired benefits of smart production, there is a need to use digital technologies to create and manage the entire flow of data. To enable smart production, it is essential to deploy digital technologies in a way so that collected raw data is converted into useful data that can be applied by equipment or humans to generate value or reduce waste in production. This requires consideration to the data flow within the production system, i.e., the entire process of converting raw data into useful data which includes data management aspects such as the collection, analysis, and visualization of data. To enable a good data flow, there is a need to combine several digital technologies. However, many manufacturing companies are facing challenges when selecting suitable digital technologies for their specific production system. Common challenges are related to the overwhelming number of advanced digital technologies available on the market, and the complexity of production system and digital technologies. This makes it a complex task to understand what digital technologies to select and the recourses and actions needed to integrate them in the production system.

Against this background, the purpose of this licentiate thesis is to examine the selection and integration of digital technologies to enable smart production within manufacturing companies. More specifically, this licentiate thesis examines the challenges and critical factors of selecting and integrating digital technologies for smart production. This was accomplished by performing a qualitative-based multiple case study involving manufacturing companies within different industries and of different sizes. The findings show that identified challenges and critical factors are related to the different phases of the data value chain: data sources and collection, data communication, data processing and storage, and data visualisation and usage. General challenges and critical factors that were related to all phases of the data value chain were also identified. Moreover, the challenges and critical factors were related to people, process, and technology aspects. This shows that there is a need for holistic perspective on the entire data value chain and different production system elements when digital technologies are selected and integrated. Furthermore, there is a need to define a structured process for the selection and integration of digital technologies, where both management and operational level are involved. 

Abstract [sv]

Med utvecklingen mot Industri 5.0 utvecklas tillverkningsföretag mot smart produktion. I smart produktion används data som en resurs för att koppla samman olika element i produktionssystemet i syfte att lära sig om och anpassa sig efter förändrade produktionsförhållanden. Vanliga mål för smart produktion inkluderar resurseffektivitet, och en hållbar produktion anpassad utifrån människan. För att åstadkomma dessa önskade fördelar, behöver tillverkningsföretag använda digitala teknologier för att skapa och hantera hela dataflödet. För att möjliggöra smart produktion är det viktigt att implementera digitala teknologier på ett sätt så att insamlad rådata omvandlas till användbar data som kan tillämpas av maskiner eller människor för att skapa värde eller minska slöseri i produktionen. Detta kräver hänsyn till dataflödet inom produktionssystemet, det vill säga hela processen att omvandla rådata till användbar data som inkluderar datahanteringsaspekter som exempelvis insamling, analys och visualisering av data. För att möjliggöra ett bra dataflöde krävs det att flera digitala teknologier kombineras. Många tillverkningsföretag står dock inför flera utmaningar när de ska välja lämpliga digitala teknologier för sitt specifika produktionssystem. Vanliga utmaningar är relaterade till det överväldigande antalet avancerade digitala teknologier som finns på marknaden, samt komplexiteten hos produktionssystem och digitala teknologier. Detta gör det till en komplex uppgift att förstå vilka digitala tekniker som ska väljas och vilka resurser och åtgärder som behövs för att integrera dem i produktionssystemet.

Mot denna bakgrund är syftet med denna licentiatuppsats att undersöka hur tillverkningsföretag ska välja och integrera digitala teknologier för att uppnå smart produktion. Mer specifikt så undersöker denna licentiatuppsats vilka utmaningar och kritiska faktorer som finns för att välja och integrera digitala teknologier för att uppnå smart produktion. Detta uppnåddes genom en kvalitativ multipel fallstudie med tillverkningsföretag inom olika branscher och av olika storlekar. Resultaten visar att identifierade utmaningar och kritiska faktorer är relaterade till de olika faserna av datavärdekedjan: datakällor och insamling, datakommunikation, databearbetning och lagring samt datavisualisering och användning. Generella utmaningar och kritiska faktorer som var relaterade till alla faser av datavärdekedjan identifierades också. Dessutom var utmaningarna och kritiska faktorerna relaterade till människa, process och tekniska aspekter. Detta visar att det finns ett behov av helhetsperspektiv på hela datavärdekedjan och olika element i produktionssystemet när digitala teknologier väljs och integreras. Dessutom finns det ett behov av att definiera en strukturerad process för val och integration av digital teknik, där både ledning och operativ nivå är involverade.

Place, publisher, year, edition, pages
Västerås: Mälardalens universitet, 2023. p. 51
Series
Mälardalen University Press Licentiate Theses, ISSN 1651-9256 ; 345
Keywords
Industry 5.0, Production Development, Digital Transformation, Smart Manufacturing
National Category
Production Engineering, Human Work Science and Ergonomics
Research subject
Industrial Systems
Identifiers
urn:nbn:se:mdh:diva-64757 (URN)978-91-7485-624-8 (ISBN)
Presentation
2023-11-30, C3-003, Mälardalens universitet, Eskilstuna, 11:07 (English)
Opponent
Supervisors
Available from: 2023-11-16 Created: 2023-11-16 Last updated: 2023-11-17Bibliographically approved

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Agerskans, NatalieAshjaei, Seyed Mohammad HosseinBruch, JessicaChirumalla, Koteshwar

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