his.sePublications
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
On transforming into the data-driven decision-making era: current state of practice in manufacturing smes
University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre. (Informationssystem, Information Systems)ORCID iD: 0000-0001-5435-9535
University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre. (Produktion och automatiseringsteknik, Production and Automation Engineering)
2018 (English)In: Advances in Manufacturing Technology XXXII: Proceedings of the 16th International Conference on Manufacturing Research, incorporating the 33rd National Conference on Manufacturing Research, September 11–13, 2018, University of Skövde, Sweden / [ed] Peter Thorvald, Keith Case, Amsterdam: IOS Press, 2018, Vol. 8, p. 337-342Conference paper, Published paper (Refereed)
Abstract [en]

Current research lacks details on how SMMEs are able to capitalize on how their IT-solutions supports data-driven decision-making. Such details are important for being able to support further development of SMMEs and assuring their sustainability and competitive edge. Prosperous SMMEs are vital due to their economical and societal importance. To alleviate the lack of details, this paper presents the results of four case studies towards SMMEs partly aimed at investigating their current state of data-driven decision-making. The findings reveal that IT-solutions in some areas are either underdeveloped or unexplored. Instead, the SMMEs tend to focus on traditional manufacturing techniques, continuous improvements in the manufacturing process, and manual support routines and thereby neglects opportunities offered in relation to e.g. incident management, product quality monitoring, and the usage of KPIs not directly linked to manufacturing.

Place, publisher, year, edition, pages
Amsterdam: IOS Press, 2018. Vol. 8, p. 337-342
Series
Advances in Transdisciplinary Engineering, ISSN 2352-751X, E-ISSN 2352-7528 ; 8
Keywords [en]
Decision making, Manufacture, Metadata, Competitive edges, Continuous improvements, Data driven decision, Incident Management, Manufacturing process, Product quality monitoring, State of practice, Traditional manufacturing, Industrial research
National Category
Production Engineering, Human Work Science and Ergonomics Other Mechanical Engineering
Research subject
Information Systems; Production and Automation Engineering
Identifiers
URN: urn:nbn:se:his:diva-16494DOI: 10.3233/978-1-61499-902-7-337ISI: 000462212700054Scopus ID: 2-s2.0-85057361916ISBN: 978-1-61499-901-0 (print)ISBN: 978-1-61499-902-7 (electronic)OAI: oai:DiVA.org:his-16494DiVA, id: diva2:1270541
Conference
16th International Conference on Manufacturing Research, incorporating the 33rd National Conference on Manufacturing Research, September 11–13, 2018, University of Skövde, Sweden
Available from: 2018-12-13 Created: 2018-12-13 Last updated: 2019-10-28Bibliographically approved
In thesis
1. Towards facilitating BI adoption in small and medium sized manufacturing companies
Open this publication in new window or tab >>Towards facilitating BI adoption in small and medium sized manufacturing companies
2019 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

This work concerns how to support Small and Medium sized Manufacturing Enterprises(SMMEs) with their Business Intelligence (BI) adoption, with the long term aim of supporting them in making better use of their BI investments and becoming (more)data-driven in their decision-making processes. Current BI research focuses primarily on larger enterprises, despite the fact that the majority of businesses are small or mediumsized. Therefore, this research focuses on the body of knowledge concerning how SMMEs can be more intelligent about their business, and better adopt BI to improve decision-making. Accordingly, the overall research aim is to create an artefact that can support SMMEs to facilitate BI adoption. An understanding of the current situation of BI adoption within SMMEs needs to be attained to achieve this, which is the focus for the first research question: What is the current state-of-practice in relation to BI adoption in SMMEs? The research question adds to current knowledge on how SMMEs are taking advantage of BI and highlights which functions within companies are currently supported by BI. Research question two identifies the main challenges that SMMEs are facing in this context: What are the main challenges for BI adoption in SMMEs? This question adds to knowledge regarding some of the barriers and hindrances SMMEs face in BI adoption. Finally, the third research question addresses how SMMEs can address the challenges in successfully adopting BI: How can the main challenges be addressed? The research question is answered by providing descriptions of work in four participating companies addressing different types of problems. Many of the challenges from literature (and from empirical data from the participating companies) regarding BI adoption are met. The outcome adds to the literature a hands-on approach for companies to address chosen problems in their settings, and addressing many of the factors previously found in the BI adoption literature. An action design research (ADR) method is used to fulfill the overall research aim. The ADR method is used to guide the development of a framework artefact based on previousliterature, and on empirical findings from working with participating companies. Theoretical background was obtained through a literature review of BI adoption and usage. Empirical material was gathered both through interviews and by reviewing documents from the companies. The work that was done in participating companies was supported by previous literature in several ways: through the use of an elicitation activity, through the core concepts of BI, and by focusing on categories presented in a BI maturity model. The principal contribution of the research is in the form of a framework: the Business Intelligence Facilitation Framework (BIFF), which includes four phases. All phases contain activities that support companies in addressing BI adoption challenges from the literature and empirical data, in order to achieve the overall research aim. This research contributes both to research and practice. From a research point of view, the framework provides a way to address many of the factors previously identified in literature that need to be in place to increase the likelihood of successful BI adoption. From a practice perspective, the framework supports practitioners offering guidance in how to improve their BI adoption, providing activities for them to take, and guidance in how to carry out the activities.

Place, publisher, year, edition, pages
Skövde: University of Skovde, 2019. p. 126
Series
Dissertation Series ; 30 (2019)
National Category
Information Systems
Research subject
Information Systems
Identifiers
urn:nbn:se:his:diva-17819 (URN)978-91-984918-2-1 (ISBN)
Public defence
2019-11-22, G110, University of Skövde, Skövde, 13:00 (English)
Opponent
Supervisors
Available from: 2019-10-30 Created: 2019-10-28 Last updated: 2019-11-08Bibliographically approved

Open Access in DiVA

No full text in DiVA

Other links

Publisher's full textScopus

Authority records BETA

Gudfinnsson, KristensStrand, Mattias

Search in DiVA

By author/editor
Gudfinnsson, KristensStrand, Mattias
By organisation
School of InformaticsThe Informatics Research CentreSchool of Engineering ScienceThe Virtual Systems Research Centre
Production Engineering, Human Work Science and ErgonomicsOther Mechanical Engineering

Search outside of DiVA

GoogleGoogle Scholar

doi
isbn
urn-nbn

Altmetric score

doi
isbn
urn-nbn
Total: 601 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf